Advancing land surface model development with satellite-based Earth observations

NASA Astrophysics Data System (ADS)

Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

2017-04-01

The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing land surface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628

Radiocarbon in Ecosystem Respiration and Soil Pore-Space CO2 with Surface Gas Flux, Air Temperature, and Soil Temperature and Moisture, Barrow, Alaska, 2012-2014

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lydia Vaughn; Margaret Torn; Rachel Porras

Dataset includes Delta14C measurements made from CO2 that was collected and purified in 2012-2014 from surface soil chambers, soil pore space, and background atmosphere. In addition to 14CO2 data, dataset includes co-located measurements of CO2 and CH4 flux, soil and air temperature, and soil moisture. Measurements and field samples were taken from intensive study site 1 areas A, B, and C, and the site 0 and AB transects, from specified positions in high-centered, flat-centered, and low centered polygons.

A reanalysis dataset of the South China Sea.

PubMed

Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992-2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability.

A reanalysis dataset of the South China Sea

PubMed Central

Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992–2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability. PMID:25977803

A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900

NASA Astrophysics Data System (ADS)

Xu, Wenhui; Li, Qingxiang; Jones, Phil; Wang, Xiaolan L.; Trewin, Blair; Yang, Su; Zhu, Chen; Zhai, Panmao; Wang, Jinfeng; Vincent, Lucie; Dai, Aiguo; Gao, Yun; Ding, Yihui

2018-04-01

A new dataset of integrated and homogenized monthly surface air temperature over global land for the period since 1900 [China Meteorological Administration global Land Surface Air Temperature (CMA-LSAT)] is developed. In total, 14 sources have been collected and integrated into the newly developed dataset, including three global (CRUTEM4, GHCN, and BEST), three regional and eight national sources. Duplicate stations are identified, and those with the higher priority are chosen or spliced. Then, a consistency test and a climate outlier test are conducted to ensure that each station series is quality controlled. Next, two steps are adopted to assure the homogeneity of the station series: (1) homogenized station series in existing national datasets (by National Meteorological Services) are directly integrated into the dataset without any changes (50% of all stations), and (2) the inhomogeneities are detected and adjusted for in the remaining data series using a penalized maximal t test (50% of all stations). Based on the dataset, we re-assess the temperature changes in global and regional areas compared with GHCN-V3 and CRUTEM4, as well as the temperature changes during the three periods of 1900-2014, 1979-2014 and 1998-2014. The best estimates of warming trends and there 95% confidence ranges for 1900-2014 are approximately 0.102 ± 0.006 °C/decade for the whole year, and 0.104 ± 0.009, 0.112 ± 0.007, 0.090 ± 0.006, and 0.092 ± 0.007 °C/decade for the DJF (December, January, February), MAM, JJA, and SON seasons, respectively. MAM saw the most significant warming trend in both 1900-2014 and 1979-2014. For an even shorter and more recent period (1998-2014), MAM, JJA and SON show similar warming trends, while DJF shows opposite trends. The results show that the ability of CMA-LAST for describing the global temperature changes is similar with other existing products, while there are some differences when describing regional temperature changes.

Moderate-Resolution Sea Surface Temperature Data for the Nearshore North Pacific

EPA Science Inventory

Coastal sea surface temperature (SST) is an important environmental characteristic defining habitat suitability for nearshore marine and estuarine organisms. The purpose of this publication is to provide access to an easy-to-use coastal SST dataset for ecologists, biogeographers...

The SeaFlux Turbulent Flux Dataset Version 1.0 Documentation

NASA Technical Reports Server (NTRS)

Clayson, Carol Anne; Roberts, J. Brent; Bogdanoff, Alec S.

2012-01-01

Under the auspices of the World Climate Research Programme (WCRP) Global Energy and Water cycle EXperiment (GEWEX) Data and Assessment Panel (GDAP), the SeaFlux Project was created to investigate producing a high-resolution satellite-based dataset of surface turbulent fluxes over the global oceans. The most current release of the SeaFlux product is Version 1.0; this represents the initial release of turbulent surface heat fluxes, associated near-surface variables including a diurnally varying sea surface temperature.

Clear-Sky Longwave Irradiance at the Earth's Surface--Evaluation of Climate Models.

NASA Astrophysics Data System (ADS)

Garratt, J. R.

2001-04-01

An evaluation of the clear-sky longwave irradiance at the earth's surface (LI) simulated in climate models and in satellite-based global datasets is presented. Algorithm-based estimates of LI, derived from global observations of column water vapor and surface (or screen air) temperature, serve as proxy `observations.' All datasets capture the broad zonal variation and seasonal behavior in LI, mainly because the behavior in column water vapor and temperature is reproduced well. Over oceans, the dependence of annual and monthly mean irradiance upon sea surface temperature (SST) closely resembles the observed behavior of column water with SST. In particular, the observed hemispheric difference in the summer minus winter column water dependence on SST is found in all models, though with varying seasonal amplitudes. The analogous behavior in the summer minus winter LI is seen in all datasets. Over land, all models have a more highly scattered dependence of LI upon surface temperature compared with the situation over the oceans. This is related to a much weaker dependence of model column water on the screen-air temperature at both monthly and annual timescales, as observed. The ability of climate models to simulate realistic LI fields depends as much on the quality of model water vapor and temperature fields as on the quality of the longwave radiation codes. In a comparison of models with observations, root-mean-square gridpoint differences in mean monthly column water and temperature are 4-6 mm (5-8 mm) and 0.5-2 K (3-4 K), respectively, over large regions of ocean (land), consistent with the intermodel differences in LI of 5-13 W m2 (15-28 W m2).

Long-term records of global radiation, carbon and water fluxes derived from multi-satellite data and a process-based model

NASA Astrophysics Data System (ADS)

Ryu, Youngryel; Jiang, Chongya

2016-04-01

To gain insights about the underlying impacts of global climate change on terrestrial ecosystem fluxes, we present a long-term (1982-2015) global radiation, carbon and water fluxes products by integrating multi-satellite data with a process-based model, the Breathing Earth System Simulator (BESS). BESS is a coupled processed model that integrates radiative transfer in the atmosphere and canopy, photosynthesis (GPP), and evapotranspiration (ET). BESS was designed most sensitive to the variables that can be quantified reliably, fully taking advantages of remote sensing atmospheric and land products. Originally, BESS entirely relied on MODIS as input variables to produce global GPP and ET during the MODIS era. This study extends the work to provide a series of long-term products from 1982 to 2015 by incorporating AVHRR data. In addition to GPP and ET, more land surface processes related datasets are mapped to facilitate the discovery of the ecological variations and changes. The CLARA-A1 cloud property datasets, the TOMS aerosol datasets, along with the GLASS land surface albedo datasets, were input to a look-up table derived from an atmospheric radiative transfer model to produce direct and diffuse components of visible and near infrared radiation datasets. Theses radiation components together with the LAI3g datasets and the GLASS land surface albedo datasets, were used to calculate absorbed radiation through a clumping corrected two-stream canopy radiative transfer model. ECMWF ERA interim air temperature data were downscaled by using ALP-II land surface temperature dataset and a region-dependent regression model. The spatial and seasonal variations of CO2 concentration were accounted by OCO-2 datasets, whereas NOAA's global CO2 growth rates data were used to describe interannual variations. All these remote sensing based datasets are used to run the BESS. Daily fluxes in 1/12 degree were computed and then aggregated to half-month interval to match with the spatial-temporal resolution of LAI3g dataset. The BESS GPP and ET products were compared to other independent datasets including MPI-BGC and CLM. Overall, the BESS products show good agreement with the other two datasets, indicating a compelling potential for bridging remote sensing and land surface models.

Global lake response to the recent warming hiatus

NASA Astrophysics Data System (ADS)

Winslow, Luke A.; Leach, Taylor H.; Rose, Kevin C.

2018-05-01

Understanding temporal variability in lake warming rates over decadal scales is important for understanding observed change in aquatic systems. We analyzed a global dataset of lake surface water temperature observations (1985‑2009) to examine how lake temperatures responded to a recent global air temperature warming hiatus (1998‑2012). Prior to the hiatus (1985‑1998), surface water temperatures significantly increased at an average rate of 0.532 °C decade‑1 (±0.214). In contrast, water temperatures did not change significantly during the hiatus (average rate ‑0.087 °C decade‑1 ±0.223). Overall, 83% of lakes in our dataset (129 of 155) had faster warming rates during the pre-hiatus period than during the hiatus period. These results demonstrate that lakes have exhibited decadal-scale variability in warming rates coherent with global air temperatures and represent an independent line of evidence for the recent warming hiatus. Our analyses provide evidence that lakes are sentinels of broader climatological processes and indicate that warming rates based on datasets where a large proportion of observations were collected during the hiatus period may underestimate longer-term trends.

Observational Evidence for Desert Amplification Using Multiple Satellite Datasets.

PubMed

Wei, Nan; Zhou, Liming; Dai, Yongjiu; Xia, Geng; Hua, Wenjian

2017-05-17

Desert amplification identified in recent studies has large uncertainties due to data paucity over remote deserts. Here we present observational evidence using multiple satellite-derived datasets that desert amplification is a real large-scale pattern of warming mode in near surface and low-tropospheric temperatures. Trend analyses of three long-term temperature products consistently confirm that near-surface warming is generally strongest over the driest climate regions and this spatial pattern of warming maximizes near the surface, gradually decays with height, and disappears in the upper troposphere. Short-term anomaly analyses show a strong spatial and temporal coupling of changes in temperatures, water vapor and downward longwave radiation (DLR), indicating that the large increase in DLR drives primarily near surface warming and is tightly associated with increasing water vapor over deserts. Atmospheric soundings of temperature and water vapor anomalies support the results of the long-term temperature trend analysis and suggest that desert amplification is due to comparable warming and moistening effects of the troposphere. Likely, desert amplification results from the strongest water vapor feedbacks near the surface over the driest deserts, where the air is very sensitive to changes in water vapor and thus efficient in enhancing the longwave greenhouse effect in a warming climate.

Exploring Antarctic Land Surface Temperature Extremes Using Condensed Anomaly Databases

NASA Astrophysics Data System (ADS)

Grant, Glenn Edwin

Satellite observations have revolutionized the Earth Sciences and climate studies. However, data and imagery continue to accumulate at an accelerating rate, and efficient tools for data discovery, analysis, and quality checking lag behind. In particular, studies of long-term, continental-scale processes at high spatiotemporal resolutions are especially problematic. The traditional technique of downloading an entire dataset and using customized analysis code is often impractical or consumes too many resources. The Condensate Database Project was envisioned as an alternative method for data exploration and quality checking. The project's premise was that much of the data in any satellite dataset is unneeded and can be eliminated, compacting massive datasets into more manageable sizes. Dataset sizes are further reduced by retaining only anomalous data of high interest. Hosting the resulting "condensed" datasets in high-speed databases enables immediate availability for queries and exploration. Proof of the project's success relied on demonstrating that the anomaly database methods can enhance and accelerate scientific investigations. The hypothesis of this dissertation is that the condensed datasets are effective tools for exploring many scientific questions, spurring further investigations and revealing important information that might otherwise remain undetected. This dissertation uses condensed databases containing 17 years of Antarctic land surface temperature anomalies as its primary data. The study demonstrates the utility of the condensate database methods by discovering new information. In particular, the process revealed critical quality problems in the source satellite data. The results are used as the starting point for four case studies, investigating Antarctic temperature extremes, cloud detection errors, and the teleconnections between Antarctic temperature anomalies and climate indices. The results confirm the hypothesis that the condensate databases are a highly useful tool for Earth Science analyses. Moreover, the quality checking capabilities provide an important method for independent evaluation of dataset veracity.

Comparing Temperature Effects on E. Coli, Salmonella, and Enterococcus Survival in Surface Waters

EPA Science Inventory

The objective of this study was to compare dependency of survival rates on temperature for indicator organisms E. coli and Enterococcus and the pathogen Salmonella in surface waters. A database of 86 survival datasets from peer-reviewed papers on inactivation of E. coli, Salmonel...

Ecoregional analysis of nearshore sea-surface temperature in the North Pacific

EPA Science Inventory

Aim Sea surface temperature (SST) has been a parameter widely-identified to be useful to the investigation of marine species distribution, migration, and invasion, especially as SSTs are predicted to be affected by climate change. Here we use a remotely-sensed dataset to focus on...

Identifying anthropogenic anomalies in air, surface and groundwater temperatures in Germany.

PubMed

Benz, Susanne A; Bayer, Peter; Blum, Philipp

2017-04-15

Human activity directly influences ambient air, surface and groundwater temperatures. The most prominent phenomenon is the urban heat island effect, which has been investigated particularly in large and densely populated cities. This study explores the anthropogenic impact on the thermal regime not only in selected urban areas, but on a countrywide scale for mean annual temperature datasets in Germany in three different compartments: measured surface air temperature, measured groundwater temperature, and satellite-derived land surface temperature. Taking nighttime lights as an indicator of rural areas, the anthropogenic heat intensity is introduced. It is applicable to each data set and provides the difference between measured local temperature and median rural background temperature. This concept is analogous to the well-established urban heat island intensity, but applicable to each measurement point or pixel of a large, even global, study area. For all three analyzed temperature datasets, anthropogenic heat intensity grows with increasing nighttime lights and declines with increasing vegetation, whereas population density has only minor effects. While surface anthropogenic heat intensity cannot be linked to specific land cover types in the studied resolution (1km×1km) and classification system, both air and groundwater show increased heat intensities for artificial surfaces. Overall, groundwater temperature appears most vulnerable to human activity, albeit the different compartments are partially influenced through unrelated processes; unlike land surface temperature and surface air temperature, groundwater temperatures are elevated in cultivated areas as well. At the surface of Germany, the highest anthropogenic heat intensity with 4.5K is found at an open-pit lignite mine near Jülich, followed by three large cities (Munich, Düsseldorf and Nuremberg) with annual mean anthropogenic heat intensities >4K. Overall, surface anthropogenic heat intensities >0K and therefore urban heat islands are observed in communities down to a population of 5000. Copyright © 2017 Elsevier B.V. All rights reserved.

Web-based interactive access, analysis and comparison of remotely sensed and in situ measured temperature data

NASA Astrophysics Data System (ADS)

Eberle, Jonas; Urban, Marcel; Hüttich, Christian; Schmullius, Christiane

2014-05-01

Numerous datasets providing temperature information from meteorological stations or remote sensing satellites are available. However, the challenging issue is to search in the archives and process the time series information for further analysis. These steps can be automated for each individual product, if the pre-conditions are complied, e.g. data access through web services (HTTP, FTP) or legal rights to redistribute the datasets. Therefore a python-based package was developed to provide data access and data processing tools for MODIS Land Surface Temperature (LST) data, which is provided by NASA Land Processed Distributed Active Archive Center (LPDAAC), as well as the Global Surface Summary of the Day (GSOD) and the Global Historical Climatology Network (GHCN) daily datasets provided by NOAA National Climatic Data Center (NCDC). The package to access and process the information is available as web services used by an interactive web portal for simple data access and analysis. Tools for time series analysis were linked to the system, e.g. time series plotting, decomposition, aggregation (monthly, seasonal, etc.), trend analyses, and breakpoint detection. Especially for temperature data a plot was integrated for the comparison of two temperature datasets based on the work by Urban et al. (2013). As a first result, a kernel density plot compares daily MODIS LST from satellites Aqua and Terra with daily means from GSOD and GHCN datasets. Without any data download and data processing, the users can analyze different time series datasets in an easy-to-use web portal. As a first use case, we built up this complimentary system with remotely sensed MODIS data and in situ measurements from meteorological stations for Siberia within the Siberian Earth System Science Cluster (www.sibessc.uni-jena.de). References: Urban, Marcel; Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane; Herold, Martin. 2013. "Comparison of Satellite-Derived Land Surface Temperature and Air Temperature from Meteorological Stations on the Pan-Arctic Scale." Remote Sens. 5, no. 5: 2348-2367. Further materials: Eberle, Jonas; Clausnitzer, Siegfried; Hüttich, Christian; Schmullius, Christiane. 2013. "Multi-Source Data Processing Middleware for Land Monitoring within a Web-Based Spatial Data Infrastructure for Siberia." ISPRS Int. J. Geo-Inf. 2, no. 3: 553-576.

Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Clayson, Carol Anne

2012-01-01

Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Goldberg, Richard; Chryssanthacopoulos, James

2014-01-01

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

Comparison of temperature effects on E. coli, Salmonella, and Enterococcus survival in surface waters

USDA-ARS?s Scientific Manuscript database

The objective of this study was to compare the dependencies of survival rates on temperature for indicator organisms E. coli and Enterococcus and the pathogen Salmonella in surface waters. A database consisting of 86 survival datasets from peer-reviewed papers on inactivation of E. coli, Salmonella...

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

USGS Publications Warehouse

Kittel, T.G.F.; Rosenbloom, N.A.; Royle, J. Andrew; Daly, Christopher; Gibson, W.P.; Fisher, H.H.; Thornton, P.; Yates, D.N.; Aulenbach, S.; Kaufman, C.; McKeown, R.; Bachelet, D.; Schimel, D.S.; Neilson, R.; Lenihan, J.; Drapek, R.; Ojima, D.S.; Parton, W.J.; Melillo, J.M.; Kicklighter, D.W.; Tian, H.; McGuire, A.D.; Sykes, M.T.; Smith, B.; Cowling, S.; Hickler, T.; Prentice, I.C.; Running, S.; Hibbard, K.A.; Post, W.M.; King, A.W.; Smith, T.; Rizzo, B.; Woodward, F.I.

2004-01-01

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5?? latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers. ?? Inter-Research 2004.

Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xiaoma; Zhou, Yuyu; Asrar, Ghassem R.

High spatiotemporal resolution air temperature (Ta) datasets are increasingly needed for assessing the impact of temperature change on people, ecosystems, and energy system, especially in the urban domains. However, such datasets are not widely available because of the large spatiotemporal heterogeneity of Ta caused by complex biophysical and socioeconomic factors such as built infrastructure and human activities. In this study, we developed a 1-km gridded dataset of daily minimum Ta (Tmin) and maximum Ta (Tmax), and the associated uncertainties, in urban and surrounding areas in the conterminous U.S. for the 2003–2016 period. Daily geographically weighted regression (GWR) models were developedmore » and used to interpolate Ta using 1 km daily land surface temperature and elevation as explanatory variables. The leave-one-out cross-validation approach indicates that our method performs reasonably well, with root mean square errors of 2.1 °C and 1.9 °C, mean absolute errors of 1.5 °C and 1.3 °C, and R 2 of 0.95 and 0.97, for Tmin and Tmax, respectively. The resulting dataset captures reasonably the spatial heterogeneity of Ta in the urban areas, and also captures effectively the urban heat island (UHI) phenomenon that Ta rises with the increase of urban development (i.e., impervious surface area). The new dataset is valuable for studying environmental impacts of urbanization such as UHI and other related effects (e.g., on building energy consumption and human health). The proposed methodology also shows a potential to build a long-term record of Ta worldwide, to fill the data gap that currently exists for studies of urban systems.« less

New NOAA-15 Advanced Microwave Sounding Unit (AMSU) Datasets for Stratospheric Research

NASA Technical Reports Server (NTRS)

Spencer, Roy W.; Braswell, William D.

1999-01-01

The NOAA-15 spacecraft launched in May 1998 carried the first Advanced Microwave Sounding Unit (AMSU). The AMSU has eleven oxygen absorption channels with weighting functions peaking from near the surface to 2 mb. Twice-daily, limb-corrected I degree gridded datasets of layer temperatures have been constructed since the AMSU went operational in early August 1998. Examples of AMSU imagery will be shown, as will preliminary analyses of daily fluctuations in tropical stratospheric temperatures and their relationship to daily variations in tropical-average rainfall measured by the Special Sensor Microwave Imager (SSM/I). The AMSU datasets are now available for other researchers to utilize.

Sea Surface Temperature for Climate Applications: A New Dataset from the European Space Agency Climate Change Initiative

NASA Astrophysics Data System (ADS)

Merchant, C. J.; Hulley, G. C.

2013-12-01

There are many datasets describing the evolution of global sea surface temperature (SST) over recent decades -- so why make another one? Answer: to provide observations of SST that have particular qualities relevant to climate applications: independence, accuracy and stability. This has been done within the European Space Agency (ESA) Climate Change Initative (CCI) project on SST. Independence refers to the fact that the new SST CCI dataset is not derived from or tuned to in situ observations. This matters for climate because the in situ observing network used to assess marine climate change (1) was not designed to monitor small changes over decadal timescales, and (2) has evolved significantly in its technology and mix of types of observation, even during the past 40 years. The potential for significant artefacts in our picture of global ocean surface warming is clear. Only by having an independent record can we confirm (or refute) that the work done to remove biases/trend artefacts in in-situ datasets has been successful. Accuracy is the degree to which SSTs are unbiased. For climate applications, a common accuracy target is 0.1 K for all regions of the ocean. Stability is the degree to which the bias, if any, in a dataset is constant over time. Long-term instability introduces trend artefacts. To observe trends of the magnitude of 'global warming', SST datasets need to be stable to <5 mK/year. The SST CCI project has produced a satellite-based dataset that addresses these characteristics relevant to climate applications. Satellite radiances (brightness temperatures) have been harmonised exploiting periods of overlapping observations between sensors. Less well-characterised sensors have had their calibration tuned to that of better characterised sensors (at radiance level). Non-conventional retrieval methods (optimal estimation) have been employed to reduce regional biases to the 0.1 K level, a target violated in most satellite SST datasets. Models for quantifying uncertainty have been developed to attach uncertainty to SST across a range of space-time scales. The stability of the data has been validated.

Globally-Gridded Interpolated Night-Time Marine Air Temperatures 1900-2014

NASA Astrophysics Data System (ADS)

Junod, R.; Christy, J. R.

2016-12-01

Over the past century, climate records have pointed to an increase in global near-surface average temperature. Near-surface air temperature over the oceans is a relatively unused parameter in understanding the current state of climate, but is useful as an independent temperature metric over the oceans and serves as a geographical and physical complement to near-surface air temperature over land. Though versions of this dataset exist (i.e. HadMAT1 and HadNMAT2), it has been strongly recommended that various groups generate climate records independently. This University of Alabama in Huntsville (UAH) study began with the construction of monthly night-time marine air temperature (UAHNMAT) values from the early-twentieth century through to the present era. Data from the International Comprehensive Ocean and Atmosphere Data Set (ICOADS) were used to compile a time series of gridded UAHNMAT, (20S-70N). This time series was homogenized to correct for the many biases such as increasing ship height, solar deck heating, etc. The time series of UAHNMAT, once adjusted to a standard reference height, is gridded to 1.25° pentad grid boxes and interpolated using the kriging interpolation technique. This study will present results which quantify the variability and trends and compare to current trends of other related datasets that include HadNMAT2 and sea-surface temperatures (HadISST & ERSSTv4).

Fort Bliss Geothermal Area Data: Temperature profile, logs, schematic model and cross section

DOE Data Explorer

Adam Brandt

2015-11-15

This dataset contains a variety of data about the Fort Bliss geothermal area, part of the southern portion of the Tularosa Basin, New Mexico. The dataset contains schematic models for the McGregor Geothermal System, a shallow temperature survey of the Fort Bliss geothermal area. The dataset also contains Century OH logs, a full temperature profile, and complete logs from well RMI 56-5, including resistivity and porosity data, drill logs with drill rate, depth, lithology, mineralogy, fractures, temperature, pit total, gases, and descriptions among other measurements as well as CDL, CNL, DIL, GR Caliper and Temperature files. A shallow (2 meter depth) temperature survey of the Fort Bliss geothermal area with 63 data points is also included. Two cross sections through the Fort Bliss area, also included, show well position and depth. The surface map included shows faults and well spatial distribution. Inferred and observed fault distributions from gravity surveys around the Fort Bliss geothermal area.

Evaluation of Greenland near surface air temperature datasets

DOE PAGES

Reeves Eyre, J. E. Jack; Zeng, Xubin

2017-07-05

Near-surface air temperature (SAT) over Greenland has important effects on mass balance of the ice sheet, but it is unclear which SAT datasets are reliable in the region. Here extensive in situ SAT measurements ( ∼  1400 station-years) are used to assess monthly mean SAT from seven global reanalysis datasets, five gridded SAT analyses, one satellite retrieval and three dynamically downscaled reanalyses. Strengths and weaknesses of these products are identified, and their biases are found to vary by season and glaciological regime. MERRA2 reanalysis overall performs best with mean absolute error less than 2 °C in all months. Ice sheet-average annual mean SAT frommore » different datasets are highly correlated in recent decades, but their 1901–2000 trends differ even in sign. Compared with the MERRA2 climatology combined with gridded SAT analysis anomalies, thirty-one earth system model historical runs from the CMIP5 archive reach  ∼  5 °C for the 1901–2000 average bias and have opposite trends for a number of sub-periods.« less

Evaluation of Greenland near surface air temperature datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reeves Eyre, J. E. Jack; Zeng, Xubin

Near-surface air temperature (SAT) over Greenland has important effects on mass balance of the ice sheet, but it is unclear which SAT datasets are reliable in the region. Here extensive in situ SAT measurements ( ∼  1400 station-years) are used to assess monthly mean SAT from seven global reanalysis datasets, five gridded SAT analyses, one satellite retrieval and three dynamically downscaled reanalyses. Strengths and weaknesses of these products are identified, and their biases are found to vary by season and glaciological regime. MERRA2 reanalysis overall performs best with mean absolute error less than 2 °C in all months. Ice sheet-average annual mean SAT frommore » different datasets are highly correlated in recent decades, but their 1901–2000 trends differ even in sign. Compared with the MERRA2 climatology combined with gridded SAT analysis anomalies, thirty-one earth system model historical runs from the CMIP5 archive reach  ∼  5 °C for the 1901–2000 average bias and have opposite trends for a number of sub-periods.« less

Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products

USGS Publications Warehouse

Ji, Lei; Senay, Gabriel B.; Verdin, James P.

2015-01-01

There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

Moving towards Hyper-Resolution Hydrologic Modeling

NASA Astrophysics Data System (ADS)

Rouf, T.; Maggioni, V.; Houser, P.; Mei, Y.

2017-12-01

Developing a predictive capability for terrestrial hydrology across landscapes, with water, energy and nutrients as the drivers of these dynamic systems, faces the challenge of scaling meter-scale process understanding to practical modeling scales. Hyper-resolution land surface modeling can provide a framework for addressing science questions that we are not able to answer with coarse modeling scales. In this study, we develop a hyper-resolution forcing dataset from coarser resolution products using a physically based downscaling approach. These downscaling techniques rely on correlations with landscape variables, such as topography, roughness, and land cover. A proof-of-concept has been implemented over the Oklahoma domain, where high-resolution observations are available for validation purposes. Hourly NLDAS (North America Land Data Assimilation System) forcing data (i.e., near-surface air temperature, pressure, and humidity) have been downscaled to 500m resolution over the study area for 2015-present. Results show that correlation coefficients between the downscaled temperature dataset and ground observations are consistently higher than the ones between the NLDAS temperature data at their native resolution and ground observations. Not only correlation coefficients are higher, but also the deviation around the 1:1 line in the density scatterplots is smaller for the downscaled dataset than the original one with respect to the ground observations. Results are therefore encouraging as they demonstrate that the 500m temperature dataset has a good agreement with the ground information and can be adopted to force the land surface model for soil moisture estimation. The study has been expanded to wind speed and direction, incident longwave and shortwave radiation, pressure, and precipitation. Precipitation is well known to vary dramatically with elevation and orography. Therefore, we are pursuing a downscaling technique based on both topographical and vegetation characteristics.

Establishment and analysis of a High-Resolution Assimilation Dataset of the water-energy cycle in China

NASA Astrophysics Data System (ADS)

Wen, Xiaohang; Dong, Wenjie; Yuan, Wenping; Zheng, Zhiyuan

For better prediction and understanding of land-atmospheric interaction, in-situ observed meteorological data acquired from the China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated using the Normalized Difference Vegetation Index (NDVI) of the Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS) and Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system. Furthermore, the WRF model produced a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC). This dataset has a horizontal resolution of 25 km for near surface meteorological data, such as air temperature, humidity, wind vectors and pressure (19 levels); soil temperature and moisture (four levels); surface temperature; downward/upward short/long radiation; 3-h latent heat flux; sensible heat flux; and ground heat flux. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method and 2) compare results of meteorological elements, such as 2 m temperature and precipitation generated by the HRADC with the gridded observation data from CMA, and surface temperature and specific humidity with Global Land Data Assimilation System (GLDAS) output data from the National Aeronautics and Space Administration (NASA). We find that the simulated results of monthly 2 m temperature from HRADC is improved compared with the control simulation and has effectively reproduced the observed patterns. The simulated special distribution of ground surface temperature and specific humidity from HRADC are much closer to GLDAS outputs. The spatial distribution of root mean square errors (RMSE) and bias of 2 m temperature between observations and HRADC is reduced compared with the bias between observations and the control run. The monthly spatial distribution of surface temperature and specific humidity from HRADC is consistent with the GLDAS outputs over China. This study could improve the land surface parameters by utilizing remote sensing data and could further improve atmospheric elements with a data assimilation system. This work provides an effective attempt at combining multi-source data with different spatial and temporal scales into numerical simulations, and the simulated results could be used in further research on the long-term climatic effects and characteristics of the water-energy cycle over China.

Surface Meteorological Station - ARL 2m, ancillary flux, Prineville - Raw Data

DOE Data Explorer

Clawson, kirk

2017-10-23

These data contain measurements from a 4-component net radiometer, as well as 2-m temperature, pressure, and relative humidity (RH). Measurements of soil moisture and temperature and soil heat fluxes also are included in the dataset.

Sensitivity of Offshore Surface Fluxes and Sea Breezes to the Spatial Distribution of Sea-Surface Temperature

NASA Astrophysics Data System (ADS)

Lombardo, Kelly; Sinsky, Eric; Edson, James; Whitney, Michael M.; Jia, Yan

2018-03-01

A series of numerical sensitivity experiments is performed to quantify the impact of sea-surface temperature (SST) distribution on offshore surface fluxes and simulated sea-breeze dynamics. The SST simulations of two mid-latitude sea-breeze events over coastal New England are performed using a spatially-uniform SST, as well as spatially-varying SST datasets of 32- and 1-km horizontal resolutions. Offshore surface heat and buoyancy fluxes vary in response to the SST distribution. Local sea-breeze circulations are relatively insensitive, with minimal differences in vertical structure and propagation speed among the experiments. The largest thermal perturbations are confined to the lowest 10% of the sea-breeze column due to the relatively high stability of the mid-Atlantic marine atmospheric boundary layer (ABL) suppressing vertical mixing, resulting in the depth of the marine layer remaining unchanged. Minimal impacts on the column-averaged virtual potential temperature and sea-breeze depth translates to small changes in sea-breeze propagation speed. This indicates that the use of datasets with a fine-scale SST may not produce more accurate sea-breeze simulations in highly stable marine ABL regimes, though may prove more beneficial in less stable sub-tropical environments.

Pacific Ocean buoy temperature date

EPA Pesticide Factsheets

Pacific Ocean buoy temperature dataThis dataset is associated with the following publication:Carbone, F., M. Landis, C.N. Gencarelli, A. Naccarato, F. Sprovieri, F. De Simone, I.M. Hedgecock, and N. Pirrone. Sea surface temperature variation linked to elemental mercury concentrations measured on Mauna Loa. GEOPHYSICAL RESEARCH LETTERS. American Geophysical Union, Washington, DC, USA, online, (2016).

Evaluation and inter-comparison of modern day reanalysis datasets over Africa and the Middle East

NASA Astrophysics Data System (ADS)

Shukla, S.; Arsenault, K. R.; Hobbins, M.; Peters-Lidard, C. D.; Verdin, J. P.

2015-12-01

Reanalysis datasets are potentially very valuable for otherwise data-sparse regions such as Africa and the Middle East. They are potentially useful for long-term climate and hydrologic analyses and, given their availability in real-time, they are particularity attractive for real-time hydrologic monitoring purposes (e.g. to monitor flood and drought events). Generally in data-sparse regions, reanalysis variables such as precipitation, temperature, radiation and humidity are used in conjunction with in-situ and/or satellite-based datasets to generate long-term gridded atmospheric forcing datasets. These atmospheric forcing datasets are used to drive offline land surface models and simulate soil moisture and runoff, which are natural indicators of hydrologic conditions. Therefore, any uncertainty or bias in the reanalysis datasets contributes to uncertainties in hydrologic monitoring estimates. In this presentation, we report on a comprehensive analysis that evaluates several modern-day reanalysis products (such as NASA's MERRA-1 and -2, ECMWF's ERA-Interim and NCEP's CFS Reanalysis) over Africa and the Middle East region. We compare the precipitation and temperature from the reanalysis products with other independent gridded datasets such as GPCC, CRU, and USGS/UCSB's CHIRPS precipitation datasets, and CRU's temperature datasets. The evaluations are conducted at a monthly time scale, since some of these independent datasets are only available at this temporal resolution. The evaluations range from the comparison of the monthly mean climatology to inter-annual variability and long-term changes. Finally, we also present the results of inter-comparisons of radiation and humidity variables from the different reanalysis datasets.

A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009

PubMed Central

Sharma, Sapna; Gray, Derek K; Read, Jordan S; O’Reilly, Catherine M; Schneider, Philipp; Qudrat, Anam; Gries, Corinna; Stefanoff, Samantha; Hampton, Stephanie E; Hook, Simon; Lenters, John D; Livingstone, David M; McIntyre, Peter B; Adrian, Rita; Allan, Mathew G; Anneville, Orlane; Arvola, Lauri; Austin, Jay; Bailey, John; Baron, Jill S; Brookes, Justin; Chen, Yuwei; Daly, Robert; Dokulil, Martin; Dong, Bo; Ewing, Kye; de Eyto, Elvira; Hamilton, David; Havens, Karl; Haydon, Shane; Hetzenauer, Harald; Heneberry, Jocelyne; Hetherington, Amy L; Higgins, Scott N; Hixson, Eric; Izmest’eva, Lyubov R; Jones, Benjamin M; Kangur, Külli; Kasprzak, Peter; Köster, Olivier; Kraemer, Benjamin M; Kumagai, Michio; Kuusisto, Esko; Leshkevich, George; May, Linda; MacIntyre, Sally; Müller-Navarra, Dörthe; Naumenko, Mikhail; Noges, Peeter; Noges, Tiina; Niederhauser, Pius; North, Ryan P; Paterson, Andrew M; Plisnier, Pierre-Denis; Rigosi, Anna; Rimmer, Alon; Rogora, Michela; Rudstam, Lars; Rusak, James A; Salmaso, Nico; Samal, Nihar R; Schindler, Daniel E; Schladow, Geoffrey; Schmidt, Silke R; Schultz, Tracey; Silow, Eugene A; Straile, Dietmar; Teubner, Katrin; Verburg, Piet; Voutilainen, Ari; Watkinson, Andrew; Weyhenmeyer, Gesa A; Williamson, Craig E; Woo, Kara H

2015-01-01

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global data set of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985–2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues. PMID:25977814

A global database of lake surface temperatures collected by in situ and satellite methods from 1985-2009.

PubMed

Sharma, Sapna; Gray, Derek K; Read, Jordan S; O'Reilly, Catherine M; Schneider, Philipp; Qudrat, Anam; Gries, Corinna; Stefanoff, Samantha; Hampton, Stephanie E; Hook, Simon; Lenters, John D; Livingstone, David M; McIntyre, Peter B; Adrian, Rita; Allan, Mathew G; Anneville, Orlane; Arvola, Lauri; Austin, Jay; Bailey, John; Baron, Jill S; Brookes, Justin; Chen, Yuwei; Daly, Robert; Dokulil, Martin; Dong, Bo; Ewing, Kye; de Eyto, Elvira; Hamilton, David; Havens, Karl; Haydon, Shane; Hetzenauer, Harald; Heneberry, Jocelyne; Hetherington, Amy L; Higgins, Scott N; Hixson, Eric; Izmest'eva, Lyubov R; Jones, Benjamin M; Kangur, Külli; Kasprzak, Peter; Köster, Olivier; Kraemer, Benjamin M; Kumagai, Michio; Kuusisto, Esko; Leshkevich, George; May, Linda; MacIntyre, Sally; Müller-Navarra, Dörthe; Naumenko, Mikhail; Noges, Peeter; Noges, Tiina; Niederhauser, Pius; North, Ryan P; Paterson, Andrew M; Plisnier, Pierre-Denis; Rigosi, Anna; Rimmer, Alon; Rogora, Michela; Rudstam, Lars; Rusak, James A; Salmaso, Nico; Samal, Nihar R; Schindler, Daniel E; Schladow, Geoffrey; Schmidt, Silke R; Schultz, Tracey; Silow, Eugene A; Straile, Dietmar; Teubner, Katrin; Verburg, Piet; Voutilainen, Ari; Watkinson, Andrew; Weyhenmeyer, Gesa A; Williamson, Craig E; Woo, Kara H

2015-01-01

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global data set of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues.

A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009

USGS Publications Warehouse

Sharma, Sapna; Gray, Derek; Read, Jordan S.; O'Reilly, Catherine; Schneider, Philipp; Qudrat, Anam; Gries, Corinna; Stefanoff, Samantha; Hampton, Stephanie; Hook, Simon; Lenters, John; Livingstone, David M.; McIntyre, Peter B.; Adrian, Rita; Allan, Mathew; Anneville, Orlane; Arvola, Lauri; Austin, Jay; Bailey, John E.; Baron, Jill S.; Brookes, Justin D; Chen, Yuwei; Daly, Robert; Ewing, Kye; de Eyto, Elvira; Dokulil, Martin; Hamilton, David B.; Havens, Karl; Haydon, Shane; Hetzenaeur, Harald; Heneberry, Jocelyn; Hetherington, Amy; Higgins, Scott; Hixson, Eric; Izmest'eva, Lyubov; Jones, Benjamin M.; Kangur, Kulli; Kasprzak, Peter; Kraemer, Benjamin; Kumagai, Michio; Kuusisto, Esko; Leshkevich, George; May, Linda; MacIntyre, Sally; Dörthe Müller-Navarra,; Naumenko, Mikhail; Noges, Peeter; Noges, Tiina; Pius Niederhauser,; North, Ryan P.; Andrew Paterson,; Plisnier, Pierre-Denis; Rigosi, Anna; Rimmer, Alon; Rogora, Michela; Rudstam, Lars G.; Rusak, James A.; Salmaso, Nico; Samal, Nihar R.; Daniel E. Schindler,; Geoffrey Schladow,; Schmidt, Silke R.; Tracey Schultz,; Silow, Eugene A.; Straile, Dietmar; Teubner, Katrin; Verburg, Piet; Voutilainen, Ari; Watkinson, Andrew; Weyhenmeyer, Gesa A.; Craig E. Williamson,; Kara H. Woo,

2015-01-01

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global data set of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985–2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues.

Inter-annual Tropospheric Aerosol Variability in Late Twentieth Century and its Impact on Tropical Atlantic and West African Climate by Direct and Semi-direct Effects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Evans, Katherine J; Hack, James J; Truesdale, John

A new high-resolution (0.9more » $$^{\\circ}$$x1.25$$^{\\circ}$$ in the horizontal) global tropospheric aerosol dataset with monthly resolution is generated using the finite-volume configuration of Community Atmosphere Model (CAM4) coupled to a bulk aerosol model and forced with recent estimates of surface emissions for the latter part of twentieth century. The surface emissions dataset is constructed from Coupled Model Inter-comparison Project (CMIP5) decadal-resolution surface emissions dataset to include REanalysis of TROpospheric chemical composition (RETRO) wildfire monthly emissions dataset. Experiments forced with the new tropospheric aerosol dataset and conducted using the spectral configuration of CAM4 with a T85 truncation (1.4$$^{\\circ}$$x1.4$$^{\\circ}$$) with prescribed twentieth century observed sea surface temperature, sea-ice and greenhouse gases reveal that variations in tropospheric aerosol levels can induce significant regional climate variability on the inter-annual timescales. Regression analyses over tropical Atlantic and Africa reveal that increasing dust aerosols can cool the North African landmass and shift convection southwards from West Africa into the Gulf of Guinea in the spring season in the simulations. Further, we find that increasing carbonaceous aerosols emanating from the southwestern African savannas can cool the region significantly and increase the marine stratocumulus cloud cover over the southeast tropical Atlantic ocean by aerosol-induced diabatic heating of the free troposphere above the low clouds. Experiments conducted with CAM4 coupled to a slab ocean model suggest that present day aerosols can shift the ITCZ southwards over the tropical Atlantic and can reduce the ocean mixed layer temperature beneath the increased marine stratocumulus clouds in the southeastern tropical Atlantic.« less

Version 2 Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF2)

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Nelkin, Eric; Ardizzone, Joe; Atlas, Robert M.; Shie, Chung-Lin; Starr, David O'C. (Technical Monitor)

2002-01-01

Information on the turbulent fluxes of momentum, moisture, and heat at the air-sea interface is essential in improving model simulations of climate variations and in climate studies. We have derived a 13.5-year (July 1987-December 2000) dataset of daily surface turbulent fluxes over global oceans from the Special Sensor Mcrowave/Imager (SSM/I) radiance measurements. This dataset, version 2 Goddard Satellite-based Surface Turbulent Fluxes (GSSTF2), has a spatial resolution of 1 degree x 1 degree latitude-longitude and a temporal resolution of 1 day. Turbulent fluxes are derived from the SSM/I surface winds and surface air humidity, as well as the 2-m air and sea surface temperatures (SST) of the NCEP/NCAR reanalysis, using a bulk aerodynamic algorithm based on the surface layer similarity theory.

Clear-sky irradiance simulation using GMAO products and its comparison to ground and CERES satellite observation

NASA Astrophysics Data System (ADS)

Ham, S. H.; Loeb, N. G.; Kato, S.; Rose, F. G.; Bosilovich, M. G.; Rutan, D. A.; Huang, X.; Collow, A.

2017-12-01

Global Modeling Assimilation Office (GMAO) GEOS assimilated datasets are used to describe temperature and humidity profiles in the Clouds and the Earth's Radiant Energy System (CERES) data processing. Given that advance versions of the assimilated data sets known as of Forward Processing (FP), FP Parallel (FPP), and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) datasets are available, we examine clear-sky irradiance calculation to see if accuracy is improved with these newer versions of GMAO datasets when their temperature and humidity profiles are used in computing irradiances. Two older versions, GEOS-5.2.0 and GEOS-5.4.1 are used for producing, respectively, Ed3 and Ed4 CERES data products. For the evaluation, CERES-derived TOA irradiances and observed ground-based surface irradiances are compared with the computed irradiances for clear skies identified by Moderate Resolution Imaging Spectroradiometer (MODIS). Surface type dependent spectral emissivity is taken from an observationally-based monthly gridded emissivity dataset. TOA longwave (LW) irradiances computed with GOES-5.2.0 temperature and humidity profiles are biased low, up to -5 Wm-2, compared to CERES-derived TOA longwave irradiance over tropical oceans. In contrast, computed longwave irradiances agree well with CERES observations with the biases less than 2 W m-2 when GOES-5.4.1, FP v5.13, or MERRA-2 temperature and humidity are used. The negative biases of the TOA LW irradiance computed with GOES-5.2.0 appear to be related to a wet bias at 500-850 hPa layer. This indicates that if the input of CERES algorithm switches from GOES-5.2.0 to FP v5.13 or MERRA-2, the bias in clear-sky longwave TOA fluxes over tropical oceans is expected to be smaller. At surface, downward LW irradiances computed with FP v5.13 and MERRA-2 are biased low, up to -10 Wm-2, compared to ground observations over tropical oceans. The magnitude of the bias in the longwave surface irradiances cannot be explained by uncertainties related to aerosol, which is estimated to be less than 2.5 W m-2. Therefore, the negative biases are likely caused by cold or dry biases in FP v5.13 and MERRA-2 datasets. We plan to continue the investigation with more ground sites.

Recent Upgrades to NASA SPoRT Initialization Datasets for the Environmental Modeling System

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Lafontaine, Frank J.; Molthan, Andrew L.; Zavodsky, Bradley T.; Rozumalski, Robert A.

2012-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its NOAA/National Weather Service (NWS) partners that can initialize specific fields for local model runs within the NOAA/NWS Science and Training Resource Center Environmental Modeling System (EMS). The suite of SPoRT products for use in the EMS consists of a Sea Surface Temperature (SST) composite that includes a Lake Surface Temperature (LST) analysis over the Great Lakes, a Great Lakes sea-ice extent within the SST composite, a real-time Green Vegetation Fraction (GVF) composite, and NASA Land Information System (LIS) gridded output. This paper and companion poster describe each dataset and provide recent upgrades made to the SST, Great Lakes LST, GVF composites, and the real-time LIS runs.

Spatiotemporal Evaluation of Reanalysis and In-situ Surface Air Temperature over Ethiopia

NASA Astrophysics Data System (ADS)

Tesfaye, T.

2017-12-01

Tewodros Woldemariam Tesfaye*1, C.T. Dhanya 2,and A.K. Gosain3 1Research Scholar, Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi-110016, India 2Assistant Professor, Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi-110016, India 3 Professor, Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi-110016, India, *e-mail: tewodros2002@gmail.com Abstract: Water resources management and modelling studies are often constrained by the scarcity of observed data, especially of the two major variables i.e., precipitation and temperature. Modellers, hence, rely on reanalysis datasets as a substitute; though its performance heavily vary depending on the data availability and regional characteristics. The present study aims at examining the ability of frequently used reanalysis datasets in capturing the spatiotemporal characteristics of maximum and minimum surface temperatures over Ethiopia and to highlight the biases, if any, in these over Ethiopian region. We considered ERA-Interim, NCEP 2, MERRA and CFSR reanalysis datasets and compared these with temperature observations from 15 synoptic stations spread over Ethiopia. In addition to the long term averages and annual cycle, a critical comparison of various extreme indices such as diurnal temperature range, warm days, warm nights, cool days, cool nights, summer days and tropical nights are also undertaken. Our results indicate that, the performance of CFSR followed by NCEP 2 is better in capturing majority of the aspects. ERA-Interim suffers a huge additive bias in the simulation of various aspects of minimum temperature in all the stations considered; while its performance is better for maximum temperature. The inferior performance of ERA-Interim is noted to be only because of the difficulty in simulating minimum temperature. Key words: ERA Interim; NCEP Reanalysis; MERRA; CFSR; Diurnal temperature range; reanalysis performance.

A TEX86 surface sediment database and extended Bayesian calibration

NASA Astrophysics Data System (ADS)

Tierney, Jessica E.; Tingley, Martin P.

2015-06-01

Quantitative estimates of past temperature changes are a cornerstone of paleoclimatology. For a number of marine sediment-based proxies, the accuracy and precision of past temperature reconstructions depends on a spatial calibration of modern surface sediment measurements to overlying water temperatures. Here, we present a database of 1095 surface sediment measurements of TEX86, a temperature proxy based on the relative cyclization of marine archaeal glycerol dialkyl glycerol tetraether (GDGT) lipids. The dataset is archived in a machine-readable format with geospatial information, fractional abundances of lipids (if available), and metadata. We use this new database to update surface and subsurface temperature calibration models for TEX86 and demonstrate the applicability of the TEX86 proxy to past temperature prediction. The TEX86 database confirms that surface sediment GDGT distribution has a strong relationship to temperature, which accounts for over 70% of the variance in the data. Future efforts, made possible by the data presented here, will seek to identify variables with secondary relationships to GDGT distributions, such as archaeal community composition.

The South American Land Data Assimilation System (SALDAS) 5-Year Retrospective Atmospheric Forcing Datasets

NASA Technical Reports Server (NTRS)

deGoncalves, Luis Gustavo G.; Shuttleworth, William J.; Vila, Daniel; Larroza, Elaine; Bottino, Marcus J.; Herdies, Dirceu L.; Aravequia, Jose A.; De Mattos, Joao G. Z.; Toll, David L.; Rodell, Matthew;

Modeling the effects of diagenesis on carbonate clumped-isotope values in deep- and shallow-water settings

NASA Astrophysics Data System (ADS)

Stolper, Daniel A.; Eiler, John M.; Higgins, John A.

2018-04-01

The measurement of multiply isotopically substituted ('clumped isotope') carbonate groups provides a way to reconstruct past mineral formation temperatures. However, dissolution-reprecipitation (i.e., recrystallization) reactions, which commonly occur during sedimentary burial, can alter a sample's clumped-isotope composition such that it partially or wholly reflects deeper burial temperatures. Here we derive a quantitative model of diagenesis to explore how diagenesis alters carbonate clumped-isotope values. We apply the model to a new dataset from deep-sea sediments taken from Ocean Drilling Project site 807 in the equatorial Pacific. This dataset is used to ground truth the model. We demonstrate that the use of the model with accompanying carbonate clumped-isotope and carbonate δ18O values provides new constraints on both the diagenetic history of deep-sea settings as well as past equatorial sea-surface temperatures. Specifically, the combination of the diagenetic model and data support previous work that indicates equatorial sea-surface temperatures were warmer in the Paleogene as compared to today. We then explore whether the model is applicable to shallow-water settings commonly preserved in the rock record. Using a previously published dataset from the Bahamas, we demonstrate that the model captures the main trends of the data as a function of burial depth and thus appears applicable to a range of depositional settings.

Reconstruction of a Three Hourly 1-km Land Surface Air Temperature Dataset in the Qinghai-Tibet Plateau

NASA Astrophysics Data System (ADS)

Zhou, J.; Ding, L.

2017-12-01

Land surface air temperature (SAT) is an important parameter in the modeling of radiation balance and energy budget of the earth surface. Generally, SAT is measured at ground meteorological stations; then SAT mapping is possible though a spatial interpolation process. The interpolated SAT map relies on the spatial distribution of ground stations, the terrain, and many other factors; thus, it has great uncertainties in regions with complicated terrain. Instead, SAT map can also be obtained through physical modeling of interactions between the land surface and the atmosphere. Such dataset generally has coarse spatial resolution (e.g. coarser than 0.1°) and cannot satisfy the applications at fine scales, e.g. 1 km. This presentation reports the reconstruction of a three hourly 1-km SAT dataset from 2001 to 2015 over the Qinghai-Tibet Plateau. The terrain in the Qinghai-Tibet Plateau, especially in the eastern part, is extremely complicated. Two SAT datasets with good qualities are used in this study. The first one is from the 3h China Meteorological Forcing Dataset with a 0.1° resolution released by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Yang et al., 2010); the second one is from the ERA-Interim product with the same temporal resolution and a 0.125° resolution. A statistical approach is developed to downscale the spatial resolution of the derived SAT to 1-km. The elevation and the normalized difference vegetation index (NDVI) are selected as two scaling factors in the downscaling approach. Results demonstrate there is significantly negative correlation between the SAT and elevation in all seasons; there is also significantly negative correlation between the SAT and NDVI in the vegetation growth seasons, while the correlation decreases in the other seasons. Therefore, a temporally dynamic downscaling approach is feasible to enhance the spatial resolution of the SAT. Compared with the SAT at the 0.1° or 0.125°, the reconstructed 1-km SAT can provide much more spatial details in areas with complicated terrain. Additionally, the 1-km SAT agrees well with the ground measured air temperatures as well as the SAT before downscaling. The reconstructed SAT will be beneficial for the modeling of surface radiation balance and energy budget over the Qinghai-Tibet Plateau.

Titan Surface Temperatures as Measured by Cassini CIRS

NASA Technical Reports Server (NTRS)

Jennings, Donald E.; Flasar, F.M.; Kunde, V.G.; Nixon, C.A.; Romani, P.N.; Samuelson, R.E.; Coustenis, A.; Courtin, R.

2009-01-01

Thermal radiation from the surface of Titan reaches space through a spectral window of low opacity at 19-microns wavelength. This radiance gives a measure of the brightness temperature of the surface. Composite Infrared Spectrometer' (CIRS) observations from Cassini during its first four years at Saturn have permitted latitude mapping of zonally averaged surface temperatures. The measurements are corrected for atmospheric opacity using the dependence of radiance on emission angle. With the more complete latitude coverage and much larger dataset of CIRS we have improved upon the original results from Voyager IRIS. CIRS measures the equatorial surface brightness temperature to be 93.7+/-0.6 K, the same as the temperature measured at the Huygens landing site. The surface brightness temperature decreases by 2 K toward the south pole and by 3 K toward the north pole. The drop in surface temperature between equator and north pole implies a 50% decrease in methane saturation vapor pressure and relative humidity; this may help explain the large northern lakes. The H2 mole fraction is derived as a by-product of our analysis and agrees with previous results. Evidence of seasonal variation in surface and atmospheric temperatures is emerging from CIRS measurements over the Cassini mission.

Comparison of Radiative Energy Flows in Observational Datasets and Climate Modeling

NASA Technical Reports Server (NTRS)

Raschke, Ehrhard; Kinne, Stefan; Rossow, William B.; Stackhouse, Paul W. Jr.; Wild, Martin

2016-01-01

This study examines radiative flux distributions and local spread of values from three major observational datasets (CERES, ISCCP, and SRB) and compares them with results from climate modeling (CMIP3). Examinations of the spread and differences also differentiate among contributions from cloudy and clear-sky conditions. The spread among observational datasets is in large part caused by noncloud ancillary data. Average differences of at least 10Wm(exp -2) each for clear-sky downward solar, upward solar, and upward infrared fluxes at the surface demonstrate via spatial difference patterns major differences in assumptions for atmospheric aerosol, solar surface albedo and surface temperature, and/or emittance in observational datasets. At the top of the atmosphere (TOA), observational datasets are less influenced by the ancillary data errors than at the surface. Comparisons of spatial radiative flux distributions at the TOA between observations and climate modeling indicate large deficiencies in the strength and distribution of model-simulated cloud radiative effects. Differences are largest for lower-altitude clouds over low-latitude oceans. Global modeling simulates stronger cloud radiative effects (CRE) by +30Wmexp -2) over trade wind cumulus regions, yet smaller CRE by about -30Wm(exp -2) over (smaller in area) stratocumulus regions. At the surface, climate modeling simulates on average about 15Wm(exp -2) smaller radiative net flux imbalances, as if climate modeling underestimates latent heat release (and precipitation). Relative to observational datasets, simulated surface net fluxes are particularly lower over oceanic trade wind regions (where global modeling tends to overestimate the radiative impact of clouds). Still, with the uncertainty in noncloud ancillary data, observational data do not establish a reliable reference.

Errors of five-day mean surface wind and temperature conditions due to inadequate sampling

NASA Technical Reports Server (NTRS)

Legler, David M.

1991-01-01

Surface meteorological reports of wind components, wind speed, air temperature, and sea-surface temperature from buoys located in equatorial and midlatitude regions are used in a simulation of random sampling to determine errors of the calculated means due to inadequate sampling. Subsampling the data with several different sample sizes leads to estimates of the accuracy of the subsampled means. The number N of random observations needed to compute mean winds with chosen accuracies of 0.5 (N sub 0.5) and 1.0 (N sub 1,0) m/s and mean air and sea surface temperatures with chosen accuracies of 0.1 (N sub 0.1) and 0.2 (N sub 0.2) C were calculated for each 5-day and 30-day period in the buoy datasets. Mean values of N for the various accuracies and datasets are given. A second-order polynomial relation is established between N and the variability of the data record. This relationship demonstrates that for the same accuracy, N increases as the variability of the data record increases. The relationship is also independent of the data source. Volunteer-observing ship data do not satisfy the recommended minimum number of observations for obtaining 0.5 m/s and 0.2 C accuracy for most locations. The effect of having remotely sensed data is discussed.

Comparison of two Centennial-scale Sea Surface Temperature Datasets in the Regional Climate Change Studies of the China Seas

NASA Astrophysics Data System (ADS)

Qingyuan, Wang; Yanan, Wang; Yiwei, Liu

2017-08-01

Two widely used sea surface temperature (SST) datasets are compared in this article. We examine characteristics in the climate variability of SST in the China Seas.Two series yielded almost the same warming trend for 1890-2013 (0.7-0.8°C/100 years). However, HadISST1 series shows much stronger warming trends during 1961-2013 and 1981-2013 than that of COBE SST2 series. The disagreement between data sets was marked after 1981. For the hiatus period 1998-2013, the cooling trends of HadISST1 series is much lower than that of COBE SST2. These differences between the two datasets are possibly caused by the different observations which are incorporated to fill with data-sparse regions since 1982. Those findings illustrate that there are some uncertainties in the estimate of SST warming patterns in certain regions. The results also indicate that the temporal and spatial deficiency of observed data is still the biggest handicap for analyzing multi-scale SST characteristics in regional area.

Global Validation of MODIS Atmospheric Profile-Derived Near-Surface Air Temperature and Dew Point Estimates

NASA Astrophysics Data System (ADS)

Famiglietti, C.; Fisher, J.; Halverson, G. H.

2017-12-01

This study validates a method of remote sensing near-surface meteorology that vertically interpolates MODIS atmospheric profiles to surface pressure level. The extraction of air temperature and dew point observations at a two-meter reference height from 2001 to 2014 yields global moderate- to fine-resolution near-surface temperature distributions that are compared to geographically and temporally corresponding measurements from 114 ground meteorological stations distributed worldwide. This analysis is the first robust, large-scale validation of the MODIS-derived near-surface air temperature and dew point estimates, both of which serve as key inputs in models of energy, water, and carbon exchange between the land surface and the atmosphere. Results show strong linear correlations between remotely sensed and in-situ near-surface air temperature measurements (R2 = 0.89), as well as between dew point observations (R2 = 0.77). Performance is relatively uniform across climate zones. The extension of mean climate-wise percent errors to the entire remote sensing dataset allows for the determination of MODIS air temperature and dew point uncertainties on a global scale.

Spatial distribution of pingos in Northern Asia

USGS Publications Warehouse

Grosse, G.; Jones, Benjamin M.

2010-01-01

Pingos are prominent periglacial landforms in vast regions of the Arctic and Subarctic. They are indicators of modern and past conditions of permafrost, surface geology, hydrology and climate. A first version of a detailed spatial geodatabase of more than 6000 pingo locations in a 3.5 ?? 106 km2 region of Northern Asia was assembled from topographic maps. A first order analysis was carried out with respect to permafrost, landscape characteristics, surface geology, hydrology, climate, and elevation datasets using a Geographic Information System (GIS). Pingo heights in the dataset vary between 2 and 37 m, with a mean height of 4.8 m. About 64% of the pingos occur in continuous permafrost with high ice content and thick sediments; another 19% in continuous permafrost with moderate ice content and thick sediments. The majority of these pingos likely formed through closed system freezing, typical of those located in drained thermokarst lake basins of northern lowlands with continuous permafrost. About 82% of the pingos are located in the tundra bioclimatic zone. Most pingos in the dataset are located in regions with mean annual ground temperatures between -3 and -11 ??C and mean annual air temperatures between -7 and -18 ??C. The dataset confirms that surface geology and hydrology are key factors for pingo formation and occurrence. Based on model predictions for near-future permafrost distribution, hundreds of pingos along the southern margins of permafrost will be located in regions with thawing permafrost by 2100, which ultimately may lead to increased occurrence of pingo collapse. Based on our dataset and previously published estimates of pingo numbers from other regions, we conclude that there are more than 11 000 pingos on Earth. ?? 2010 Author(s).

A 20-year collection of sub-surface salinity and temperature observations for the Australian shelf seas

NASA Astrophysics Data System (ADS)

Proctor, R.; Mancini, S.; Hoenner, X.; Tattersall, K.; Pasquer, B.; Galibert, G.; Moltmann, T.

2016-02-01

Salinity and temperature measurements from different sources have been assembled into a common data structure in a relational database. Quality Control flags have been mapped to a common scheme and associated to each measurement. For datasets like gliders, moorings or ship underway which are sampled at high temporal resolution (e.g. data every second) a binning and sub-sampling approach has been applied to some datasets in order to reduce the number of measurements to hourly sampling. After averaging approximately 25 Million measurements are available in this dataset collection. A national shelf and coastal data atlas has been created using all the temperature and salinity measurements that pass various quality control checks. These observations have been binned spatially on a horizontal grid of ¼ degree with standard vertical levels (every 10 meters from the surface to 500m depth) and temporally on a monthly time range over the period January 1995 to December 2014. The number of observations in each bin has been determined and additional statistics, the mean, the standard deviation, minimum and maximum values, have been calculated, enabling a degree of uncertainty to be associated with any measurement. The data atlas is available as a Web Feature Service.

A modern pollen-climate dataset from the Darjeeling area, eastern Himalaya: Assessing its potential for past climate reconstruction

NASA Astrophysics Data System (ADS)

Ghosh, Ruby; Bruch, Angela A.; Portmann, Felix; Bera, Subir; Paruya, Dipak Kumar; Morthekai, P.; Ali, Sheikh Nawaz

2017-10-01

Relying on the ability of pollen assemblages to differentiate among elevationally stratified vegetation zones, we assess the potential of a modern pollen-climate dataset from the Darjeeling area, eastern Himalaya, in past climate reconstructions. The dataset includes 73 surface samples from 25 sites collected from a c. 130-3600 m a.s.l. elevation gradient along a horizontal distance of c. 150 km and 124 terrestrial pollen taxa, which are analysed with respect to various climatic and environmental variables such as mean annual temperature (MAT), mean annual precipitation (MAP), mean temperature of coldest quarter (MTCQ), mean temperature of warmest quarter (MTWQ), mean precipitation of driest quarter (MPDQ), mean precipitation of wettest quarter (MPWQ), AET (actual evapotranspiration) and MI (moisture index). To check the reliability of the modern pollen-climate relationships different ordination methods are employed and subsequently tested with Huisman-Olff-Fresco (HOF) models. A series of pollen-climate parameter transfer functions using weighted-averaging regression and calibration partial least squares (WA-PLS) models are developed to reconstruct past climate changes from modern pollen data, and have been cross-validated. Results indicate that three of the environmental variables i.e., MTCQ, MPDQ and MI have strong potential for past climate reconstruction based on the available surface pollen dataset. The potential of the present modern pollen-climate relationship for regional quantitative paleoclimate reconstruction is further tested on a Late Quaternary fossil pollen profile from the Darjeeling foothill region with previously reconstructed and quantified climate. The good agreement with existing data allows for new insights in the hydroclimatic conditions during the Last glacial maxima (LGM) with (winter) temperature being the dominant controlling factor for glacial changes during the LGM in the eastern Himalaya.

Sea surface temperature and salinity from French research vessels, 2001–2013

PubMed Central

Gaillard, Fabienne; Diverres, Denis; Jacquin, Stéphane; Gouriou, Yves; Grelet, Jacques; Le Menn, Marc; Tassel, Joelle; Reverdin, Gilles

2015-01-01

French Research vessels have been collecting thermo-salinometer (TSG) data since 1999 to contribute to the Global Ocean Surface Underway Data (GOSUD) programme. The instruments are regularly calibrated and continuously monitored. Water samples are taken on a daily basis by the crew and later analysed in the laboratory. We present here the delayed mode processing of the 2001–2013 dataset and an overview of the resulting quality. Salinity measurement error was a few hundredths of a unit or less on the practical salinity scale (PSS), due to careful calibration and instrument maintenance, complemented with a rigorous adjustment on water samples. In a global comparison, these data show excellent agreement with an ARGO-based salinity gridded product. The Sea Surface Salinity and Temperature from French REsearch SHips (SSST-FRESH) dataset is very valuable for the ‘calibration and validation’ of the new satellite observations delivered by the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions. PMID:26504523

Establishment and analysis of a High-Resolution Assimilation Dataset of the water-energy cycle in China

NASA Astrophysics Data System (ADS)

Zhu, X.; Wen, X.; Zheng, Z.

2017-12-01

For better prediction and understanding of land-atmospheric interaction, in-situ observed meteorological data acquired from the China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated using the Normalized Difference Vegetation Index (NDVI) of the Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS) and Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system. Furthermore, the WRF model produced a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC). This dataset has a horizontal resolution of 25 km for near surface meteorological data, such as air temperature, humidity, wind vectors and pressure (19 levels); soil temperature and moisture (four levels); surface temperature; downward/upward short/long radiation; 3-h latent heat flux; sensible heat flux; and ground heat flux. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method and 2) compare results of meteorological elements, such as 2 m temperature and precipitation generated by the HRADC with the gridded observation data from CMA, and surface temperature and specific humidity with Global LandData Assimilation System (GLDAS) output data from the National Aeronautics and Space Administration (NASA). We found that the satellite-derived GVF from MODIS increased over southeast China compared with the default model over the whole year. The simulated results of soil temperature, net radiation and surface energy flux from the HRADC are improved compared with the control simulation and are close to GLDAS outputs. The values of net radiation from HRADC are higher than the GLDAS outputs, and the differences in the simulations are large in the east region but are smaller in northwest China and on the Qinghai-Tibet Plateau. The spatial distribution of the sensible heat flux and the ground heat flux from HRADC is consistent with the GLDAS outputs in summer. In general, the simulated results from HRADC are an improvement on the control simulation and can present the characteristics of the spatial and temporal variation of the water-energy cycle in China.

The Special Sensor Microwave Imager Wind Dataset: A Source of Quantitative Information for the Ocean-to-Land Advection

NASA Technical Reports Server (NTRS)

Otterman, J.; Ardizzone, J.; Atlas, R.; Demaree, G.; Huth, R.; Jaagus, J.; Koslowsky, D.; Przybylak, R.; Wos, A.; Atlas, Robert (Technical Monitor)

1999-01-01

It is well recognized that advection from the North Atlantic has a profound effect on the climatic conditions in central Europe. A new dataset of the ocean-surface winds, derived from the Special Sensor Microwave Imager, SSM/1, is now available. This satellite instrument measures the wind speed, but not the direction. However, variational analysis developed at the Data Assimilation Office, NASA Goddard Space Flight Center, by combining the SSM/I measurements with wind vectors measured from ships, etc., produced global maps of the ocean surface winds suitable for climate analysis. From this SSM/I dataset, a specific index I(sub na) of the North Atlantic surface winds has been developed, which pertinently quantifies the low-level advection into central Europe. For a selected time-period, the index I(sub na) reports the average of the amplitude of the wind, averaging only the speed when the direction is from the southwest (when the wind is from another direction, the contribution counts to the average as zero speed). Strong correlations were found between February I(sub na) and the surface air temperatures in Europe 50-60 deg N. In the present study, we present the correlations between I(sub na) and temperature I(sub s), and also the sensitivity of T(sub s), to an increase in I(sub na), in various seasons and various regions. We specifically analyze the flow of maritime-air from the North Atlantic that produced two extraordinary warm periods: February 1990, and early-winter 2000/2001. The very cold December 2001 was clearly due to a northerly flow. Our conclusion is that the SSM/I dataset is very useful for providing insight to the forcing of climatic fluctuations in Europe.

High-frequency fluctuations of surface temperatures in an urban environment

NASA Astrophysics Data System (ADS)

Christen, Andreas; Meier, Fred; Scherer, Dieter

2012-04-01

This study presents an attempt to resolve fluctuations in surface temperatures at scales of a few seconds to several minutes using time-sequential thermography (TST) from a ground-based platform. A scheme is presented to decompose a TST dataset into fluctuating, high-frequency, and long-term mean parts. To demonstrate the scheme's application, a set of four TST runs (day/night, leaves-on/leaves-off) recorded from a 125-m-high platform above a complex urban environment in Berlin, Germany is used. Fluctuations in surface temperatures of different urban facets are measured and related to surface properties (material and form) and possible error sources. A number of relationships were found: (1) Surfaces with surface temperatures that were significantly different from air temperature experienced the highest fluctuations. (2) With increasing surface temperature above (below) air temperature, surface temperature fluctuations experienced a stronger negative (positive) skewness. (3) Surface materials with lower thermal admittance (lawns, leaves) showed higher fluctuations than surfaces with high thermal admittance (walls, roads). (4) Surface temperatures of emerged leaves fluctuate more compared to trees in a leaves-off situation. (5) In many cases, observed fluctuations were coherent across several neighboring pixels. The evidence from (1) to (5) suggests that atmospheric turbulence is a significant contributor to fluctuations. The study underlines the potential of using high-frequency thermal remote sensing in energy balance and turbulence studies at complex land-atmosphere interfaces.

Mapping the surface characteristics of the Mojave with remote sensing for terrestrial habitat modeling

NASA Astrophysics Data System (ADS)

Nowicki, S. A.; Skuse, R. J.

2012-12-01

High-resolution ecological and climate modeling requires quantification of surface characteristics such as rock abundance, soil induration and surface roughness at fine-scale, since these features can affect the micro and macro habitat of a given area and ultimately determine the assemblage of plant and animal species that may occur there. Our objective is to develop quantitative data layers of thermophysical properties of the entire Mojave Desert Ecoregion for applications to habitat modeling being conducted by the USGS Western Ecological Research Center. These research efforts are focused on developing habitat models and a better physical understanding of the Mojave Desert, which have implications the development of solar and wind energy resources, military installation expansion and residential development planned for the Mojave. Thus there is a need to improve our understanding of the mechanical composition and thermal characteristics of natural and modified surfaces in the southwestern US at as high-resolution as possible. Since the Mojave is a sparsely-vegetated, arid landscape with little precipitation, remote sensing-based thermophysical analyses using Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) day and nighttime imagery are ideal for determining the physical properties of the surface. New mosaicking techniques for thermal imagery acquired at different dates, seasons and temperatures have allowed for the highest-resolution mosaics yet generated at 100m/pixel for thermal infrared wavelengths. Among our contributions is the development of seamless day and night ASTER mosaics of land surface temperatures that are calibrated to Moderate Resolution Imaging Spectroradiometer (MODIS) coincident observations to produce both a seamless mosaic and quantitative temperatures across the region that varies spectrally and thermophysically over a large number of orbit tracks. Products derived from this dataset include surface rock abundance, apparent thermal inertia, and diurnal/seasonal thermal regime. Additionally, the combination of moderate and high-resolution thermal observations are used to map the spatial and temporal variation of significant rain storms that intermittently increase the surface moisture. The resulting thermally-derived layers are in the process of being combined with composition, vegetation and surface reflectance datasets to map the Mojave at the highest VNIR resolution (20m/pixel) and compared to currently-available lower-resolution datasets.

Brady's Geothermal Field - Analysis of Pressure Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, David

*This submission provides corrections to GDR Submissions 844 and 845* Poroelastic Tomography (PoroTomo) by Adjoint Inverse Modeling of Data from Hydrology. The 3 *csv files containing pressure data are the corrected versions of the pressure dataset found in Submission 844. The dataset has been corrected in the sense that the atmospheric pressure has been subtracted from the total pressure measured in the well. Also, the transducers used at wells 56A-1 and SP-2 are sensitive to surface temperature fluctuations. These temperature effects have been removed from the corrected datasets. The 4th *csv file contains corrected version of the pumping data foundmore » in Submission 845. The data has been corrected in the sense that the data from several wells that were used during the PoroTomo deployment pumping tests that were not included in the original dataset has been added. In addition, several other minor changes have been made to the pumping records due to flow rate instrument calibration issues that were discovered.« less

Establishment and analysis of High-Resolution Assimilation Dataset of water-energy cycle over China

NASA Astrophysics Data System (ADS)

Wen, Xiaohang; Liao, Xiaohan; Dong, Wenjie; Yuan, Wenping

2015-04-01

For better prediction and understanding of water-energy exchange process and land-atmospheric interaction, the in-situ observed meteorological data which were acquired from China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated by the Normalized Difference Vegetation Index (NDVI) of Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS), Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system were also integrated in the WRF model over China. Further, the High-Resolution Assimilation Dataset of water-energy cycle over China (HRADC) was produced by WRF model. This dataset include 25 km horizontal resolution near surface meteorological data such as air temperature, humidity, ground temperature, and pressure at 19 levels, soil temperature and soil moisture at 4 levels, green vegetation coverage, latent heat flux, sensible heat flux, and ground heat flux for 3 hours. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method; 2) Compare results of meteorological elements such as 2 m temperature, precipitation and ground temperature generated by the HRADC with the gridded observation data from CMA, and Global Land Data Assimilation System (GLDAS) output data from National Aeronautics and Space Administration (NASA). It is found that the results of 2 m temperature were improved compared with the control simulation and has effectively reproduced the observed patterns, and the simulated results of ground temperature, 0-10 cm soil temperature and specific humidity were as much closer to GLDAS outputs. Root mean square errors are reduced in assimilation run than control run, and the assimilation run of ground temperature, 0-10 cm soil temperature, radiation and surface fluxes were agreed well with the GLDAS outputs over China. The HRADC could be used in further research on the long period climatic effects and characteristics of water-energy cycle over China.

Heat Coma Temperature and Supercooling Point in Oceanic Sea Skaters (Heteroptera, Gerridae)

PubMed Central

Harada, Tetsuo

2018-01-01

Heat coma temperatures (HCTs) and super cooling points (SCPs) were examined for nearly 1000 oceanic sea skaters collected from in the Pacific and Indian Oceans representing four Halobates species; H. germanus, H. micans, H. sericeus, and H. sp. Analysis was conducted using the entire dataset because a negative correlation was seen between the HCTs and SCPs in all four species. A weak negative correlation was seen between HCTs and SCPs with a cross tolerance between warmer HCTs and colder SCPs. The weakness of the correlation may be due to the large size of the dataset and to the variability in ocean surface temperature. The negative correlation does however suggest that oceanic sea skaters may have some form of cross tolerance with a common physiological mechanism for their high and low temperature tolerances. PMID:29401693

Over 10 million seawater temperature records for the United Kingdom Continental Shelf between 1880 and 2014 from 17 Cefas (United Kingdom government) marine data systems

NASA Astrophysics Data System (ADS)

Morris, David J.; Pinnegar, John K.; Maxwell, David L.; Dye, Stephen R.; Fernand, Liam J.; Flatman, Stephen; Williams, Oliver J.; Rogers, Stuart I.

2018-01-01

The datasets described here bring together quality-controlled seawater temperature measurements from over 130 years of departmental government-funded marine science investigations in the UK (United Kingdom). Since before the foundation of a Marine Biological Association fisheries laboratory in 1902 and through subsequent evolutions as the Directorate of Fisheries Research and the current Centre for Environment Fisheries & Aquaculture Science, UK government marine scientists and observers have been collecting seawater temperature data as part of oceanographic, chemical, biological, radiological, and other policy-driven research and observation programmes in UK waters. These datasets start with a few tens of records per year, rise to hundreds from the early 1900s, thousands by 1959, and hundreds of thousands by the 1980s, peaking with > 1 million for some years from 2000 onwards. The data source systems vary from time series at coastal monitoring stations or offshore platforms (buoys), through repeated research cruises or opportunistic sampling from ferry routes, to temperature extracts from CTD (conductivity, temperature, depth) profiles, oceanographic, fishery and plankton tows, and data collected from recreational scuba divers or electronic devices attached to marine animals. The datasets described have not been included in previous seawater temperature collation exercises (e.g. International Comprehensive Ocean-Atmosphere Data Set, Met Office Hadley Centre sea surface temperature data set, the centennial in situ observation-based estimates of sea surface temperatures), although some summary data reside in the British Oceanographic Data Centre (BODC) archive, the Marine Environment Monitoring and Assessment National (MERMAN) database and the International Council for the Exploration of the Sea (ICES) data centre. We envisage the data primarily providing a biologically and ecosystem-relevant context for regional assessments of changing hydrological conditions around the British Isles, although cross-matching with satellite-derived data for surface temperatures at specific times and in specific areas is another area in which the data could be of value (see e.g. Smit et al., 2013). Maps are provided indicating geographical coverage, which is generally within and around the UK Continental Shelf area, but occasionally extends north from Labrador and Greenland to east of Svalbard and southward to the Bay of Biscay. Example potential uses of the data are described using plots of data in four selected groups of four ICES rectangles covering areas of particular fisheries interest. The full dataset enables extensive data synthesis, for example in the southern North Sea where issues of spatial and numerical bias from a data source are explored. The full dataset also facilitates the construction of long-term temperature time series and an examination of changes in the phenology (seasonal timing) of ecosystem processes. This is done for a wide geographic area with an exploration of the limitations of data coverage over long periods. Throughout, we highlight and explore potential issues around the simple combination of data from the diverse and disparate sources collated here. The datasets are available on the Cefas Data Hub (https://www.cefas.co.uk/cefas-data-hub/). The referenced data sources are listed in Sect. 5.

Evaluating new SMAP soil moisture for drought monitoring in the rangelands of the US High Plains

USGS Publications Warehouse

Velpuri, Naga Manohar; Senay, Gabriel B.; Morisette, Jeffrey T.

2016-01-01

Level 3 soil moisture datasets from the recently launched Soil Moisture Active Passive (SMAP) satellite are evaluated for drought monitoring in rangelands.Validation of SMAP soil moisture (SSM) with in situ and modeled estimates showed high level of agreement.SSM showed the highest correlation with surface soil moisture (0-5 cm) and a strong correlation to depths up to 20 cm.SSM showed a reliable and expected response of capturing seasonal dynamics in relation to precipitation, land surface temperature, and evapotranspiration.Further evaluation using multi-year SMAP datasets is necessary to quantify the full benefits and limitations for drought monitoring in rangelands.

A Modeling and Verification Study of Summer Precipitation Systems Using NASA Surface Initialization Datasets

NASA Technical Reports Server (NTRS)

Jonathan L. Case; Kumar, Sujay V.; Srikishen, Jayanthi; Jedlovec, Gary J.

2010-01-01

One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse-type convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within parameterization schemes, model resolution limitations, and uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture, soil temperature, and sea surface temperature (SST) are necessary to better simulate the interactions between the surface and atmosphere, and ultimately improve predictions of summertime pulse convection. This paper describes a sensitivity experiment using the Weather Research and Forecasting (WRF) model. Interpolated land and ocean surface fields from a large-scale model are replaced with high-resolution datasets provided by unique NASA assets in an experimental simulation: the Land Information System (LIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) SSTs. The LIS is run in an offline mode for several years at the same grid resolution as the WRF model to provide compatible land surface initial conditions in an equilibrium state. The MODIS SSTs provide detailed analyses of SSTs over the oceans and large lakes compared to current operational products. The WRF model runs initialized with the LIS+MODIS datasets result in a reduction in the overprediction of rainfall areas; however, the skill is almost equally as low in both experiments using traditional verification methodologies. Output from object-based verification within NCAR s Meteorological Evaluation Tools reveals that the WRF runs initialized with LIS+MODIS data consistently generated precipitation objects that better matched observed precipitation objects, especially at higher precipitation intensities. The LIS+MODIS runs produced on average a 4% increase in matched precipitation areas and a simultaneous 4% decrease in unmatched areas during three months of daily simulations.

Joint variability of global runoff and global sea surface temperatures

USGS Publications Warehouse

McCabe, G.J.; Wolock, D.M.

2008-01-01

Global land surface runoff and sea surface temperatures (SST) are analyzed to identify the primary modes of variability of these hydroclimatic data for the period 1905-2002. A monthly water-balance model first is used with global monthly temperature and precipitation data to compute time series of annual gridded runoff for the analysis period. The annual runoff time series data are combined with gridded annual sea surface temperature data, and the combined dataset is subjected to a principal components analysis (PCA) to identify the primary modes of variability. The first three components from the PCA explain 29% of the total variability in the combined runoff/SST dataset. The first component explains 15% of the total variance and primarily represents long-term trends in the data. The long-term trends in SSTs are evident as warming in all of the oceans. The associated long-term trends in runoff suggest increasing flows for parts of North America, South America, Eurasia, and Australia; decreasing runoff is most notable in western Africa. The second principal component explains 9% of the total variance and reflects variability of the El Ni??o-Southern Oscillation (ENSO) and its associated influence on global annual runoff patterns. The third component explains 5% of the total variance and indicates a response of global annual runoff to variability in North Aflantic SSTs. The association between runoff and North Atlantic SSTs may explain an apparent steplike change in runoff that occurred around 1970 for a number of continental regions.

Surface temperature dataset for North America obtained by application of optimal interpolation algorithm merging tree-ring chronologies and climate model output

NASA Astrophysics Data System (ADS)

Chen, Xin; Xing, Pei; Luo, Yong; Nie, Suping; Zhao, Zongci; Huang, Jianbin; Wang, Shaowu; Tian, Qinhua

2017-02-01

A new dataset of surface temperature over North America has been constructed by merging climate model results and empirical tree-ring data through the application of an optimal interpolation algorithm. Errors of both the Community Climate System Model version 4 (CCSM4) simulation and the tree-ring reconstruction were considered to optimize the combination of the two elements. Variance matching was used to reconstruct the surface temperature series. The model simulation provided the background field, and the error covariance matrix was estimated statistically using samples from the simulation results with a running 31-year window for each grid. Thus, the merging process could continue with a time-varying gain matrix. This merging method (MM) was tested using two types of experiment, and the results indicated that the standard deviation of errors was about 0.4 °C lower than the tree-ring reconstructions and about 0.5 °C lower than the model simulation. Because of internal variabilities and uncertainties in the external forcing data, the simulated decadal warm-cool periods were readjusted by the MM such that the decadal variability was more reliable (e.g., the 1940-1960s cooling). During the two centuries (1601-1800 AD) of the preindustrial period, the MM results revealed a compromised spatial pattern of the linear trend of surface temperature, which is in accordance with the phase transition of the Pacific decadal oscillation and Atlantic multidecadal oscillation. Compared with pure CCSM4 simulations, it was demonstrated that the MM brought a significant improvement to the decadal variability of the gridded temperature via the merging of temperature-sensitive tree-ring records.

NASA SPoRT Initialization Datasets for Local Model Runs in the Environmental Modeling System

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaFontaine, Frank J.; Molthan, Andrew L.; Carcione, Brian; Wood, Lance; Maloney, Joseph; Estupinan, Jeral; Medlin, Jeffrey M.; Blottman, Peter; Rozumalski, Robert A.

2011-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its National Weather Service (NWS) partners that can be used to initialize local model runs within the Weather Research and Forecasting (WRF) Environmental Modeling System (EMS). These real-time datasets consist of surface-based information updated at least once per day, and produced in a composite or gridded product that is easily incorporated into the WRF EMS. The primary goal for making these NASA datasets available to the WRF EMS community is to provide timely and high-quality information at a spatial resolution comparable to that used in the local model configurations (i.e., convection-allowing scales). The current suite of SPoRT products supported in the WRF EMS include a Sea Surface Temperature (SST) composite, a Great Lakes sea-ice extent, a Greenness Vegetation Fraction (GVF) composite, and Land Information System (LIS) gridded output. The SPoRT SST composite is a blend of primarily the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared and Advanced Microwave Scanning Radiometer for Earth Observing System data for non-precipitation coverage over the oceans at 2-km resolution. The composite includes a special lake surface temperature analysis over the Great Lakes using contributions from the Remote Sensing Systems temperature data. The Great Lakes Environmental Research Laboratory Ice Percentage product is used to create a sea-ice mask in the SPoRT SST composite. The sea-ice mask is produced daily (in-season) at 1.8-km resolution and identifies ice percentage from 0 100% in 10% increments, with values above 90% flagged as ice.

Getting the temperature right: Understanding thermal emission from airless bodies

NASA Astrophysics Data System (ADS)

Bandfield, J.; Greenhagen, B. T.; Hayne, P. O.; Williams, J. P.; Paige, D. A.

2016-12-01

Thermal infrared measurements are crucial for understanding a wide variety of processes present on airless bodies throughout the solar system. Although these data can be complex, they also contain an enormous amount of useful information. By building a framework for understanding thermal infrared datasets, significant advances are possible in the understanding of regolith development, detection of H2O and OH-, characterizing the nature and magnitude of Yarkovsky and YORP effects, and determination of the properties of newly identified asteroids via telescopic measurements. Airless bodies can have both extremely rough and insulating surfaces. For example, these two properties allow for sunlit and shaded or buried lunar materials separated by just a few centimeters to vary by 200K. In this sense, there is no "correct" temperature interpretable from orbital, or even in-situ, measurements. The surface contains a wide mixture of temperatures in the field of view, and rougher surfaces greatly enhance this anisothermality. We have used the Lunar Reconnaissance Orbiter Diviner Radiometer to characterize these effects by developing new targeting and analysis methods, including extended off-nadir observations and combined surface roughness and thermal modeling (Fig. 1). These measurements and models have shown up to 100K brightness temperature differences from measurements that differ only in the viewing angle of the observation. In addition, the thermal emission near 3 μm can be highly dependent on the surface roughness, resulting in more extensive and prominent lunar 3 μm H2O and OH-absorptions than indicated in data corrected by isothermal models. The datasets serve as a foundation for the derivation and understanding of surface spectral and thermophysical properties. Roughness and anisothermality effects are likely to dominate infrared measurements from many spacecraft, including LRO, Dawn, BepiColombo, OSIRIS-REx, Hayabusa-2, and Europa Clipper.

Saturn's icy satellites investigated by Cassini-VIMS. IV. Daytime temperature maps

NASA Astrophysics Data System (ADS)

Filacchione, Gianrico; D'Aversa, Emiliano; Capaccioni, Fabrizio; Clark, Roger N.; Cruikshank, Dale P.; Ciarniello, Mauro; Cerroni, Priscilla; Bellucci, Giancarlo; Brown, Robert H.; Buratti, Bonnie J.; Nicholson, Phillip D.; Jaumann, Ralf; McCord, Thomas B.; Sotin, Christophe; Stephan, Katrin; Dalle Ore, Cristina M.

2016-06-01

The spectral position of the 3.6 μm continuum peak measured on Cassini-VIMS I/F spectra is used as a marker to infer the temperature of the regolith particles covering the surfaces of Saturn's icy satellites. This feature is characterizing the crystalline water ice spectrum which is the dominant compositional endmember of the satellites' surfaces. Laboratory measurements indicate that the position of the 3.6 μm peak of pure water ice is temperature-dependent, shifting towards shorter wavelengths when the sample is cooled, from about 3.65 μm at T=123 K to about 3.55 μm at T=88 K. A similar method was already applied to VIMS Saturn's rings mosaics to retrieve ring particles temperature (Filacchione, G., Ciarniello, M., Capaccioni, F., et al., 2014. Icarus, 241, 45-65). We report here about the daytime temperature variations observed on the icy satellites as derived from three different VIMS observation types: (a) a sample of 240 disk-integrated I/F observations of Saturn's regular satellites collected by VIMS during years 2004-2011 with solar phase in the 20°-40° range, corresponding to late morning-early afternoon local times. This dataset is suitable to exploit the temperature variations at hemispherical scale, resulting in average temperature T <88 K for Mimas, T ≪88 K for Enceladus, T <88 K for Tethys, T=98-118 K for Dione, T=108-128 K for Rhea, T=118-128 K for Hyperion, T=128-148 and T > 168 K for Iapetus' trailing and leading hemispheres, respectively. A typical ±5 K uncertainty is associated to the temperature retrieval. On Tethys and Dione, for which observations on both leading and trailing hemispheres are available, in average daytime temperatures higher of about 10 K on the trailing than on the leading hemisphere are inferred. (b) Satellites disk-resolved observations taken at 20-40 km pixel-1 resolution are suitable to map daytime temperature variations across surfaces' features, such as Enceladus' tiger stripes and Tethys' equatorial dark lens. These datasets allow to disentangle solar illumination conditions from temperature distribution when observing surface's features with strong thermal contrast. (c) Daytime average maps covering large regions of the surfaces are used to compare the inferred temperature with geomorphological features (impact craters, chasmatae, equatorial radiation lenses and active areas) and albedo variations. Temperature maps are built by mining the complete VIMS dataset collected in years 2004-2009 (pre-equinox) and in 2009-2012 (post equinox) by selecting pixels with max 150 km pixel-1 resolution. VIMS-derived temperature maps allow to identify thermal anomalies across the equatorial lens of Mimas and Tethys. A temperature T > 115K is measured above Enceladus' Damascus and Alexandria sulci in the south pole region. VIMS has the sensitivity to follow seasonal temperature changes: on Tethys, Dione and Rhea higher temperature are measured above the south hemisphere during pre-equinox and above the north hemisphere during post-equinox epochs. The measured temperature distribution appears correlated with surface albedo features: in fact temperature increases on low albedo units located on Tethys, Dione and Rhea trailing hemispheres. The thermal anomaly region on Rhea's Inktomi crater detected by CIRS (Howett, C. J. A., Spencer, J. R., Hurford, T., et al., 2014. Icarus, 241, 239-247) is confirmed by VIMS: this area appears colder with respect to surrounding terrains when observed at the same local solar time.

Historical instrumental climate data for Australia - quality and utility for palaeoclimatic studies

NASA Astrophysics Data System (ADS)

Nicholls, Neville; Collins, Dean; Trewin, Blair; Hope, Pandora

2006-10-01

The quality and availability of climate data suitable for palaeoclimatic calibration and verification for the Australian region are discussed and documented. Details of the various datasets, including problems with the data, are presented. High-quality datasets, where such problems are reduced or even eliminated, are discussed. Many climate datasets are now analysed onto grids, facilitating the preparation of regional-average time series. Work is under way to produce such high-quality, gridded datasets for a variety of hitherto unavailable climate data, including surface humidity, pan evaporation, wind, and cloud. An experiment suggests that only a relatively small number of palaeoclimatic time series could provide a useful estimate of long-term changes in Australian annual average temperature. Copyright

Detecting Changes of Thermal Environment over the Bohai Coastal Region by Spectral Change Vector Analysis

NASA Astrophysics Data System (ADS)

Hu, Y.; Jia, G.

2009-12-01

Change vector analysis (CVA) is an effective approach for detecting and characterizing land-cover change by comparing pairs of multi-spectral and multi-temporal datasets over certain area derived from various satellite platforms. NDVI is considered as an effective detector for biophysical changes due to its sensitivity to red and near infrared signals, while land surface temperature (LST) is considered as a valuable indicator for changes of ground thermal conditions. Here we try to apply CVA over satellite derived LST datasets to detect changes of land surface thermal properties parallel to climate change and anthropogenic influence in a city cluster since 2001. In this study, monthly land surface temperature datasets from 2001-2008 derived from MODIS collection 5 were used to examine change pattern of thermal environment over the Bohai coastal region by using spectral change vector analysis. The results from principle component analysis (PCA) for LST show that the PC 1-3 contain over 80% information on monthly variations and these PCA components represent the main processes of land thermal environment change over the study area. Time series of CVA magnitude combined with land cover information show that greatest change occurred in urban and heavily populated area, featured with expansion of urban heat island, while moderate change appeared in grassland area in the north. However few changes were observed over large plain area and forest area. Strong signals also are related to economy level and especially the events of surface cover change, such as emergence of railway and port. Two main processes were also noticed about the changes of thermal environment. First, weak signal was detected in mostly natural area influenced by interannual climate change in temperate broadleaf forest area. Second, land surface temperature changes were controlled by human activities as 1) moderate change of LST happened in grassland influenced by grazing and 2) urban heat island was intensifier in major cities, such as Beijing and Tianjin. Further, the continual drier climate combined with human actions over past fifties years have intensified land thermal pattern change and the continuation will be an important aspects to understand land surface processes and local climate change. Land surface temperature trends from 2000-2008 over the Bohai coastal region

Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

NASA Astrophysics Data System (ADS)

Jones, Philip D.; Harpham, Colin; Troccoli, Alberto; Gschwind, Benoit; Ranchin, Thierry; Wald, Lucien; Goodess, Clare M.; Dorling, Stephen

2017-07-01

The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979-2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas

NASA Astrophysics Data System (ADS)

Diaconescu, Emilia Paula; Mailhot, Alain; Brown, Ross; Chaumont, Diane

2018-03-01

This study focuses on the evaluation of daily precipitation and temperature climate indices and extremes simulated by an ensemble of 12 Regional Climate Model (RCM) simulations from the ARCTIC-CORDEX experiment with surface observations in the Canadian Arctic from the Adjusted Historical Canadian Climate Dataset. Five global reanalyses products (ERA-Interim, JRA55, MERRA, CFSR and GMFD) are also included in the evaluation to assess their potential for RCM evaluation in data sparse regions. The study evaluated the means and annual anomaly distributions of indices over the 1980-2004 dataset overlap period. The results showed that RCM and reanalysis performance varied with the climate variables being evaluated. Most RCMs and reanalyses were able to simulate well climate indices related to mean air temperature and hot extremes over most of the Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to topographic effects. Overall performance was generally poor for indices related to cold extremes. Likewise, only a few RCM simulations and reanalyses were able to provide realistic simulations of precipitation extreme indicators. The multi-reanalysis ensemble provided superior results to individual datasets for climate indicators related to mean air temperature and hot extremes, but not for other indicators. These results support the use of reanalyses as reference datasets for the evaluation of RCM mean air temperature and hot extremes over northern Canada, but not for cold extremes and precipitation indices.

In Situ Global Sea Surface Salinity and Variability from the NCEI Global Thermosalinograph Database

NASA Astrophysics Data System (ADS)

Wang, Z.; Boyer, T.; Zhang, H. M.

2017-12-01

Sea surface salinity (SSS) plays an important role in the global ocean circulations. The variations of sea surface salinity are key indicators of changes in air-sea water fluxes. Using nearly 30 years of in situ measurements of sea surface salinity from thermosalinographs, we will evaluate the variations of the sea surface salinity in the global ocean. The sea surface salinity data used are from our newly-developed NCEI Global Thermosalinograph Database - NCEI-TSG. This database provides a comprehensive set of quality-controlled in-situ sea-surface salinity and temperature measurements collected from over 340 vessels during the period 1989 to the present. The NCEI-TSG is the world's most complete TSG dataset, containing all data from the different TSG data assembly centers, e.g. COAPS (SAMOS), IODE (GOSUD) and AOML, with more historical data from NCEI's archive to be added. Using this unique dataset, we will investigate the spatial variations of the global SSS and its variability. Annual and interannual variability will also be studied at selected regions.

Climate data, analysis and models for the study of natural variability and anthropogenic change

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, Philip D.

Gridded Temperature Under prior/current support, we completed and published (Jones et al., 2012) the fourth major update to our global land dataset of near-surface air temperatures, CRUTEM4. This is one of the most widely used records of the climate system, having been updated, maintained and further developed with DoE support since the 1980s. We have continued to update the CRUTEM4 (Jones et al., 2012) database that is combined with marine data to produce HadCRUT4 (Morice et al., 2012). The emphasis in our use of station temperature data is to access as many land series that have been homogenized by Nationalmore » Meteorological Services (NMSs, including NCDC/NOAA, Asheville, NC). Unlike the three US groups monitoring surface temperatures in a similar way, we do not infill areas that have no or missing data. We can only infill such regions in CRUTEM4 by accessing more station temperature series. During early 2014, we have begun the extensive task of updating as many of these series as possible using data provided by some NMSs and also through a number of research projects and programs around the world. All the station data used in CRUTEM4 have been available since 2009, but in Osborn and Jones (2014) we have made this more usable using a Google Earth interface (http://www.cru.uea.ac.uk/cru/data/crutem/ge/ ). We have recently completed the update of our infilled land multi-variable dataset (CRU TS 3.10, Harris et al., 2014). This additionally produces complete land fields (except for the Antarctic) for temperature, precipitation, diurnal temperature range, vapour pressure and sunshine/cloud. Using this dataset we have calculated sc-PDSI (self-calibrating Palmer Drought Severity Index) data and compared with other PDSI datasets (Trenberth et al., 2014). Also using CRU TS 3.10 and Reanalysis datasets, we showed no overall increase in global temperature variability despite changing regional patterns (Huntingford et al., 2013). Harris et al. (2014) is an update of an earlier dataset (Mitchell and Jones, 2005) which also had earlier DoE support. The earlier dataset has been cited over 1700 times according to ResearcherID on 31/July/2014 and the recent paper has already been cited 22 times. Analyses of Temperature Data Using the ERA-Interim estimate of the absolute surface air temperature of the Earth (instead of in the more normal form of anomalies) we compared the result against estimates we produced in 1999 with earlier DoE support. The two estimates are surprisingly close (differing by a couple of tenths of a degree Celsius), with the average temperature of the world (for 1981-2010) being very close to 14°C (Jones and Harpham, 2013). We have assessed ERA-Interim against station temperatures from manned and automatic weather station measurements across the Antarctic (Jones and Lister, 2014). Agreement is generally excellent across the Antarctic Peninsula and the sparsely sampled western parts of Antarctica. Differences tend to occur over eastern Antarctica where ERA-Interim is biased warm (up to 6°C) in the interior of the continent and biased cool (up to 6°C) for some of the coastal locations. Opportunities presented themselves during 2012 for collaborative work with a couple of Chinese groups. Three papers develop new temperature series for China as a whole and also for the eastern third of China (Wang et al., 2014, Cao et al., 2013 and Zhao et al., 2014). A dataset of ~400 daily Chinese temperature stations has been added to the CRU datasets. The latter paper finds that urban effects are generally about 10% of the long-term warming trend across eastern China. A fourth paper (Wang et al., 2013) illustrates issues with comparisons between reanalyses and surface temperatures across China, a method that has been widely used by some to suggest urban heating effects are much larger in the region. ERA-Interim can be used but NCEP/NCAR comparisons are very dependent on the period analysed. Earlier a new temperature dataset of homogenized records was developed for China (Li et al., 2009). Urbanization has also been addressed for London (Jones and Lister, 2009) where two rural sites have not warmed more than a city centre site since 1900. Additionally, in Ethymiadis and Jones (2010) we show that land air temperatures agree with marine data around coastal areas, further illustrating that urbanization is not a major component of large-scale surface air temperature change. Early instrumental data (before the development of modern thermometer screens) have always been suspected of being biased warm in summer, due to possible direct exposure to the sun. Two studies (Böhm et al., 2010 and Brunet et al., 2010) show this for the Greater Alpine Region (GAR) and for mainland Spain respectively. The issue is important before about 1870 in the GAR and before about 1900 in Spain. After correction for the problems, summer temperature estimates before these dates are cooler by about 0.4°C. In Jones and Wigley (2010), we discussed the importance of the biases in global temperature estimation. Exposure and to a lesser extent urbanization are the most important biases for the land areas, but both are dwarfed by the necessary adjustments for bucket SST measurements before about 1950. Individual station homogeneity is only important at the local scale. This was additionally illustrated by Hawkins and Jones (2013) where we replicated the temperature record developed by Guy Stewart Callendar in papers in 1938 and 1961. Analyses of Daily Climate Data Work here indicates that ERA-Interim (at least in Europe, Cornes and Jones, 2013, discussed in more detail in this proposal) can be used to monitor extremes (using the ETCCDI software – see Zhang et al., 2011). Additionally, also as a result of Chinese collaboration, a new method of daily temperature homogenization has been developed (Li et al., 2014). In Cornes and Jones (2011) we assessed storm activity in the northeast Atlantic region using daily gridded data. Even though the grid resolution is coarse (5° by 5° lat/long) the changes in storm activity are similar to those developed from the pressure triangle approach with station data. Analyses of humidity and pressure data In Simmons et al. (2010) we showed a reduction in relative humidity over low-latitude and mid-latitude land areas for the 10 years to 2008, based on monthly anomalies of surface air temperature and humidity from ECMWF reanalyses (ERA-40 and ERA-Interim) and our earlier land-only dataset (CRUTEM3) and synoptic humidity observations (HadCRUH). Updates of this station-based humidity dataset (now called HadISDH) extend the record, showing continued reductions (Willett et al., 2013). Analyses of Proxy Temperature Data In Vinther et al. (2010), relationships between the seasonal stable isotope data from Greenland Ice Cores and Greenland and Icelandic instrumental temperatures were investigated for the past 150-200 years. The winter season stable isotope data are found to be influenced by the North Atlantic Oscillation (NAO) and very closely related to SW Greenland temperatures. The summer season stable isotope data display higher correlations with Icelandic summer temperatures and North Atlantic SST conditions than with local SW Greenland temperatures. In Jones et al. (2014) we use these winter isotope reconstructions to show the expected inverse correlation (due to the NAO) with winter-season documentary reconstructions from the Netherlands and Sweden over the last 800 years. Finally, in this section Jones et al. (2013) shows the agreement between tree-ring width measurements from Northern Sweden and Finland and an assessment of the link to explosive volcanic eruptions. An instrumental record for the region in the early 19th century indicates that the summer of 1816 was only slightly below normal, explaining why this year has normal growth for both ring width and density. GCM/RCM/Reanalysis Evaluation In this section we have intercompared daily temperature extremes across Europe in Cornes and Jones (2013) using station data, E-OBS and ERA-Interim. We have additionally considered the impact of the urban issue on the global scale using the results of the Compo et al. (2011) Reanalyses, 20CR. These only make use of SST and station pressure data. Across the world’s land areas, they indicate similar warming since 1900 to that which has occurred (Compo et al., 2013), again illustrating that urbanization is not the cause of the long-term warming. Changes in HadCRUH global land surface specific humidity and CRUTEM3 surface temperatures from 1973 to 1999 were compared to the CMIP3 archive of climate model simulations with 20th Century forcings (Willett et al., 2010). The models reproduce the magnitude of observed interannual variance over all large regions. Observed and modelled trends and temperature-humidity relationships are comparable with the exception of the extra-tropical Southern Hemisphere where observations exhibit no trend but models exhibit moistening.« less

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Smith, William L., Jr.; Bedka, Kristopher M.; Nguyen, Louis; Palikonda, Rabindra; Hong, Gang; Trepte, Qing Z.; Chee, Thad; Scarino, Benjamin; Spangenberg, Douglas A.;

Turbulence structure of the near-surface boundary layer in complex terrain

NASA Astrophysics Data System (ADS)

Sfyri, Eleni; Rotach, Mathias Walter; Stiperski, Ivana; Bosveld, Fred; Lehner, Manuela; Obleitner, Friedrich

2017-04-01

Monin-Obukhov Similarity Theory (MOST) is evaluated in two cases: truly complex terrain (CT) and horizontally inhomogeneous and flat (HIF) terrain. CT data are derived from 5 measurement sites, which differ in terms of slope, orientation and surface roughness at the Inn Valley of Austria (i-Box) and HIF data come from one measurement site at the Cabauw experimental site (Netherlands). The applicability of the surface-layer, 'ideal' similarity relations is examined for both data-sets and the non-dimensional variances of temperature and humidity as a function of stability (z/L, where L is the Obukhov length) are compared for each type of terrain. Large deviations from the reference curves in case of temperature are observed in both CT and HIF, leading to the conclusion that these deviations are not due to the complex terrain but due to inappropriate near-neutral description of the reference curves. It is found here that the non-dimensional temperature variance exhibits a -1 slope in the near-neutral region, for both CT and HIF datasets. In addition, the constant-fluxes hypothesis of the MOST is evaluated at one i-Box site. It is found that only about 1% of the data show constant momentum, sensible and latent heat fluxes with height. Therefore, local scaling instead of surface layer scaling is being used in this study.

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

NASA Astrophysics Data System (ADS)

Minnis, P.; Smith, W., Jr.; Bedka, K. M.; Nguyen, L.; Palikonda, R.; Hong, G.; Trepte, Q.; Chee, T.; Scarino, B. R.; Spangenberg, D.; Sun-Mack, S.; Fleeger, C.; Ayers, J. K.; Chang, F. L.; Heck, P. W.

2014-12-01

Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-real time globally from both geostationary (GEO) and low-earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.

The mark of vegetation change on Earth's surface energy balance: data-driven diagnostics and model validation

NASA Astrophysics Data System (ADS)

Cescatti, A.; Duveiller, G.; Hooker, J.

2017-12-01

Changing vegetation cover not only affects the atmospheric concentration of greenhouse gases but also alters the radiative and non-radiative properties of the surface. The result of competing biophysical processes on Earth's surface energy balance varies spatially and seasonally, and can lead to warming or cooling depending on the specific vegetation change and on the background climate. To date these effects are not accounted for in land-based climate policies because of the complexity of the phenomena, contrasting model predictions and the lack of global data-driven assessments. To overcome the limitations of available observation-based diagnostics and of the on-going model inter-comparison, here we present a new benchmarking dataset derived from satellite remote sensing. This global dataset provides the potential changes induced by multiple vegetation transitions on the single terms of the surface energy balance. We used this dataset for two major goals: 1) Quantify the impact of actual vegetation changes that occurred during the decade 2000-2010, showing the overwhelming role of tropical deforestation in warming the surface by reducing evapotranspiration despite the concurrent brightening of the Earth. 2) Benchmark a series of ESMs against data-driven metrics of the land cover change impacts on the various terms of the surface energy budget and on the surface temperature. We anticipate that the dataset could be also used to evaluate future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

The Surface Brightness Contribution of II Peg: A Comparison of TiO Band Analysis and Doppler Imaging

NASA Astrophysics Data System (ADS)

Senavci, H. V.; O'Neal, D.; Hussain, G. A. J.; Barnes, J. R.

2015-01-01

We investigate the surface brightness contribution of the very well known active SB1 binary II Pegasi , to determine the star spot filling factor and the spot temperature parameters. In this context, we analyze 54 spectra of the system taken over 6 nights in September - October of 1996, using the 2.1m Otto Struve Telescope equipped with SES at the McDonald Observatory. We measure the spot temperatures and spot filling factors by fitting TiO molecular bands in this spectroscopic dataset, with model atmosphere approximation using ATLAS9 and with proxy stars obtained with the same instrument. The same dataset is then used to also produce surface spot maps using the Doppler imaging technique. We compare the spot filling factors obtained with the two independent techniques in order to better characterise the spot properties of the system and to better assess the limitations inherent to both techniques. The results obtained from both techniques show that the variation of spot filling factor as a function of phase agree well with each other, while the amount of TiO and DI spot

Contribution of tropical cyclones to abnormal sea surface temperature warming in the Yellow Sea in December 2004

NASA Astrophysics Data System (ADS)

Kim, Taekyun; Choo, Sung-Ho; Moon, Jae-Hong; Chang, Pil-Hun

2017-12-01

Unusual sea surface temperature (SST) warming occurred over the Yellow Sea (YS) in December 2004. To identify the causes of the abnormal SST warming, we conducted an analysis on atmospheric circulation anomalies induced by tropical cyclones (TCs) and their impacts on upper ocean characteristics using multiple datasets. With the analysis of various datasets, we explored a new aspect of the relationship between TC activity and SST. The results show that there is a significant link between TC activity over the Northwest Pacific (NWP) and SST in the YS. The integrated effect of consecutive TCs activity induces a large-scale atmospheric cyclonic circulation anomaly over the NWP and consequently anomalous easterly winds over the YS and East China Sea. The mechanism of the unusually warm SST in the YS can be explained by considering TCs acting as an important source of Ekman heat transport that results in substantial intrusion of relatively warm surface water into the YS interior. Furthermore, TC-related circulation anomalies contribute to the retention of the resulting warm SST anomalies in the entire YS.

Southern Ocean Climate and Sea Ice Anomalies Associated with the Southern Oscillation.

NASA Astrophysics Data System (ADS)

Kwok, R.; Comiso, J. C.

2002-03-01

The anomalies in the climate and sea ice cover of the Southern Ocean and their relationships with the Southern Oscillation (SO) are investigated using a 17-yr dataset from 1982 to 1998. The polar climate anomalies are correlated with the Southern Oscillation index (SOI) and the composites of these anomalies are examined under the positive (SOI > 0), neutral (0 > SOI > 1), and negative (SOI < 1) phases of SOI. The climate dataset consists of sea level pressure, wind, surface air temperature, and sea surface temperature fields, while the sea ice dataset describes its extent, concentration, motion, and surface temperature. The analysis depicts, for the first time, the spatial variability in the relationship of the above variables with the SOI. The strongest correlation between the SOI and the polar climate anomalies are found in the Bellingshausen, Amundsen, and Ross Seas. The composite fields reveal anomalies that are organized in distinct large-scale spatial patterns with opposing polarities at the two extremes of SOI, and suggest oscillations that are closely linked to the SO. Within these sectors, positive (negative) phases of the SOI are generally associated with lower (higher) sea level pressure, cooler (warmer) surface air temperature, and cooler (warmer) sea surface temperature in these sectors. Associations between these climate anomalies and the behavior of the Antarctic sea ice cover are evident. Recent anomalies in the sea ice cover that are clearly associated with the SOI include the following: the record decrease in the sea ice extent in the Bellingshausen Sea from mid-1988 to early 1991; the relationship between Ross Sea SST and the ENSO signal, and reduced sea ice concentration in the Ross Sea; and the shortening of the ice season in the eastern Ross Sea, Amundsen Sea, far western Weddell Sea and lengthening of the ice season in the western Ross Sea, Bellinghausen Sea, and central Weddell Sea gyre during the period 1988-94. Four ENSO episodes over the last 17 years contributed to a negative mean in the SOI (0.5). In each of these episodes, significant retreats in ice cover of the Bellingshausen and Amundsen Seas were observed showing a unique association of this region of the Antarctic with the Southern Oscillation.

Global Surface Temperatures of the Moon

NASA Astrophysics Data System (ADS)

Williams, J. P.; Paige, D. A.; Greenhagen, B. T.; Sefton-Nash, E.

2015-12-01

The Diviner instrument aboard the Lunar Reconnaissance Orbiter (LRO) is providing the most comprehensive view of how regoliths on airless body store and exchange thermal energy with the space environment. Approximately a quarter trillion calibrated radiance measurements of the Moon, acquired over 5.5 years by Diviner, have been compiled into a 0.5° resolution global dataset with a 0.25 hour local time resolution. Maps generated with this dataset provide a global perspective of the surface energy balance of the Moon and reveal the complex and extreme nature of the lunar surface thermal environment. Daytime maximum temperatures are sensitive to the radiative properties of the surface and are ~387-397 K at the equator, dropping to ~95 K before sunrise. Asymmetry between the morning and afternoon temperatures is observed due to the thermal inertia of the regolith with the dusk terminator ~30 K warmer than the dawn terminator at the equator. An increase in albedo with incidence angle is required to explain the observed temperatures with latitude. At incidence angles >40° topography and surface roughness result in increasing anisothermality between spectral passbands and scatter in temperatures. Minimum temperatures reflect variations in thermophysical properties (Figure). Impact craters are found to modify regolith properties over large distances. The thermal signature of Tycho is asymmetric consistent with an oblique impact coming from the west. Some prominent crater rays are visible in the thermal data and require material with a higher thermal inertial than nominal regolith. The influence of the formation of the Orientale basin on the regolith properties is observable over a substantial portion of the western hemisphere despite its age (~3.8 Gyr), and may have contributed to mixing of highland and mare material on the southwest margin of Oceanus Procellarum where the gradient in radiative properties at the mare-highland contact are observed to be broad (~200 km).

An End-to-End simulator for the development of atmospheric corrections and temperature - emissivity separation algorithms in the TIR spectral domain

NASA Astrophysics Data System (ADS)

Rock, Gilles; Fischer, Kim; Schlerf, Martin; Gerhards, Max; Udelhoven, Thomas

2017-04-01

The development and optimization of image processing algorithms requires the availability of datasets depicting every step from earth surface to the sensor's detector. The lack of ground truth data obliges to develop algorithms on simulated data. The simulation of hyperspectral remote sensing data is a useful tool for a variety of tasks such as the design of systems, the understanding of the image formation process, and the development and validation of data processing algorithms. An end-to-end simulator has been set up consisting of a forward simulator, a backward simulator and a validation module. The forward simulator derives radiance datasets based on laboratory sample spectra, applies atmospheric contributions using radiative transfer equations, and simulates the instrument response using configurable sensor models. This is followed by the backward simulation branch, consisting of an atmospheric correction (AC), a temperature and emissivity separation (TES) or a hybrid AC and TES algorithm. An independent validation module allows the comparison between input and output dataset and the benchmarking of different processing algorithms. In this study, hyperspectral thermal infrared scenes of a variety of surfaces have been simulated to analyze existing AC and TES algorithms. The ARTEMISS algorithm was optimized and benchmarked against the original implementations. The errors in TES were found to be related to incorrect water vapor retrieval. The atmospheric characterization could be optimized resulting in increasing accuracies in temperature and emissivity retrieval. Airborne datasets of different spectral resolutions were simulated from terrestrial HyperCam-LW measurements. The simulated airborne radiance spectra were subjected to atmospheric correction and TES and further used for a plant species classification study analyzing effects related to noise and mixed pixels.

A Useful Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data

NASA Astrophysics Data System (ADS)

Rivalland, Vincent; Tardy, Benjamin; Huc, Mireille; Hagolle, Olivier; Marcq, Sébastien; Boulet, Gilles

2016-04-01

Land Surface temperature (LST) is a critical variable for studying the energy and water budgets at the Earth surface, and is a key component of many aspects of climate research and services. The Landsat program jointly carried out by NASA and USGS has been providing thermal infrared data for 40 years, but no associated LST product has been yet routinely proposed to community. To derive LST values, radiances measured at sensor-level need to be corrected for the atmospheric absorption, the atmospheric emission and the surface emissivity effect. Until now, existing LST products have been generated with multi channel methods such as the Temperature/Emissivity Separation (TES) adapted to ASTER data or the generalized split-window algorithm adapted to MODIS multispectral data. Those approaches are ill-adapted to the Landsat mono-window data specificity. The atmospheric correction methodology usually used for Landsat data requires detailed information about the state of the atmosphere. This information may be obtained from radio-sounding or model atmospheric reanalysis and is supplied to a radiative transfer model in order to estimate atmospheric parameters for a given coordinate. In this work, we present a new automatic tool dedicated to Landsat thermal data correction which improves the common atmospheric correction methodology by introducing the spatial dimension in the process. The python tool developed during this study, named LANDARTs for LANDsat Automatic Retrieval of surface Temperature, is fully automatic and provides atmospheric corrections for a whole Landsat tile. Vertical atmospheric conditions are downloaded from the ERA Interim dataset from ECMWF meteorological organization which provides them at 0.125 degrees resolution, at a global scale and with a 6-hour-time step. The atmospheric correction parameters are estimated on the atmospheric grid using the commercial software MODTRAN, then interpolated to 30m resolution. We detail the processing steps implemented in LANDARTs and propose a local and spatial validation of the LST products from Landsat dataset archive over two climatically contrasted zones: south-west France and centre of Tunisia. In both sites, long term datasets of in-situ surface temperature measurements have been compared to LST obtained for Landsat data processed by LANDARTs and filtered from clouds. This temporal comparison presents RMSE between 1.84K and 2.55K. Then, Landsat LST products are compared to ASTER kinetic surface temperature products on two synchronous dates from both zones. This comparison presents satisfactory RMSE about 2.55K with a good correlation coefficient of 0.9. Finally, a sensibility analysis to the spatial variation of parameters presents a variability reaching 2K at the Landsat image scale and confirms the improved accuracy in Landsat LST estimation linked to our spatial approach.

The Spatial Coherence of Interannual Temperature Variations in the Antarctic Peninsula

NASA Technical Reports Server (NTRS)

King, John C.; Comiso, Josefino C.; Koblinsky, Chester J. (Technical Monitor)

2002-01-01

Over 50 years of observations from climate stations on the west coast of the Antarctic Peninsula show that this is a region of extreme interannual variability in near-surface temperatures. The region has also experienced more rapid warming than any other part of the Southern Hemisphere. In this paper we use a new dataset of satellite-derived surface temperatures to define the extent of the region of extreme variability more clearly than was possible using the sparse station data. The region in which satellite surface temperatures correlate strongly with west Peninsula station temperatures is found to be quite small and is largely confined to the seas just west of the Peninsula, with a northward and eastward extension into the Scotia Sea and a southward extension onto the western slopes of Palmer Land. Correlation of Peninsula surface temperatures with surface temperatures over the rest of continental Antarctica is poor confirming that the west Peninsula is in a different climate regime. The analysis has been used to identify sites where ice core proxy records might be representative of variations on the west coast of the Peninsula. Of the five existing core sites examined, only one is likely to provide a representative record for the west coast.

Development of Gridded Ensemble Precipitation and Temperature Datasets for the Contiguous United States Plus Hawai'i and Alaska

NASA Astrophysics Data System (ADS)

Newman, A. J.; Clark, M. P.; Nijssen, B.; Wood, A.; Gutmann, E. D.; Mizukami, N.; Longman, R. J.; Giambelluca, T. W.; Cherry, J.; Nowak, K.; Arnold, J.; Prein, A. F.

2016-12-01

Gridded precipitation and temperature products are inherently uncertain due to myriad factors. These include interpolation from a sparse observation network, measurement representativeness, and measurement errors. Despite this inherent uncertainty, uncertainty is typically not included, or is a specific addition to each dataset without much general applicability across different datasets. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits their utility to support land surface and hydrologic modeling techniques such as data assimilation, probabilistic forecasting and verification. To address this gap, we have developed a first of its kind gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980-2012 over the United States (including Alaska and Hawaii). A longer, higher resolution version (1970-present, 1/16th degree) has also been implemented to support real-time hydrologic- monitoring and prediction in several regional US domains. We will present the development and evaluation of the dataset, along with initial applications of the dataset for ensemble data assimilation and probabilistic evaluation of high resolution regional climate model simulations. We will also present results on the new high resolution products for Alaska and Hawaii (2 km and 250 m respectively), to complete the first ensemble observation based product suite for the entire 50 states. Finally, we will present plans to improve the ensemble dataset, focusing on efforts to improve the methods used for station interpolation and ensemble generation, as well as methods to fuse station data with numerical weather prediction model output.

A Satellite-Derived Climate-Quality Data Record of the Clear-Sky Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Comiso, Josefino C.; DiGirolamo, Nicolo E.; Shuman, Christopher A.; Key, Jeffrey R.; Koenig, Lora S.

2011-01-01

We have developed a climate-quality data record of the clear-sky surface temperature of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra ice-surface temperature (1ST) algorithm. A climate-data record (CDR) is a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change. We present daily and monthly Terra MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 at 6.25-km spatial resolution on a polar stereographic grid within +/-3 hours of 17:00Z or 2:00 PM Local Solar Time. Preliminary validation of the ISTs at Summit Camp, Greenland, during the 2008-09 winter, shows that there is a cold bias using the MODIS IST which underestimates the measured surface temperature by approximately 3 C when temperatures range from approximately -50 C to approximately -35 C. The ultimate goal is to develop a CDR that starts in 1981 with the Advanced Very High Resolution (AVHRR) Polar Pathfinder (APP) dataset and continues with MODIS data from 2000 to the present. Differences in the APP and MODIS cloud masks have so far precluded the current IST records from spanning both the APP and MODIS IST time series in a seamless manner though this will be revisited when the APP dataset has been reprocessed. The Greenland IST climate-quality data record is suitable for continuation using future Visible Infrared Imager Radiometer Suite (VIIRS) data and will be elevated in status to a CDR when at least 9 more years of climate-quality data become available either from MODIS Terra or Aqua, or from the VIIRS. The complete MODIS IST data record will be available online in the summer of 2011.

Long-term variation of Surface Ozone, NO2, temperature and relative humidity on crop yield over Andhra Pradesh (AP), India

NASA Astrophysics Data System (ADS)

Arunachalam, M. S.; Obili, Manjula; Srimurali, M.

2016-07-01

Long-term variation of Surface Ozone, NO2, Temperature, Relative humidity and crop yield datasets over thirteen districts of Andhra Pradesh(AP) has been studied with the help of OMI, MODIS, AIRS, ERA-Interim re-analysis and Directorate of Economics and Statistics (DES) of AP. Inter comparison of crop yield loss estimates according to exposure metrics such as AOT40 (accumulated ozone exposure over a threshold of 40) and non-linear variation of surface temperature for twenty and eighteen varieties of two major crop growing seasons namely, kharif (April-September) and rabi (October-March), respectively has been made. Study is carried to establish a new crop-yield-exposure relationship for different crop cultivars of AP. Both ozone and temperature are showing a correlation coefficient of 0.66 and 0.87 with relative humidity; and 0.72 and 0.80 with NO2. Alleviation of high surface ozone results in high food security and improves the economy thereby reduces the induced warming of the troposphere caused by ozone. Keywords: Surface Ozone, NO2, Temperature, Relative humidity, Crop yield, AOT 40.

A comparison of spatial interpolation methods for soil temperature over a complex topographical region

NASA Astrophysics Data System (ADS)

Wu, Wei; Tang, Xiao-Ping; Ma, Xue-Qing; Liu, Hong-Bin

2016-08-01

Soil temperature variability data provide valuable information on understanding land-surface ecosystem processes and climate change. This study developed and analyzed a spatial dataset of monthly mean soil temperature at a depth of 10 cm over a complex topographical region in southwestern China. The records were measured at 83 stations during the period of 1961-2000. Nine approaches were compared for interpolating soil temperature. The accuracy indicators were root mean square error (RMSE), modelling efficiency (ME), and coefficient of residual mass (CRM). The results indicated that thin plate spline with latitude, longitude, and elevation gave the best performance with RMSE varying between 0.425 and 0.592 °C, ME between 0.895 and 0.947, and CRM between -0.007 and 0.001. A spatial database was developed based on the best model. The dataset showed that larger seasonal changes of soil temperature were from autumn to winter over the region. The northern and eastern areas with hilly and low-middle mountains experienced larger seasonal changes.

Challenges in Extracting Information From Large Hydrogeophysical-monitoring Datasets

NASA Astrophysics Data System (ADS)

Day-Lewis, F. D.; Slater, L. D.; Johnson, T.

2012-12-01

Over the last decade, new automated geophysical data-acquisition systems have enabled collection of increasingly large and information-rich geophysical datasets. Concurrent advances in field instrumentation, web services, and high-performance computing have made real-time processing, inversion, and visualization of large three-dimensional tomographic datasets practical. Geophysical-monitoring datasets have provided high-resolution insights into diverse hydrologic processes including groundwater/surface-water exchange, infiltration, solute transport, and bioremediation. Despite the high information content of such datasets, extraction of quantitative or diagnostic hydrologic information is challenging. Visual inspection and interpretation for specific hydrologic processes is difficult for datasets that are large, complex, and (or) affected by forcings (e.g., seasonal variations) unrelated to the target hydrologic process. New strategies are needed to identify salient features in spatially distributed time-series data and to relate temporal changes in geophysical properties to hydrologic processes of interest while effectively filtering unrelated changes. Here, we review recent work using time-series and digital-signal-processing approaches in hydrogeophysics. Examples include applications of cross-correlation, spectral, and time-frequency (e.g., wavelet and Stockwell transforms) approaches to (1) identify salient features in large geophysical time series; (2) examine correlation or coherence between geophysical and hydrologic signals, even in the presence of non-stationarity; and (3) condense large datasets while preserving information of interest. Examples demonstrate analysis of large time-lapse electrical tomography and fiber-optic temperature datasets to extract information about groundwater/surface-water exchange and contaminant transport.

Spatio-temporal Root Zone Soil Moisture Estimation for Indo - Gangetic Basin from Satellite Derived (AMSR-2 and SMOS) Surface Soil Moisture

NASA Astrophysics Data System (ADS)

Sure, A.; Dikshit, O.

2017-12-01

Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.

Lack of cool, not warm, extremes distinguishes late 20th Century climate in 979-year Tasmanian summer temperature reconstruction

NASA Astrophysics Data System (ADS)

Allen, K. J.; Cook, E. R.; Evans, R.; Francey, R.; Buckley, B. M.; Palmer, J. G.; Peterson, M. J.; Baker, P. J.

2018-03-01

Very few annually resolved millennial-length temperature reconstructions exist for the Southern Hemisphere. Here we present four 979-year reconstructions for southeastern Australia for the austral summer months of December-February. Two of the reconstructions are based on the Australian Water Availability Project dataset and two on the Berkeley Earth Surface Temperature dataset. For each climate data set, one reconstruction is based solely on Lagarostrobos franklinii (restricted reconstructions) while the other is based on multiple Tasmanian conifer species (unrestricted reconstructions). Each reconstruction calibrates ~50-60% of the variance in the temperature datasets depending on the number of tree-ring records available for the reconstruction. We found little difference in the temporal variability of the reconstructions, although extremes are amplified in the restricted reconstructions relative to the unrestricted reconstructions. The reconstructions highlight the occurrence of numerous individual years, especially in the 15th-17th Centuries, for which temperatures were comparable with those of the late 20th Century. The 1950-1999 period, however, stands out as the warmest 50-year period on average for the past 979 years, with a sustained shift away from relatively low mean temperatures, the length of which is unique in the 979-year record. The reconstructions are strongly and positively related to temperatures across the southeast of the Australian continent, negatively related to temperatures in the north and northeast of the continent, and uncorrelated with temperatures in the west. The lack of a strong relationship with temperatures across the continent highlights the necessity of a sub-regional focus for Australasian temperature reconstructions.

Comparing AIRS/AMSU-A Satellite and MERRA/MERRA-2 Reanalysis products with In-situ Station Observations at Summit, Greenland

NASA Astrophysics Data System (ADS)

Hearty, T. J., III; Vollmer, B.; Wei, J. C.; Huwe, P. M.; Albayrak, A.; Wu, D. L.; Cullather, R. I.; Meyer, D. L.; Lee, J. N.; Blaisdell, J. M.; Susskind, J.; Nowicki, S.

2017-12-01

The surface air and skin temperatures reported by the Atmospheric Infrared Sounder (AIRS), the Modern-Era Retrospective analysis for Research and Applications (MERRA), and MERRA-2 at Summit, Greenland are compared with near surface air temperatures measured at National Oceanic and Atmospheric Administration (NOAA) and Greenland Climate Network (GC-Net) weather stations. Therefore this investigation requires familiarity with a heterogeneous set of swath, grid, and point data in several different formats, different granularity, and different sampling. We discuss the current subsetting capabilities available at the GES DISC (Goddard Earth Sciences Data Information Services Center) to perform the inter-comparisons necessary to evaluate the quality and trustworthiness of these datasets. We also explore potential future services which may assist users with this type of intercomparison. We find the AIRS Surface Skin Temperature (TS) is best correlated with the NOAA 2 m air temperature (T2M) but it tends to be colder than the station measurements. The difference may be the result of the frequent near surface temperature inversions in the region. The AIRS Surface Air Temperature (SAT) is also well correlated with the NOAA T2M but it has a warm bias with respect to the NOAA T2M during the cold season and a larger standard error than surface temperature. This suggests that the extrapolation of the temperature profile to the surface is not valid for the strongest inversions. Comparing the temperature lapse rate derived from the 2 stations shows that the lapse rate can increase closer to the surface. We also find that the difference between the AIRS SAT and TS is sensitive to near surface inversions. The MERRA-2 surface and near surface temperatures show improvements over MERRA but little sensitivity to near surface temperature inversions.

CO2 CH4 flux Air temperature Soil temperature and Soil moisture, Barrow, Alaska 2013 ver. 1

DOE Data Explorer

Margaret Torn

2015-01-14

This dataset consists of field measurements of CO2 and CH4 flux, as well as soil properties made during 2013 in Areas A-D of Intensive Site 1 at the Next-Generation Ecosystem Experiments (NGEE) Arctic site near Barrow, Alaska. Included are i) measurements of CO2 and CH4 flux made from June to September (ii) Calculation of corresponding Gross Primary Productivity (GPP) and CH4 exchange (transparent minus opaque) between atmosphere and the ecosystem (ii) Measurements of Los Gatos Research (LGR) chamber air temperature made from June to September (ii) measurements of surface layer depth, type of surface layer, soil temperature and soil moisture from June to September.

On the Trend of the Annual Mean, Maximum, and Minimum Temperature and the Diurnal Temperature Range in the Armagh Observatory, Northern Ireland, Dataset, 1844 -2012

NASA Technical Reports Server (NTRS)

Wilson, Robert M.

2013-01-01

Examined are the annual averages, 10-year moving averages, decadal averages, and sunspot cycle (SC) length averages of the mean, maximum, and minimum surface air temperatures and the diurnal temperature range (DTR) for the Armagh Observatory, Northern Ireland, during the interval 1844-2012. Strong upward trends are apparent in the Armagh surface-air temperatures (ASAT), while a strong downward trend is apparent in the DTR, especially when the ASAT data are averaged by decade or over individual SC lengths. The long-term decrease in the decadaland SC-averaged annual DTR occurs because the annual minimum temperatures have risen more quickly than the annual maximum temperatures. Estimates are given for the Armagh annual mean, maximum, and minimum temperatures and the DTR for the current decade (2010-2019) and SC24.

Evaluation of bulk heat fluxes from atmospheric datasets

NASA Astrophysics Data System (ADS)

Farmer, Benton

Heat fluxes at the air-sea interface are an important component of the Earth's heat budget. In addition, they are an integral factor in determining the sea surface temperature (SST) evolution of the oceans. Different representations of these fluxes are used in both the atmospheric and oceanic communities for the purpose of heat budget studies and, in particular, for forcing oceanic models. It is currently difficult to quantify the potential impact varying heat flux representations have on the ocean response. In this study, a diagnostic tool is presented that allows for a straightforward comparison of surface heat flux formulations and atmospheric data sets. Two variables, relaxation time (RT) and the apparent temperature (T*), are derived from the linearization of the bulk formulas. They are then calculated to compare three bulk formulae and five atmospheric datasets. Additionally, the linearization is expanded to the second order to compare the amount of residual flux present. It is found that the use of a bulk formula employing a constant heat transfer coefficient produces longer relaxation times and contains a greater amount of residual flux in the higher order terms of the linearization. Depending on the temperature difference, the residual flux remaining in the second order and above terms can reach as much as 40--50% of the total residual on a monthly time scale. This is certainly a non-negligible residual flux. In contrast, a bulk formula using a stability and wind dependent transfer coefficient retains much of the total flux in the first order term, as only a few percent remain in the residual flux. Most of the difference displayed among the bulk formulas stems from the sensitivity to wind speed and the choice of a constant or spatially varying transfer coefficient. Comparing the representation of RT and T* provides insight into the differences among various atmospheric datasets. In particular, the representations of the western boundary current, upwelling, and the Indian monsoon regions of the oceans have distinct characteristics within each dataset. Localized regions, such as the eastern Mexican and Central American coasts, are also shown to have variability among the datasets. The use of this technique for the evaluation of bulk formulae and datasets is an efficient method for identifying the unique characteristics of each. Furthermore, insight into the heat fluxes produced by particular bulk formula or dataset can be gained.

Comparative analysis of different underlying surfaces using a high-resolution assimilation dataset in semi-arid areas in China

NASA Astrophysics Data System (ADS)

Ruan, Jinshuai; Wen, Xiaohang; Fan, Guangzhou; Li, Deqin; Hua, Wei; Wang, Bingyun; Zhang, Yi; Zhang, Mingjun; Wang, Chao; Wang, Lei

2017-11-01

To study the land surface and atmospheric meteorological characteristics of non-uniform underlying surfaces in the semi-arid area of Northeast China, we use a "High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC)". The grid points of three different underlying surfaces were selected, and their meteorological elements were averaged for each type (i.e., mixed forest, grassland, and cropland). For 2009, we compared and analyzed the different components of leaf area index (LAI), soil temperature and moisture, surface albedo, precipitation, and surface energy for various underlying surfaces in Northeast China. The results indicated that the LAI of mixed forest and cropland during the summer is greater than 5 m2 m-2 and below 2.5 m2 m-2 for grassland; in the winter and spring seasons, the Green Vegetation Fraction (GVF) is below 30%. The soil temperature and moisture both vary greatly. Throughout the year, the mixed forest is dominated by latent heat evaporation; in grasslands and croplands, the sensible heat flux and the latent heat flux are approximately equal, and the GVF contributed more to latent heat flux than sensible heat flux in the summer. This study compares meteorological characteristics between three different underlying surfaces of the semi-arid area of Northeast China and makes up for the insufficiency of purely using observations for the study. This research is important for understanding the water-energy cycle and transport in the semi-arid area.

Australian snowpack in the NARCliM ensemble: evaluation, bias correction and future projections

NASA Astrophysics Data System (ADS)

Luca, Alejandro Di; Evans, Jason P.; Ji, Fei

2017-10-01

In this study we evaluate the ability of an ensemble of high-resolution Regional Climate Model simulations to represent snow cover characteristics over the Australian Alps and go on to asses future projections of snowpack characteristics. Our results show that the ensemble presents a cold temperature bias and overestimates total precipitation leading to a general overestimation of the snow cover as compared with MODIS satellite data. We then produce a new set of snowpack characteristics by running a temperature based snow melt/accumulation model forced by bias corrected temperature and precipitation fields. While some positive snow cover biases remain, the bias corrected (BC) dataset show large improvements regarding the simulation of total amounts, seasonality and spatial distribution of the snow cover compared with MODIS products. Both the raw and BC datasets are then used to assess future changes in the snowpack characteristics. Both datasets show robust increases in near-surface temperatures and decreases in snowfall that lead to a substantial reduction of the snowpack over the Australian Alps. The snowpack decreases by about 15 and 60% by 2030 and 2070 respectively. While the BC data introduce large differences in the simulation of the present climate snowpack, in relative terms future changes appear to be similar to those obtained using the raw data. Future temperature projections show a clear dependence with elevation through the snow-albedo feedback effect that affects snowpack projections. Uncertainties in future projections of the snowpack are large in both datasets and are mainly dominated by the choice of the lateral boundary conditions.

Comparing historical and modern methods of Sea Surface Temperature measurement - Part 1: Review of methods, field comparisons and dataset adjustments

NASA Astrophysics Data System (ADS)

Matthews, J. B. R.

2012-09-01

Sea Surface Temperature (SST) measurements have been obtained from a variety of different platforms, instruments and depths over the post-industrial period. Today most measurements come from ships, moored and drifting buoys and satellites. Shipboard methods include temperature measurement of seawater sampled by bucket and in engine cooling water intakes. Engine intake temperatures are generally thought to average a few tenths of a °C warmer than simultaneous bucket temperatures. Here I review SST measurement methods, studies comparing shipboard methods by field experiment and adjustments applied to SST datasets to account for variable methods. In opposition to contemporary thinking, I find average bucket-intake temperature differences reported from field studies inconclusive. Non-zero average differences often have associated standard deviations that are several times larger than the averages themselves. Further, average differences have been found to vary widely between ships and between cruises on the same ship. The cause of non-zero average differences is typically unclear given the general absence of additional temperature observations to those from buckets and engine intakes. Shipboard measurements appear of variable quality, highly dependent upon the accuracy and precision of the thermometer used and the care of the observer where manually read. Methods are generally poorly documented, with written instructions not necessarily reflecting actual practices of merchant mariners. Measurements cannot be expected to be of high quality where obtained by untrained sailors using thermometers of low accuracy and precision.

Preliminary results and assessment of the MAR outputs over High Mountain Asia

NASA Astrophysics Data System (ADS)

Linares, M.; Tedesco, M.; Margulis, S. A.; Cortés, G.; Fettweis, X.

2017-12-01

Lack of ground measurements has made the use of regional climate models (RCMs) over the High Mountain Asia (HMA) pivotal for understanding the impact of climate change on the hydrological cycle and on the cryosphere. Here, we show an analysis of the assessment of the outputs of Modèle Atmosphérique Régionale (MAR) model RCM over the HMA region as part of the NASA-funded project `Understanding and forecasting changes in High Mountain Asia snow hydrology via a novel Bayesian reanalysis and modeling approach'. The first step was to evaluate the impact of the different forcings on MAR outputs. To this aim, we performed simulations for the 2007 - 2008 and 2014 - 2015 years forcing MAR at its boundaries either with reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) or from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The comparison between the outputs obtained with the two forcings indicates that the impact on MAR simulations depends on specific parameters. For example, in case of surface pressure the maximum percentage error is 0.09 % while the 2-m air temperature has a maximum percentage error of 103.7%. Next, we compared the MAR outputs with reanalysis data fields over the region of interest. In particular, we evaluated the following parameters: surface pressure, snow depth, total cloud cover, two meter temperature, horizontal wind speed, vertical wind speed, wind speed, surface new solar radiation, skin temperature, surface sensible heat flux, and surface latent heat flux. Lastly, we report results concerning the assessment of MAR surface albedo and surface temperature over the region through MODIS remote sensing products. Next steps are to determine whether RCMs and reanalysis datasets are effective at capturing snow and snowmelt runoff processes in the HMA region through a comparison with in situ datasets. This will help determine what refinements are necessary to improve RCM outputs.

The Status of the NASA MEaSUREs Combined ASTER and MODIS Emissivity Over Land (CAMEL) Products

NASA Astrophysics Data System (ADS)

Borbas, E. E.; Feltz, M.; Hulley, G. C.; Knuteson, R. O.; Hook, S. J.

2017-12-01

As part of a NASA MEaSUREs Land Surface Temperature and Emissivity project, the University of Wisconsin, Space Science and Engineering Center and the NASA's Jet Propulsion Laboratory have developed a global monthly mean emissivity Earth System Data Record (ESDR). The CAMEL ESDR was produced by merging two current state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UWIREMIS), and the JPL ASTER Global Emissivity Dataset v4 (GEDv4). The dataset includes monthly global data records of emissivity, uncertainty at 13 hinge points between 3.6-14.3 µm, and Principal Components Analysis (PCA) coefficients at 5 kilometer resolution for years 2003 to 2015. A high spectral resolution algorithm is also provided for HSR applications. The dataset is currently being tested in sounder retrieval algorithm (e.g. CrIS, IASI) and has already been implemented in RTTOV-12 for immediate use in numerical weather modeling and data assimilation. This poster will present the current status of the dataset.

Relating Radiative Fluxes on Arctic Sea Ice Area Using Arctic Observation and Reanalysis Integrated System (ArORIS)

NASA Astrophysics Data System (ADS)

Sledd, A.; L'Ecuyer, T. S.

2017-12-01

With Arctic sea ice declining rapidly and Arctic temperatures rising faster than the rest of the globe, a better understanding of the Arctic climate, and ice cover-radiation feedbacks in particular, is needed. Here we present the Arctic Observation and Reanalysis Integrated System (ArORIS), a dataset of integrated products to facilitate studying the Arctic using satellite, reanalysis, and in-situ datasets. The data include cloud properties, radiative fluxes, aerosols, meteorology, precipitation, and surface properties, to name just a few. Each dataset has uniform grid-spacing, time-averaging and naming conventions for ease of use between products. One intended use of ArORIS is to assess Arctic radiation and moisture budgets. Following that goal, we use observations from ArORIS - CERES-EBAF radiative fluxes and NSIDC sea ice fraction and area to quantify relationships between the Arctic energy balance and surface properties. We find a discernable difference between energy budgets for years with high and low September sea ice areas. Surface fluxes are especially responsive to the September sea ice minimum in months both leading up to September and the months following. In particular, longwave fluxes at the surface show increased sensitivity in the months preceding September. Using a single-layer model of solar radiation we also investigate the individual responses of surface and planetary albedos to changes in sea ice area. By partitioning the planetary albedo into surface and atmospheric contributions, we find that the atmospheric contribution to planetary albedo is less sensitive to changes in sea ice area than the surface contribution. Further comparisons between observations and reanalyses can be made using the available datasets in ArORIS.

Forest fire danger index based on modifying Nesterov Index, fuel, and anthropogenic activities using MODIS TERRA, AQUA and TRMM satellite datasets

NASA Astrophysics Data System (ADS)

Suresh Babu, K. V.; Roy, Arijit; Ramachandra Prasad, P.

2016-05-01

Forest fire has been regarded as one of the major causes of degradation of Himalayan forests in Uttarakhand. Forest fires occur annually in more than 50% of forests in Uttarakhand state, mostly due to anthropogenic activities and spreads due to moisture conditions and type of forest fuels. Empirical drought indices such as Keetch-Byram drought index, the Nesterov index, Modified Nesterov index, the Zhdanko index which belongs to the cumulative type and the Angstrom Index which belongs to the daily type have been used throughout the world to assess the potential fire danger. In this study, the forest fire danger index has been developed from slightly modified Nesterov index, fuel and anthropogenic activities. Datasets such as MODIS TERRA Land Surface Temperature and emissivity (MOD11A1), MODIS AQUA Atmospheric profile product (MYD07) have been used to determine the dew point temperature and land surface temperature. Precipitation coefficient has been computed from Tropical Rainfall measuring Mission (TRMM) product (3B42RT). Nesterov index has been slightly modified according to the Indian context and computed using land surface temperature, dew point temperature and precipitation coefficient. Fuel type danger index has been derived from forest type map of ISRO based on historical fire location information and disturbance danger index has been derived from disturbance map of ISRO. Finally, forest fire danger index has been developed from the above mentioned indices and MODIS Thermal anomaly product (MOD14) has been used for validating the forest fire danger index.

Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xiaoma; Zhou, Yuyu; Asrar, Ghassem R.

High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. Moderate Resolution Imaging Spectroradiometer (MODIS) LST is one of such high spatiotemporal datasets that are widely used. But, it has large amount of missing values primarily because of clouds. Gapfilling the missing values is an important approach to create high spatiotemporal LST datasets. However current gapfilling methods have limitations in terms of accuracy and time required to assemble the data over large areas (e.g., national and continental levels). In this study, we developed a 3-step hybridmore » method by integrating a combination of daily merging, spatiotemporal gapfilling, and temporal interpolation methods, to create a high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. We applied this method in urban and surrounding areas for the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates that its root mean squared error (RMSE) to be 3.3K for mid-daytime (1:30 pm) and 2.7K for mid-13 nighttime (1:30 am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products. This seamless daily (mid-daytime and mid-nighttime) LST product with 1 km spatial resolution is of great value for studying effects of urbanization (e.g., urban heat island) and the related impacts on people, ecosystems, energy systems and other infrastructure for cities.« less

Spatially variable stage-driven groundwater-surface water interaction inferred from time-frequency analysis of distributed temperature sensing data

USGS Publications Warehouse

Mwakanyamale, Kisa; Slater, Lee; Day-Lewis, Frederick D.; Elwaseif, Mehrez; Johnson, Carole D.

2012-01-01

Characterization of groundwater-surface water exchange is essential for improving understanding of contaminant transport between aquifers and rivers. Fiber-optic distributed temperature sensing (FODTS) provides rich spatiotemporal datasets for quantitative and qualitative analysis of groundwater-surface water exchange. We demonstrate how time-frequency analysis of FODTS and synchronous river stage time series from the Columbia River adjacent to the Hanford 300-Area, Richland, Washington, provides spatial information on the strength of stage-driven exchange of uranium contaminated groundwater in response to subsurface heterogeneity. Although used in previous studies, the stage-temperature correlation coefficient proved an unreliable indicator of the stage-driven forcing on groundwater discharge in the presence of other factors influencing river water temperature. In contrast, S-transform analysis of the stage and FODTS data definitively identifies the spatial distribution of discharge zones and provided information on the dominant forcing periods (≥2 d) of the complex dam operations driving stage fluctuations and hence groundwater-surface water exchange at the 300-Area.

A Regularized Neural Net Approach for Retrieval of Atmospheric and Surface Temperatures with the IASI Instrument

NASA Technical Reports Server (NTRS)

Aires, F.; Chedin, A.; Scott, N. A.; Rossow, W. B.; Hansen, James E. (Technical Monitor)

2001-01-01

Abstract In this paper, a fast atmospheric and surface temperature retrieval algorithm is developed for the high resolution Infrared Atmospheric Sounding Interferometer (IASI) space-borne instrument. This algorithm is constructed on the basis of a neural network technique that has been regularized by introduction of a priori information. The performance of the resulting fast and accurate inverse radiative transfer model is presented for a large divE:rsified dataset of radiosonde atmospheres including rare events. Two configurations are considered: a tropical-airmass specialized scheme and an all-air-masses scheme.

Hurricane Data Analysis Tool

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, Dana; Leptoukh, Gregory

2011-01-01

In order to facilitate Earth science data access, the NASA Goddard Earth Sciences Data Information Services Center (GES DISC) has developed a web prototype, the Hurricane Data Analysis Tool (HDAT; URL: http://disc.gsfc.nasa.gov/HDAT), to allow users to conduct online visualization and analysis of several remote sensing and model datasets for educational activities and studies of tropical cyclones and other weather phenomena. With a web browser and few mouse clicks, users can have a full access to terabytes of data and generate 2-D or time-series plots and animation without downloading any software and data. HDAT includes data from the NASA Tropical Rainfall Measuring Mission (TRMM), the NASA Quick Scatterometer(QuikSCAT) and NECP Reanalysis, and the NCEP/CPC half-hourly, 4-km Global (60 N - 60 S) IR Dataset. The GES DISC archives TRMM data. The daily global rainfall product derived from the 3-hourly multi-satellite precipitation product (3B42 V6) is available in HDAT. The TRMM Microwave Imager (TMI) sea surface temperature from the Remote Sensing Systems is in HDAT as well. The NASA QuikSCAT ocean surface wind and the NCEP Reanalysis provide ocean surface and atmospheric conditions, respectively. The global merged IR product, also known as, the NCEP/CPC half-hourly, 4-km Global (60 N -60 S) IR Dataset, is one of TRMM ancillary datasets. They are globally-merged pixel-resolution IR brightness temperature data (equivalent blackbody temperatures), merged from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 & GMS). The GES DISC has collected over 10 years of the data beginning from February of 2000. This high temporal resolution (every 30 minutes) dataset not only provides additional background information to TRMM and other satellite missions, but also allows observing a wide range of meteorological phenomena from space, such as, hurricanes, typhoons, tropical cyclones, mesoscale convection system, etc. Basic functions include selection of area of interest and time, single imagery, overlay of two different products, animation,a time skip capability and different image size outputs. Users can save an animation as a file (animated gif) and import it in other presentation software, such as, Microsoft PowerPoint. Since the tool can directly access the real data, more features and functionality can be added in the future.

Sea Temperature Fiducial Reference Measurements for the Validation and Data Gap Bridging of Satellite SST Data Products

NASA Astrophysics Data System (ADS)

Wimmer, Werenfrid

2016-08-01

The Infrared Sea surface temperature Autonomous Radiometer (ISAR) was developed to provide reference data for the validation of satellite Sea Surface Temperature at the Skin interface (SSTskin) temperature data products, particularly the Advanced Along Track Scanning Radiometer (AATSR). Since March 2004 ISAR instruments have been deployed nearly continuously on ferries crossing the English Channel and the Bay of Biscay, between Portsmouth (UK) and Bilbao/Santander (Spain). The resulting twelve years of ISAR data, including an individual uncertainty estimate for each SST record, are calibrated with traceability to national standards (National Institute of Standards and Technology, USA (NIST) and National Physical Laboratory, Teddigton, UK (NPL), Fiducial Reference Measurements for satellite derived surface temperature product validation (FRM4STS)). They provide a unique independent in situ reference dataset against which to validate satellite derived products. We present results of the AATSR validation, and show the use of ISAR fiducial reference measurements as a common traceable validation data source for both AATSR and Sea and Land Surface Temperature Radiometer (SLSTR). ISAR data were also used to review performance of the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Sea Surface Temperature (SST) analysis before and after the demise of ESA Environmental Satellite (Envisat) when AATSR inputs ceased This demonstrates use of the ISAR reference data set for validating the SST climatologies that will bridge the data gap between AATSR and SLSTR.

Validation of Noah-simulated Soil Temperature in the North American Land Data Assimilation System Phase 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xia, Youlong; Ek, Michael; Sheffield, Justin

2013-02-25

Soil temperature can exhibit considerable memory from weather and climate signals and is among the most important initial conditions in numerical weather and climate models. Consequently, a more accurate long-term land surface soil temperature dataset is needed to improve weather and climate simulation and prediction, and is also important for the simulation of agricultural crop yield and ecological processes. The North-American Land Data Assimilation (NLDAS) Phase 2 (NLDAS-2) has generated 31-years (1979-2009) of simulated hourly soil temperature data with a spatial resolution of 1/8o. This dataset has not been comprehensively evaluated to date. Thus, the ultimate purpose of the presentmore » work is to assess Noah-simulated soil temperature for different soil depths and timescales. We used long-term (1979-2001) observed monthly mean soil temperatures from 137 cooperative stations over the United States to evaluate simulated soil temperature for three soil layers (0-10 cm, 10-40 cm, 40-100 cm) for annual and monthly timescales. We used short-term (1997-1999) observed soil temperature from 72 Oklahoma Mesonet stations to validate simulated soil temperatures for three soil layers and for daily and hourly timescales. The results showed that the Noah land surface model (Noah LSM) generally matches observed soil temperature well for different soil layers and timescales. At greater depths, the simulation skill (anomaly correlation) decreased for all time scales. The monthly mean diurnal cycle difference between simulated and observed soil temperature revealed large midnight biases in the cold season due to small downward longwave radiation and issues related to model parameters.« less

Construction and Analysis of Long-Term Surface Temperature Dataset in Fujian Province

NASA Astrophysics Data System (ADS)

Li, W. E.; Wang, X. Q.; Su, H.

2017-09-01

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.

Does using different modern climate datasets impact pollen-based paleoclimate reconstructions in North America during the past 2,000 years

NASA Astrophysics Data System (ADS)

Ladd, Matthew; Viau, Andre

2013-04-01

Paleoclimate reconstructions rely on the accuracy of modern climate datasets for calibration of fossil records under the assumption of climate normality through time, which means that the modern climate operates in a similar manner as over the past 2,000 years. In this study, we show how using different modern climate datasets have an impact on a pollen-based reconstruction of mean temperature of the warmest month (MTWA) during the past 2,000 years for North America. The modern climate datasets used to explore this research question include the: Whitmore et al., (2005) modern climate dataset; North American Regional Reanalysis (NARR); National Center For Environmental Prediction (NCEP); European Center for Medium Range Weather Forecasting (ECMWF) ERA-40 reanalysis; WorldClim, Global Historical Climate Network (GHCN) and New et al., which is derived from the CRU dataset. Results show that some caution is advised in using the reanalysis data on large-scale reconstructions. Station data appears to dampen out the variability of the reconstruction produced using station based datasets. The reanalysis or model-based datasets are not recommended for paleoclimate large-scale North American reconstructions as they appear to lack some of the dynamics observed in station datasets (CRU) which resulted in warm-biased reconstructions as compared to the station-based reconstructions. The Whitmore et al. (2005) modern climate dataset appears to be a compromise between CRU-based datasets and model-based datasets except for the ERA-40. In addition, an ultra-high resolution gridded climate dataset such as WorldClim may only be useful if the pollen calibration sites in North America have at least the same spatial precision. We reconstruct the MTWA to within +/-0.01°C by using an average of all curves derived from the different modern climate datasets, demonstrating the robustness of the procedure used. It may be that the use of an average of different modern datasets may reduce the impact of uncertainty of paleoclimate reconstructions, however, this is yet to be determined with certainty. Future evaluation using for example the newly developed Berkeley earth surface temperature datasets should be tested against the paleoclimate record.

A Satellite-Derived Climate-Quality Data Record of the Clear-Sky Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Comiso, Josefino C.; DiGirolamo, Nikolo E.; Shuman, Christopher A.; Key, Jeffrey R.; Koenig, Lora S.

2012-01-01

We have developed a climate-quality data record of the clear-sky surface temperature of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) ice-surface temperature (1ST) algorithm. A climate-data record (CDR) is a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change. We present daily and monthly MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 at 6.25-km spatial resolution on a polar stereographic grid. This record will be elevated in status to a CDR when at least nine more years of data become available either from MODIS Terra or Aqua, or from the Visible Infrared Imager Radiometer Suite (VIIRS) to be launched in October 2011. Our ultimate goal is to develop a CDR that starts in 1981 with the Advanced Very High Resolution (AVHRR) Polar Pathfinder (APP) dataset and continues with MODIS data from 2000 to the present, and into the VIIRS era. Differences in the APP and MODIS cloud masks have so far precluded the current 1ST records from spanning both the APP and MODIS time series in a seamless manner though this will be revisited when the APP dataset has been reprocessed. The complete MODIS 1ST daily and monthly data record is available online.

Coupled Land Surface-Subsurface Hydrogeophysical Inverse Modeling to Estimate Soil Organic Carbon Content in an Arctic Tundra

NASA Astrophysics Data System (ADS)

Tran, A. P.; Dafflon, B.; Hubbard, S.

2017-12-01

Soil organic carbon (SOC) is crucial for predicting carbon climate feedbacks in the vulnerable organic-rich Arctic region. However, it is challenging to achieve this property due to the general limitations of conventional core sampling and analysis methods. In this study, we develop an inversion scheme that uses single or multiple datasets, including soil liquid water content, temperature and ERT data, to estimate the vertical profile of SOC content. Our approach relies on the fact that SOC content strongly influences soil hydrological-thermal parameters, and therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. The scheme includes several advantages. First, this is the first time SOC content is estimated by using a coupled hydrogeophysical inversion. Second, by using the Community Land Model, we can account for the land surface dynamics (evapotranspiration, snow accumulation and melting) and ice/liquid phase transition. Third, we combine a deterministic and an adaptive Markov chain Monte Carlo optimization algorithm to better estimate the posterior distributions of desired model parameters. Finally, the simulated subsurface variables are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using synthetic experiments. The results show that compared to inversion of single dataset, joint inversion of these datasets significantly reduces parameter uncertainty. The joint inversion approach is able to estimate SOC content within the shallow active layer with high reliability. Next, we apply the scheme to estimate OC content along an intensive ERT transect in Barrow, Alaska using multiple datasets acquired in the 2013-2015 period. The preliminary results show a good agreement between modeled and measured soil temperature, thaw layer thickness and electrical resistivity. The accuracy of estimated SOC content will be evaluated by comparison with measurements from soil samples along the transect. Our study presents a new surface-subsurface, deterministic-stochastic hydrogeophysical inversion approach, as well as the benefit of including multiple types of data to estimate SOC and associated hydrological-thermal dynamics.

Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets

USDA-ARS?s Scientific Manuscript database

Two satellites are currently monitoring surface soil moisture (SM) from L-band observations: SMOS (Soil Moisture and Ocean Salinity), a European Space Agency (ESA) satellite that was launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration...

Recent land cover changes and sensitivity of the model simulations to various land cover datasets for China

NASA Astrophysics Data System (ADS)

Chen, Liang; Ma, Zhuguo; Mahmood, Rezaul; Zhao, Tianbao; Li, Zhenhua; Li, Yanping

2017-08-01

Reliable land cover data are important for improving numerical simulation by regional climate model, because the land surface properties directly affect climate simulation by partitioning of energy, water and momentum fluxes and by determining temperature and moisture at the interface between the land surface and atmosphere. China has experienced significant land cover change in recent decades and accurate representation of these changes is, hence, essential. In this study, we used a climate model to examine the changes experienced in the regional climate because of the different land cover data in recent decades. Three sets of experiments are performed using the same settings, except for the land use/cover (LC) data for the years 1990, 2000, 2009, and the model default LC data. Three warm season periods are selected, which represented a wet (1998), normal (2000) and a dry year (2011) for China in each set of experiment. The results show that all three sets of land cover experiments simulate a warm bias relative to the control with default LC data for near-surface temperature in summertime in most parts of China. It is especially noticeable in the southwest China and south of the Yangtze River, where significant changes of LC occurred. Deforestation in southwest China and to the south of Yangtze River in the experiment cases may have contributed to the negative precipitation bias relative to the control cases. Large LC changes in northwestern Tibetan Plateau for 2000 and 2009 datasets are also associated with changes in surface temperature, precipitation, and heat fluxes. Wind anomalies and energy budget changes are consistent with the precipitation and temperature changes.

Variability of hydrological extreme events in East Asia and their dynamical control: a comparison between observations and two high-resolution global climate models

NASA Astrophysics Data System (ADS)

Freychet, N.; Duchez, A.; Wu, C.-H.; Chen, C.-A.; Hsu, H.-H.; Hirschi, J.; Forryan, A.; Sinha, B.; New, A. L.; Graham, T.; Andrews, M. B.; Tu, C.-Y.; Lin, S.-J.

2017-02-01

This work investigates the variability of extreme weather events (drought spells, DS15, and daily heavy rainfall, PR99) over East Asia. It particularly focuses on the large scale atmospheric circulation associated with high levels of the occurrence of these extreme events. Two observational datasets (APHRODITE and PERSIANN) are compared with two high-resolution global climate models (HiRAM and HadGEM3-GC2) and an ensemble of other lower resolution climate models from CMIP5. We first evaluate the performance of the high resolution models. They both exhibit good skill in reproducing extreme events, especially when compared with CMIP5 results. Significant differences exist between the two observational datasets, highlighting the difficulty of having a clear estimate of extreme events. The link between the variability of the extremes and the large scale circulation is investigated, on monthly and interannual timescales, using composite and correlation analyses. Both extreme indices DS15 and PR99 are significantly linked to the low level wind intensity over East Asia, i.e. the monsoon circulation. It is also found that DS15 events are strongly linked to the surface temperature over the Siberian region and to the land-sea pressure contrast, while PR99 events are linked to the sea surface temperature anomalies over the West North Pacific. These results illustrate the importance of the monsoon circulation on extremes over East Asia. The dependencies on of the surface temperature over the continent and the sea surface temperature raise the question as to what extent they could affect the occurrence of extremes over tropical regions in future projections.

Land surface dynamics monitoring using microwave passive satellite sensors

NASA Astrophysics Data System (ADS)

Guijarro, Lizbeth Noemi

Soil moisture, surface temperature and vegetation are variables that play an important role in our environment. There is growing demand for accurate estimation of these geophysical parameters for the research of global climate models (GCMs), weather, hydrological and flooding models, and for the application to agricultural assessment, land cover change, and a wide variety of other uses that meet the needs for the study of our environment. The different studies covered in this dissertation evaluate the capabilities and limitations of microwave passive sensors to monitor land surface dynamics. The first study evaluates the 19 GHz channel of the SSM/I instrument with a radiative transfer model and in situ datasets from the Illinois stations and the Oklahoma Mesonet to retrieve land surface temperature and surface soil moisture. The surface temperatures were retrieved with an average error of 5 K and the soil moisture with an average error of 6%. The results show that the 19 GHz channel can be used to qualitatively predict the spatial and temporal variability of surface soil moisture and surface temperature at regional scales. In the second study, in situ observations were compared with sensor observations to evaluate aspects of low and high spatial resolution at multiple frequencies with data collected from the Southern Great Plains Experiment (SGP99). The results showed that the sensitivity to soil moisture at each frequency is a function of wavelength and amount of vegetation. The results confirmed that L-band is more optimal for soil moisture, but each sensor can provide soil moisture information if the vegetation water content is low. The spatial variability of the emissivities reveals that resolution suffers considerably at higher frequencies. The third study evaluates C- and X-bands of the AMSR-E instrument. In situ datasets from the Soil Moisture Experiments (SMEX03) in South Central Georgia were utilized to validate the AMSR-E soil moisture product and to derive surface soil moisture with a radiative transfer model. The soil moisture was retrieved with an average error of 2.7% at X-band and 6.7% at C-band. The AMSR-E demonstrated its ability to successfully infer soil moisture during the SMEX03 experiment.

Refining surface net radiation estimates in arid and semi-arid climates of Iran

NASA Astrophysics Data System (ADS)

Golkar, Foroogh; Rossow, William B.; Sabziparvar, Ali Akbar

2018-06-01

Although the downwelling fluxes exhibit space-time scales of dependency on characteristic of atmospheric variations, especially clouds, the upward fluxes and, hence the net radiation, depends on the variation of surface properties, particularly surface skin temperature and albedo. Evapotranspiration at the land surface depends on the properties of that surface and is determined primarily by the net surface radiation, mostly absorbed solar radiation. Thus, relatively high spatial resolution net radiation data are needed for evapotranspiration studies. Moreover, in more arid environments, the diurnal variations of surface (air and skin) temperature can be large so relatively high (sub-daily) time resolution net radiation is also needed. There are a variety of radiation and surface property products available but they differ in accuracy, space-time resolution and information content. This situation motivated the current study to evaluate multiple sources of information to obtain the best net radiation estimate with the highest space-time resolution from ISCCP FD dataset. This study investigates the accuracy of the ISCCP FD and AIRS surface air and skin temperatures, as well as the ISCCP FD and MODIS surface albedos and aerosol optical depths as the leading source of uncertainty in ISCCP FD dataset. The surface air temperatures, 10-cm soil temperatures and surface solar insolation from a number of surface sites are used to judge the best combinations of data products, especially on clear days. The corresponding surface skin temperatures in ISCCP FD, although they are known to be biased somewhat high, disagreed more with AIRS measurements because of the mismatch of spatial resolutions. The effect of spatial resolution on the comparisons was confirmed using the even higher resolution MODIS surface skin temperature values. The agreement of ISCCP FD surface solar insolation with surface measurements is good (within 2.4-9.1%), but the use of MODIS aerosol optical depths as an alternative was checked and found to not improve the agreement. The MODIS surface albedos differed from the ISCCP FD values by no more than 0.02-0.07, but because these differences are mostly at longer wavelengths, they did not change the net solar radiation very much. Therefore to obtain the best estimate of surface net radiation with the best combination of spatial and temporal resolution, we developed a method to adjust the ISCCP FD surface longwave fluxes using the AIRS surface air and skin temperatures to obtain the higher spatial resolution of the latter (45 km), while retaining the 3-h time intervals of the former. Overall, the refinements reduced the ISCCP FD longwave flux magnitudes by about 25.5-42.1 W/m2 RMS (maximum difference -27.5 W/m2 for incoming longwave radiation and -59 W/m2 for outgoing longwave radiation) with the largest differences occurring at 9:00 and 12:00 UTC near local noon. Combining the ISCCP FD net shortwave radiation data and the AIRS-modified net longwave radiation data changed the total net radiation for summertime by 4.64 to 61.5 W/m2 and for wintertime by 1.06 to 41.88 W/m2 (about 11.1-39.2% of the daily mean).

Improved Modeling of Land-Atmosphere Interactions using a Coupled Version of WRF with the Land Information System

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaCasse, Katherine M.; Santanello, Joseph A., Jr.; Lapenta, William M.; Petars-Lidard, Christa D.

2007-01-01

The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many hydrometeorological processes. Accurate and high-resolution representations of surface properties such as sea-surface temperature (SST), vegetation, soil temperature and moisture content, and ground fluxes are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of weather and climate phenomena. The NASA/NWS Short-term Prediction Research and Transition (SPORT) Center is currently investigating the potential benefits of assimilating high-resolution datasets derived from the NASA moderate resolution imaging spectroradiometer (MODIS) instruments using the Weather Research and Forecasting (WRF) model and the Goddard Space Flight Center Land Information System (LIS). The LIS is a software framework that integrates satellite and ground-based observational and modeled data along with multiple land surface models (LSMs) and advanced computing tools to accurately characterize land surface states and fluxes. The LIS can be run uncoupled to provide a high-resolution land surface initial condition, and can also be run in a coupled mode with WRF to integrate surface and soil quantities using any of the LSMs available in LIS. The LIS also includes the ability to optimize the initialization of surface and soil variables by tuning the spin-up time period and atmospheric forcing parameters, which cannot be done in the standard WRF. Among the datasets available from MODIS, a leaf-area index field and composite SST analysis are used to improve the lower boundary and initial conditions to the LIS/WRF coupled model over both land and water. Experiments will be conducted to measure the potential benefits from using the coupled LIS/WRF model over the Florida peninsula during May 2004. This month experienced relatively benign weather conditions, which will allow the experiments to focus on the local and mesoscale impacts of the high-resolution MODIS datasets and optimized soil and surface initial conditions. Follow-on experiments will examine the utility of such an optimized WRF configuration for more complex weather scenarios such as convective initiation. This paper will provide an overview of the experiment design and present preliminary results from selected cases in May 2004.

High Spatial Resolution Forecasting of Long-Term Monthly Precipitation and Mean Temperature Trends in Data Scarce Regions

NASA Astrophysics Data System (ADS)

Mosier, T. M.; Hill, D. F.; Sharp, K. V.

2013-12-01

High spatial resolution time-series data are critical for many hydrological and earth science studies. Multiple groups have developed historical and forecast datasets of high-resolution monthly time-series for regions of the world such as the United States (e.g. PRISM for hindcast data and MACA for long-term forecasts); however, analogous datasets have not been available for most data scarce regions. The current work fills this data need by producing and freely distributing hindcast and forecast time-series datasets of monthly precipitation and mean temperature for all global land surfaces, gridded at a 30 arc-second resolution. The hindcast data are constructed through a Delta downscaling method, using as inputs 0.5 degree monthly time-series and 30 arc-second climatology global weather datasets developed by Willmott & Matsuura and WorldClim, respectively. The forecast data are formulated using a similar downscaling method, but with an additional step to remove bias from the climate variable's probability distribution over each region of interest. The downscaling package is designed to be compatible with a number of general circulation models (GCM) (e.g. with GCMs developed for the IPCC AR4 report and CMIP5), and is presently implemented using time-series data from the NCAR CESM1 model in conjunction with 30 arc-second future decadal climatologies distributed by the Consultative Group on International Agricultural Research. The resulting downscaled datasets are 30 arc-second time-series forecasts of monthly precipitation and mean temperature available for all global land areas. As an example of these data, historical and forecast 30 arc-second monthly time-series from 1950 through 2070 are created and analyzed for the region encompassing Pakistan. For this case study, forecast datasets corresponding to the future representative concentration pathways 45 and 85 scenarios developed by the IPCC are presented and compared. This exercise highlights a range of potential meteorological trends for the Pakistan region and more broadly serves to demonstrate the utility of the presented 30 arc-second monthly precipitation and mean temperature datasets for use in data scarce regions.

Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures.

PubMed

Rebaudo, François; Faye, Emile; Dangles, Olivier

2016-01-01

A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies.

Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures

PubMed Central

Rebaudo, François; Faye, Emile; Dangles, Olivier

2016-01-01

A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies. PMID:27148077

The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset

NASA Technical Reports Server (NTRS)

Welch, Ronald M.

1993-01-01

A series of cloud and sea ice retrieval algorithms are being developed in support of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Science Team objectives. These retrievals include the following: cloud fractional area, cloud optical thickness, cloud phase (water or ice), cloud particle effective radius, cloud top heights, cloud base height, cloud top temperature, cloud emissivity, cloud 3-D structure, cloud field scales of organization, sea ice fractional area, sea ice temperature, sea ice albedo, and sea surface temperature. Due to the problems of accurately retrieving cloud properties over bright surfaces, an advanced cloud classification method was developed which is based upon spectral and textural features and artificial intelligence classifiers.

Prediction of brain tissue temperature using near-infrared spectroscopy.

PubMed

Holper, Lisa; Mitra, Subhabrata; Bale, Gemma; Robertson, Nicola; Tachtsidis, Ilias

2017-04-01

Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of [Formula: see text] (animal dataset) and [Formula: see text] (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of [Formula: see text] (animal dataset) and [Formula: see text] (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications.

High-resolution daily gridded datasets of air temperature and wind speed for Europe

NASA Astrophysics Data System (ADS)

Brinckmann, S.; Krähenmann, S.; Bissolli, P.

2015-08-01

New high-resolution datasets for near surface daily air temperature (minimum, maximum and mean) and daily mean wind speed for Europe (the CORDEX domain) are provided for the period 2001-2010 for the purpose of regional model validation in the framework of DecReg, a sub-project of the German MiKlip project, which aims to develop decadal climate predictions. The main input data sources are hourly SYNOP observations, partly supplemented by station data from the ECA&D dataset (http://www.ecad.eu). These data are quality tested to eliminate erroneous data and various kinds of inhomogeneities. Grids in a resolution of 0.044° (5 km) are derived by spatial interpolation of these station data into the CORDEX area. For temperature interpolation a modified version of a regression kriging method developed by Krähenmann et al. (2011) is used. At first, predictor fields of altitude, continentality and zonal mean temperature are chosen for a regression applied to monthly station data. The residuals of the monthly regression and the deviations of the daily data from the monthly averages are interpolated using simple kriging in a second and third step. For wind speed a new method based on the concept used for temperature was developed, involving predictor fields of exposure, roughness length, coastal distance and ERA Interim reanalysis wind speed at 850 hPa. Interpolation uncertainty is estimated by means of the kriging variance and regression uncertainties. Furthermore, to assess the quality of the final daily grid data, cross validation is performed. Explained variance ranges from 70 to 90 % for monthly temperature and from 50 to 60 % for monthly wind speed. The resulting RMSE for the final daily grid data amounts to 1-2 °C and 1-1.5 m s-1 (depending on season and parameter) for daily temperature parameters and daily mean wind speed, respectively. The datasets presented in this article are published at http://dx.doi.org/10.5676/DWD_CDC/DECREG0110v1.

The global surface temperatures of the Moon as measured by the Diviner Lunar Radiometer Experiment

NASA Astrophysics Data System (ADS)

Williams, J.-P.; Paige, D. A.; Greenhagen, B. T.; Sefton-Nash, E.

2017-02-01

The Diviner Lunar Radiometer Experiment onboard the Lunar Reconnaissance Orbiter (LRO) has been acquiring solar reflectance and mid-infrared radiance measurements nearly continuously since July of 2009. Diviner is providing the most comprehensive view of how regoliths on airless bodies store and exchange thermal energy with the space environment. Approximately a quarter trillion calibrated radiance measurements of the Moon, acquired over 5.5 years by Diviner, have been compiled into a 0.5° resolution global dataset with a 0.25 h local time resolution. Maps generated with this dataset provide a global perspective of the surface energy balance of the Moon and reveal the complex and extreme nature of the lunar surface thermal environment. Our achievable map resolution, both spatially and temporally, will continue to improve with further data acquisition. Daytime maximum temperatures are sensitive to the albedo of the surface and are ∼387-397 K at the equator, dropping to ∼95 K just before sunrise, though anomalously warm areas characterized by high rock abundances can be > 50 K warmer than the zonal average nighttime temperatures. An asymmetry is observed between the morning and afternoon temperatures due to the thermal inertia of the lunar regolith with the dusk terminator ∼30 K warmer than the dawn terminator at the equator. An increase in albedo with incidence angle is required to explain the observed decrease in temperatures with latitude. At incidence angles exceeding ∼40°, topography and surface roughness influence temperatures resulting in increasing scatter in temperatures and anisothermality between Diviner channels. Nighttime temperatures are sensitive to the thermophysical properties of the regolith. High thermal inertia (TI) materials such as large rocks, remain warmer during the long lunar night and result in anomalously warm nighttime temperatures and anisothermality in the Diviner channels. Anomalous maximum and minimum temperatures are highlighted by subtracting the zonal mean temperatures from maps. Terrains can be characterized as low or high reflectance and low or high TI. Low maximum temperatures result from high reflectance surfaces while low minimum temperatures from low-TI material. Conversely, high maximum temperatures result from dark surface, and high minimum temperatures from high-TI materials. Impact craters are found to modify regolith properties over large distances. The thermal signature of Tycho is asymmetric, consistent with an oblique impact coming from the west. Some prominent crater rays are visible in the thermal data and require material with a higher thermal inertial than nominal regolith. The influence of the formation of the Orientale basin on the regolith properties is observable over a substantial portion of the western hemisphere despite its age (∼3.8 Gyr), and may have contributed to mixing of highland and mare material on the southwest margin of Oceanus Procellarum where the gradient in radiative properties at the mare-highland contact is broad (∼200 km).

Simulated MERTIS observation of the Rudaki-Kuiper craters area on Mercury

NASA Astrophysics Data System (ADS)

D'Amore, M.; Helbert, J.; Maturilli, A.; Ferrari, S.; Bauch, K.; D'Incecco, P.; Hiesinger, H.; Head, J. W.; Holsclaw, G. M.; Lorin, D. D.; Denevi, B. W.; Stockstill-Cahill, K. R.

2013-12-01

The MErcury Radiometer and Thermal infrared Imaging Spectrometer (MERTIS) is part of the payload of the BepiColombo mission. The mission is scheduled for launch in 2015 with arrival at Mercury in 2021. To achieve MERTIS's scientific goals the instrument maps the surface of Mercury with a spatial resolution of 500m for the spectrometer channel and 2km for the radiometer channel. MERTIS spans wavelength ranges of 7-14 and 7-40 μm with its two channels. Among it scientific goals, MERTIS will infer rock-forming minerals, map surface composition, and study surface temperature variations on Mercury with an uncooled microbolometer detector. To exploit the full potential of the unique MERTIS dataset, an extensive calibration campaign has been performed. This includes radiometric, spectral, and geometric calibration. In addition we have performed measurement of analog materials at temperatures of up to 500°C - similar to the peak temperatures expected at Mercury - with the MERTIS qualification model in the Planetary Emissivity Laboratory. These measurements allow for the evaluation of the MERTIS performance in direct comparison with the laboratory spectrometer. They also enable the creation of synthetic MERTIS datasets. For this purpose we use data from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft as baseline. MESSENGER can provide geological information as well as spectral information in the UV, visible and near-infrared wavelengths range. For a first test we have selected the Kuiper-Rudaki region. The region has been extensively covered by measurements from the MESSENGER spacecraft. Recent analysis of observations by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument on the MESSENGER spacecraft with an unsupervised hierarchical clustering method shows at global scales two major units: a Polar region (PR) spectrally flat and redder than the equatorial region (ER). The study area is primarily classified as a homogeneous expanse of the equatorial region (ER) cluster. Further clustering shows that the study area belongs to the 'core' ER, in opposition to some smaller patches of a transitional sub-unit, that are transitional region between global ER and the polar region (PR) cluster. Assuming a set of several potential mineralogies for the study area and modeled surface temperature at different local times we can obtain at PEL the spectra in the mid-infrared spectral range. Combining these with our knowledge of the MERTIS performance we can produce simulated MERTIS datasets of the study region at different point of the mission.

Validation of the Aura Microwave Limb Sounder Temperature and Geopotential Height Measurements

NASA Technical Reports Server (NTRS)

Schwartz, M. J.; Lambert, A.; Manney, G. L.; Read, W. G.; Livesey, N. J.; Froidevaux, L.; Ao, C. O.; Bernath, P. F.; Boone, C. D.; Cofield, R. E.;

Soil Mesocosm CO2 Emissions after 13C-glucose Addition, Soil Physical and Chemical Characteristics, and Microbial Biomass, Barrow, Alaska, 2014-2016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lydia Vaughn; Biao Zhu; Carolin Bimueller

Measurements made from a 2014-2016 field glucose addition experiment. Dataset includes measurements of surface trace gas emissions (Delta13C of ecosystem respiration and source-partitioned surface CO2 flux, CH4 flux, and GPP), soil profile information (concentrations of carbon, nitrogen, and soil microbial biomass carbon, Delta13C of soil organic matter and microbial biomass, gravimetric water content, and bulk density), soil mineral nitrogen availability, and field-measured soil temperature, air temperature and soil moisture. Experiment was conducted in a region of high-centered polygons on the BEO. Data will be available Fall 2017.

Seasonality of change: Summer warming rates do not fully represent effects of climate change on lake temperatures

USGS Publications Warehouse

Winslow, Luke; Read, Jordan S.; Hansen, Gretchen J. A.; Rose, Kevin C.; Robertson, Dale M.

2017-01-01

Responses in lake temperatures to climate warming have primarily been characterized using seasonal metrics of surface-water temperatures such as summertime or stratified period average temperatures. However, climate warming may not affect water temperatures equally across seasons or depths. We analyzed a long-term dataset (1981–2015) of biweekly water temperature data in six temperate lakes in Wisconsin, U.S.A. to understand (1) variability in monthly rates of surface- and deep-water warming, (2) how those rates compared to summertime average trends, and (3) if monthly heterogeneity in water temperature trends can be predicted by heterogeneity in air temperature trends. Monthly surface-water temperature warming rates varied across the open-water season, ranging from 0.013 in August to 0.073°C yr−1 in September (standard deviation [SD]: 0.025°C yr−1). Deep-water trends during summer varied less among months (SD: 0.006°C yr−1), but varied broadly among lakes (–0.056°C yr−1 to 0.035°C yr−1, SD: 0.034°C yr−1). Trends in monthly surface-water temperatures were well correlated with air temperature trends, suggesting monthly air temperature trends, for which data exist at broad scales, may be a proxy for seasonal patterns in surface-water temperature trends during the open water season in lakes similar to those studied here. Seasonally variable warming has broad implications for how ecological processes respond to climate change, because phenological events such as fish spawning and phytoplankton succession respond to specific, seasonal temperature cues.

Regional Contrasts of the Warming Rate over Land Significantly Depend on the Calculation Methods of Mean Air Temperature

NASA Astrophysics Data System (ADS)

Wang, Kaicun; Zhou, Chunlüe

2016-04-01

Global analyses of surface mean air temperature (Tm) are key datasets for climate change studies and provide fundamental evidences for global warming. However, the causes of regional contrasts in the warming rate revealed by such datasets, i.e., enhanced warming rates over the northern high latitudes and the "warming hole" over the central U.S., are still under debate. Here we show these regional contrasts depends on the calculation methods of Tm. Existing global analyses calculated Tm from daily minimum and maximum temperatures (T2). We found that T2 has a significant standard deviation error of 0.23 °C/decade in depicting the regional warming rate from 2000 to 2013 but can be reduced by two-thirds using Tm calculated from observations at four specific times (T4), which samples diurnal cycle of land surface air temperature more often. From 1973 to 1997, compared with T4, T2 significantly underestimated the warming rate over the central U.S. and overestimated the warming rate over the northern high latitudes. The ratio of the warming rate over China to that over the U.S. reduces from 2.3 by T2 to 1.4 by T4. This study shows that the studies of regional warming can be substantially improved by T4 instead of T2.

A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Abul Ehsan Bhuiyan, Md; Nikolopoulos, Efthymios I.; Anagnostou, Emmanouil N.; Quintana-Seguí, Pere; Barella-Ortiz, Anaïs

2018-02-01

This study investigates the use of a nonparametric, tree-based model, quantile regression forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning 11 years (2000-2010). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate ensemble for these conditions. Evaluation of the combined product was based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5 km 1 h-1 resolution. Furthermore, to evaluate relative improvements and the overall impact of the combined product in hydrological response, we used the generated ensemble to force a distributed hydrological model (the SURFEX land surface model and the RAPID river routing scheme) and compared its streamflow simulation results with the corresponding simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20-99 and 44-88 %, respectively, when considering the ensemble mean.

Global Sea Surface Temperature: A Harmonized Multi-sensor Time-series from Satellite Observations

NASA Astrophysics Data System (ADS)

Merchant, C. J.

2017-12-01

This paper presents the methods used to obtain a new global sea surface temperature (SST) dataset spanning the early 1980s to the present, intended for use as a climate data record (CDR). The dataset provides skin SST (the fundamental measurement) and an estimate of the daily mean SST at depths compatible with drifting buoys (adjusting for skin and diurnal variability). The depth SST provided enables the CDR to be used with in situ records and centennial-scale SST reconstructions. The new SST timeseries is as independent as possible from in situ observations, and from 1995 onwards is harmonized to an independent satellite reference (namely, SSTs from the Advanced Along Track Scanning Radiometer (Advanced ATSR)). This maximizes the utility of our new estimates of variability and long-term trends in interrogating previous datasets tied to in situ observations. The new SSTs include full resolution (swath, level 2) data, single-sensor gridded data (level 3, 0.05 degree latitude-longitude grid) and a multi-sensor optimal analysis (level 4, same grid). All product levels are consistent. All SSTs have validated uncertainty estimates attached. The sensors used include all Advanced Very High Resolution Radiometers from NOAA-6 onwards and the ATSR series. AVHRR brightness temperatures (BTs) are calculated from counts using a new in-flight re-calibration for each sensor, ultimately linked through to the AATSR BT calibration by a new harmonization technique. Artefacts in AVHRR BTs linked to varying instrument temperature, orbital regime and solar contamination are significantly reduced. These improvements in the AVHRR BTs (level 1) translate into improved cloud detection and SST (level 2). For cloud detection, we use a Bayesian approach for all sensors. For the ATSRs, SSTs are derived with sufficient accuracy and sensitivity using dual-view coefficients. This is not the case for single-view AVHRR observations, for which a physically based retrieval is employed, using a hybrid maximum a posteriori / maximum likelihood retrieval, which optimises retrieval uncertainty and SST sensitivity for climate applications. Validation results will be presented along with examples of the variability and trends in SST evident in the dataset.

OSI SAF Sea Surface Temperature reprocessing of MSG/SEVIRI archive.

NASA Astrophysics Data System (ADS)

Saux Picart, Stéphane; Legendre, Gerard; Marsouin, Anne; Péré, Sonia; Roquet, Hervé

2017-04-01

The Ocean and Sea-Ice Satellite Application Facility (OSI-SAF) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) is planning to deliver a reprocessing of Sea Surface Temperature (SST) from Spinning Enhanced Visible and Infrared Imager/Meteosat Second Generation (SEVIRI/MSG) archive (2004-2012) by the end of 2016. This reprocessing is drawing from experiences of the OSI SAF team in near real time processing of MSG/SEVIRI data. The retrieval method consist in a non-linear split-window algorithm including the algorithm correction scheme developed by Le Borgne et al. (2011). The bias correction relies on simulations of infrared brightness temperatures performed using Numerical Weather Prediction model atmospheric profiles of water vapour and temperature, and RTTOV radiative transfer model. The cloud mask used is the Climate SAF reprocessing of the MSG/SEVIRI archive. It is consistent over the period in consideration. Atmospheric Saharan dusts have a strong impact on the retrieved SST, they are taken into consideration through the computation of the Saharan Dust Index (Merchant et al., 2006) which is then used to determine an empirical correction applied to SST. The MSG/SEVIRI SST reprocessing dataset consist in hourly level 3 composite of sub-skin temperature projected onto a regular 0.05° grid over the region delimited by 60N,60S and 60W,60E. This presentation gives an overview of the data and methods used for the reprocessing, the products and validation results against drifting buoys measurements extracted from the ERA Clim dataset.

Characterization of surface properties over permafrost soils using a high resolution mid-infrared camera as part of the Carbon in the Arctic Vulnerability Experiment (CARVE)

NASA Astrophysics Data System (ADS)

Steiner, N.; McDonald, K. C.; Podest, E.; Dinardo, S. J.; Miller, C. E.

2016-12-01

Freeze/thaw and hydrologic cycling have important influence over surface processes in Arctic ecosystems and in Arctic carbon cycling. The seasonal freezing and thawing of soils bracket negative and positive modes of CO2 and CH4 flux of the bulk landscape. Hydrologic processes, such as seasonal inundation of thawed tundra create a complex microtopography where greenhouse-gas sources and sinks occur over short distances. Because of a high spatial variability hydrologic features must be mapped at fine resolution. These mappings can then be compared to local and regional scale observations of surface conditions, such as temperature and freeze/thaw state, to create better estimates of these important surface fields. The Carbon in the Arctic Vulnerability Experiment (CARVE) monitors carbon gas cycling in Alaskan using aircraft-deployed gas sampling instruments along with remote sensing observations of the land surface condition. A nadir-pointed, forward looking infrared (FLIR) imager mounted on the CARVE air-craft is used to measure upwelling mid-infrared spectral radiance at 3-5 microns. The FLIR instrument was operated during the spring, summer and fall seasons, 2013 through 2015. The instantaneous field of view (IFOV) of the FLIR instrument allows for a sub-meter resolution from a height of 500 m. High resolution data products allows for the discrimination of individual landscape components such as soil, vegetation and surface water features in the image footprint. We assess the effectiveness of the FLIR thermal images in monitoring thawing and inundation processes at very high resolutions. Analyses of FLIR datasets over focused study areas emphasizing exploration of the FLIR dataset utility for detailed land surface characterization as related to surface moisture and temperature. Emphasis is given to the Barrow CMDL station site and employ the tram-based data collections there. We will also examine potential at other high latitude sites of interest, e.g. Atqasuk, Ivotuk Alaska and tundra polygon sites under study by collaborators at UT Austin. The combination of high resolution temperature observations with associated estimates of temperature from other instruments can be used to discriminate hydrologic from temperature features in the mid-infrared to produce a high-resolution hydrology product.

Human influence on sub-regional surface air temperature change over India.

PubMed

Dileepkumar, R; AchutaRao, Krishna; Arulalan, T

2018-06-12

Human activities have been implicated in the observed increase in Global Mean Surface Temperature. Over regional scales where climatic changes determine societal impacts and drive adaptation related decisions, detection and attribution (D&A) of climate change can be challenging due to the greater contribution of internal variability, greater uncertainty in regionally important forcings, greater errors in climate models, and larger observational uncertainty in many regions of the world. We examine the causes of annual and seasonal surface air temperature (TAS) changes over sub-regions (based on a demarcation of homogeneous temperature zones) of India using two observational datasets together with results from a multimodel archive of forced and unforced simulations. Our D&A analysis examines sensitivity of the results to a variety of optimal fingerprint methods and temporal-averaging choices. We can robustly attribute TAS changes over India between 1956-2005 to anthropogenic forcing mostly by greenhouse gases and partially offset by other anthropogenic forcings including aerosols and land use land cover change.

Effects of short-term variability of meteorological variables on soil temperature in permafrost regions

NASA Astrophysics Data System (ADS)

Beer, Christian; Porada, Philipp; Ekici, Altug; Brakebusch, Matthias

2018-03-01

Effects of the short-term temporal variability of meteorological variables on soil temperature in northern high-latitude regions have been investigated. For this, a process-oriented land surface model has been driven using an artificially manipulated climate dataset. Short-term climate variability mainly impacts snow depth, and the thermal diffusivity of lichens and bryophytes. These impacts of climate variability on insulating surface layers together substantially alter the heat exchange between atmosphere and soil. As a result, soil temperature is 0.1 to 0.8 °C higher when climate variability is reduced. Earth system models project warming of the Arctic region but also increasing variability of meteorological variables and more often extreme meteorological events. Therefore, our results show that projected future increases in permafrost temperature and active-layer thickness in response to climate change will be lower (i) when taking into account future changes in short-term variability of meteorological variables and (ii) when representing dynamic snow and lichen and bryophyte functions in land surface models.

[An operational remote sensing algorithm of land surface evapotranspiration based on NOAA PAL dataset].

PubMed

Hou, Ying-Yu; He, Yan-Bo; Wang, Jian-Lin; Tian, Guo-Liang

2009-10-01

Based on the time series 10-day composite NOAA Pathfinder AVHRR Land (PAL) dataset (8 km x 8 km), and by using land surface energy balance equation and "VI-Ts" (vegetation index-land surface temperature) method, a new algorithm of land surface evapotranspiration (ET) was constructed. This new algorithm did not need the support from meteorological observation data, and all of its parameters and variables were directly inversed or derived from remote sensing data. A widely accepted ET model of remote sensing, i. e., SEBS model, was chosen to validate the new algorithm. The validation test showed that both the ET and its seasonal variation trend estimated by SEBS model and our new algorithm accorded well, suggesting that the ET estimated from the new algorithm was reliable, being able to reflect the actual land surface ET. The new ET algorithm of remote sensing was practical and operational, which offered a new approach to study the spatiotemporal variation of ET in continental scale and global scale based on the long-term time series satellite remote sensing images.

Constraining the Sensitivity of Amazonian Rainfall with Observations of Surface Temperature

NASA Astrophysics Data System (ADS)

Dolman, A. J.; von Randow, C.; de Oliveira, G. S.; Martins, G.; Nobre, C. A.

2016-12-01

Earth System models generally do a poor job in predicting Amazonian rainfall, necessitating the need to look for observational constraints on their predictability. We use observed surface temperature and precipitation of the Amazon and a set of 21 CMIP5 models to derive an observational constraint of the sensitivity of rainfall to surface temperature (dP/dT). From first principles such a relation between the surface temperature of the earth and the amount of precipitation through the surface energy balance should exist, particularly in the tropics. When de-trended anomalies in surface temperature and precipitation from a set of datasets are plotted, a clear linear relation between surface temperature and precipitation appears. CMIP5 models show a similar relation with relatively cool models having a larger sensitivity, producing more rainfall. Using the ensemble of models and the observed surface temperature we were able to derive an emerging constraint, reducing the dPdt sensitivity of the CMIP5 model from -0.75 mm day-1 0C-1 (+/- 0.54 SD) to -0.77 mm day-1 0C-1 with a reduced uncertainty of about a factor 5. dPdT from the observation is -0.89 mm day-1 0C-1 . We applied the method to wet and dry season separately noticing that in the wet season we shifted the mean and reduced uncertainty, while in the dry season we very much reduced uncertainty only. The method can be applied to other model simulations such as specific deforestation scenarios to constrain the sensitivity of rainfall to surface temperature. We discuss the implications of the constrained sensitivity for future Amazonian predictions.

Prediction of brain tissue temperature using near-infrared spectroscopy

PubMed Central

Holper, Lisa; Mitra, Subhabrata; Bale, Gemma; Robertson, Nicola; Tachtsidis, Ilias

2017-01-01

Abstract. Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of R2=0.713±0.157 (animal dataset) and R2=0.798±0.087 (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of 0.436±0.283°C (animal dataset) and 0.162±0.149°C (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications. PMID:28630878

Influence of reanalysis datasets on dynamically downscaling the recent past

NASA Astrophysics Data System (ADS)

Moalafhi, Ditiro B.; Evans, Jason P.; Sharma, Ashish

2017-08-01

Multiple reanalysis datasets currently exist that can provide boundary conditions for dynamic downscaling and simulating local hydro-climatic processes at finer spatial and temporal resolutions. Previous work has suggested that there are two reanalyses alternatives that provide the best lateral boundary conditions for downscaling over southern Africa. This study dynamically downscales these reanalyses (ERA-I and MERRA) over southern Africa to a high resolution (10 km) grid using the WRF model. Simulations cover the period 1981-2010. Multiple observation datasets were used for both surface temperature and precipitation to account for observational uncertainty when assessing results. Generally, temperature is simulated quite well, except over the Namibian coastal plain where the simulations show anomalous warm temperature related to the failure to propagate the influence of the cold Benguela current inland. Precipitation tends to be overestimated in high altitude areas, and most of southern Mozambique. This could be attributed to challenges in handling complex topography and capturing large-scale circulation patterns. While MERRA driven WRF exhibits slightly less bias in temperature especially for La Nina years, ERA-I driven simulations are on average superior in terms of RMSE. When considering multiple variables and metrics, ERA-I is found to produce the best simulation of the climate over the domain. The influence of the regional model appears to be large enough to overcome the small difference in relative errors present in the lateral boundary conditions derived from these two reanalyses.

A century of ocean warming on Florida Keys coral reefs: historic in situ observations

USGS Publications Warehouse

Kuffner, Ilsa B.; Lidz, Barbara H.; Hudson, J. Harold; Anderson, Jeffery S.

2015-01-01

There is strong evidence that global climate change over the last several decades has caused shifts in species distributions, species extinctions, and alterations in the functioning of ecosystems. However, because of high variability on short (i.e., diurnal, seasonal, and annual) timescales as well as the recency of a comprehensive instrumental record, it is difficult to detect or provide evidence for long-term, site-specific trends in ocean temperature. Here we analyze five in situ datasets from Florida Keys coral reef habitats, including historic measurements taken by lighthouse keepers, to provide three independent lines of evidence supporting approximately 0.8 °C of warming in sea surface temperature (SST) over the last century. Results indicate that the warming observed in the records between 1878 and 2012 can be fully accounted for by the warming observed in recent decades (from 1975 to 2007), documented using in situ thermographs on a mid-shore patch reef. The magnitude of warming revealed here is similar to that found in other SST datasets from the region and to that observed in global mean surface temperature. The geologic context and significance of recent ocean warming to coral growth and population dynamics are discussed, as is the future prognosis for the Florida reef tract.

Possible role of the dimming/brightening in observed temperatures across Europe since the second half of the 20th century

NASA Astrophysics Data System (ADS)

van den Besselaar, E. J. M.; Sanchez-Lorenzo, A.; Wild, M.; Klein Tank, A. M. G.

2012-04-01

The surface solar radiation (SSR) is the fundamental source of energy in the climate system, and consequently the source of life on our planet, due to its central role in the surface energy balance. Therefore, a significant impact on temperatures is expected due to the widespread dimming/brightening phenomenon observed since the second half of the 20th century (Wild, 2009). Previous studies pointed out the effects of SSR trends in temperatures series over Europe (Makowski et al., 2009; Philipona et al., 2009), although the lack of long-term SSR series limits these results. This work describes an updated sunshine duration (SS) dataset compiled by the European Climate Assessment and Dataset (ECA&D) project based on around 300 daily time series over Europe covering the 1961-2010 period. The relationship between the SS and temperature series is analysed based on four temperature variables: maximum (TX), minimum (TN) and mean temperature (TG), as well as the diurnal temperature range (DTR). Regional and pan-European mean series of SS and temperatures are constructed. The analyses are performed on annual and seasonal scale, and focusing on the interannual and decadal agreement between the variables. The results show strong positive correlations on interannual scales between SS and temperatures over Europe, especially for the DTR and TX during the summer period and regions in Central Europe. Interestingly, the SS and temperatures series show a tendency towards higher correlations in the smoothed series, both for different regions and temperature variables. These results confirm the relationship between temperature and SS trends over Europe since the second half of the 20th century, which has been speculated to partially decrease (increase) temperatures during the dimming (brightening) period (Makowski et al., 2009; Wild, 2009). Further research is needed to confirm this cause-effect relationship currently found only using correlation analysis.

Measuring the Surface Temperature of the Cryosphere using Remote Sensing

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.

2012-01-01

A general description of the remote sensing of cryosphere surface temperatures from satellites will be provided. This will give historical information on surface-temperature measurements from space. There will also be a detailed description of measuring the surface temperature of the Greenland Ice Sheet using Moderate-Resolution Imaging Spectroradiometer (MODIS) data which will be the focus of the presentation. Enhanced melting of the Greenland Ice Sheet has been documented in recent literature along with surface-temperature increases measured using infrared satellite data since 1981. Using a recently-developed climate data record, trends in the clear-sky ice-surface temperature (IST) of the Greenland Ice Sheet have been studied using the MODIS IST product. Daily and monthly MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 are now freely available to download at 6.25-km spatial resolution on a polar stereographic grid. Maps showing the maximum extent of melt for the entire ice sheet and for the six major drainage basins have been developed from the MODIS IST dataset. Twelve-year trends of the duration of the melt season on the ice sheet vary in different drainage basins with some basins melting progressively earlier over the course of the study period. Some (but not all) of the basins also show a progressively-longer duration of melt. The consistency of this IST record, with temperature and melt records from other sources will be discussed.

Characterization of the thermal structure inside an urban canyon: field measurements and validation of a simple model

NASA Astrophysics Data System (ADS)

Giovannini, Lorenzo; Zardi, Dino; de Franceschi, Massimiliano

2013-04-01

The results of measurement campaigns are analyzed to investigate the thermal structure in an urban canyon, and to validate a simplified model simulating the air and surface temperatures from surface energy budgets. Starting from measurements at roof-top level, the model provides time series of air and surface temperatures, as well as surface fluxes. Two campaigns were carried out in summer 2007 and in winter 2008/09 in a street of the city of Trento (Italy). Temperature sensors were placed at various levels near the walls flanking the canyon and on a traffic light in the street center. Furthermore, the atmosphere above the mean roof-top level was monitored by a weather station on top of a tower located nearby. Air temperatures near the walls, being strongly influenced by direct solar radiation, display considerable contrasts between the opposite sides of the canyon. On the other hand, when solar radiation is weak or absent, the temperature field remains rather homogeneous.Moreover, air temperature inside the canyon is generally higher than above roof level, with larger differences during summertime. Air temperatures from the above street measurements are well simulated by the model in both seasons. Furthermore, the modeled surface temperatures are tested against a dataset of wall surface temperatures from the Advanced Tools for Rational Energy Use Towards Sustainability-Photocatalytic Innovative Coverings Applications for Depollution (ATREUS-PICADA) experiment, and a very good agreement is found. Results suggest that themodel is a reliable and convenient tool for simplified assessment of climatic conditions occurring in urban canyons under various weather situations.

A closer look at temperature changes with remote sensing

NASA Astrophysics Data System (ADS)

Metz, Markus; Rocchini, Duccio; Neteler, Markus

2014-05-01

Temperature is a main driver for important ecological processes. Time series temperature data provide key environmental indicators for various applications and research fields. High spatial and temporal resolution is crucial in order to perform detailed analyses in various fields of research. While meteorological station data are commonly used, they often lack completeness or are not distributed in a representative way. Remotely sensed thermal images from polar orbiting satellites are considered to be a good alternative to the scarce meteorological data as they offer almost continuous coverage of the Earth with very high temporal resolution. A drawback of temperature data obtained by satellites is the occurrence of gaps (due to clouds, aerosols) that must be filled. We have reconstructed a seamless and gap-free time series for land surface temperature (LST) at continental scale for Europe from MODIS LST products (Moderate Resolution Imaging Sensor instruments onboard the Terra and Aqua satellites), keeping the temporal resolution of four records per day and enhancing the spatial resolution from 1 km to 250 m. Here we present a new procedure to reconstruct MODIS LST time series with unprecedented detail in space and time, at the same time providing continental coverage. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. We selected as auxiliary variables datasets which are globally available in order to propose a worldwide reproducible method. Compared to existing similar datasets, the substantial quantitative difference translates to a qualitative difference in applications and results. We consider both our dataset and the new procedure for its creation to be of utmost interest to a broad interdisciplinary audience. Moreover, we provide examples for its implications and applications, such as disease risk assessment, epidemiology, environmental monitoring, and temperature anomalies. In the near future, aggregated derivatives of our dataset (following the BIOCLIM variable scheme) will be freely made online available for direct usage in GIS based applications.

Variation of Surface Air Temperature in Relation to El Nino and Cataclysmic Volcanic Eruptions, 1796-1882

NASA Technical Reports Server (NTRS)

Wilson, Robert M.

1999-01-01

During the contemporaneous interval of 1796-1882 a number of significant decreases in temperature are found in the records of Central England and Northern Ireland. These decreases appear to be related to the occurrences of El Nino and/or cataclysmic volcanic eruptions. For example, a composite of residual temperatures of the Central England dataset, centering temperatures on the yearly onsets of 20 El Nino of moderate to stronger strength, shows that, on average, the change in temperature varied by about +/- 0.3 C from normal being warmer during the boreal fall-winter leading up to the El Nino year and cooler during the spring-summer of the El Nino year. Also, the influence of El Nino on Central England temperatures appears to last about 1-2 years. Similarly, a composite of residual temperatures of the Central England dataset, centering temperatures on the month of eruption for 26 cataclysmic volcanic eruptions, shows that, on average, the change in temperature decreased by about 0.1 - 0.2 C, typically, 1-2 years after the eruption, although for specific events, like Tambora, the decrease was considerably greater. Additionally, tropical eruptions appear to produce greater changes in temperature than extratropical eruptions, and eruptions occurring in boreal spring-summer appear to produce greater changes in temperature than those occurring in fall-winter.

Annual global mean temperature explains reproductive success in a marine vertebrate from 1955 to 2010.

PubMed

Mauck, Robert A; Dearborn, Donald C; Huntington, Charles E

2018-04-01

The salient feature of anthropogenic climate change over the last century has been the rise in global mean temperature. However, global mean temperature is not used as an explanatory variable in studies of population-level response to climate change, perhaps because the signal-to-noise ratio of this gross measure makes its effect difficult to detect in any but the longest of datasets. Using a population of Leach's storm-petrels breeding in the Bay of Fundy, we tested whether local, regional, or global temperature measures are the best index of reproductive success in the face of climate change in species that travel widely between and within seasons. With a 56-year dataset, we found that annual global mean temperature (AGMT) was the single most important predictor of hatching success, more so than regional sea surface temperatures (breeding season or winter) and local air temperatures at the nesting colony. Storm-petrel reproductive success showed a quadratic response to rising temperatures, in that hatching success increased up to some critical temperature, and then declined when AGMT exceeded that temperature. The year at which AGMT began to consistently exceed that critical temperature was 1988. Importantly, in this population of known-age individuals, the impact of changing climate was greatest on inexperienced breeders: reproductive success of inexperienced birds increased more rapidly as temperatures rose and declined more rapidly after the tipping point than did reproductive success of experienced individuals. The generality of our finding that AGMT is the best predictor of reproductive success in this system may hinge on two things. First, an integrative global measure may be best for species in which individuals move across an enormous spatial range, especially within seasons. Second, the length of our dataset and our capacity to account for individual- and age-based variation in reproductive success increase our ability to detect a noisy signal. © 2017 John Wiley & Sons Ltd.

The response of the SSM/I to the marine environment. I - An analytic model for the atmospheric component of observed brightness temperatures

NASA Technical Reports Server (NTRS)

Petty, Grant W.; Katsaros, Kristina B.

1992-01-01

A detailed parameterization is developed for the contribution of the nonprecipitating atmosphere to the microwave brightness temperatures observed by the Special Sensor Microwave/Imager (SSM/I). The atmospheric variables considered include the viewing angle, the integrated water vapor amount and scale height, the effective tropospheric lapse rate and near-surface temperature, the total cloud liquid water, the effective cloud height, and the surface pressure. The dependence of the radiative variables on meteorological variables is determined for each of the SSM/I frequencies 19.35, 22.235, 37.0, and 85.5 GHz, based on the values computed from 16,893 maritime temperature and humidity profiles representing all latitude belts and all seasons. A comparison of the predicted brightness temperatures with brightness temperatures obtained by direct numerical integration of the radiative transfer equation for the radiosonde-profile dataset yielded rms differences well below 1 K for all four SSM/I frequencies.

Understanding and Reconciling Differences in Surface and Satellite-Based Lower Troposphere Temperatures

NASA Astrophysics Data System (ADS)

Hausfather, Z.; Thorne, P.; Mears, C. A.

2017-12-01

One of the main remaining uncertainties in global temperatures over the past few decades is the disagreement between surface and microwave sounding unit (MSU) satellite-based observations of the lower troposphere. Reconciling these will prove an important step in improving our understanding of modern climate change, and help resolve an issue that has been frequently brought to the attention of policymakers and highlighted as a reason to distrust climate observations. To assess differences between surface and satellite records, we examine data from radiosondes, from atmospheric reanalysis, from numerous different satellites, from surface observations over the land and ocean, and from global climate models. Controlling for spatial coverage, we determine where these datasets agree and disagree, isolate the differences, and identify for common factors to explain the divergences. We find large systemic differences between surface and lower troposphere warming in MSU/AMSU records compared to radiosondes, reanalysis products, and climate models that suggest possible residual inhomogeneities in satellite records. We further show that no reasonable subset of surface temperature records exhibits as little warming over the last two decades as satellite observations, suggesting that inhomogeneities in the surface record are very likely not responsible for the divergence.

MEaSUReS Land Surface Temperature and Emissivity data records

NASA Astrophysics Data System (ADS)

Cawse-Nicholson, K.; Hook, S. J.; Gulley, G.; Borbas, E. E.; Knuteson, R. O.

2017-12-01

The NASA MEaSUReS program was put into place to produce long-term, well calibrated and validated data records for Earth Science research. As part of this program, we have developed three Earth System Data Records (ESDR) to measure Land Surface Temperature (LST) and emissivity: a high spatial resolution (1km) LST product using Low Earth Orbiting (LEO) satellites; a high temporal resolution (hourly over North America) LST product using Geostationary (GEO) satellites; and a Combined ASTER MODIS Emissivity for Land (CAMEL) ESDR. CAMEL was produced by merging two state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UWIREMIS), and the JPL ASTER Global Emissivity Dataset v4 (GEDv4). The CAMEL ESDR is currently available for download, and is being tested in sounder retrieval schemes (e.g. CrIS, IASI, AIRS) to reduce uncertainties in water vapor retrievals, and has already been implemented in the radiative transfer software RTTOV v12 for immediate use in numerical weather modeling and data assimilation systems. The LEO-LST product combines two existing MODIS products, using an uncertainty analysis approach to optimize accuracy over different landcover classes. Validation of these approaches for retrieving LST have shown that they are complementary, with the split-window approach (MxD11) being more stable over heavily vegetated regions and the physics-based approach (MxD21) demonstrating higher accuracy in semi-arid and arid regions where the largest variations in emissivity exist, both spatially and spectrally. The GEO LST-ESDR product uses CAMEL ESDR for improved temperature-emissivity separation, and the same atmospheric correction as the LEO LST product to ensure consistency across all three data records.

Recent Global Warming as Observed by AIRS and Depicted in GISSTEMP and MERRA-2

NASA Technical Reports Server (NTRS)

Susskind, Joel; Lee, Jae; Iredell, Lena

2017-01-01

AIRS Version-6 monthly mean level-3 surface temperature products confirm the result, depicted in the GISSTEMP dataset, that the earth's surface temperature has been warming since early 2015, though not before that. AIRS is at a higher spatial resolution than GISSTEMP, and produces sharper spatial features which are otherwise in excellent agreement with those of GISSTEMP. Version-6 AO Ts anomalies are consistent with those of Version-6 AIRS/AMSU. Version-7 AO anomalies should be even more accurate, especially at high latitudes. ARCs of MERRA-2 Ts anomalies are spurious as a result of a discontinuity which occurred somewhere between 2007 and 2008. This decreases global mean trends.

Assimilation of neural network soil moisture in land surface models

NASA Astrophysics Data System (ADS)

Rodriguez-Fernandez, Nemesio; de Rosnay, Patricia; Albergel, Clement; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Richaume, Philippe; Muñoz-Sabater, Joaquin; Drusch, Matthias

2017-04-01

In this study a set of land surface data assimilation (DA) experiments making use of satellite derived soil moisture (SM) are presented. These experiments have two objectives: (1) to test the information content of satellite remote sensing of soil moisture for numerical weather prediction (NWP) models, and (2) to test a simplified assimilation of these data through the use of a Neural Network (NN) retrieval. Advanced Scatterometer (ASCAT) and Soil Moisture and Ocean Salinity (SMOS) data were used. The SMOS soil moisture dataset was obtained specifically for this project training a NN using SMOS brightness temperatures as input and using as reference for the training European Centre for Medium-Range Weather Forecasts (ECMWF) H-TESSEL SM fields. In this way, the SMOS NN SM dataset has a similar climatology to that of the model and it does not present a global bias with respect to the model. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. This system is very computationally efficient and allows to perform long surface assimilation experiments (one whole year, 2012). SMOS NN SM DA experiments are compared to ASCAT SM DA experiments. In both cases, experiments with and without 2 m air temperature and relative humidity DA are discussed using different observation errors for the ASCAT and SMOS datasets. Seasonal, geographical and soil-depth-related differences between the results of those experiments are presented and discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives in general similar results to the model open loop with no assimilation even if significant differences can be seen for specific sites with in situ measurements. The sensitivity to observation errors to the SM dataset slightly differs depending on the networks of in situ measurements, however it is relatively low for the tests conducted here. Finally, the effect of the soil moisture analysis on the NWP is evaluated comparing experiments for different configurations of the system, with and without (Open Loop) soil moisture data assimilation. ssimilation of ASCAT soil moisture improves the forecast in the tropics and adds information with respect to the near surface conventional observations. In contrast, SMOS degrades the forecast in the Tropics in July-September. In the Southern hemisphere ASCAT degrades the forecast in July-September both alone and using 2m air temperature and relative humidity. On the other hand, experiments using SMOS (even without screen level variables) improve the forecast for all the seasons, in particular, in July-December. In the northern hemisphere both with ASCAT and SMOS, the experiments using 2m air temperature and relative humidity improve the forecast in April-September. SMOS alone has a significant positive effect in July-September for experiments with low observation error. Maps of the forecast skill with respect to the open loop experiment show that SMOS improves the forecast in North America and to a lesser extent in northern Asia for up to 72 hours.

Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

NASA Astrophysics Data System (ADS)

Phuong Tran, Anh; Dafflon, Baptiste; Hubbard, Susan S.

2017-09-01

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface-subsurface hydrological-thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon-climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological-thermal processes associated with annual freeze-thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets - including soil liquid water content, temperature and electrical resistivity tomography (ERT) data - to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological-thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface-subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice-liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological-thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological-thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface-subsurface, deterministic-stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological-thermal dynamics.

Sampling biases in datasets of historical mean air temperature over land.

PubMed

Wang, Kaicun

2014-04-10

Global mean surface air temperature (Ta) has been reported to have risen by 0.74°C over the last 100 years. However, the definition of mean Ta is still a subject of debate. The most defensible definition might be the integral of the continuous temperature measurements over a day (Td0). However, for technological and historical reasons, mean Ta over land have been taken to be the average of the daily maximum and minimum temperature measurements (Td1). All existing principal global temperature analyses over land rely heavily on Td1. Here, I make a first quantitative assessment of the bias in the use of Td1 to estimate trends of mean Ta using hourly Ta observations at 5600 globally distributed weather stations from the 1970s to 2013. I find that the use of Td1 has a negligible impact on the global mean warming rate. However, the trend of Td1 has a substantial bias at regional and local scales, with a root mean square error of over 25% at 5° × 5° grids. Therefore, caution should be taken when using mean Ta datasets based on Td1 to examine high resolution details of warming trends.

Is the global mean temperature trend too low?

NASA Astrophysics Data System (ADS)

Venema, Victor; Lindau, Ralf

2015-04-01

The global mean temperature trend may be biased due to similar technological and economic developments worldwide. In this study we want to present a number of recent results that suggest that the global mean temperature trend might be steeper as generally thought. In the Global Historical Climate Network version 3 (GHCNv3) the global land surface temperature is estimated to have increased by about 0.8°C between 1880 and 2012. In the raw temperature record, the increase is 0.6°C; the 0.2°C difference is due to homogenization adjustments. Given that homogenization can only reduce biases, this 0.2°C stems from a partial correction of bias errors and it seems likely that the real non-climatic trend bias will be larger. Especially in regions with sparser networks, homogenization will not be able to improve the trend much. Thus if the trend bias in these regions is similar to the bias for more dense networks (industrialized countries), one would expect the real bias to be larger. Stations in sparse networks are representative for a larger region and are given more weight in the computation of the global mean temperature. If all stations are given equal weight, the homogenization adjustments of the GHCNv3 dataset are about 0.4°C per century. In the subdaily HadISH dataset one break with mean size 0.12°C is found every 15 years for the period 1973-2013. That would be a trend bias of 0.78°C per century on a station by station basis. Unfortunately, these estimates strongly focus on Western countries having more stations. It is known from the literature that rich countries have a (statistically insignificant) stronger trend in the global datasets. Regional datasets can be better homogenized than global ones, the main reason being that global datasets do not contain all stations known to the weather services. Furthermore, global datasets use automatic homogenization methods and have less or no metadata. Thus while regional data can be biased themselves, comparing them with global datasets can provide some indication on biases. Compared to the global BEST dataset for the same countries, the national datasets of Austria, Italy and Switzerland have a 0.36°C per century stronger trend since 1901. For the trend since 1960 we can also take Australia, France and Slovenia into account and find a trend bias of 0.40°C per century. Relative to CRUCY the trend biases are smaller and only statistically significant for the period since 1980. The most direct way to study biases in the temperature records is by making parallel measurements with historical measurement set-ups. Several recent parallel data studies for the transition to Stevenson screens suggest larger biases: Austria 0.2°C, Spain 0.5 & 0.6°C. As well as older tropical ones: India 0.42°C and Sri Lanka 0.37°C. The smaller values from the Parker (1994) review mainly stem from parallel measurements from North-West Europe, which may have less problems with exposure. Furthermore, the influence of many historical transitions, especially the ones that could cause an artificial smaller trend, have not been studied in detail yet. We urgently need to study improvements of exposure (especially in the (sub-)tropics), increases in watering and irrigation, mechanical ventilation, better paints, relocations to airports, and relocations to suburbs of stations that started in the cities and from village centers to pasture, for example. Our current understanding surprisingly suggests that the more recent period may have the largest biases, but it could also be that even the best datasets are unable to improve earlier data sufficiently. If the temperature trend were actually larger it would reduce discrepancies between studies for a number of problems in climatology. For example, the estimates of transient climate sensitivity using instrumental data are lower as the one using climate models, volcanic eruptions or paleo data. Furthermore, several changes observed in the climate system are larger than expected. On the other hand, a large trend in the land surface temperature would make the discrepancy with the tropospheric temperature even larger (radiosondes and satellites) and it would introduce a larger difference between land and sea temperature trends. Concluding, at the moment there is no strong evidence yet that the temperature trend is underestimated. However, we do have a considerable amount of evidence that suggests that there is a moderate, but climatologically important bias that we should study with urgency. As far as we know there are no estimates for the remaining uncertainty in the global mean trend after homogenization. Also studies into the causes of cooling biases are a pressing need. (Many have contributed to this study, but it is not clear at this moment who would be official collaborators; they will be added later.)

Application of spatially gridded temperature and land cover data sets for urban heat island analysis

USGS Publications Warehouse

Gallo, Kevin; Xian, George Z.

2014-01-01

Two gridded data sets that included (1) daily mean temperatures from 2006 through 2011 and (2) satellite-derived impervious surface area, were combined for a spatial analysis of the urban heat-island effect within the Dallas-Ft. Worth Texas region. The primary advantage of using these combined datasets included the capability to designate each 1 × 1 km grid cell of available temperature data as urban or rural based on the level of impervious surface area within the grid cell. Generally, the observed differences in urban and rural temperature increased as the impervious surface area thresholds used to define an urban grid cell were increased. This result, however, was also dependent on the size of the sample area included in the analysis. As the spatial extent of the sample area increased and included a greater number of rural defined grid cells, the observed urban and rural differences in temperature also increased. A cursory comparison of the spatially gridded temperature observations with observations from climate stations suggest that the number and location of stations included in an urban heat island analysis requires consideration to assure representative samples of each (urban and rural) environment are included in the analysis.

A 7.5-Year Dataset of SSM/I-Derived Surface Turbulent Fluxes Over Global Oceans

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Shie, Chung-Lin; Atlas, Robert M.; Ardizzone, Joe; Nelkin, Eric; Einaudi, Franco (Technical Monitor)

2001-01-01

The surface turbulent fluxes of momentum, latent heat, and sensible heat over global oceans are essential to weather, climate and ocean problems. Wind stress is the major forcing for driving the oceanic circulation, while Evaporation is a key component of hydrological cycle and surface heat budget. We have produced a 7.5-year (July 1987-December 1994) dataset of daily, individual monthly-mean and climatological (1988-94) monthly-mean surface turbulent fluxes over the global oceans from measurements of the Special Sensor Microwave/Imager (SSM/I) on board the US Defense Meteorological Satellite Program F8, F10, and F11 satellites. It has a spatial resolution of 2.0x2.5 latitude-longitude. Daily turbulent fluxes are derived from daily data of SSM/I surface winds and specific humidity, National Centers for Environmental Prediction (NCEP) sea surface temperatures, and European Centre for Medium-Range Weather Forecasts (ECMWF) air-sea temperature differences, using a stability-dependent bulk scheme. The retrieved instantaneous surface air humidity (with a 25-km resolution) IS found to be generally accurate as compared to the collocated radiosonde observations over global oceans. The surface wind speed and specific humidity (latent heat flux) derived from the F10 SSM/I are found to be -encrally smaller (larger) than those retrieved from the F11 SSM/I. The F11 SSM/I appears to have slightly better retrieval accuracy for surface wind speed and humidity as compared to the F10 SSM/I. This difference may be due to the orbital drift of the F10 satellite. The daily wind stresses and latent heat fluxes retrieved from F10 and F11 SSM/Is show useful accuracy as verified against the research quality in si -neasurerrients (IMET buoy, RV Moana Wave, and RV Wecoma) in the western Pacific warm pool during the TOGA COARE Intensive observing period (November 1992-February 1993). The 1988-94 seasonal-mean turbulent fluxes and input variables derived from FS and F11 SSM/Is show reasonable patterns related to seasonal variations of atmospheric general circulation. This dataset of SSM/I-derived turbulent fluxes is useful for climate studies, forcing of ocean models, and validation of coupled ocean-atmosphere global models and can be accessed through the NASA/GSFC Distributed Active Archive Center.

Climate Trend Detection using Sea-Surface Temperature Data-sets from the (A)ATSR and AVHRR Space Sensors.

NASA Astrophysics Data System (ADS)

Llewellyn-Jones, D. T.; Corlett, G. K.; Remedios, J. J.; Noyes, E. J.; Good, S. A.

2007-05-01

Sea-Surface Temperature (SST) is an important indicator of global change, designated by GCOS as an essential Climate Variable (ECV). The detection of trends in Global SST requires rigorous measurements that are not only global, but also highly accurate and consistent. Space instruments can provide the means to achieve these required attributes in SST data. This paper presents an analysis of 15 years of SST data from two independent data sets, generated from the (A)ATSR and AVHRR series of sensors respectively. The analyses reveal trends of increasing global temperature between 0.13°C to 0.18 °C, per decade, closely matching that expected from some current predictions. A high level of consistency in the results from the two independent observing systems is seen, which gives increased confidence in data from both systems and also enables comparative analyses of the accuracy and stability of both data sets to be carried out. The conclusion is that these satellite SST data-sets provide important means to quantify and explore the processes of climate change. An analysis based upon singular value decomposition, allowing the removal of gross transitory disturbances, notably the El Niño, in order to examine regional areas of change other than the tropical Pacific, is also presented. Interestingly, although El Niño events clearly affect SST globally, they are found to have a non- significant (within error) effect on the calculated trends, which changed by only 0.01 K/decade when the pattern of El Niño and the associated variations was removed from the SST record. Although similar global trends were calculated for these two independent data sets, larger regional differences are noted. Evidence of decreased temperatures after the eruption of Mount Pinatubo in 1991 was also observed. The methodology demonstrated here can be applied to other data-sets, which cover long time-series observations of geophysical observations in order to characterise long-term change.

The effect of radiometer placement and view on inferred directional and hemispheric radiometric temperatures of a urban canopy

NASA Astrophysics Data System (ADS)

Adderley, C.; Christen, A.; Voogt, J. A.

2015-02-01

Any radiometer at a fixed location has a biased view when observing a convoluted, three dimensional surface such as an urban canopy. The goal of this contribution is to determine the bias of various sensors views observing a simple urban residential neighbourhood (nadir, oblique, hemispherical) over a 24 h cycle under clear weather conditions. The error in measuring longwave radiance (L) and/or inferring surface temperatures (T0) is quantified for different times over a diurnal cycle. Panoramic time-sequential thermography (PTST) data was recorded by a thermal camera on a hydraulic mast above a residential canyon in Vancouver, BC. The dataset resolved sub-facet temperature variability of all representative urban facets in a 360° swath repetitively over a 24 h cycle. This dataset is used along with computer graphics and vision techniques to project measured fields of L for a given time and pixel onto texture sheets of a three-dimensional urban surface model at a resolution of centimetres. The resulting dataset attributes L of each pixel on the texture sheets to different urban facets and associates facet location, azimuth, slope, material, and sky view factor. The texture sheets of L are used to calculate the complete surface temperature (T0,C) and to simulate the instantaneous field of view (IFOV) of narrow and hemispheric radiometers observing the same urban surface (in absence of emissivity and atmospheric effects). The simulated directional (T0,d) and hemispheric (T0,h) radiometric temperatures inferred from various biased views are compared to T0,C. For a range of simulated off-nadir (ϕ) and azimuth (Ω) angles, T0,d (ϕ, Ω) and T0,C differ between -2.7 and +2.9 K over the course of the day. The effects of effective anisotropy are highest in the daytime, particularly around sunrise and sunset when different views can lead to differences in T0,d (ϕ, Ω) that are as high as 3.5 K. For a sensor with a narrow IFOV in the nadir of the urban surface, T0,d (ϕ = 0°) differs from T0,C by -2.2 K (day) and by +1.6 K (night). Simulations of the IFOV of hemispherical, downward-facing pyrgeometers at 270 positions show considerable variations in the measured L and inferred hemispherical radiometeric temperature T0,h as a function of both horizontal placement and height. The root mean squared error (RMSE) between different horizontal positions in retrieving outgoing longwave emittance L↑ decreased exponentially with height, and was 11.2, 6.3 and 2.0 W m-2 at 2, 3, and 5 times the mean building height zb. Generally, above 3.5 zb the horizontal positional error is less than the typical accuracy of common pyrgeometers. The average T0,h over 24 h determined from the hemispherical radiometer sufficiently above an urban surface is in close agreement with the average T0,C. However, over the course of the day, the difference between T0,h and T0,C shows an RMSE of 1.8 K (9.9 W m-2) because the relative contributions of facets within the projected IFOV of a pyrgeometer do not correspond to their fractions of the complete urban surface.

EnviroAtlas - Potential Evapotranspiration 1950 - 2099 for the Conterminous United States

EPA Pesticide Factsheets

The EnviroAtlas Climate Scenarios were generated from NASA Earth Exchange (NEX) Downscaled Climate Projections (NEX-DCP30) ensemble averages (the average of over 30 available climate models) for each of the four representative concentration pathways (RCP) for the contiguous U.S. at 30 arc-second (approx. 800 m2) spatial resolution. In addition to the three climate variables provided by the NEX-DCP30 dataset (minimum monthly temperature, maximum monthly temperature, and precipitation) a corresponding estimate of potential evapotranspiration (PET) was developed to match the spatial and temporal scales of the input dataset. PET represents the cumulative amount of water returned to the atmosphere due to evaporation from Earth00e2??s surface and plant transpiration under ideal circumstances (i.e., a vegetated surface shading the ground and unlimited water supply). PET was calculated using the Hamon PET equation (Hamon, 1961) and CBM model for daylength (Forsythe et al. 1995) for the 4 RCPs (2.6, 4.5, 6.0, 8.5) and organized by season (Winter, Spring, Summer, and Fall) and annually for the years 2006 00e2?? 2099. Additionally, PET was calculated for the ensemble average of all historic runs and organized similarly for the years 1950 00e2?? 2005. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-u

Transitioning Enhanced Land Surface Initialization and Model Verification Capabilities to the Kenya Meteorological Department (KMD)

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Zavodsky, Bradley T.; Srikishen, Jayanthi; Limaye, Ashutosh; Blankenship, Clay B.

2016-01-01

Flooding, severe weather, and drought are key forecasting challenges for the Kenya Meteorological Department (KMD), based in Nairobi, Kenya. Atmospheric processes leading to convection, excessive precipitation and/or prolonged drought can be strongly influenced by land cover, vegetation, and soil moisture content, especially during anomalous conditions and dry/wet seasonal transitions. It is thus important to represent accurately land surface state variables (green vegetation fraction, soil moisture, and soil temperature) in Numerical Weather Prediction (NWP) models. The NASA SERVIR and the Short-term Prediction Research and Transition (SPoRT) programs in Huntsville, AL have established a working partnership with KMD to enhance its regional modeling capabilities. SPoRT and SERVIR are providing experimental land surface initialization datasets and model verification capabilities for capacity building at KMD. To support its forecasting operations, KMD is running experimental configurations of the Weather Research and Forecasting (WRF; Skamarock et al. 2008) model on a 12-km/4-km nested regional domain over eastern Africa, incorporating the land surface datasets provided by NASA SPoRT and SERVIR. SPoRT, SERVIR, and KMD participated in two training sessions in March 2014 and June 2015 to foster the collaboration and use of unique land surface datasets and model verification capabilities. Enhanced regional modeling capabilities have the potential to improve guidance in support of daily operations and high-impact weather and climate outlooks over Eastern Africa. For enhanced land-surface initialization, the NASA Land Information System (LIS) is run over Eastern Africa at 3-km resolution, providing real-time land surface initialization data in place of interpolated global model soil moisture and temperature data available at coarser resolutions. Additionally, real-time green vegetation fraction (GVF) composites from the Suomi-NPP VIIRS instrument is being incorporated into the KMD-WRF runs, using the product generated by NOAA/NESDIS. Model verification capabilities are also being transitioned to KMD using NCAR's Model *Corresponding author address: Jonathan Case, ENSCO, Inc., 320 Sparkman Dr., Room 3008, Huntsville, AL, 35805. Email: Jonathan.Case-1@nasa.gov Evaluation Tools (MET; Brown et al. 2009) software in conjunction with a SPoRT-developed scripting package, in order to quantify and compare errors in simulated temperature, moisture and precipitation in the experimental WRF model simulations. This extended abstract and accompanying presentation summarizes the efforts and training done to date to support this unique regional modeling initiative at KMD. To honor the memory of Dr. Peter J. Lamb and his extensive efforts in bolstering weather and climate science and capacity-building in Africa, we offer this contribution to the special Peter J. Lamb symposium. The remainder of this extended abstract is organized as follows. The collaborating international organizations involved in the project are presented in Section 2. Background information on the unique land surface input datasets is presented in Section 3. The hands-on training sessions from March 2014 and June 2015 are described in Section 4. Sample experimental WRF output and verification from the June 2015 training are given in Section 5. A summary is given in Section 6, followed by Acknowledgements and References.

Impact of automatization in temperature series in Spain and comparison with the POST-AWS dataset

NASA Astrophysics Data System (ADS)

Aguilar, Enric; López-Díaz, José Antonio; Prohom Duran, Marc; Gilabert, Alba; Luna Rico, Yolanda; Venema, Victor; Auchmann, Renate; Stepanek, Petr; Brandsma, Theo

2016-04-01

Climate data records are most of the times affected by inhomogeneities. Especially inhomogeneities introducing network-wide biases are sometimes related to changes happening almost simultaneously in an entire network. Relative homogenization is difficult in these cases, especially at the daily scale. A good example of this is the substitution of manual observations (MAN) by automatic weather stations (AWS). Parallel measurements (i.e. records taken at the same time with the old (MAN) and new (AWS) sensors can provide an idea of the bias introduced and help to evaluate the suitability of different correction approaches. We present here a quality controlled dataset compiled under the DAAMEC Project, comprising 46 stations across Spain and over 85,000 parallel measurements (AWS-MAN) of daily maximum and minimum temperature. We study the differences between both sensors and compare it with the available metadata to account for internal inhomogeneities. The differences between both systems vary much across stations, with patterns more related to their particular settings than to climatic/geographical reasons. The typical median biases (AWS-MAN) by station (comprised between the interquartile range) oscillate between -0.2°C and 0.4 in daily maximum temperature and between -0.4°C and 0.2°C in daily minimum temperature. These and other results are compared with a larger network, the Parallel Observations Scientific Team, a working group of the International Surface Temperatures Initiative (ISTI-POST) dataset, which comprises our stations, as well as others from different countries in America, Asia and Europe.

How are the wetlands over tropical basins impacted by the extreme hydrological events?

NASA Astrophysics Data System (ADS)

Al-Bitar, A.; Parrens, M.; Frappart, F.; Papa, F.; Kerr, Y. H.; Cretaux, J. F.; Wigneron, J. P.

2016-12-01

Wetlands play a crucial role in tropical basins and still many questions remain unanswered on how extreme events (like El-Nino) impacts them. Answering these questions is challenging as monitoring of inland water surfaces via remote sensing over tropical areas is a difficult task because of impact of vegetation and cloud cover. Several microwave based products have been elaborated to monitor these surfaces (Papa et al. 2010). In this study we combine the use of L-band microwave brightness temperatures and altimetric data from SARAL/ALTIKA to derive water storage maps at relatively high (7days) temporal frequency. The area of interest concerns the Amazon, Congo and GBH basins A first order radiative model is used to derive surface water over land from the brightness temperature measured by ESA SMOS mission at coarse resolution (25 km x 25 km) and 7-days frequency. An initial investigation of the use of the SMAP mission for the same purpose will be also presented. The product is compared to the static land cover map such as ESA CCI and the International Geosphere-Biosphere Program (IGBP) and also dynamic maps from SWAPS. It is then combined to the altimetric data to derive water storage maps. The water surfaces and water storage products are then compared to precipitation data from GPM TRMM datasets, ground water storage change from GRACE and river discharge data from field data. The amplitudes and time shifts of the signals is compared based on the sub-basin definition from Hydroshed database. The dataset is then divided into years of strong and weak El-Nino signal and the anomaly is between the two dataset is compared. The results show a strong influence of EL-Nino on the time shift of the different components showing that the hydrological regime of wetlands is highly impacted by these extreme events. This can have dramatic impacts on the ecosystem as the wetlands are vulnerable with a high biodiversity.

Development of a Climate-Data Record (CDR) of the Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, Dorthy K.; Comiso, Josefino C.; Shuman, Christopher A.; DiGirolamo, Nicolo E.; Stock, Larry V.

2010-01-01

Regional "clear sky" surface temperature increases since the early 1980s in the Arctic, measured using Advanced Very High Resolution Radiometer (AVHRR) infrared data, range from 0.57+/-0.02 deg C to 72+/-0.10 deg C per decade. Arctic warming has important implications for ice-sheet mass balance because much of the periphery of the Greenland Ice Sheet is already near 0 deg C during the melt season, and is thus vulnerable to rapid melting if temperatures continue to increase. An increase in melting of the ice sheet would accelerate sea-level rise, an issue affecting potentially billions of people worldwide. To quantify the ice-surface temperature (IST) of the Greenland Ice Sheet, and to provide an IST dataset of Greenland for modelers that provides uncertainties, we are developing a climate-data record (CDR) of daily "clear-sky" IST of the Greenland Ice Sheet, from 1982 to the present using AVHRR (1982 - present) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data (2000 - present) at a resolution of approximately 5 km. Known issues being addressed in the production of the CDR are: time-series bias caused by cloud cover (surface temperatures can be different under clouds vs. clear areas) and cross-calibration in the overlap period between AVHRR instruments, and between AVHRR and MODIS instruments. Because of uncertainties, mainly due to clouds, time-series of satellite IST do not necessarily correspond with actual surface temperatures. The CDR will be validated by comparing results with automatic-weather station data and with satellite-derived surface-temperature products and biases will be calculated.

Titan's Thermal Emission: Analysis Of Near-surface Temperatures Via Mid-infrared Measurements

NASA Astrophysics Data System (ADS)

Sadino, Jeff; Parrish, P. D.; Orton, G. S.; Burl, M. C.; Davies, A. G.; Irwin, P. G.; Teanby, N. A.; Flasar, F. M.; Cassini/CIRS investigation Team

2006-09-01

After Courtin and Kim 2002, tropospheric and near-surface temperatures of Titan may be obtained by examining mid-infrared radiances at 300 and 500 wavenumbers (33 and 20 microns). Here, the measured radiance is (respectively) sensitive to the temperature near the tropopause and sufficient to discern variations in surface topography and emissivity. Our search, as a function of location and time, compares brightness temperatures derived from measurements by the Cassini Composite Infrared Spectrometer (CIRS) and variations of radiance as a function of Titan's rotation derived from ground-based measurements at NASA's Infrared Telescope Facility. Although the variation of the tropopause and zonal near-surface temperatures are fairly homogenous, similar to Courtin and Kim 2002, the meridional distribution of near-surface temperatures varies symmetrically from Equator to pole. While no significant thermal variations suggestive of localized hotspots have yet been observed, such diversity is suggestive of active surface geology, in support of other optical and near-infrared investigations. Although the spatial coverage of the CIRS dataset is severely limited, the approximately 10 degrees field of view (450km at the Equator) is de-convolved somewhat to extract meaningful, sub-pixel maps of Titan's surface. Courtin, R. and Kim, S. (2002). Planet. and Sp. Sci., 50: 309-321. The acquisition of data described here was accomplished through the coordinated effort of Cassini-Huygens project staff, Deep Space Network personnel and the CIRS instrument and science-planning teams with funding provided by the National Research Council, NASA/JPL and NASA/GSFC and the UK Particle Physics and Astronomy council.

Quantifying the Terrestrial Surface Energy Fluxes Using Remotely-Sensed Satellite Data

NASA Astrophysics Data System (ADS)

Siemann, Amanda Lynn

The dynamics of the energy fluxes between the land surface and the atmosphere drive local and regional climate and are paramount to understand the past, present, and future changes in climate. Although global reanalysis datasets, land surface models (LSMs), and climate models estimate these fluxes by simulating the physical processes involved, they merely simulate our current understanding of these processes. Global estimates of the terrestrial, surface energy fluxes based on observations allow us to capture the dynamics of the full climate system. Remotely-sensed satellite data is the source of observations of the land surface which provide the widest spatial coverage. Although net radiation and latent heat flux global, terrestrial, surface estimates based on remotely-sensed satellite data have progressed, comparable sensible heat data products and ground heat flux products have not progressed at this scale. Our primary objective is quantifying and understanding the terrestrial energy fluxes at the Earth's surface using remotely-sensed satellite data with consistent development among all energy budget components [through the land surface temperature (LST) and input meteorology], including validation of these products against in-situ data, uncertainty assessments, and long-term trend analysis. The turbulent fluxes are constrained by the available energy using the Bowen ratio of the un-constrained products to ensure energy budget closure. All final products are within uncertainty ranges of literature values, globally. When validated against the in-situ estimates, the sensible heat flux estimates using the CFSR air temperature and constrained with the products using the MODIS albedo produce estimates closest to the FLUXNET in-situ observations. Poor performance over South America is consistent with the largest uncertainties in the energy budget. From 1984-2007, the longwave upward flux increase due to the LST increase drives the net radiation decrease, and the decrease in the available energy balances the decrease in the sensible heat flux. These datasets are useful for benchmarking climate models and LSM output at the global annual scale and the regional scale subject to the regional uncertainties and performance. Future work should improve the input data, particularly the temperature gradient and Zilitinkevich empirical constant, to reduce uncertainties.

An Examination of the Hadley Sea-Surface Temperature Time Series for the Nino 3.4 Region

NASA Technical Reports Server (NTRS)

Wilson, Robert M.

2010-01-01

The Hadley sea-surface temperature (HadSST) dataset is investigated for the interval 1871-2008. The purpose of this investigation is to determine the degree of success in identifying and characterizing El Nino (EN) southern (ENSO) extreme events, both EN and La Nina (LN) events. Comparisons are made against both the Southern Oscillation Index for the same time interval and with published values of the Oceanic Nino Index for the interval since 1950. Some 60 ENSO extreme events are identified in the HadSST dataset, consisting of 33 EN and 27 LN events. Also, preferential associations are found to exist between the duration of ENSO extreme events and their maximum anomalous excursion temperatures and between the recurrence rate for an EN event and the duration of the last known EN event. Because the present ongoing EN is a strong event, it should persist 11 months or longer, inferring that the next EN event should not be expected until June 2012 or later. Furthermore, the decadal sum of EN-related months is found to have increased somewhat steadily since the decade of 1920-1929, suggesting that the present decade (2010-2019) possibly will see about 3-4 EN events, totaling about 37 +/- 3 EN-related months (i.e., months that meet the definition for the occurrence of an EN event).

High-resolution grids of hourly meteorological variables for Germany

NASA Astrophysics Data System (ADS)

Krähenmann, S.; Walter, A.; Brienen, S.; Imbery, F.; Matzarakis, A.

2018-02-01

We present a 1-km2 gridded German dataset of hourly surface climate variables covering the period 1995 to 2012. The dataset comprises 12 variables including temperature, dew point, cloud cover, wind speed and direction, global and direct shortwave radiation, down- and up-welling longwave radiation, sea level pressure, relative humidity and vapour pressure. This dataset was constructed statistically from station data, satellite observations and model data. It is outstanding in terms of spatial and temporal resolution and in the number of climate variables. For each variable, we employed the most suitable gridding method and combined the best of several information sources, including station records, satellite-derived data and data from a regional climate model. A module to estimate urban heat island intensity was integrated for air and dew point temperature. Owing to the low density of available synop stations, the gridded dataset does not capture all variations that may occur at a resolution of 1 km2. This applies to areas of complex terrain (all the variables), and in particular to wind speed and the radiation parameters. To achieve maximum precision, we used all observational information when it was available. This, however, leads to inhomogeneities in station network density and affects the long-term consistency of the dataset. A first climate analysis for Germany was conducted. The Rhine River Valley, for example, exhibited more than 100 summer days in 2003, whereas in 1996, the number was low everywhere in Germany. The dataset is useful for applications in various climate-related studies, hazard management and for solar or wind energy applications and it is available via doi: 10.5676/DWD_CDC/TRY_Basis_v001.

Web-GIS visualisation of permafrost-related Remote Sensing products for ESA GlobPermafrost

NASA Astrophysics Data System (ADS)

Haas, A.; Heim, B.; Schaefer-Neth, C.; Laboor, S.; Nitze, I.; Grosse, G.; Bartsch, A.; Kaab, A.; Strozzi, T.; Wiesmann, A.; Seifert, F. M.

2016-12-01

The ESA GlobPermafrost (www.globpermafrost.info) provides a remote sensing service for permafrost research and applications. The service comprises of data product generation for various sites and regions as well as specific infrastructure allowing overview and access to datasets. Based on an online user survey conducted within the project, the user community extensively applies GIS software to handle remote sensing-derived datasets and requires preview functionalities before accessing them. In response, we develop the Permafrost Information System PerSys which is conceptualized as an open access geospatial data dissemination and visualization portal. PerSys will allow visualisation of GlobPermafrost raster and vector products such as land cover classifications, Landsat multispectral index trend datasets, lake and wetland extents, InSAR-based land surface deformation maps, rock glacier velocity fields, spatially distributed permafrost model outputs, and land surface temperature datasets. The datasets will be published as WebGIS services relying on OGC-standardized Web Mapping Service (WMS) and Web Feature Service (WFS) technologies for data display and visualization. The WebGIS environment will be hosted at the AWI computing centre where a geodata infrastructure has been implemented comprising of ArcGIS for Server 10.4, PostgreSQL 9.2 and a browser-driven data viewer based on Leaflet (http://leafletjs.com). Independently, we will provide an `Access - Restricted Data Dissemination Service', which will be available to registered users for testing frequently updated versions of project datasets. PerSys will become a core project of the Arctic Permafrost Geospatial Centre (APGC) within the ERC-funded PETA-CARB project (www.awi.de/petacarb). The APGC Data Catalogue will contain all final products of GlobPermafrost, allow in-depth dataset search via keywords, spatial and temporal coverage, data type, etc., and will provide DOI-based links to the datasets archived in the long-term, open access PANGAEA data repository.

Climate Inferences From Geothermal Measurements in South America

NASA Astrophysics Data System (ADS)

Gurza Fausto, Edmundo; Harris, Robert; Montenegro, Alvaro; Tassara, Andrés; Beltrami, Hugo

2013-04-01

We present the data and analysis of 26 borehole temperature logs from South America. The dataset consists of a combination of 15 new borehole logs measured during 2012 distributed between three sites in Chile. These sites are located near Vallenar, Sierra Gorda and Sierra Limon Verde. Six temperature logs were measured during 1994 at sites near Michilla, Mansa Mina and the region of El Loa (Springer et al., Tectonophysics, 1998). Four logs were obtained from the NOAA Paleoclimatology Borehole Database located in Villa Staff, Toquepala and Talara in Peru. These data were analyzed for climate variability signals of the surface temperature and changes in the earth's surface energy balance. The analysis suggests regionalized temperature changes in ground surface temperatures with anomalies ranging from -0.1 to -0.3 K for Vallenar, -0.2 to -0.9 K in Sierra Gorda and 0.0 to 0.5 K for Sierra Limon Verde. We place the results within the context of surface air temperature yearly means obtained from existing meteorological and proxy paleoclimatic data between Peru and Northern Chile. The use of geothermal measurements for climate variability studies provides a further understanding of the climatic and energy cycles of the Southern Hemisphere, where meteorological data can be scarce to non-existent. Analysis of borehole temperature data have contributed significantly to estimating the last millennium surface temperature changes. Additionally, recent analysis have contributed to evaluate the Earth's energy balance by providing a quantitative value for the energy absorbed by the continents in the later part of the 20th century. Knowledge of the surface energy flux is important for understanding the solid Earth - atmosphere boundary condition, land cover changes, and their impact on regional and global climate models.

Impacts of wind farms on surface air temperatures

PubMed Central

Baidya Roy, Somnath; Traiteur, Justin J.

2010-01-01

Utility-scale large wind farms are rapidly growing in size and numbers all over the world. Data from a meteorological field campaign show that such wind farms can significantly affect near-surface air temperatures. These effects result from enhanced vertical mixing due to turbulence generated by wind turbine rotors. The impacts of wind farms on local weather can be minimized by changing rotor design or by siting wind farms in regions with high natural turbulence. Using a 25-y-long climate dataset, we identified such regions in the world. Many of these regions, such as the Midwest and Great Plains in the United States, are also rich in wind resources, making them ideal candidates for low-impact wind farms. PMID:20921371

Climate applications for NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature

NASA Astrophysics Data System (ADS)

Boyer, T.; Banzon, P. V. F.; Liu, G.; Saha, K.; Wilson, C.; Stachniewicz, J. S.

2015-12-01

Few sea surface temperature (SST) datasets from satellites have the long temporal span needed for climate studies. The NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) on a 1/4° grid, produced at National Centers for Environmental Information, is based primarily on SSTs from the Advanced Very High Resolution Radiometer (AVHRR), available from 1981 to the present. AVHRR data can contain biases, particularly when aerosols are present. Over the three decade span, the largest departure of AVHRR SSTs from buoy temperatures occurred during the Mt Pinatubo and El Chichon eruptions. Therefore, in DOISST, AVHRR SSTs are bias-adjusted to match in situ SSTs prior to interpolation. This produces a consistent time series of complete SST fields that is suitable for modelling and investigating local climate phenomena like El Nino or the Pacific warm blob in a long term context. Because many biological processes and animal distributions are temperature dependent, there are also many ecological uses of DOISST (e.g., coral bleaching thermal stress, fish and marine mammal distributions), thereby providing insights into resource management in a changing ocean. The advantages and limitations of using DOISST for different applications will be discussed.

Experiment of Rain Retrieval over Land Using Surface Emissivity Map Derived from TRMM TMI and JRA25

NASA Astrophysics Data System (ADS)

Furuzawa, Fumie; Masunaga, Hirohiko; Nakamura, Kenji

2010-05-01

We are developing a data-set of global land surface emissivity calculated from TRMM TMI brightness temperature (TB) and atmospheric profile data of Japanese 25-year Reanalysis Project (JRA-25) for the region identified as no-rain by TRMM PR, assuming zero cloud liquid water beyond 0-C level. For the evaluation, some characteristics of global monthly emissivity maps, for example, dependency of emissivity on each TMI frequency or each local time or seasonal/annual variation are checked. Moreover, these data are classified based on JRA25 land type or soilwetness and compared. Histogram of polarization difference of emissivity is similar to that of TB and mostly reflects the variability of land type or soil wetness, while histogram of vertical emissivity show a small difference. Next, by interpolating this instantaneous dataset with Gaussian function weighting, we derive an emissivity over neighboring rainy region and assess the interpolated emissivity by running radiative transfer model using PR rain profile and comparing with observed TB. Preliminary rain retrieval from the emissivities for some frequencies and TBs is evaluated based on PR rain profile and TMI rain rate. Moreover, another method is tested to estimate surface temperature from two emissivities, based on their statistical relation for each land type. We will show the results for vertical and horizontal emissivities of each frequency.

Implementing DOIs for Oceanographic Satellite Data at PO.DAAC

NASA Astrophysics Data System (ADS)

Hausman, J.; Tauer, E.; Chung, N.; Chen, C.; Moroni, D. F.

2013-12-01

The Physical Oceanographic Distributed Active Archive Center (PO.DAAC) is NASA's archive for physical oceanographic satellite data. It distributes over 500 datasets from gravity, ocean wind, sea surface topography, sea ice, ocean currents, salinity, and sea surface temperature satellite missions. A dataset is a collection of granules/files that share the same mission/project, versioning, processing level, spatial, and temporal characteristics. The large number of datasets is partially due to the number of satellite missions, but mostly because a single satellite mission typically has multiple versions or even temporal and spatial resolutions of data. As a result, a user might mistake one dataset for a different dataset from the same satellite mission. Due to the PO.DAAC'S vast variety and volume of data and growing requirements to report dataset usage, it has begun implementing DOIs for the datasets it archives and distributes. However, this was not as simple as registering a name for a DOI and providing a URL. Before implementing DOIs multiple questions needed to be answered. What are the sponsor and end-user expectations regarding DOIs? At what level does a DOI get assigned (dataset, file/granule)? Do all data get a DOI, or only selected data? How do we create a DOI? How do we create landing pages and manage them? What changes need to be made to the data archive, life cycle policy and web portal to accommodate DOIs? What if the data also exists at another archive and a DOI already exists? How is a DOI included if the data were obtained via a subsetting tool? How does a researcher or author provide a unique, definitive reference (standard citation) for a given dataset? This presentation will discuss how these questions were answered through changes in policy, process, and system design. Implementing DOIs is not a trivial undertaking, but as DOIs are rapidly becoming the de facto approach, it is worth the effort. Researchers have historically referenced the source satellite and data center (or archive), but scientific writings do not typically provide enough detail to point to a singular, uniquely identifiable dataset. DOIs provide the means to help researchers be precise in their data citations and provide needed clarity, standardization and permanence.

Spatiotemporal Variations in the Difference between Satellite-observed Land Surface Temperature and Station-based Near-surface Air Temperature

NASA Astrophysics Data System (ADS)

Lian, X.

2016-12-01

There is an increasing demand to integrate land surface temperature (LST) into climate research due to its global coverage, which requires a comprehensive knowledge of its distinctive characteristics compared to near-surface air temperature ( ). Using satellite observations and in-situ station-based datasets, we conducted a global-scale assessment of the spatial, seasonal, and interannual variations in the difference between daytime maximum LST and daytime maximum ( , LST - ) during 2003-2014. Spatially, LST is generally higher than over arid and sparsely vegetated regions in the mid-low latitudes, but LST is lower than in the tropical rainforests due to strong evaporative cooling, and in the high-latitude regions due to snow-induced radiative cooling. Seasonally, is negative in tropical regions throughout the year, while it displays a pronounced seasonality in both the mid-latitudes and boreal regions. The seasonality in the mid-latitudes is a result of the asynchronous responses of LST and to the seasonal cycle of radiation and vegetation abundance, whereas in the boreal regions, seasonality is mainly caused by the change in snow cover. At an interannual scale, only a small proportion of the land surface displays a statistically significant trend (P <0.05) due to the short time span of current measurements. Our study identified substantial spatial heterogeneity and seasonality in , as well as its determinant environmental drivers, and thus provides a useful reference for monitoring near-surface temperature changes using remote sensing, particularly in remote regions.

Evaluation of energy fluxes in the NCEP climate forecast system version 2.0 (CFSv2)

NASA Astrophysics Data System (ADS)

Rai, Archana; Saha, Subodh Kumar

2018-01-01

The energy fluxes at the surface and top of the atmosphere (TOA) from a long free run by the NCEP climate forecast system version 2.0 (CFSv2) are validated against several observation and reanalysis datasets. This study focuses on the annual mean energy fluxes and tries to link it with the systematic cold biases in the 2 m air temperature, particularly over the land regions. The imbalance in the long term mean global averaged energy fluxes are also evaluated. The global averaged imbalance at the surface and at the TOA is found to be 0.37 and 6.43 Wm-2, respectively. It is shown that CFSv2 overestimates the land surface albedo, particularly over the snow region, which in turn contributes to the cold biases in 2 m air temperature. On the other hand, surface albedo is highly underestimated over the coastal region around Antarctica and that may have contributed to the warm bias over that oceanic region. This study highlights the need for improvements in the parameterization of snow/sea-ice albedo scheme for a realistic simulation of surface temperature and that may have implications on the global energy imbalance in the model.

GLEAM v3: updated land evaporation and root-zone soil moisture datasets

NASA Astrophysics Data System (ADS)

Martens, Brecht; Miralles, Diego; Lievens, Hans; van der Schalie, Robin; de Jeu, Richard; Fernández-Prieto, Diego; Verhoest, Niko

2016-04-01

Evaporation determines the availability of surface water resources and the requirements for irrigation. In addition, through its impacts on the water, carbon and energy budgets, evaporation influences the occurrence of rainfall and the dynamics of air temperature. Therefore, reliable estimates of this flux at regional to global scales are of major importance for water management and meteorological forecasting of extreme events. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to the limited global coverage of in situ measurements. Remote sensing techniques can help to overcome the lack of ground data. However, evaporation is not directly observable from satellite systems. As a result, recent efforts have focussed on combining the observable drivers of evaporation within process-based models. The Global Land Evaporation Amsterdam Model (GLEAM, www.gleam.eu) estimates terrestrial evaporation based on daily satellite observations of meteorological drivers of terrestrial evaporation, vegetation characteristics and soil moisture. Since the publication of the first version of the model in 2011, GLEAM has been widely applied for the study of trends in the water cycle, interactions between land and atmosphere and hydrometeorological extreme events. A third version of the GLEAM global datasets will be available from the beginning of 2016 and will be distributed using www.gleam.eu as gateway. The updated datasets include separate estimates for the different components of the evaporative flux (i.e. transpiration, bare-soil evaporation, interception loss, open-water evaporation and snow sublimation), as well as variables like the evaporative stress, potential evaporation, root-zone soil moisture and surface soil moisture. A new dataset using SMOS-based input data of surface soil moisture and vegetation optical depth will also be distributed. The most important updates in GLEAM include the revision of the soil moisture data assimilation system, the evaporative stress functions and the infiltration of rainfall. In this presentation, we will highlight the changes of the methodology and present the new datasets, their validation against in situ observations and the comparisons against alternative datasets of terrestrial evaporation, such as GLDAS-Noah, ERA-Interim and previous GLEAM datasets. Preliminary results indicate that the magnitude and the spatio-temporal variability of the evaporation estimates have been slightly improved upon previous versions of the datasets.

A high-resolution European dataset for hydrologic modeling

NASA Astrophysics Data System (ADS)

Ntegeka, Victor; Salamon, Peter; Gomes, Goncalo; Sint, Hadewij; Lorini, Valerio; Thielen, Jutta

2013-04-01

There is an increasing demand for large scale hydrological models not only in the field of modeling the impact of climate change on water resources but also for disaster risk assessments and flood or drought early warning systems. These large scale models need to be calibrated and verified against large amounts of observations in order to judge their capabilities to predict the future. However, the creation of large scale datasets is challenging for it requires collection, harmonization, and quality checking of large amounts of observations. For this reason, only a limited number of such datasets exist. In this work, we present a pan European, high-resolution gridded dataset of meteorological observations (EFAS-Meteo) which was designed with the aim to drive a large scale hydrological model. Similar European and global gridded datasets already exist, such as the HadGHCND (Caesar et al., 2006), the JRC MARS-STAT database (van der Goot and Orlandi, 2003) and the E-OBS gridded dataset (Haylock et al., 2008). However, none of those provide similarly high spatial resolution and/or a complete set of variables to force a hydrologic model. EFAS-Meteo contains daily maps of precipitation, surface temperature (mean, minimum and maximum), wind speed and vapour pressure at a spatial grid resolution of 5 x 5 km for the time period 1 January 1990 - 31 December 2011. It furthermore contains calculated radiation, which is calculated by using a staggered approach depending on the availability of sunshine duration, cloud cover and minimum and maximum temperature, and evapotranspiration (potential evapotranspiration, bare soil and open water evapotranspiration). The potential evapotranspiration was calculated using the Penman-Monteith equation with the above-mentioned meteorological variables. The dataset was created as part of the development of the European Flood Awareness System (EFAS) and has been continuously updated throughout the last years. The dataset variables are used as inputs to the hydrological calibration and validation of EFAS as well as for establishing long-term discharge "proxy" climatologies which can then in turn be used for statistical analysis to derive return periods or other time series derivatives. In addition, this dataset will be used to assess climatological trends in Europe. Unfortunately, to date no baseline dataset at the European scale exists to test the quality of the herein presented data. Hence, a comparison against other existing datasets can therefore only be an indication of data quality. Due to availability, a comparison was made for precipitation and temperature only, arguably the most important meteorological drivers for hydrologic models. A variety of analyses was undertaken at country scale against data reported to EUROSTAT and E-OBS datasets. The comparison revealed that while the datasets showed overall similar temporal and spatial patterns, there were some differences in magnitudes especially for precipitation. It is not straightforward to define the specific cause for these differences. However, in most cases the comparatively low observation station density appears to be the principal reason for the differences in magnitude.

Analysis of the Meteorology Associated with the 1997 NASA Glenn Twin Otter Icing Events

NASA Technical Reports Server (NTRS)

Bernstein, Ben C.

2000-01-01

This part of the document contains an analysis of the meteorology associated with the premier icing encounters from the January-March 1997 NASA Twin Otter dataset. The purpose of this analysis is to provide a meteorological context for the aircraft data collected during these flights. For each case, the following data elements are presented: (1) A detailed discussion of the Twin Otter encounter, including locations, liquid water contents, temperatures and microphysical makeup of the clouds and precipitation aloft, (2) Upper-air charts, providing hand-analyzed locations of lows, troughs, ridges, saturated/unsaturated air, temperatures, warm/cold advection, and jet streams, (3) Balloon-borne soundings, providing vertical profiles of temperature, moisture and winds, (4) Infrared satellite data, providing cloud locations and cloud top temperature, (5) 3-hourly surface charts, providing hand-analyzed locations of lows, highs, fronts, precipitation (including type) and cloud cover, (6) Hourly plots of icing pilot reports, providing the icing intensity, icing type, icing altitudes and aircraft type, (7) Hourly, regional radar mosaics, providing fine resolution of the locations of precipitation (including intensity and type), pilot reports of icing (including intensity and type), surface observations of precipitation type and Twin Otter tracks for a one hour window centered on the time of the radar data, and (8) Plots of data from individual NEXRAD radars at times and elevation angles that have been matched to Twin Otter flight locations. Outages occurred in nearly every dataset at some point. All relevant data that was available is presented here. All times are in UTC and all heights are in feet above mean sea level (MSL).

Parametrization of Land Surface Temperature Fields with Optical and Microwave Remote Sensing in Brazil's Atlantic Forest

NASA Astrophysics Data System (ADS)

McDonald, K. C.; Khan, A.; Carnaval, A. C.

2016-12-01

Brazil is home to two of the largest and most biodiverse ecosystems in the world, primarily encompassed in forests and wetlands. A main region of interest in this project is Brazil's Atlantic Forest (AF). Although this forest is only a fraction of the size of the Amazon rainforest, it harbors significant biological richness, making it one of the world's major hotspots for biodiversity. The AF is located on the East to Southeast region of Brazil, bordering the Atlantic Ocean. As luscious and biologically rich as this region is, the area covered by the Atlantic Forest has been diminishing over past decades, mainly due to human influences and effects of climate change. We examine 1 km resolution Land Surface Temperature (LST) data from NASA's Moderate-resolution Imaging Spectroradiometer (MODIS) combined with 25 km resolution radiometric temperature derived from NASA's Advanced Microwave Scanning Radiometer on EOS (AMSR-E) to develop a capability employing both in combination to assess LST. Since AMSR-E is a microwave remote sensing instrument, products derived from its measurements are minimally effected by cloud cover. On the other hand, MODIS data are heavily influenced by cloud cover. We employ a statistical downscaling technique to the coarse-resolution AMSR-E datasets to enhance its spatial resolution to match that of MODIS. Our approach employs 16-day composite MODIS LST data in combination with synergistic ASMR-E radiometric brightness temperature data to develop a combined, downscaled dataset. Our goal is to use this integrated LST retrieval with complementary in situ station data to examine associated influences on regional biodiversity

Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

NASA Astrophysics Data System (ADS)

Cannon, A. J.; Hsieh, W. W.

2008-02-01

Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pearson product-moment correlation in the double-barreled network is replaced by the biweight midcorrelation, and the mean squared error (mse) in the inverse mapping networks can be replaced by the mean absolute error (mae). Robust variants of NLCCA are demonstrated on a synthetic dataset and are used to forecast sea surface temperatures in the tropical Pacific Ocean based on the sea level pressure field. Results suggest that adoption of the biweight midcorrelation can lead to improved performance, especially when a strong, common event exists in both predictor/predictand datasets. Replacing the mse by the mae leads to improved performance on the synthetic dataset, but not on the climate dataset except at the longest lead time, which suggests that the appropriate cost function for the inverse mapping networks is more problem dependent.

Recently amplified arctic warming has contributed to a continual global warming trend

NASA Astrophysics Data System (ADS)

Huang, Jianbin; Zhang, Xiangdong; Zhang, Qiyi; Lin, Yanluan; Hao, Mingju; Luo, Yong; Zhao, Zongci; Yao, Yao; Chen, Xin; Wang, Lei; Nie, Suping; Yin, Yizhou; Xu, Ying; Zhang, Jiansong

2017-12-01

The existence and magnitude of the recently suggested global warming hiatus, or slowdown, have been strongly debated1-3. Although various physical processes4-8 have been examined to elucidate this phenomenon, the accuracy and completeness of observational data that comprise global average surface air temperature (SAT) datasets is a concern9,10. In particular, these datasets lack either complete geographic coverage or in situ observations over the Arctic, owing to the sparse observational network in this area9. As a consequence, the contribution of Arctic warming to global SAT changes may have been underestimated, leading to an uncertainty in the hiatus debate. Here, we constructed a new Arctic SAT dataset using the most recently updated global SATs2 and a drifting buoys based Arctic SAT dataset11 through employing the `data interpolating empirical orthogonal functions' method12. Our estimate of global SAT rate of increase is around 0.112 °C per decade, instead of 0.05 °C per decade from IPCC AR51, for 1998-2012. Analysis of this dataset shows that the amplified Arctic warming over the past decade has significantly contributed to a continual global warming trend, rather than a hiatus or slowdown.

A global analysis of the urban heat island effect based on multisensor satellite data

NASA Astrophysics Data System (ADS)

Xiao, J.; Frolking, S. E.; Milliman, T. E.; Schneider, A.; Friedl, M. A.

2017-12-01

Human population is rapidly urbanizing. In much of the world, cities are prone to hotter weather than surrounding rural areas - so-called `urban heat islands' - and this effect can have mortal consequences during heat waves. During the daytime, when the surface energy balance is driven by incoming solar radiation, the magnitude of urban warming is strongly influenced by surface albedo and the capacity to evaporate water (i.e., there is a strong relationship between vegetated land fraction and the ratio of sensible to latent heat loss or Bowen ratio). At nighttime, urban cooling is often inhibited by the thermal inertia of the built environment and anthropogenic heat exhaust from building and transportation energy use. We evaluated a suite of global remote sensing data sets representing a range of urban characteristics against MODIS-derived land-surface temperature differences between urban and surrounding rural areas. We included two new urban datasets in this analysis - MODIS-derived change in global urban extent and global urban microwave backscatter - along with several MODIS standard products and DMSP/OLS nighttime lights time series data. The global analysis spanned a range of urban characteristics that likely influence the magnitude of daytime and/or nighttime urban heat islands - urban size, population density, building density, state of development, impervious fraction, eco-climatic setting. Specifically, we developed new satellite datasets and synthesizing these with existing satellite data into a global database of urban land surface parameters, used two MODIS land surface temperature products to generate time series of daytime and nighttime urban heat island effects for 30 large cities across the globe, and empirically analyzed these data to determine specifically which remote sensing-based characterizations of global urban areas have explanatory power with regard to both daytime and nighttime urban heat islands.

Observational Evidence of Changes in Soil Temperatures across Eurasian Continent

NASA Astrophysics Data System (ADS)

Zhang, T.

2015-12-01

Soil temperature is one of the key climate change indicators and plays an important role in plant growth, agriculture, carbon cycle and ecosystems as a whole. In this study, variability and changes in ground surface and soil temperatures up to 3.20 m were investigated based on data and information obtained from hydrometeorological stations across Eurasian continent since the early 1950s. Ground surface and soil temperatures were measured daily by using the same standard method and by the trained professionals across Eurasian continent, which makes the dataset unique and comparable over a large study region. Using the daily soil temperature profiles, soil seasonal freeze depth was also obtained through linear interpolation. Preliminary results show that soil temperatures at various depths have increased dramatically, almost twice as much as air temperature increase over the same period. Regionally, soil temperature increase was more dramatically in high northern latitudes than mid/lower latitude regions. Air temperature changes alone may not be able to fully explain the magnitude of changes in soil temperatures. Further study indicates that snow cover establishment started later in autumn and snow cover disappearance occurred earlier in spring, while winter snow depth became thicker with a decreasing trend of snow density. Changes in snow cover conditions may play an important role in changes of soil temperatures over the Eurasian continent.

Observed Local Impacts of Global Irrigation on Surface Temperature

NASA Astrophysics Data System (ADS)

Chen, L.; Dirmeyer, P.

2017-12-01

Agricultural irrigation has significant potential for altering local climate through reducing soil albedo, increasing evapotranspiration, and enabling greater leaf area. Numerous studies using regional or global climate models have demonstrated the cooling effects of irrigation on mean and extreme temperature, especially over regions where irrigation is extensive. However, these model-based results have not been validated due to the limitations of observational datasets. In this study, multiple satellite-based products, including the Moderate Resolution Imaging Spectroradiometer (MODIS) and Soil Moisture Active Passive (SMAP) data sets, are used to isolate and quantify the local impacts of irrigation on surface climate over the irrigated regions, which are derived from the Global Map of Irrigation Areas (GMIA). The relationships among soil moisture, albedo, evapotranspiration, and surface temperature are explored. Strong evaporative cooling of irrigation on daytime surface temperature is found over the arid and semi-arid regions, such as California's Central Valley, the Great Plains, and central Asia. However, the cooling effects are less evident in most areas of eastern China, India, and the Lower Mississippi River Basin in spite of extensive irrigation over these regions. Results are also compared with irrigation experiments using the Community Earth System Model (CESM) to assess the model's ability to represent land-atmosphere interactions in regards to irrigation.

PERSPECTIVE Working towards a community-wide understanding of satellite skin temperature observations

NASA Astrophysics Data System (ADS)

Shreve, Cheney

2010-12-01

With more than sixty free and publicly available high-quality datasets, including ecosystem variables, radiation budget variables, and land cover products, the MODIS instrument and the MODIS scientific team have contributed significantly to scientific investigations of ecosystems across the globe. The MODIS instrument, launched in December 1999, has 36 spectral bands, a viewing swath of 2330 km, and acquires data at 250 m, 500 m, and 1000 m spatial resolution every one to two days. Radiation budget variables include surface reflectance, skin temperature, emissivity, and albedo, to list a few. Ecosystem variables include several vegetation indices and productivity measures. Land cover characteristics encompass land cover classifications as well as model parameters and vegetation classifications. Many of these products are instrumental in constraining global climate models and climate change studies, as well as monitoring events such as the recent flooding in Pakistan, the unprecedented oil spill in the Gulf of Mexico, or phytoplankton bloom in the Barents Sea. While product validation efforts by the MODIS scientific team are both vigorous and continually improving, validation is unquestionably one of the most difficult tasks when dealing with remotely derived datasets, especially at the global scale. The quality and availability of MODIS data have led to widespread usage in the scientific community that has further contributed to validation and development of the MODIS products. In their recent paper entitled 'Land surface skin temperature climatology: benefitting from the strengths of satellite observations', Jin and Dickinson review the scientific theory behind, and demonstrate application of, a MODIS temperature product: surface skin temperature. Utilizing datasets from the Global Historical Climatological Network (GHCN), daily skin and air temperature from the Atmospheric Radiation Measurement (ARM) program, and MODIS products (skin temperature, albedo, land cover, water vapor, cloud cover), they show that skin temperature is clearly a different physical parameter from air temperature and varies from air temperature in magnitude, response to atmospheric conditions, and diurnal phase. Although the accuracy of skin temperature (Tskin) algorithms has improved to within 0.5-1°C for field measurements and clear-sky satellite observations (Becker and Li 1995, Goetz et al 1995, Wan and Dozier 1996), general confusion regarding the physical definition of 'surface temperature' and how it can be used for climate studies has persisted throughout the scientific community and limited the applications of these data (Jin and Dickinson 2010). For example, satellite sea surface temperature was used as evidence of global climate change instead of skin temperature in the IPCC 2001 and 2007 reports (Jin and Dickinson 2010). This work provides clarity in the theoretical definition of temperature variables, demonstrates the difference between air and skin temperature, and aids the understanding of the MODIS Tskin product, which could be very beneficial for future climate studies. As outlined by Jin and Dickinson, 'surface temperature' is a vague term commonly used in reference to air temperature, aerodynamic temperature, and skin temperature. Air temperature (Tair), or thermodynamic temperature, is measured by an in situ instrument usually 1.5-2 m above the ground. Aerodynamic temperature (Taero) refers to the temperature at the height of the roughness length of heat. Satellite derived skin temperature (Tskin) is the radiometric temperature derived from the inverse of Planck's function. While these different temperature variables are typically correlated, they differ as a result of environmental conditions (e.g. land cover and sky conditions; Jin and Dickinson 2010). With an extensive network of Tair measurements, some have questioned the benefits of using Tskin at all (Peterson et al 1997, 1998). Tskin and Tair can vary depending on land cover or sky conditions and variations may be large, e.g., for sparsely vegetated areas where net radiation is largely balanced by sensible heat flux (Hall et al 1992, Sun and Mahrt 1995, Jin et al 1997). Tskin can be higher than Taero at midday and lower at night (Sun and Mahrt 1995) and some models use Taero to approximate surface radiative temperature (Hubband and Monteith 1986). One of the strengths of the MODIS instrument is the simultaneous collection of surface and atmospheric conditions. By incorporating a range of MODIS variables in their comparison to Tskin, the authors examine the relationship of Tskin to atmospheric and surface conditions. Results from their global evaluation of Tskin highlight its variability on an inter-annual basis, its variation with solar zenith angle, and diurnal variations, which are not achievable with Tair measurements. Comparison with land cover type illustrates the seasonality of Tskin for different land covers. Comparison with the enhanced vegetation index (EVI) suggests more vegetation reduces skin temperature. Using the MODIS albedo, they demonstrate a clear relationship between yearly averaged Tskin and land surface albedo. Lastly, their examination of water vapor and cloud cover in comparison to Tskin suggests similar seasonality between these two variables. The MODIS Tskin product is not without uncertainty; retrieving Tskin requires a calculation of radiative transfer to account for atmospheric emission and molecular absorption, which is time and resource intensive (Jin and Dickinson 2010). Additionally, surface emissivity, instrument noise, and view angle geometry contribute to error in Tskin estimations (Jin and Dickinson 2010). The transparency of the scientific theory underlying this work, and the clear demonstration of the distinction between temperature measures on varying scales, demonstrates the usefulness of Tskin despite the uncertainties. Perhaps equally as important is the tone; in a time when the controversy surrounding climate change is peaking and the very ethics of the scientific community are being questioned, it is more critical than ever to be transparent in one's work and to assist the scientific community in understanding the tools we have available to us for investigating climate change. References Becker F and Li Z-L 1995 Surface temperature and emissivity at different scales: definition, measurement and related problems Remote Sensing Rev. 12 225-53 Goetz S J, Halthore R, Hall F G and Markham B L 1995 Surface temperature retrieval in a temperate grassland with multi-resolution sensors J. Geophys. Res. Atmos. 100 25397-410 Hall F G, Huemmrich K F, Goetz P J, Sellers P J and Nickeson J E 1992 Satellite remote sensing of the surface energy balance: success, failures and unresolved issues in FIFE J. Geophys. Res. Atmos. 97 19061-90 Jin M and Dickinson R E 2010 Land surface skin temperature climatology: benefitting from the strengths of satellite observations Environ. Res. Lett. 5 044004 Jin M, Dickinson R E and Vogelmann A M 1997 A comparison of CCM2/BATS skin temperature and surface-air temperature with satellite and surface observations J. Climate 10 1505-24 Hubband N D S and Monteith J L 1986 Radiative surface temperature and energy balance of a wheat canopy Boundary Layer Meteorol. 36 107-16 Peterson T C and Vose R S 1997 An overview of the Global Historical Climatology Network temperature data base Bull. Am. Meteorol. Soc. 78 2837-49 Peterson T C, Karl T R, Jamason P F, Knight R and Easterling D R 1998 The first difference method: maximizing station density for the calculation of long-term global temperature change J. Geophys. Res. Atmos. 103 25967-74 Sun J and Mahrt L 1995 Determination of surface fluxes from the surface radiative temperature Atmos. Sci. 52 1096-106 Wan Z and Dozier J 1996 A generalized split-window algorithm for retrieving land-surface temperature from space IEEE Trans. Geosci. Remote Sensing 34 892-905

Intercomparison of the Extended Reconstructed Sea Surface Temperature v4 and v3b Datasets

NASA Astrophysics Data System (ADS)

Wang, Jinping; Chen, Xianyao

2018-04-01

Version 4 (v4) of the Extended Reconstructed Sea Surface Temperature (ERSST) dataset is compared with its precedent, the widely used version 3b (v3b). The essential upgrades applied to v4 lead to remarkable differences in the characteristics of the sea surface temperature (SST) anomaly (SSTa) in both the temporal and spatial domains. First, the largest discrepancy of the global mean SSTa values around the 1940s is due to ship-observation corrections made to reconcile observations from buckets and engine intake thermometers. Second, differences in global and regional mean SSTa values between v4 and v3b exhibit a downward trend (around -0.032°C per decade) before the 1940s, an upward trend (around 0.014°C per decade) during the period of 1950-2015, interdecadal oscillation with one peak around the 1980s, and two troughs during the 1960s and 2000s, respectively. This does not derive from treatments of the polar or the other data-void regions, since the difference of the SSTa does not share the common features. Third, the spatial pattern of the ENSO-related variability of v4 exhibits a wider but weaker cold tongue in the tropical region of the Pacific Ocean compared with that of v3b, which could be attributed to differences in gap-filling assumptions since the latter features satellite observations whereas the former features in situ ones. This intercomparison confirms that the structural uncertainty arising from underlying assumptions on the treatment of diverse SST observations even in the same SST product family is the main source of significant SST differences in the temporal domain. Why this uncertainty introduces artificial decadal oscillations remains unknown.

The Impact of Sea-Surface Winds on Meteorological Conditions in Israel: An Initial Study

NASA Technical Reports Server (NTRS)

Otterman, J.; Saaroni, H.; Atlas, R.; Ardizzone, J.; Ben-Dor, E.; Druyan, L.; Jusem, C. J.; Karnieli, A.; Terry, J.

2000-01-01

The SSM/I (Spectral Sensor Microwave Imager) dataset is used to monitor surface wind speed and direction at four locations over the Eastern Mediterranean during December 1998 - January 1999. Time series of these data are compared to concurrent series of precipitation, surface temperature, humidity and winds at selected Israeli stations: Sde Dov (coastal), Bet Dagan (5 km. inland), Jerusalem (Judean Hills), Hafetz Haim (3 km. inland) and Sde Boker (central Negev). December 1998 and the beginning of January 1999 were dry in Israel, but significant precipitation was recorded at many stations during the second half of January (1999). SSM/I data show a surge in westerly surface winds west of Israel (32 N, 32.5 E) on 15 January, coinciding with the renewal of precipitation. We discuss the relevant circulation and pressure patterns during this transition in the context of the evolving meteorological conditions at the selected Israeli locations. The SSM/I dataset of near ocean surface winds, available for the last 12 years, is described. We analyze lagged correlation between these data and the Israeli station data and investigate possibility of predictive skill. Application of such relationships to short-term weather prediction would require real-time access to the SSM/I observations.

Daily temperature records from a mesonet in the foothills of the Canadian Rocky Mountains, 2005-2010

NASA Astrophysics Data System (ADS)

Wood, Wendy H.; Marshall, Shawn J.; Whitehead, Terri L.; Fargey, Shannon E.

2018-03-01

Near-surface air temperatures were monitored from 2005 to 2010 in a mesoscale network of 230 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada. The monitoring network covers a range of elevations from 890 to 2880 m above sea level and an area of about 18 000 km2, sampling a variety of topographic settings and surface environments with an average spatial density of one station per 78 km2. This paper presents the multiyear temperature dataset from this study, with minimum, maximum, and mean daily temperature data available at https://doi.org/10.1594/PANGAEA.880611. In this paper, we describe the quality control and processing methods used to clean and filter the data and assess its accuracy. Overall data coverage for the study period is 91 %. We introduce a weather-system-dependent gap-filling technique to estimate the missing 9 % of data. Monthly and seasonal distributions of minimum, maximum, and mean daily temperature lapse rates are shown for the region.

Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System (GLDAS) Land Surface States

NASA Technical Reports Server (NTRS)

Gottschalck, Jon; Meng, Jesse; Rodel, Matt; Houser, paul

2005-01-01

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. NASA's Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type). Precipitation is arguably the most important input to LSMs. Many precipitation datasets have been produced using satellite and rain gauge observations and weather forecast models. In this study, seven different global precipitation datasets were evaluated over the United States, where dense rain gauge networks contribute to reliable precipitation maps. We then used the seven datasets as inputs to GLDAS simulations, so that we could diagnose their impacts on output stocks and fluxes of water. In terms of totals, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) had the closest agreement with the US rain gauge dataset for all seasons except winter. The CMAP precipitation was also the most closely correlated in time with the rain gauge data during spring, fall, and winter, while the satellitebased estimates performed best in summer. The GLDAS simulations revealed that modeled soil moisture is highly sensitive to precipitation, with differences in spring and summer as large as 45% depending on the choice of precipitation input.

Time-series modeling and prediction of global monthly absolute temperature for environmental decision making

NASA Astrophysics Data System (ADS)

Ye, Liming; Yang, Guixia; Van Ranst, Eric; Tang, Huajun

2013-03-01

A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined (DSC) approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average (SARIMA) models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term (˜10-year) environmental planning and decision making.

The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Case, Jonathan; Kozlowski, Danielle; Molthan, Andrew

2012-01-01

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting entities, including a number of National Weather Service offices. SPoRT transitions real-time NASA products and capabilities to its partners to address specific operational forecast challenges. One challenge that forecasters face is applying convection-allowing numerical models to predict mesoscale convective weather. In order to address this specific forecast challenge, SPoRT produces real-time mesoscale model forecasts using the Weather Research and Forecasting (WRF) model that includes unique NASA products and capabilities. Currently, the SPoRT configuration of the WRF model (SPoRT-WRF) incorporates the 4-km Land Information System (LIS) land surface data, 1-km SPoRT sea surface temperature analysis and 1-km Moderate resolution Imaging Spectroradiometer (MODIS) greenness vegetation fraction (GVF) analysis, and retrieved thermodynamic profiles from the Atmospheric Infrared Sounder (AIRS). The LIS, SST, and GVF data are all integrated into the SPoRT-WRF through adjustments to the initial and boundary conditions, and the AIRS data are assimilated into a 9-hour SPoRT WRF forecast each day at 0900 UTC. This study dissects the overall impact of the NASA datasets and the individual surface and atmospheric component datasets on daily mesoscale forecasts. A case study covering the super tornado outbreak across the Ce ntral and Southeastern United States during 25-27 April 2011 is examined. Three different forecasts are analyzed including the SPoRT-WRF (NASA surface and atmospheric data), the SPoRT WRF without AIRS (NASA surface data only), and the operational National Severe Storms Laboratory (NSSL) WRF (control with no NASA data). The forecasts are compared qualitatively by examining simulated versus observed radar reflectivity. Differences between the simulated reflectivity are further investigated using convective parameters along with model soundings to determine the impacts of the various NASA datasets. Additionally, quantitative evaluation of select meteorological parameters is performed using the Meteorological Evaluation Tools model verification package to compare forecasts to in situ surface and upper air observations.

HadISD: a quality-controlled global synoptic report database for selected variables at long-term stations from 1973-2011

NASA Astrophysics Data System (ADS)

Dunn, R. J. H.; Willett, K. M.; Thorne, P. W.; Woolley, E. V.; Durre, I.; Dai, A.; Parker, D. E.; Vose, R. S.

2012-10-01

This paper describes the creation of HadISD: an automatically quality-controlled synoptic resolution dataset of temperature, dewpoint temperature, sea-level pressure, wind speed, wind direction and cloud cover from global weather stations for 1973-2011. The full dataset consists of over 6000 stations, with 3427 long-term stations deemed to have sufficient sampling and quality for climate applications requiring sub-daily resolution. As with other surface datasets, coverage is heavily skewed towards Northern Hemisphere mid-latitudes. The dataset is constructed from a large pre-existing ASCII flatfile data bank that represents over a decade of substantial effort at data retrieval, reformatting and provision. These raw data have had varying levels of quality control applied to them by individual data providers. The work proceeded in several steps: merging stations with multiple reporting identifiers; reformatting to netCDF; quality control; and then filtering to form a final dataset. Particular attention has been paid to maintaining true extreme values where possible within an automated, objective process. Detailed validation has been performed on a subset of global stations and also on UK data using known extreme events to help finalise the QC tests. Further validation was performed on a selection of extreme events world-wide (Hurricane Katrina in 2005, the cold snap in Alaska in 1989 and heat waves in SE Australia in 2009). Some very initial analyses are performed to illustrate some of the types of problems to which the final data could be applied. Although the filtering has removed the poorest station records, no attempt has been made to homogenise the data thus far, due to the complexity of retaining the true distribution of high-resolution data when applying adjustments. Hence non-climatic, time-varying errors may still exist in many of the individual station records and care is needed in inferring long-term trends from these data. This dataset will allow the study of high frequency variations of temperature, pressure and humidity on a global basis over the last four decades. Both individual extremes and the overall population of extreme events could be investigated in detail to allow for comparison with past and projected climate. A version-control system has been constructed for this dataset to allow for the clear documentation of any updates and corrections in the future.

Diurnal Variations of Titan's Surface Temperatures From Cassini -CIRS Observations

NASA Astrophysics Data System (ADS)

Cottini, Valeria; Nixon, Conor; Jennings, Don; Anderson, Carrie; Samuelson, Robert; Irwin, Patrick; Flasar, F. Michael

The Cassini Composite Infrared Spectrometer (CIRS) observations of Saturn's largest moon, Titan, are providing us with the ability to detect the surface temperature of the planet by studying its outgoing radiance through a spectral window in the thermal infrared at 19 m (530 cm-1) characterized by low opacity. Since the first acquisitions of CIRS Titan data the in-strument has gathered a large amount of spectra covering a wide range of latitudes, longitudes and local times. We retrieve the surface temperature and the atmospheric temperature pro-file by modeling proper zonally averaged spectra of nadir observations with radiative transfer computations. Our forward model uses the correlated-k approximation for spectral opacity to calculate the emitted radiance, including contributions from collision induced pairs of CH4, N2 and H2, haze, and gaseous emission lines (Irwin et al. 2008). The retrieval method uses a non-linear least-squares optimal estimation technique to iteratively adjust the model parameters to achieve a spectral fit (Rodgers 2000). We show an accurate selection of the wide amount of data available in terms of footprint diameter on the planet and observational conditions, together with the retrieved results. Our results represent formal retrievals of surface brightness temperatures from the Cassini CIRS dataset using a full radiative transfer treatment, and we compare to the earlier findings of Jennings et al. (2009). The application of our methodology over wide areas has increased the planet coverage and accuracy of our knowledge of Titan's surface brightness temperature. In particular we had the chance to look for diurnal variations in surface temperature around the equator: a trend with slowly increasing temperature toward the late afternoon reveals that diurnal temperature changes are present on Titan surface. References: Irwin, P.G.J., et al.: "The NEMESIS planetary atmosphere radiative transfer and retrieval tool" (2008). JQSRT, Vol. 109, pp. 1136-1150, 2008. Rodgers, C. D.: "Inverse Methods For Atmospheric Sounding: Theory and Practice". World Scientific, Singapore, 2000. Jennings, D.E., et al.: "Titan's Surface Brightness Temperatures." Ap. J. L., Vol. 691, pp. L103-L105, 2009.

Regional climate change study requires new temperature datasets

NASA Astrophysics Data System (ADS)

Wang, K.; Zhou, C.

2016-12-01

Analyses of global mean air temperature (Ta), i. e., NCDC GHCN, GISS, and CRUTEM4, are the fundamental datasets for climate change study and provide key evidence for global warming. All of the global temperature analyses over land are primarily based on meteorological observations of the daily maximum and minimum temperatures (Tmax and Tmin) and their averages (T2) because in most weather stations, the measurements of Tmax and Tmin may be the only choice for a homogenous century-long analysis of mean temperature. Our studies show that these datasets are suitable for long-term global warming studies. However, they may introduce substantial bias in quantifying local and regional warming rates, i.e., with a root mean square error of more than 25% at 5°x 5° grids. From 1973 to 1997, the current datasets tend to significantly underestimate the warming rate over the central U.S. and overestimate the warming rate over the northern high latitudes. Similar results revealed during the period 1998-2013, the warming hiatus period, indicate the use of T2 enlarges the spatial contrast of temperature trends. This because T2 over land only sample air temperature twice daily and cannot accurately reflect land-atmosphere and incoming radiation variations in the temperature diurnal cycle. For better regional climate change detection and attribution, we suggest creating new global mean air temperature datasets based on the recently available high spatiotemporal resolution meteorological observations, i.e., daily four observations weather station since 1960s, These datasets will not only help investigate dynamical processes on temperature variances but also help better evaluate the reanalyzed and modeled simulations of temperature and make some substantial improvements for other related climate variables in models, especially over regional and seasonal aspects.

Regional climate change study requires new temperature datasets

NASA Astrophysics Data System (ADS)

Wang, Kaicun; Zhou, Chunlüe

2017-04-01

Analyses of global mean air temperature (Ta), i. e., NCDC GHCN, GISS, and CRUTEM4, are the fundamental datasets for climate change study and provide key evidence for global warming. All of the global temperature analyses over land are primarily based on meteorological observations of the daily maximum and minimum temperatures (Tmax and Tmin) and their averages (T2) because in most weather stations, the measurements of Tmax and Tmin may be the only choice for a homogenous century-long analysis of mean temperature. Our studies show that these datasets are suitable for long-term global warming studies. However, they may have substantial biases in quantifying local and regional warming rates, i.e., with a root mean square error of more than 25% at 5 degree grids. From 1973 to 1997, the current datasets tend to significantly underestimate the warming rate over the central U.S. and overestimate the warming rate over the northern high latitudes. Similar results revealed during the period 1998-2013, the warming hiatus period, indicate the use of T2 enlarges the spatial contrast of temperature trends. This is because T2 over land only samples air temperature twice daily and cannot accurately reflect land-atmosphere and incoming radiation variations in the temperature diurnal cycle. For better regional climate change detection and attribution, we suggest creating new global mean air temperature datasets based on the recently available high spatiotemporal resolution meteorological observations, i.e., daily four observations weather station since 1960s. These datasets will not only help investigate dynamical processes on temperature variances but also help better evaluate the reanalyzed and modeled simulations of temperature and make some substantial improvements for other related climate variables in models, especially over regional and seasonal aspects.

Variability of Winter Air Temperature in Mid-Latitude Europe

NASA Technical Reports Server (NTRS)

Otterman, J.; Ardizzone, J.; Atlas, R.; Bungato, D.; Cierniewski, J.; Jusem, J. C.; Przybylak, R.; Schubert, S.; Starr, D.; Walczewski, J.

2002-01-01

The aim of this paper is to report extreme winter/early-spring air temperature (hereinafter temperature) anomalies in mid-latitude Europe, and to discuss the underlying forcing to these interannual fluctuations. Warm advection from the North Atlantic in late winter controls the surface-air temperature, as indicated by the substantial correlation between the speed of the surface southwesterlies over the eastern North Atlantic (quantified by a specific Index Ina) and the 2-meter level air temperatures (hereinafter Ts) over Europe, 45-60 deg N, in winter. In mid-March and subsequently, the correlation drops drastically (quite often it is negative). This change in the relationship between Ts and Ina marks a transition in the control of the surface-air temperature: absorption of insolation replaces the warm advection as the dominant control. This forcing by maritime-air advection in winter was demonstrated in a previous publication, and is re-examined here in conjunction with extreme fluctuations of temperatures in Europe. We analyze here the interannual variability at its extreme by comparing warm-winter/early-spring of 1989/90 with the opposite scenario in 1995/96. For these two December-to-March periods the differences in the monthly mean temperature in Warsaw and Torun, Poland, range above 10 C. Short-term (shorter than a month) fluctuations of the temperature are likewise very strong. We conduct pentad-by-pentad analysis of the surface-maximum air temperature (hereinafter Tmax), in a selected location, examining the dependence on Ina. The increased cloudiness and higher amounts of total precipitable water, corollary effects to the warm low-level advection. in the 1989/90 winter, enhance the positive temperature anomalies. The analysis of the ocean surface winds is based on the Special Sensor Microwave/Imager (SSM/I) dataset; ascent rates, and over land wind data are from the European Centre for Medium-Range Weather Forecasts (ECMWF); maps of 2-m temperature, cloud cover and precipitable water are from the National Centers for Environmental Prediction (NCEP) Reanalysis.

Uncertainty Propagation of Non-Parametric-Derived Precipitation Estimates into Multi-Hydrologic Model Simulations

NASA Astrophysics Data System (ADS)

Bhuiyan, M. A. E.; Nikolopoulos, E. I.; Anagnostou, E. N.

2017-12-01

Quantifying the uncertainty of global precipitation datasets is beneficial when using these precipitation products in hydrological applications, because precipitation uncertainty propagation through hydrologic modeling can significantly affect the accuracy of the simulated hydrologic variables. In this research the Iberian Peninsula has been used as the study area with a study period spanning eleven years (2000-2010). This study evaluates the performance of multiple hydrologic models forced with combined global rainfall estimates derived based on a Quantile Regression Forests (QRF) technique. In QRF technique three satellite precipitation products (CMORPH, PERSIANN, and 3B42 (V7)); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset are being utilized in this study. A high-resolution, ground-based observations driven precipitation dataset (named SAFRAN) available at 5 km/1 h resolution is used as reference. Through the QRF blending framework the stochastic error model produces error-adjusted ensemble precipitation realizations, which are used to force four global hydrological models (JULES (Joint UK Land Environment Simulator), WaterGAP3 (Water-Global Assessment and Prognosis), ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) and SURFEX (Stands for Surface Externalisée) ) to simulate three hydrologic variables (surface runoff, subsurface runoff and evapotranspiration). The models are forced with the reference precipitation to generate reference-based hydrologic simulations. This study presents a comparative analysis of multiple hydrologic model simulations for different hydrologic variables and the impact of the blending algorithm on the simulated hydrologic variables. Results show how precipitation uncertainty propagates through the different hydrologic model structures to manifest in reduction of error in hydrologic variables.

A study of the determination of grain boundary diffusivity and energy through the thermally grown oxide ridges on a Fe-22Cr alloy surface

NASA Astrophysics Data System (ADS)

Lin, Yu; Laughlin, David E.; Zhu, Jingxi

2017-03-01

The grain boundaries (GBs) present in polycrystalline materials are important with respect to materials behaviour and properties. During the transient stage of oxidation, the higher GB diffusivity results in heterogeneous oxidation structures in the form of oxide ridges that emerge along the alloy GBs. In an attempt to delve into the more fundamental aspects of the GBs, such as GB energy, the size of the oxide ridges was quantitatively measured by atomic force microscopy on the post oxidation surface of a Fe-22 wt % Cr alloy after an oxidation exposure at 800 °C in dry air. The GB diffusivity was calculated utilising the ridge size data and the relationship between the GB diffusivity and the GB characteristics was determined. Furthermore, the GB energy was calculated from the GB diffusivity data, also to make comparison with the data available in the literature. The absolute value of the calculated GB energy was quite close to the values reported in the literature. However, compared to the extremely low temperature (0 K) data-set from the literature, the data-set obtained from this study showed much less spread. The smaller variation range may be attributed to the higher temperature condition (1073 K) in this study.

Improved prediction of severe thunderstorms over the Indian Monsoon region using high-resolution soil moisture and temperature initialization

PubMed Central

Osuri, K. K.; Nadimpalli, R.; Mohanty, U. C.; Chen, F.; Rajeevan, M.; Niyogi, D.

2017-01-01

The hypothesis that realistic land conditions such as soil moisture/soil temperature (SM/ST) can significantly improve the modeling of mesoscale deep convection is tested over the Indian monsoon region (IMR). A high resolution (3 km foot print) SM/ST dataset prepared from a land data assimilation system, as part of a national monsoon mission project, showed close agreement with observations. Experiments are conducted with (LDAS) and without (CNTL) initialization of SM/ST dataset. Results highlight the significance of realistic land surface conditions on numerical prediction of initiation, movement and timing of severe thunderstorms as compared to that currently being initialized by climatological fields in CNTL run. Realistic land conditions improved mass flux, convective updrafts and diabatic heating in the boundary layer that contributed to low level positive potential vorticity. The LDAS run reproduced reflectivity echoes and associated rainfall bands more efficiently. Improper representation of surface conditions in CNTL run limit the evolution boundary layer processes and thereby failed to simulate convection at right time and place. These findings thus provide strong support to the role land conditions play in impacting the deep convection over the IMR. These findings also have direct implications for improving heavy rain forecasting over the IMR, by developing realistic land conditions. PMID:28128293

Validation of newly designed regional earth system model (RegESM) for Mediterranean Basin

NASA Astrophysics Data System (ADS)

Turuncoglu, Ufuk Utku; Sannino, Gianmaria

2017-05-01

We present a validation analysis of a regional earth system model system (RegESM) for the Mediterranean Basin. The used configuration of the modeling system includes two active components: a regional climate model (RegCM4) and an ocean modeling system (ROMS). To assess the performance of the coupled modeling system in representing the climate of the basin, the results of the coupled simulation (C50E) are compared to the results obtained by a standalone atmospheric simulation (R50E) as well as several observation datasets. Although there is persistent cold bias in fall and winter, which is also seen in previous studies, the model reproduces the inter-annual variability and the seasonal cycles of sea surface temperature (SST) in a general good agreement with the available observations. The analysis of the near-surface wind distribution and the main circulation of the sea indicates that the coupled model can reproduce the main characteristics of the Mediterranean Sea surface and intermediate layer circulation as well as the seasonal variability of wind speed and direction when it is compared with the available observational datasets. The results also reveal that the simulated near-surface wind speed and direction have poor performance in the Gulf of Lion and surrounding regions that also affects the large positive SST bias in the region due to the insufficient horizontal resolution of the atmospheric component of the coupled modeling system. The simulated seasonal climatologies of the surface heat flux components are also consistent with the CORE.2 and NOCS datasets along with the overestimation in net long-wave radiation and latent heat flux (or evaporation, E), although a large observational uncertainty is found in these variables. Also, the coupled model tends to improve the latent heat flux by providing a better representation of the air-sea interaction as well as total heat flux budget over the sea. Both models are also able to reproduce the temporal evolution of the inter-annual anomaly of surface air temperature and precipitation (P) over defined sub-regions. The Mediterranean water budget (E, P and E-P) estimates also show that the coupled model has high skill in the representation of water budget of the Mediterranean Sea. To conclude, the coupled model reproduces climatological land surface fields and the sea surface variables in the range of observation uncertainty and allow studying air-sea interaction and main regional climate characteristics of the basin.

A Multiyear Dataset of SSM/I-Derived Global Ocean Surface Turbulent Fluxes

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Shie, Chung-Lin; Atlas, Robert M.; Ardizzone, Joe; Nelkin, Eric; Einaudi, Franco (Technical Monitor)

2001-01-01

The surface turbulent fluxes of momentum, latent heat, and sensible heat over global oceans are essential to weather, climate and ocean problems. Evaporation is a key component of the hydrological cycle and the surface heat budget, while the wind stress is the major forcing for driving the oceanic circulation. The global air-sea fluxes of momentum, latent and sensible heat, radiation, and freshwater (precipitation-evaporation) are the forcing for driving oceanic circulation and, hence, are essential for understanding the general circulation of global oceans. The global air-sea fluxes are required for driving ocean models and validating coupled ocean-atmosphere global models. We have produced a 7.5-year (July 1987-December 1994) dataset of daily surface turbulent fluxes over the global oceans from the Special Sensor microwave/Imager (SSM/I) data. Daily turbulent fluxes were derived from daily data of SSM/I surface winds and specific humidity, National Centers for Environmental Prediction (NCEP) sea surface temperatures, and European Centre for Medium-Range Weather Forecasts (ECMWF) air-sea temperature differences, using a stability-dependent bulk scheme. The retrieved instantaneous surface air humidity (with a 25-km resolution) validated well with that of the collocated radiosonde observations over the global oceans. Furthermore, the retrieved daily wind stresses and latent heat fluxes were found to agree well with that of the in situ measurements (IMET buoy, RV Moana Wave, and RV Wecoma) in the western Pacific warm pool during the TOGA COARE intensive observing period (November 1992-February 1993). The global distributions of 1988-94 seasonal-mean turbulent fluxes will be presented. In addition, the global distributions of 1990-93 annual-means turbulent fluxes and input variables will be compared with those of UWM/COADS covering the same period. The latter is based on the COADS (comprehensive ocean-atmosphere data set) and is recognized to be one of the best climatological analyses of fluxes derived from ship observations.

Surface-Wind Anomalies in North-Atlantic and North Pacific from SSM/I Observations: Influence on Temperature of Adjoining Land Regions

NASA Technical Reports Server (NTRS)

Otterman, Joseph; Atlas, R.; Ingraham, J.; Ardizzone, J.; Starr, D.; Terry, J.

1998-01-01

Surface winds over the oceans are derived from Special Sensor Microwave Imager (SSM/I) measurements, assigning direction by Variational Analysis Method (VAM). Validations by comparison with other measurements indicate highly-satisfactory data quality. Providing global coverage from 1988, the dataset is a convenient source for surface-wind climatology. In this study, the interannual variability of zonal winds is analyzed concentrating on the westerlies in North Atlantic and North Pacific, above 30 N. Interannual differences in the westerlies exceeding 10 m sec (exp -1) are observed over large regions, often accompanied by changes of the same magnitude in the easterlies below 30 N. We concentrate on February/March, since elevated temperatures, by advancing snow-melt, can produce early spring. The extremely strong westerlies in 1997 observed in these months over North Atlantic (and also North Pacific) apparently contributed to large surface-temperature anomalies in western Europe, on the order of +3 C above the climatic monthly average for England and France. At these latitudes strong positive anomalies extended in a ring around the globe. We formulated an Index of South westerlies for the North Atlantic, which can serve as an indicator for day-by-day advection effects into Europe. In comparing 1997 and 1998 with the previous years, we establish significant correlations with the temperature anomalies (one to five days later, depending on the region, and on the season). This variability of the ocean-surface winds and of the temperature anomalies on land may be related to the El Nino/La Nina oscillations. Such large temperature fluctuations over large areas, whatever the cause, can be regarded as noise in attempts to assess long-term trends in global temperature.

Version 2 of the IASI NH3 neural network retrieval algorithm: near-real-time and reanalysed datasets

NASA Astrophysics Data System (ADS)

Van Damme, Martin; Whitburn, Simon; Clarisse, Lieven; Clerbaux, Cathy; Hurtmans, Daniel; Coheur, Pierre-François

2017-12-01

Recently, Whitburn et al.(2016) presented a neural-network-based algorithm for retrieving atmospheric ammonia (NH3) columns from Infrared Atmospheric Sounding Interferometer (IASI) satellite observations. In the past year, several improvements have been introduced, and the resulting new baseline version, Artificial Neural Network for IASI (ANNI)-NH3-v2.1, is documented here. One of the main changes to the algorithm is that separate neural networks were trained for land and sea observations, resulting in a better training performance for both groups. By reducing and transforming the input parameter space, performance is now also better for observations associated with favourable sounding conditions (i.e. enhanced thermal contrasts). Other changes relate to the introduction of a bias correction over land and sea and the treatment of the satellite zenith angle. In addition to these algorithmic changes, new recommendations for post-filtering the data and for averaging data in time or space are formulated. We also introduce a second dataset (ANNI-NH3-v2.1R-I) which relies on ERA-Interim ECMWF meteorological input data, along with surface temperature retrieved from a dedicated network, rather than the operationally provided Eumetsat IASI Level 2 (L2) data used for the standard near-real-time version. The need for such a dataset emerged after a series of sharp discontinuities were identified in the NH3 time series, which could be traced back to incremental changes in the IASI L2 algorithms for temperature and clouds. The reanalysed dataset is coherent in time and can therefore be used to study trends. Furthermore, both datasets agree reasonably well in the mean on recent data, after the date when the IASI meteorological L2 version 6 became operational (30 September 2014).

Electron collisions with atoms, ions, molecules, and surfaces: Fundamental science empowering advances in technology

PubMed Central

Bartschat, Klaus; Kushner, Mark J.

2016-01-01

Electron collisions with atoms, ions, molecules, and surfaces are critically important to the understanding and modeling of low-temperature plasmas (LTPs), and so in the development of technologies based on LTPs. Recent progress in obtaining experimental benchmark data and the development of highly sophisticated computational methods is highlighted. With the cesium-based diode-pumped alkali laser and remote plasma etching of Si3N4 as examples, we demonstrate how accurate and comprehensive datasets for electron collisions enable complex modeling of plasma-using technologies that empower our high-technology–based society. PMID:27317740

Interaction potential for indium phosphide: a molecular dynamics and first-principles study of the elastic constants, generalized stacking fault and surface energies.

PubMed

Branicio, Paulo Sergio; Rino, José Pedro; Gan, Chee Kwan; Tsuzuki, Hélio

2009-03-04

Indium phosphide is investigated using molecular dynamics (MD) simulations and density-functional theory calculations. MD simulations use a proposed effective interaction potential for InP fitted to a selected experimental dataset of properties. The potential consists of two- and three-body terms that represent atomic-size effects, charge-charge, charge-dipole and dipole-dipole interactions as well as covalent bond bending and stretching. Predictions are made for the elastic constants as a function of density and temperature, the generalized stacking fault energy and the low-index surface energies.

Moisture transport and Atmospheric circulation in the Arctic

NASA Astrophysics Data System (ADS)

Woods, Cian; Caballero, Rodrigo

2013-04-01

Cyclones are an important feature of the Mid-Latitudes and Arctic Climates. They are a main transporter of warm moist energy from the sub tropics to the poles. The Arctic Winter is dominated by highly stable conditions for most of the season due to a low level temperature inversion caused by a radiation deficit at the surface. This temperature inversion is a ubiquitous feature of the Arctic Winter Climate and can persist for up to weeks at a time. The inversion can be destroyed during the passage of a cyclone advecting moisture and warming the surface. In the absence of an inversion, and in the presence of this warm moist air mass, clouds can form quite readily and as such influence the radiative processes and energy budget of the Arctic. Wind stress caused by a passing cyclones also has the tendency to cause break-up of the ice sheet by induced rotation, deformation and divergence at the surface. For these reasons, we wish to understand the mechanisms of warm moisture advection into the Arctic from lower latitudes and how these mechanisms are controlled. The body of work in this area has been growing and gaining momentum in recent years (Stramler et al. 2011; Morrison et al. 2012; Screen et al. 2011). However, there has been no in depth analysis of the underlying dynamics to date. Improving our understanding of Arctic dynamics becomes increasingly important in the context of climate change. Many models agree that a northward shift of the storm track is likely in the future, which could have large impacts in the Arctic, particularly the sea ice. A climatology of six-day forward and backward trajectories starting from multiple heights around 70 N is constructed using the 22 year ECMWF reanalysis dataset (ERA-INT). The data is 6 hourly with a horizontal resolution of 1 degree on 16 pressure levels. Our methodology here is inspired by previous studies examining flow patterns through cyclones in the mid-latitudes. We apply these earlier mid-latitude methods in the Arctic. We investigate an Arctic trajectory dataset and provide a phenomenological/descriptive analysis of these trajectories, including key meteorological variables carried along trajectories. The trajectory climatology is linked to a previously established cyclone climatology dataset from Hanley and Caballero (2011). We associate trajectories and the meteorological variables they are carrying to cyclones in this dataset. A climatology of 'Arctic-influencing' cyclones is constructed from the cyclone dataset. The resilience of the polar vortex and its effect on circulation, via blocking and breaking, is examined in relation to our trajectory climatology.

Linking the Local Climate Zones and Land Surface Temperature to Investigate the Surface Urban Heat Island, a Case Study of San Antonio, Texas, U.S.

NASA Astrophysics Data System (ADS)

Zhao, Chunhong

2018-04-01

The Local Climate Zones (LCZs) concept was initiated in 2012 to improve the documentation of Urban Heat Island (UHI) observations. Despite the indispensable role and initial aim of LCZs concept in metadata reporting for atmospheric UHI research, its role in surface UHI investigation also needs to be emphasized. This study incorporated LCZs concept to study surface UHI effect for San Antonio, Texas. LCZ map was developed by a GIS-based LCZs classification scheme with the aid of airborne Lidar dataset and other freely available GIS data. Then, the summer LST was calculated based Landsat imagery, which was used to analyse the relations between LST and LCZs and the statistical significance of the differences of LST among the typical LCZs, in order to test if LCZs are able to efficiently facilitate SUHI investigation. The linkage of LCZs and land surface temperature (LST) indicated that the LCZs mapping can be used to compare and investigate the SUHI. Most of the pairs of LCZs illustrated significant differences in average LSTs with considerable significance. The intra-urban temperature comparison among different urban classes contributes to investigate the influence of heterogeneous urban morphology on local climate formation.

The worth of data to reduce predictive uncertainty of an integrated catchment model by multi-constraint calibration

NASA Astrophysics Data System (ADS)

Koch, J.; Jensen, K. H.; Stisen, S.

2017-12-01

Hydrological models that integrate numerical process descriptions across compartments of the water cycle are typically required to undergo thorough model calibration in order to estimate suitable effective model parameters. In this study, we apply a spatially distributed hydrological model code which couples the saturated zone with the unsaturated zone and the energy portioning at the land surface. We conduct a comprehensive multi-constraint model calibration against nine independent observational datasets which reflect both the temporal and the spatial behavior of hydrological response of a 1000km2 large catchment in Denmark. The datasets are obtained from satellite remote sensing and in-situ measurements and cover five keystone hydrological variables: discharge, evapotranspiration, groundwater head, soil moisture and land surface temperature. Results indicate that a balanced optimization can be achieved where errors on objective functions for all nine observational datasets can be reduced simultaneously. The applied calibration framework was tailored with focus on improving the spatial pattern performance; however results suggest that the optimization is still more prone to improve the temporal dimension of model performance. This study features a post-calibration linear uncertainty analysis. This allows quantifying parameter identifiability which is the worth of a specific observational dataset to infer values to model parameters through calibration. Furthermore the ability of an observation to reduce predictive uncertainty is assessed as well. Such findings determine concrete implications on the design of model calibration frameworks and, in more general terms, the acquisition of data in hydrological observatories.

ARM Research in the Equatorial Western Pacific: A Decade and Counting

NASA Technical Reports Server (NTRS)

Long, C. N.; McFarlane, S. A.; DelGenio, A.; Minnis, P.; Ackerman, T. S.; Mather, J.; Comstock, J.; Mace, G. G.; Jensen, M.; Jakob, C.

2013-01-01

The tropical western Pacific (TWP) is an important climatic region. Strong solar heating, warm sea surface temperatures, and the annual progression of the intertropical convergence zone (ITCZ) across this region generate abundant convective systems, which through their effects on the heat and water budgets have a profound impact on global climate and precipitation. In order to accurately evaluate tropical cloud systems in models, measurements of tropical clouds, the environment in which they reside, and their impact on the radiation and water budgets are needed. Because of the remote location, ground-based datasets of cloud, atmosphere, and radiation properties from the TWP region have come primarily from short-term field experiments. While providing extremely useful information on physical processes, these short-term datasets are limited in statistical and climatological information. To provide longterm measurements of the surface radiation budget in the tropics and the atmospheric properties that affect it, the Atmospheric Radiation Measurement program established a measurement site on Manus Island, Papua New Guinea, in 1996 and on the island republic of Nauru in late 1998. These sites provide unique datasets now available for more than 10 years on Manus and Nauru. This article presents examples of the scientific use of these datasets including characterization of cloud properties, analysis of cloud radiative forcing, model studies of tropical clouds and processes, and validation of satellite algorithms. New instrumentation recently installed at the Manus site will provide expanded opportunities for tropical atmospheric science.

Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery

NASA Astrophysics Data System (ADS)

Weng, Qihao; Fu, Peng

2014-11-01

Land surface temperature is a key parameter for monitoring urban heat islands, assessing heat related risks, and estimating building energy consumption. These environmental issues are characterized by high temporal variability. A possible solution from the remote sensing perspective is to utilize geostationary satellites images, for instance, images from Geostationary Operational Environmental System (GOES) and Meteosat Second Generation (MSG). These satellite systems, however, with coarse spatial but high temporal resolution (sub-hourly imagery at 3-10 km resolution), often limit their usage to meteorological forecasting and global climate modeling. Therefore, how to develop efficient and effective methods to disaggregate these coarse resolution images to a proper scale suitable for regional and local studies need be explored. In this study, we propose a least square support vector machine (LSSVM) method to achieve the goal of downscaling of GOES image data to half-hourly 1-km LSTs by fusing it with MODIS data products and Shuttle Radar Topography Mission (SRTM) digital elevation data. The result of downscaling suggests that the proposed method successfully disaggregated GOES images to half-hourly 1-km LSTs with accuracy of approximately 2.5 K when validated against with MODIS LSTs at the same over-passing time. The synthetic LST datasets were further explored for monitoring of surface urban heat island (UHI) in the Los Angeles region by extracting key diurnal temperature cycle (DTC) parameters. It is found that the datasets and DTC derived parameters were more suitable for monitoring of daytime- other than nighttime-UHI. With the downscaled GOES 1-km LSTs, the diurnal temperature variations can well be characterized. An accuracy of about 2.5 K was achieved in terms of the fitted results at both 1 km and 5 km resolutions.

A comparison between general circulation model simulations using two sea surface temperature datasets for January 1979

NASA Technical Reports Server (NTRS)

Ose, Tomoaki; Mechoso, Carlos; Halpern, David

1994-01-01

Simulations with the UCLA atmospheric general circulation model (AGCM) using two different global sea surface temperature (SST) datasets for January 1979 are compared. One of these datasets is based on Comprehensive Ocean-Atmosphere Data Set (COADS) (SSTs) at locations where there are ship reports, and climatology elsewhere; the other is derived from measurements by instruments onboard NOAA satellites. In the former dataset (COADS SST), data are concentrated along shipping routes in the Northern Hemisphere; in the latter dataset High Resolution Infrared Sounder (HIRS SST), data cover the global domain. Ensembles of five 30-day mean fields are obtained from integrations performed in the perpetual-January mode. The results are presented as anomalies, that is, departures of each ensemble mean from that produced in a control simulation with climatological SSTs. Large differences are found between the anomalies obtained using COADS and HIRS SSTs, even in the Northern Hemisphere where the datasets are most similar to each other. The internal variability of the circulation in the control simulation and the simulated atmospheric response to anomalous forcings appear to be linked in that the pattern of geopotential height anomalies obtained using COADS SSTs resembles the first empirical orthogonal function (EOF 1) in the control simulation. The corresponding pattern obtained using HIRS SSTs is substantially different and somewhat resembles EOF 2 in the sector from central North America to central Asia. To gain insight into the reasons for these results, three additional simulations are carried out with SST anomalies confined to regions where COADS SSTs are substantially warmer than HIRS SSTs. The regions correspond to warm pools in the northwest and northeast Pacific, and the northwest Atlantic. These warm pools tend to produce positive geopotential height anomalies in the northeastern part of the corresponding oceans. Both warm pools in the Pacific produce large-scale circulation anomalies with a pattern that resembles that obtained using COADS SSTs as well as EOF 1 of the control simulation; the warm pool in the Atlantic does not. These results suggest that the differences obtained with COADS SSTs and HIRS SSTs are mostly due to the differences in the datasets over the northern Pacific. There was a blocking episode near Greenland in late January 1979. Both simulations with warm SST anomalies over the northwest and northeast Pacific show a tendency toward increased incidence of North Atlantic blocking; the simulation with warm SST anomalies over the northwest Atlantic shows a tendency toward decreased incidence. These results suggest that features in both SST datasets that do not have a counterpart in the other dataset contribute signficantly to the differences between the simulated and observed fields. The results of this study imply that uncertainties in current SST distributions for the world oceans can be as important as the SST anomalies themselves in terms of their impact on the atmospheric circulation. Caution should be exercised, therefore, when linking anomalous circulation and SST patterns, especially in long-range prediction.

Evaluation of Historical and Projected Surface Air Temperature Simulations over China in CMIP5

NASA Astrophysics Data System (ADS)

Chen, L.; Frauenfeld, O. W.

2013-12-01

Projections of future temperature in China are crucial for assessments of climate change and implementation of appropriate adaptation and mitigation strategies. With the upcoming Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5), the fifth phase of the Coupled Model Intercomparison Project (CMIP5) was developed for assessing the latest state-of-the-art climate models and their projections. In this study, monthly surface air temperature from 20 CMIP5 models and four experiments (historical, RCP 2.6, RCP 4.5, and RCP 8.5) were used to investigate the temperature variability over China during the 20th century, and future changes for the 21st century. Two observational datasets (CRU TS 3.1 and the global terrestrial air temperature dataset from the University of Delaware) were adopted to evaluate the performance of the CMIP5 multimodel ensemble average, the performance of individual models, as well as the possible improvements in CMIP5 relative to CMIP3. Results show that both CMIP3 and CMIP5 have cold biases over most parts of China. CMIP5 displays a slightly better agreement with the observations than CMIP3, but substantial cold biases still exist over the Tibetan Plateau, especially in the cold season. These biases are also characterized by the greatest discrepancies among the individual models, indicating the models' limitations over this mountainous region. Both CMIP3 and CMIP5 show poor agreement with observed 20th-century temperature trends such that the spatial and seasonal patterns of the trends are not captured in the multimodel ensemble averages. Comparing individual models we find that MPI-ESM-LR, CanESM2, MIROC-ESM, and CCSM4 exhibit better skill than the other models in this part of the world. Projections of future temperature suggest that there will be a gradual increase in annual surface air temperature in China during the 21st century at a rate of 0.60°C/decade and 0.27°C/decade under the RCP 8.5 and RCP 4.5 scenarios, respectively. RCP 2.6 shows the slowest warming at a rate of 0.10°C/decade for the whole 21st century, but temperature will increase until 2040, and then remain stable or even decrease slightly. Based on the three emission scenarios, annual temperatures are projected to rise by 1.7-5.7°C by the end of the 21st century, and the greatest warming will occur over northern China and the Tibetan Plateau.

Modeling the hydrogeophysical response of lake talik evolution

USGS Publications Warehouse

Minsley, Burke J.; Wellman, Tristan; Walvoord, Michelle Ann; Revil, Andre

2014-01-01

Geophysical methods provide valuable information about subsurface permafrost and its relation to dynamic hydrologic systems. Airborne electromagnetic data from interior Alaska are used to map the distribution of permafrost, geological features, surface water, and groundwater. To validate and gain further insight into these field datasets, we also explore the geophysical response to hydrologic simulations of permafrost evolution by implementing a physical property relationship that connects geology, temperature, and ice saturation to changes in electrical properties.

Crystal cryocooling distorts conformational heterogeneity in a model Michaelis complex of DHFR

PubMed Central

Keedy, Daniel A.; van den Bedem, Henry; Sivak, David A.; Petsko, Gregory A.; Ringe, Dagmar; Wilson, Mark A.; Fraser, James S.

2014-01-01

Summary Most macromolecular X-ray structures are determined from cryocooled crystals, but it is unclear whether cryocooling distorts functionally relevant flexibility. Here we compare independently acquired pairs of high-resolution datasets of a model Michaelis complex of dihydrofolate reductase (DHFR), collected by separate groups at both room and cryogenic temperatures. These datasets allow us to isolate the differences between experimental procedures and between temperatures. Our analyses of multiconformer models and time-averaged ensembles suggest that cryocooling suppresses and otherwise modifies sidechain and mainchain conformational heterogeneity, quenching dynamic contact networks. Despite some idiosyncratic differences, most changes from room temperature to cryogenic temperature are conserved, and likely reflect temperature-dependent solvent remodeling. Both cryogenic datasets point to additional conformations not evident in the corresponding room-temperature datasets, suggesting that cryocooling does not merely trap pre-existing conformational heterogeneity. Our results demonstrate that crystal cryocooling consistently distorts the energy landscape of DHFR, a paragon for understanding functional protein dynamics. PMID:24882744

Building a better search engine for earth science data

NASA Astrophysics Data System (ADS)

Armstrong, E. M.; Yang, C. P.; Moroni, D. F.; McGibbney, L. J.; Jiang, Y.; Huang, T.; Greguska, F. R., III; Li, Y.; Finch, C. J.

2017-12-01

Free text data searching of earth science datasets has been implemented with varying degrees of success and completeness across the spectrum of the 12 NASA earth sciences data centers. At the JPL Physical Oceanography Distributed Active Archive Center (PO.DAAC) the search engine has been developed around the Solr/Lucene platform. Others have chosen other popular enterprise search platforms like Elasticsearch. Regardless, the default implementations of these search engines leveraging factors such as dataset popularity, term frequency and inverse document term frequency do not fully meet the needs of precise relevancy and ranking of earth science search results. For the PO.DAAC, this shortcoming has been identified for several years by its external User Working Group that has assigned several recommendations to improve the relevancy and discoverability of datasets related to remotely sensed sea surface temperature, ocean wind, waves, salinity, height and gravity that comprise a total count of over 500 public availability datasets. Recently, the PO.DAAC has teamed with an effort led by George Mason University to improve the improve the search and relevancy ranking of oceanographic data via a simple search interface and powerful backend services called MUDROD (Mining and Utilizing Dataset Relevancy from Oceanographic Datasets to Improve Data Discovery) funded by the NASA AIST program. MUDROD has mined and utilized the combination of PO.DAAC earth science dataset metadata, usage metrics, and user feedback and search history to objectively extract relevance for improved data discovery and access. In addition to improved dataset relevance and ranking, the MUDROD search engine also returns recommendations to related datasets and related user queries. This presentation will report on use cases that drove the architecture and development, and the success metrics and improvements on search precision and recall that MUDROD has demonstrated over the existing PO.DAAC search interfaces.

Are changes in the phytoplankton community structure altering the flux of CO2 in regions of the North Atlantic?

NASA Astrophysics Data System (ADS)

Ostle, C.; Landschutzer, P.; Johnson, M.; Schuster, U.; Watson, A. J.; Edwards, M.; Robinson, C.

2016-02-01

The North Atlantic Ocean is a globally important sink of carbon dioxide (CO2). However, the strength of the sink varies temporally and regionally. This study uses a neural network method to map the surface ocean pCO2 (partial pressure of CO2) and flux of CO2from the atmosphere to the ocean alongside measurements of plankton abundance collected from the Continuous Plankton Recorder (CPR) survey to determine the relationship between regional changes in phytoplankton community structure and regional differences in carbon flux. Despite increasing sea surface temperatures, the Grand Banks of Newfoundland show a decrease in sea surface pCO2 of -2 µatm yr-1 from 1993 to 2011. The carbon flux in the North Sea is variable over the same period. This is in contrast to most of the open ocean within the North Atlantic, where increases in sea surface pCO2 follow the trend of increasing CO2 in the atmosphere, i.e. the flux or sink remains constant. The increasing CO2 sink in the Grand Banks of Newfoundland and the variable sink in the North Sea correlate with changes in phytoplankton community composition. This study investigates the biogeochemical and oceanographic mechanisms potentially linking increasing sea surface temperature, changes in phytoplankton community structure and the changing carbon sink in these two important regions of the Atlantic Ocean. The use of volunteer ships to concurrently collect these datasets demonstrates the potential to investigate relationships between plankton community structure and carbon flux in a cost-effective way. These results not only have implications for plankton-dynamic biogeochemical models, but also likely influence carbon export, as different phytoplankton communities have different carbon export efficiencies. Extending and maintaining such datasets is critical to improving our understanding of and monitoring carbon cycling in the surface ocean and improving climate model accuracy.

On the Correlation of Specific Film Thickness and Gear Pitting Life

NASA Technical Reports Server (NTRS)

Krantz, Timothy Lewis

2014-01-01

The effect of the lubrication regime on gear performance has been recognized, qualitatively, for decades. Often the lubrication regime is characterized by the specific film thickness defined as the ratio of lubricant film thickness to the composite surface roughness. It can be difficult to combine results of studies to create a cohesive and comprehensive dataset. In this work gear surface fatigue lives for a wide range of specific film values were studied using tests done with common rigs, speeds, lubricant temperatures, and test procedures. This study includes previously reported data, results of an additional 50 tests, and detailed information from lab notes and tested gears. The dataset comprised 258 tests covering specific film values [0.47 to 5.2]. The experimentally determined surface fatigue lives, quantified as 10-percent life estimates, ranged from 8.7 to 86.8 million cycles. The trend is one of increasing life for increasing specific film. The trend is nonlinear. The observed trends were found to be in good agreement with data and recommended practice for gears and bearings. The results obtained will perhaps allow for the specific film parameter to be used with more confidence and precision to assess gear surface fatigue for purpose of design, rating, and technology development.

On the Correlation of Specific Film Thickness and Gear Pitting Life

NASA Technical Reports Server (NTRS)

Krantz, Timothy L.

2015-01-01

The effect of the lubrication regime on gear performance has been recognized, qualitatively, for decades. Often the lubrication regime is characterized by the specific film thickness defined as the ratio of lubricant film thickness to the composite surface roughness. It can be difficult to combine results of studies to create a cohesive and comprehensive dataset. In this work gear surface fatigue lives for a wide range of specific film values were studied using tests done with common rigs, speeds, lubricant temperatures, and test procedures. This study includes previously reported data, results of an additional 50 tests, and detailed information from lab notes and tested gears. The dataset comprised 258 tests covering specific film values (0.47 to 5.2). The experimentally determined surface fatigue lives, quantified as 10-percent life estimates, ranged from 8.7 to 86.8 million cycles. The trend is one of increasing life for increasing specific film. The trend is nonlinear. The observed trends were found to be in good agreement with data and recommended practice for gears and bearings. The results obtained will perhaps allow for the specific film parameter to be used with more confidence and precision to assess gear surface fatigue for purpose of design, rating, and technology development.

Oceanic influence on seasonal malaria outbreaks over Senegal and Sahel

NASA Astrophysics Data System (ADS)

Diouf, Ibrahima; Rodríguez de Fonseca, Belen; Deme, Abdoulaye; Cisse Cisse, Moustapha; Ndione Ndione, Jaques-Andre; Gaye, Amadou T.; Suarez, Roberto

2015-04-01

Beyond assessment and analysis of observed and simulated malaria parameters, this study is furthermore undertaken in the framework of predictability of malaria outbreaks in Senegal and remote regions in Sahel, which are found to take place two months after the rainy season. The predictors are the sea surface temperature anomalous patterns at different ocean basins mainly over the Pacific and Atlantic as they are related to changes in air temperature, humidity, rainfall and wind. A relationship between El Niño and anomalous malaria parameters is found. The malaria parameters are calculated with the Liverpool Malaria Model (LMM) using meteorological datasets from different reanalysis products. A hindcast of these parameters is performed using the Sea Surface temperature based Statistical Seasonal ForeCAST (S4CAST) model developed at UCM in order to predict malaria parameters some months in advance. The results of this work will be useful for decision makers to better access to climate forecasts and application on malaria transmission risk.

Atlas of the Earth's radiation budget as measured by Nimbus-7: May 1979 to May 1980

NASA Technical Reports Server (NTRS)

Kyle, H. Lee; Hucek, Richard R.; Vallette, Brenda J.

1991-01-01

This atlas describes the seasonal changes in the Earth's radiation budget for the 13-month period, May 1979 to May 1980. It helps to illustrate the strong feedback mechanisms by which the Earth's climate interacts with the top-of-the-atmosphere insolation to modify the energy that various regions absorb from the Sun. Cloud type and cloud amount, which are linked to the surface temperature and the regional climate, are key elements in this interaction. Annual, seasonal, and monthly maps of the albedo, outgoing longwave and net radiation, noontime cloud cover, and mean diurnal surface temperatures are presented. Annual and seasonal net cloud forcing maps are also given. All of the quantities were derived from Nimbus-7 satellite measurements except for the temperatures, which were used in the cloud detection algorithm and came originally from the Air Force 3-dimensional nephanalysis dataset. The seasonal changes are described. The interaction of clouds and the radiation budget is briefly discussed.

Determination of optimum viewing angles for the angular normalization of land surface temperature over vegetated surface.

PubMed

Ren, Huazhong; Yan, Guangjian; Liu, Rongyuan; Li, Zhao-Liang; Qin, Qiming; Nerry, Françoise; Liu, Qiang

2015-03-27

Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to perform such normalizations on LST. The normally kernel-driven bi-directional reflectance distribution function (BRDF) is first extended to the thermal infrared (TIR) domain as TIR-BRDF model, and its uncertainty is shown to be less than 0.3 K when used to fit the hemispheric directional thermal radiation. A local optimum three-angle combination is found and verified using the TIR-BRDF model based on two patterns: the single-point pattern and the linear-array pattern. The TIR-BRDF is applied to an airborne multi-angular dataset to retrieve LST at nadir (Te-nadir) from different viewing directions, and the results show that this model can obtain reliable Te-nadir from 3 to 4 directional observations with large angle intervals, thus corresponding to large temperature angular variations. The Te-nadir is generally larger than temperature of the slant direction, with a difference of approximately 0.5~2.0 K for vegetated pixels and up to several Kelvins for non-vegetated pixels. The findings of this paper will facilitate the future development of multi-angular thermal infrared sensors.

Determination of Optimum Viewing Angles for the Angular Normalization of Land Surface Temperature over Vegetated Surface

PubMed Central

Ren, Huazhong; Yan, Guangjian; Liu, Rongyuan; Li, Zhao-Liang; Qin, Qiming; Nerry, Françoise; Liu, Qiang

2015-01-01

Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to perform such normalizations on LST. The normally kernel-driven bi-directional reflectance distribution function (BRDF) is first extended to the thermal infrared (TIR) domain as TIR-BRDF model, and its uncertainty is shown to be less than 0.3 K when used to fit the hemispheric directional thermal radiation. A local optimum three-angle combination is found and verified using the TIR-BRDF model based on two patterns: the single-point pattern and the linear-array pattern. The TIR-BRDF is applied to an airborne multi-angular dataset to retrieve LST at nadir (Te-nadir) from different viewing directions, and the results show that this model can obtain reliable Te-nadir from 3 to 4 directional observations with large angle intervals, thus corresponding to large temperature angular variations. The Te-nadir is generally larger than temperature of the slant direction, with a difference of approximately 0.5~2.0 K for vegetated pixels and up to several Kelvins for non-vegetated pixels. The findings of this paper will facilitate the future development of multi-angular thermal infrared sensors. PMID:25825975

The Extremely Warm Early Winter 2000 in Europe: What is the Forcing

NASA Technical Reports Server (NTRS)

Otterman, J.; Angell, J. K.; Atlas, R.; Ardizzone, J.; Demaree, G.; Jusem, J. C.; Koslowsky, D.; Terry, J.; Einaudi, Franco (Technical Monitor)

2001-01-01

High variability characterizes the winter climate of central Europe: interannual fluctuations in the surface-air temperature as large as 18 C over large areas are fairly common. The extraordinary early-winter 2000 in Europe appears to be a departure to an unprecedented extreme of the existing climate patterns. Such anomalous events affect agriculture, forestry, fuel consumption, etc., and thus deserve in-depth analysis. Our analysis indicates that the high anomalies of the surface-air temperature are predominantly due to the southwesterly flow from the eastern North Atlantic, with a weak contribution by southerly flow from the western Mediterranean. Backward trajectories based on the SSM/I and NCEP Reanalysis datasets traced from west-central Europe indicate that the warm air masses flowing into Europe originate in the southern North Atlantic, where the surface-air temperatures exceed by 15c or more the climatic norms in Europe for late-November or early-December. Because such large ocean-to-continent temperature differences characterize the winter conditions, we refer to this episode which started in late November as occurring in the early winter. In this season, with the sun low over the horizon in Europe, absorption of insolation by the surface has little significance. The effect of cloudiness, a corollary to the low-level maritime-air advection, is a warming by a reduction of heat loss (greenhouse effect). In contrast, in the summer, clouds, by reducing absorption of insolation, produce a cooling, effect at the surface.

Study of variability of permittivity and its mapping over lunar surface and subsurface using multisensors datasets

NASA Astrophysics Data System (ADS)

Calla, O. P. N.; Mathur, Shubhra; Gadri, Kishan Lal; Jangid, Monika

2016-12-01

In the present paper, permittivity maps of equatorial lunar surface are generated using brightness temperature (TB) data obtained from Microwave Radiometer (MRM) of Chang'e-1 and physical temperature (TP) data obtained from Diviner of Lunar Reconnaissance Orbiter (LRO). Here, permittivity mapping is not carried out above 60° latitudes towards the lunar poles due to large anomaly in the physical temperature obtained from the Diviner. Microwave frequencies, which are used to generate these maps are 3 GHz, 7.8 GHz, 19.35 GHz and 37 GHz. Permittivity values are simulated using TB values at these four frequencies. Here, weighted average of physical temperature obtained from Diviner are used to compute permittivity at each microwave frequencies. Longer wavelengths of microwave signals give information of more deeper layers of the lunar surface as compared to smaller wavelength. Initially, microwave emissivity is estimated using TB values from MRM and physical temperature (TP) from Diviner. From estimated emissivity the real part of permittivity (ε), is calculated using Fresnel equations. The permittivity maps of equatorial lunar surface is generated. The simulated permittivity values are normalized with respect to density for easy comparison of simulated permittivity values with the permittivity values of Apollo samples as well as with the permittivity values of Terrestrial Analogue of Lunar Soil (TALS) JSC-1A. Lower value of dielectric constant (ε‧) indicates that the corresponding lunar surface is smooth and doesn't have rough rocky terrain. Thus a future lunar astronaut can use these data to decide proper landing site for future lunar missions. The results of this paper will serve as input to future exploration of lunar surface.

Ikh Turgen Mountain Glacier Change and 3d Surface Extents Prediction Using Long Term Landsat Image and Climate Data

NASA Astrophysics Data System (ADS)

Nasanbat, Elbegjargal; Erdenebat, Erdenetogtokh; Chogsom, Bolorchuluun; Lkhamjav, Ochirkhuyag; Nanzad, Lkhagvadorj

2018-04-01

The glacier is most important the freshwater resources and indicator of the climate change. The researchers noted that during last decades the glacier is melting due to global warming. The study calculates a spatial distribution of protentional change of glacier coverage in the Ikh Turgen mountain of Western Mongolia, and it integrates long-term climate data and satellite datasets. Therefore, in this experiment has tried to estimation three-dimensional surface area of the glacier. For this purpose, Normalized difference snow index (NDSI) was applied to decision tree approach, using Landsat MSS, TM, ETM+ and LC8 imagery for 1975-2016, a surface and slope for digital elevation model, precipitation and air temperature historical data of meteorological station. The potential volume area significantly changed glacier cover of the Ikh Turgen Mountain, and the area affected by highly variable precipitation and air temperature regimes. Between 1972 and 2016, a potential area of glacier area has been decreased in Ikh Turgen mountain region.

Thermodynamic Data Rescue and Informatics for Deep Carbon Science

NASA Astrophysics Data System (ADS)

Zhong, H.; Ma, X.; Prabhu, A.; Eleish, A.; Pan, F.; Parsons, M. A.; Ghiorso, M. S.; West, P.; Zednik, S.; Erickson, J. S.; Chen, Y.; Wang, H.; Fox, P. A.

2017-12-01

A large number of legacy datasets are contained in geoscience literature published between 1930 and 1980 and not expressed external to the publication text in digitized formats. Extracting, organizing, and reusing these "dark" datasets is highly valuable for many within the Earth and planetary science community. As a part of the Deep Carbon Observatory (DCO) data legacy missions, the DCO Data Science Team and Extreme Physics and Chemistry community identified thermodynamic datasets related to carbon, or more specifically datasets about the enthalpy and entropy of chemicals, as a proof of principle analysis. The data science team endeavored to develop a semi-automatic workflow, which includes identifying relevant publications, extracting contained datasets using OCR methods, collaborative reviewing, and registering the datasets via the DCO Data Portal where the 'Linked Data' feature of the data portal provides a mechanism for connecting rescued datasets beyond their individual data sources, to research domains, DCO Communities, and more, making data discovery and retrieval more effective.To date, the team has successfully rescued, deposited and registered additional datasets from publications with thermodynamic sources. These datasets contain 3 main types of data: (1) heat content or enthalpy data determined for a given compound as a function of temperature using high-temperature calorimetry, (2) heat content or enthalpy data determined for a given compound as a function of temperature using adiabatic calorimetry, and (3) direct determination of heat capacity of a compound as a function of temperature using differential scanning calorimetry. The data science team integrated these datasets and delivered a spectrum of data analytics including visualizations, which will lead to a comprehensive characterization of the thermodynamics of carbon and carbon-related materials.

A Climate-Data Record of the "Clear-Sky" Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, D. K.; Comiso, J. C.; Digirolamo, N. E.; Stock, L. V.; Riggs, G. A.; Shuman, C. A.

2009-01-01

We are developing a climate-data record (CDR of daily "clear-sky" ice-surface temperature (IST) of the Greenland Ice Sheet, from 1982 to the present using Advanced Very High Resolution Radiometer (AVHRR) (1982 - present) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data (2000 - present) at a resolution of approximately 5 km. The CDR will be continued in the National Polar-orbiting Operational Environmental Satellite System Visible/Infrared Imager Radiometer Suite era. Two algorithms remain under consideration. One algorithm under consideration is based on the split-window technique used in the Polar Pathfinder dataset (Fowler et al., 2000 & 21007). Another algorithm under consideration, developed by Comiso (2006), uses a single channel of AVHRR data (channel 4) in conjunction with meteorological-station data to account for atmospheric effects and drift between AVHRR instruments. Known issues being addressed in the production of the CDR are: tune-series bias caused by cloud cover (surface temperatures can be different under clouds vs. clear areas) and cross-calibration in the overlap period between AVHRR instruments, and between AVHRR and MODIS instruments. Because of uncertainties, mainly due to clouds (Stroeve & Steffen, 1998; Wang and Key, 2005; Hall et al., 2008 and Koenig and Hall, submitted), time-series of satellite 1S'1" do not necessarily correspond to actual surface temperatures. The CDR will be validated by comparing results with automatic-,",eather station (AWS) data and with satellite-derived surface-temperature products. Regional "clear-sky" surface temperature increases in the Arctic, measured from AVHRR infrared data, range from 0.57+/-0.02 deg C (Wang and Key, 2005) to 0.72+/-0.10 deg C (Comiso, 2006) per decade since the early 1980s. Arctic warming has important implications for ice-sheet mass balance because much of the periphery of the Greenland Ice Sheet is already near 0 deg C during the melt season, and is thus vulnerable to rapid melting if temperatures continue to increase. References

Titan's Surface Temperatures Maps from Cassini - CIRS Observations

NASA Astrophysics Data System (ADS)

Cottini, Valeria; Nixon, C. A.; Jennings, D. E.; Anderson, C. M.; Samuelson, R. E.; Irwin, P. G. J.; Flasar, F. M.

2009-09-01

The Cassini Composite Infrared Spectrometer (CIRS) observations of Saturn's largest moon, Titan, are providing us with the ability to detect the surface temperature of the planet by studying its outgoing radiance through a spectral window in the thermal infrared at 19 μm (530 cm-1) characterized by low opacity. Since the first acquisitions of CIRS Titan data the instrument has gathered a large amount of spectra covering a wide range of latitudes, longitudes and local times. We retrieve the surface temperature and the atmospheric temperature profile by modeling proper zonally averaged spectra of nadir observations with radiative transfer computations. Our forward model uses the correlated-k approximation for spectral opacity to calculate the emitted radiance, including contributions from collision induced pairs of CH4, N2 and H2, haze, and gaseous emission lines (Irwin et al. 2008). The retrieval method uses a non-linear least-squares optimal estimation technique to iteratively adjust the model parameters to achieve a spectral fit (Rodgers 2000). We show an accurate selection of the wide amount of data available in terms of footprint diameter on the planet and observational conditions, together with the retrieved results. Our results represent formal retrievals of surface brightness temperatures from the Cassini CIRS dataset using a full radiative transfer treatment, and we compare to the earlier findings of Jennings et al. (2009). In future, application of our methodology over wide areas should greatly increase the planet coverage and accuracy of our knowledge of Titan's surface brightness temperature. References: Irwin, P.G.J., et al.: "The NEMESIS planetary atmosphere radiative transfer and retrieval tool" (2008). JQSRT, Vol. 109, pp. 1136-1150, 2008. Rodgers, C. D.: "Inverse Methods For Atmospheric Sounding: Theory and Practice". World Scientific, Singapore, 2000. Jennings, D.E., et al.: "Titan's Surface Brightness Temperatures." Ap. J. L., Vol. 691, pp. L103-L105, 2009.

An automated quasi-continuous capillary refill timing device

PubMed Central

Blaxter, L L; Morris, D E; Crowe, J A; Henry, C; Hill, S; Sharkey, D; Vyas, H; Hayes-Gill, B R

2016-01-01

Capillary refill time (CRT) is a simple means of cardiovascular assessment which is widely used in clinical care. Currently, CRT is measured through manual assessment of the time taken for skin tone to return to normal colour following blanching of the skin surface. There is evidence to suggest that manually assessed CRT is subject to bias from ambient light conditions, a lack of standardisation of both blanching time and manually applied pressure, subjectiveness of return to normal colour, and variability in the manual assessment of time. We present a novel automated system for CRT measurement, incorporating three components: a non-invasive adhesive sensor incorporating a pneumatic actuator, a diffuse multi-wavelength reflectance measurement device, and a temperature sensor; a battery operated datalogger unit containing a self contained pneumatic supply; and PC based data analysis software for the extraction of refill time, patient skin surface temperature, and sensor signal quality. Through standardisation of the test, it is hoped that some of the shortcomings of manual CRT can be overcome. In addition, an automated system will facilitate easier integration of CRT into electronic record keeping and clinical monitoring or scoring systems, as well as reducing demands on clinicians. Summary analysis of volunteer (n = 30) automated CRT datasets are presented, from 15 healthy adults and 15 healthy children (aged from 5 to 15 years), as their arms were cooled from ambient temperature to 5°C. A more detailed analysis of two typical datasets is also presented, demonstrating that the response of automated CRT to cooling matches that of previously published studies. PMID:26642080

Role of Internal Variability in Surface Temperature and Precipitation Change Uncertainties over India.

NASA Astrophysics Data System (ADS)

Achutarao, K. M.; Singh, R.

2017-12-01

There are various sources of uncertainty in model projections of future climate change. These include differences in the formulation of climate models, internal variability, and differences in scenarios. Internal variability in a climate system represents the unforced change due to the chaotic nature of the climate system and is considered irreducible (Deser et al., 2012). Internal variability becomes important at regional scales where it can dominate forced changes. Therefore it needs to be carefully assessed in future projections. In this study we segregate the role of internal variability in the future temperature and precipitation projections over the Indian region. We make use of the Coupled Model Inter-comparison Project - phase 5 (CMIP5; Taylor et al., 2012) database containing climate model simulations carried out by various modeling centers around the world. While the CMIP5 experimental protocol recommended producing numerous ensemble members, only a handful of the modeling groups provided multiple realizations. Having a small number of realizations is a limitation in producing a quantification of internal variability. We therefore exploit the Community Earth System Model Large Ensemble (CESM-LE; Kay et al., 2014) dataset which contains a 40 member ensemble of a single model- CESM1 (CAM5) to explore the role of internal variability in Future Projections. Surface air temperature and precipitation change projections over regional and sub-regional scale are analyzed under the IPCC emission scenario (RCP8.5) for different seasons and homogeneous climatic zones over India. We analyze the spread in projections due to internal variability in the CESM-LE and CMIP5 datasets over these regions.

Co-variation of Temperature and Precipitation in CMIP5 Models and Satellite Observations

NASA Technical Reports Server (NTRS)

Liu, Chunlei; Allan, Richard P.; Huffman, George J.

2013-01-01

Current variability of precipitation (P) and its response to surface temperature (T) are analysed using coupled (CMIP5) and atmosphere-only (AMIP5) climate model simulations and compared with observational estimates.There is striking agreement between Global Precipitation Climatology Project (GPCP) observed and AMIP5)simulated P anomalies over land both globally and in the tropics suggesting that prescribed sea surface temperature and realistic radiative forcings are sufficient for simulating the interannual variability in continental P. Differences between the observed and simulated P variability over the ocean, originate primarily from the wet tropical regions, in particular the western Pacific, but are reduced slightly after 1995. All datasets show positive responses of P to T globally of around 2 % K for simulations and 3-4 % K in GPCP observations but model responses over the tropical oceans are around 3 times smaller than GPCP over the period 1988-2005. The observed anticorrelation between land and ocean P, linked with El Nio Southern Oscillation, is captured by the simulations. All data sets over the tropical ocean show a tendency for wet regions to become wetter and dry regions drier with warming. Over the wet region (greater than or equal to 75 precipitation percentile), the precipitation response is 13-15%K for GPCP and 5%K for models while trends in P are 2.4% decade for GPCP, 0.6% decade for CMIP5 and 0.9decade for AMIP5 suggesting that models are underestimating the precipitation responses or a deficiency exists in the satellite datasets.

Publishing high-quality climate data on the semantic web

NASA Astrophysics Data System (ADS)

Woolf, Andrew; Haller, Armin; Lefort, Laurent; Taylor, Kerry

2013-04-01

The effort over more than a decade to establish the semantic web [Berners-Lee et. al., 2001] has received a major boost in recent years through the Open Government movement. Governments around the world are seeking technical solutions to enable more open and transparent access to Public Sector Information (PSI) they hold. Existing technical protocols and data standards tend to be domain specific, and so limit the ability to publish and integrate data across domains (health, environment, statistics, education, etc.). The web provides a domain-neutral platform for information publishing, and has proven itself beyond expectations for publishing and linking human-readable electronic documents. Extending the web pattern to data (often called Web 3.0) offers enormous potential. The semantic web applies the basic web principles to data [Berners-Lee, 2006]: using URIs as identifiers (for data objects and real-world 'things', instead of documents) making the URIs actionable by providing useful information via HTTP using a common exchange standard (serialised RDF for data instead of HTML for documents) establishing typed links between information objects to enable linking and integration Leading examples of 'linked data' for publishing PSI may be found in both the UK (http://data.gov.uk/linked-data) and US (http://www.data.gov/page/semantic-web). The Bureau of Meteorology (BoM) is Australia's national meteorological agency, and has a new mandate to establish a national environmental information infrastructure (under the National Plan for Environmental Information, NPEI [BoM, 2012a]). While the initial approach is based on the existing best practice Spatial Data Infrastructure (SDI) architecture, linked-data is being explored as a technological alternative that shows great promise for the future. We report here the first trial of government linked-data in Australia under data.gov.au. In this initial pilot study, we have taken BoM's new high-quality reference surface temperature dataset, Australian Climate Observations Reference Network - Surface Air Temperature (ACORN-SAT) [BoM, 2012b]. This dataset contains daily homogenised surface temperature observations for 112 locations around Australia, dating back to 1910. An ontology for the dataset was developed [Lefort et. al., 2012], based on the existing Semantic Sensor Network ontology [Compton et. al., 2012] and the W3C RDF Data Cube vocabulary [W3C, 2012]. Additional vocabularies were developed, e.g. for BoM weather stations and rainfall districts. The dataset was converted to RDF and loaded into an RDF triplestore. The Linked-Data API (http://code.google.com/p/linked-data-api) was used to configure specific URI query patterns (e.g. for observation timeseries slices by station), and a SPARQL endpoint was provided for direct querying. In addition, some demonstration 'mash-ups' were developed, providing an interactive browser-based interface to the temperature timeseries. References [Berners-Lee et. al., 2001] Tim Berners-Lee, James Hendler and Ora Lassila (2001), "The Semantic Web", Scientific American, May 2001. [Berners-Lee, 2006] Tim Berners-Lee (2006), "Linked Data - Design Issues", W3C [http://www.w3.org/DesignIssues/LinkedData.html] [BoM, 2012a] Bureau of Meteorology (2012), "Environmental information" [http://www.bom.gov.au/environment/] [BoM, 2012b] Bureau of Meteorology (2012), "Australian Climate Observations Reference Network - Surface Air Temperature" [http://www.bom.gov.au/climate/change/acorn-sat/] [Compton et. al., 2012] Michael Compton, Payam Barnaghi, Luis Bermudez, Raul Garcia-Castro, Oscar Corcho, Simon Cox, John Graybeal, Manfred Hauswirth, Cory Henson, Arthur Herzog, Vincent Huang, Krzysztof Janowicz, W. David Kelsey, Danh Le Phuoc, Laurent Lefort, Myriam Leggieri, Holger Neuhaus, Andriy Nikolov, Kevin Page, Alexandre Passant, Amit Sheth, Kerry Taylor (2012), "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", J. Web Semantics, 17 (2012) [http://dx.doi.org/10.1016/j.websem.2012.05.003] [Lefort et. al., 2012] Laurent Lefort, Josh Bobruk, Armin Haller, Kerry Taylor and Andrew Woolf (2012), "A Linked Sensor Data Cube for a 100 Year Homogenised daily temperature dataset", Proc. Semantic Sensor Networks 2012 [http://ceur-ws.org/Vol-904/paper10.pdf] [W3C, 2012] W3C (2012), "The RDF Data Cube Vocabulary", [http://www.w3.org/TR/vocab-data-cube/

Application of a Global-to-Beam Irradiance Model to the NASA GEWEX SRB Dataset: An Extension of the NASA Surface Meteorology and Solar Energy Datasets

NASA Technical Reports Server (NTRS)

Zhang, Taiping; Stackhouse, Paul W., Jr.; Chandler, William S.; Westberg, David J.

2014-01-01

The DIRINDEX model was designed to estimate hourly solar beam irradiances from hourly global horizontal irradiances. This model was applied to the NASA GEWEX SRB(Rel. 3.0) 3-hourly global horizontal irradiance data to derive3-hourly global maps of beam, or direct normal, irradiance for the period from January 2000 to December 2005 at the 1 deg. x 1 deg. resolution. The DIRINDEX model is a combination of the DIRINT model, a quasi-physical global-to-beam irradiance model based on regression of hourly observed data, and a broadband simplified version of the SOLIS clear-sky beam irradiance model. In this study, the input variables of the DIRINDEX model are 3-hourly global horizontal irradiance, solar zenith angle, dew-point temperature, surface elevation, surface pressure, sea-level pressure, aerosol optical depth at 700 nm, and column water vapor. The resulting values of the 3-hourly direct normal irradiance are then used to compute daily and monthly means. The results are validated against the ground-based BSRN data. The monthly means show better agreement with the BSRN data than the results from an earlier endeavor which empirically derived the monthly mean direct normal irradiance from the GEWEX SRB monthly mean global horizontal irradiance. To assimilate the observed information into the final results, the direct normal fluxes from the DIRINDEX model are adjusted according to the comparison statistics in the latitude-longitude-cosine of solar zenith angle phase space, in which the inverse-distance interpolation is used for the adjustment. Since the NASA Surface meteorology and Solar Energy derives its data from the GEWEX SRB datasets, the results discussed herein will serve to extend the former.

Linking Satellite Derived Land Surface Temperature with Cholera: A Case Study for South Sudan

NASA Astrophysics Data System (ADS)

Aldaach, H. S. V.; Jutla, A.; Akanda, A. S.; Colwell, R. R.

2014-12-01

A sudden onset of cholera in South Sudan, in April 2014 in Northern Bari in Juba town resulted in more than 400 cholera cases after four weeks of initial outbreak with a case of fatality rate of CFR 5.4%. The total number of reported cholera cases for the period of April to July, 2014 were 5,141 including 114 deaths. With the limited efficacy of cholera vaccines, it is necessary to develop mechanisms to predict cholera occurrence and thereafter devise intervention strategies for mitigating impacts of the disease. Hydroclimatic processes, primarily precipitation and air temperature are related to epidemic and episodic outbreak of cholera. However, due to coarse resolution of both datasets, it is not possible to precisely locate the geographical location of disease. Here, using Land Surface Temperature (LST) from MODIS sensors, we have developed an algorithm to identify regions susceptible for cholera. Conditions for occurrence of cholera were detectable at least one month in advance in South Sudan and were statistically sensitive to hydroclimatic anomalies of land surface and air temperature, and precipitation. Our results indicate significant spatial and temporal averaging required to infer usable information from LST over South Sudan. Preliminary results that geographically location of cholera outbreak was identifiable within 1km resolution of the LST data.

Dataset of working conditions and thermo-economic performances for hybrid organic Rankine plants fed by solar and low-grade energy sources.

PubMed

Scardigno, Domenico; Fanelli, Emanuele; Viggiano, Annarita; Braccio, Giacobbe; Magi, Vinicio

2016-06-01

This article provides the dataset of operating conditions of a hybrid organic Rankine plant generated by the optimization procedure employed in the research article "A genetic optimization of a hybrid organic Rankine plant for solar and low-grade energy sources" (Scardigno et al., 2015) [1]. The methodology used to obtain the data is described. The operating conditions are subdivided into two separate groups: feasible and unfeasible solutions. In both groups, the values of the design variables are given. Besides, the subset of feasible solutions is described in details, by providing the thermodynamic and economic performances, the temperatures at some characteristic sections of the thermodynamic cycle, the net power, the absorbed powers and the area of the heat exchange surfaces.

Effects of pressure on morphology and structure of bio-char from pressurized entrained-flow pyrolysis of microalgae.

PubMed

Maliutina, Kristina; Tahmasebi, Arash; Yu, Jianglong

2018-06-01

The present dataset describes the entrained-flow pyrolysis of Microalgae Chlorella vulgaris and the results obtained during bio-char characterization. The dataset includes a brief explanation of the experimental procedure, experimental conditions and the influence of pyrolysis conditions on bio-chars morphology and carbon structure. The data show an increase in sphericity and surface smoothness of bio-chars at higher pressures and temperatures. Data confirmed that the swelling ratio of bio-chars increased with pressure up to 2.0 MPa. Consequently, changes in carbon structure of bio-chars were investigated using Raman spectroscopy. The data showed the increase in carbon order of chars at elevated pressures. Changes in the chemical structure of bio-char as a function of pyrolysis conditions were investigated using FTIR analysis.

The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William L.; Khan, Maudood N.

2006-01-01

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

Influence of Sub-grid-Scale Isentropic Transports on McRAS Evaluations using ARM-CART SCM Datasets

NASA Technical Reports Server (NTRS)

Sud, Y. C.; Walker, G. K.; Tao, W. K.

2004-01-01

In GCM-physics evaluations with the currently available ARM-CART SCM datasets, McRAS produced very similar character of near surface errors of simulated temperature and humidity containing typically warm and moist biases near the surface and cold and dry biases aloft. We argued it must have a common cause presumably rooted in the model physics. Lack of vertical adjustment of horizontal transport was thought to be a plausible source. Clearly, debarring such a freedom would force the incoming air to diffuse into the grid-cell which would naturally bias the surface air to become warm and moist while the upper air becomes cold and dry, a characteristic feature of McRAS biases. Since, the errors were significantly larger in the two winter cases that contain potentially more intense episodes of cold and warm advective transports, it further reaffirmed our argument and provided additional motivation to introduce the corrections. When the horizontal advective transports were suitably modified to allow rising and/or sinking following isentropic pathways of subgrid scale motions, the outcome was to cool and dry (or warm and moisten) the lower (or upper) levels. Ever, crude approximations invoking such a correction reduced the temperature and humidity biases considerably. The tests were performed on all the available ARM-CART SCM cases with consistent outcome. With the isentropic corrections implemented through two different numerical approximations, virtually similar benefits were derived further confirming the robustness of our inferences. These results suggest the need for insentropic advective transport adjustment in a GCM due to subgrid scale motions.

Brown CA et al 2016 Dataset

EPA Pesticide Factsheets

This dataset contains the research described in the following publication:Brown, C.A., D. Sharp, and T. Mochon Collura. 2016. Effect of Climate Change on Water Temperature and Attainment of Water Temperature Criteria in the Yaquina Estuary, Oregon (USA). Estuarine, Coastal and Shelf Science. 169:136-146, doi: 10.1016/j.ecss.2015.11.006.This dataset is associated with the following publication:Brown , C., D. Sharp, and T. MochonCollura. Effect of Climate Change on Water Temperature and Attainment of Water Temperature Criteria in the Yaquina Estuary, Oregon (USA). ESTUARINE, COASTAL AND SHELF SCIENCE. Elsevier Science Ltd, New York, NY, USA, 169: 136-146, (2016).

Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

NASA Astrophysics Data System (ADS)

Gelati, Emiliano; Decharme, Bertrand; Calvet, Jean-Christophe; Minvielle, Marie; Polcher, Jan; Fairbairn, David; Weedon, Graham P.

2018-04-01

Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO2-responsive ISBA-A-gs LSM coupled with the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25-75.5° N, 11.5° W-62.5° E, at 0.5° resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them are based on the ERA-Interim reanalysis (ERA-I). The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature and SW and LW radiation. At the monthly timescale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early-autumn LAI. Forcing uncertainty impacts on simulated river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter-spring high flows and underestimate summer-autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely explained by forcing uncertainty, we suggest several research directions. These range from further investigating the discrepancies between LSMs and remote sensing retrievals to developing new model components to represent physical and anthropogenic processes.

Hot mill process parameters impacting on hot mill tertiary scale formation

NASA Astrophysics Data System (ADS)

Kennedy, Jonathan Ian

For high end steel applications surface quality is paramount to deliver a suitable product. A major cause of surface quality issues is from the formation of tertiary scale. The scale formation depends on numerous factors such as thermo-mechanical processing routes, chemical composition, thickness and rolls used. This thesis utilises a collection of data mining techniques to better understand the influence of Hot Mill process parameters on scale formation at Port Talbot Hot Strip Mill in South Wales. The dataset to which these data mining techniques were applied was carefully chosen to reduce process variation. There are several main factors that were considered to minimise this variability including time period, grade and gauge investigated. The following data mining techniques were chosen to investigate this dataset: Partial Least Squares (PLS); Logit Analysis; Principle Component Analysis (PCA); Multinomial Logistical Regression (MLR); Adaptive Neuro Inference Fuzzy Systems (ANFIS). The analysis indicated that the most significant variable for scale formation is the temperature entering the finishing mill. If the temperature is controlled on entering the finishing mill scale will not be formed. Values greater than 1070 °C for the average Roughing Mill and above 1050 °C for the average Crop Shear temperature are considered high, with values greater than this increasing the chance of scale formation. As the temperature increases more scale suppression measures are required to limit scale formation, with high temperatures more likely to generate a greater amount of scale even with fully functional scale suppression systems in place. Chemistry is also a significant factor in scale formation, with Phosphorus being the most significant of the chemistry variables. It is recommended that the chemistry specification for Phosphorus be limited to a maximum value of 0.015 % rather than 0.020 % to limit scale formation. Slabs with higher values should be treated with particular care when being processed through the Hot Mill to limit scale formation.

Numerical analysis of one-dimensional temperature data for groundwater/surface-water exchange with 1DTempPro

NASA Astrophysics Data System (ADS)

Voytek, E. B.; Drenkelfuss, A.; Day-Lewis, F. D.; Healy, R. W.; Lane, J. W.; Werkema, D. D.

2012-12-01

Temperature is a naturally occurring tracer, which can be exploited to infer the movement of water through the vadose and saturated zones, as well as the exchange of water between aquifers and surface-water bodies, such as estuaries, lakes, and streams. One-dimensional (1D) vertical temperature profiles commonly show thermal amplitude attenuation and increasing phase lag of diurnal or seasonal temperature variations with propagation into the subsurface. This behavior is described by the heat-transport equation (i.e., the convection-conduction-dispersion equation), which can be solved analytically in 1D under certain simplifying assumptions (e.g., sinusoidal or steady-state boundary conditions and homogeneous hydraulic and thermal properties). Analysis of 1D temperature profiles using analytical models provides estimates of vertical groundwater/surface-water exchange. The utility of these estimates can be diminished when the model assumptions are violated, as is common in field applications. Alternatively, analysis of 1D temperature profiles using numerical models allows for consideration of more complex and realistic boundary conditions. However, such analyses commonly require model calibration and the development of input files for finite-difference or finite-element codes. To address the calibration and input file requirements, a new computer program, 1DTempPro, is presented that facilitates numerical analysis of vertical 1D temperature profiles. 1DTempPro is a graphical user interface (GUI) to the USGS code VS2DH, which numerically solves the flow- and heat-transport equations. Pre- and post-processor features within 1DTempPro allow the user to calibrate VS2DH models to estimate groundwater/surface-water exchange and hydraulic conductivity in cases where hydraulic head is known. This approach improves groundwater/ surface-water exchange-rate estimates for real-world data with complexities ill-suited for examination with analytical methods. Additionally, the code allows for time-varying temperature and hydraulic boundary conditions. Here, we present the approach and include examples for several datasets from stream/aquifer systems.

ARM Research in the Equatorial Western Pacific: A Decade and Counting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Long, Charles N.; McFarlane, Sally A.; Del Genio, Anthony D.

2013-05-22

The tropical western Pacific (TWP) is an important climatic region. Strong solar heating, warm sea surface temperatures and the annual progression of the Intertropical Convergence Zone (ITCZ) across this region generate abundant convective systems, which through their effects on the heat and water budgets have a profound impact on global climate and precipitation. To accurately represent tropical cloud systems in models, measurements of tropical clouds, the environment in which they reside, and their impact on the radiation and water budgets are needed. Because of the remote location, ground-based datasets of cloud, atmosphere, and radiation properties from the TWP region havemore » traditionally come primarily from short-term field experiments. While providing extremely useful information on physical processes, these datasets are limited in statistical and climatological information because of their short duration. To provide long-term measurements of the surface radiation budget in the tropics, and the atmospheric properties that affect it, the Atmospheric Radiation Measurement program established a measurement site on Manus Island, Papua New Guinea in 1996 and on the island republic of Nauru in late 1998. These sites provide unique datasets available from more than 10 years of operation in the equatorial western Pacific on Manus and Nauru. We present examples of the scientific use of these datasets including characterization of cloud properties, analysis of cloud radiative forcing, model studies of tropical clouds and processes, and validation of satellite algorithms. We also note new instrumentation recently installed at the Manus site that will expand opportunities for tropical atmospheric science.« less

Noah-MP-Crop: Enhancing cropland representation in the community land surface modeling system

NASA Astrophysics Data System (ADS)

Liu, X.; Chen, F.; Barlage, M. J.; Zhou, G.; Niyogi, D.

2015-12-01

Croplands are important in land-atmosphere interactions and in modifying local and regional weather and climate. Despite their importance, croplands are poorly represented in the current version of the coupled Weather Research and Forecasting (WRF)/ Noah land-surface modeling system, resulting in significant surface temperature and humidity biases across agriculture- dominated regions of the United States. This study aims to improve the WRF weather forecasting and regional climate simulations during the crop growing season by enhancing the representation of cropland in the Noah-MP land model. We introduced dynamic crop growth parameterization into Noah-MP and evaluated the enhanced model (Noah-MP-Crop) at both the field and regional scales with multiple crop biomass datasets, surface fluxes and soil moisture/temperature observations. We also integrated a detailed cropland cover map into WRF, enabling the model to simulate corn and soybean field across the U.S. Great Plains. Results show marked improvement in the Noah-MP-Crop performance in simulating leaf area index (LAI), crop biomass, soil temperature, and surface fluxes. Enhanced cropland representation is not only crucial for improving weather forecasting but can also help assess potential impacts of weather variability on regional hydrometeorology and crop yields. In addition to its applications to WRF, Noah-MP-Crop can be applied in high-spatial-resolution regional crop yield modeling and drought assessments

A dynamic aerodynamic resistance approach to calculate high resolution sensible heat fluxes in urban areas

NASA Astrophysics Data System (ADS)

Crawford, Ben; Grimmond, Sue; Kent, Christoph; Gabey, Andrew; Ward, Helen; Sun, Ting; Morrison, William

2017-04-01

Remotely sensed data from satellites have potential to enable high-resolution, automated calculation of urban surface energy balance terms and inform decisions about urban adaptations to environmental change. However, aerodynamic resistance methods to estimate sensible heat flux (QH) in cities using satellite-derived observations of surface temperature are difficult in part due to spatial and temporal variability of the thermal aerodynamic resistance term (rah). In this work, we extend an empirical function to estimate rah using observational data from several cities with a broad range of surface vegetation land cover properties. We then use this function to calculate spatially and temporally variable rah in London based on high-resolution (100 m) land cover datasets and in situ meteorological observations. In order to calculate high-resolution QH based on satellite-observed land surface temperatures, we also develop and employ novel methods to i) apply source area-weighted averaging of surface and meteorological variables across the study spatial domain, ii) calculate spatially variable, high-resolution meteorological variables (wind speed, friction velocity, and Obukhov length), iii) incorporate spatially interpolated urban air temperatures from a distributed sensor network, and iv) apply a modified Monte Carlo approach to assess uncertainties with our results, methods, and input variables. Modeled QH using the aerodynamic resistance method is then compared to in situ observations in central London from a unique network of scintillometers and eddy-covariance measurements.

Variability of Surface Temperature and Melt on the Greenland Ice Sheet, 2000-2011

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Comiso, Josefino, C.; Shuman, Christopher A.; Koenig, Lora S.; DiGirolamo, Nicolo E.

2012-01-01

Enhanced melting along with surface-temperature increases measured using infrared satellite data, have been documented for the Greenland Ice Sheet. Recently we developed a climate-quality data record of ice-surface temperature (IST) of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) 1ST product -- http://modis-snow-ice.gsfc.nasa.gov. Using daily and mean monthly MODIS 1ST maps from the data record we show maximum extent of melt for the ice sheet and its six major drainage basins for a 12-year period extending from March of 2000 through December of 2011. The duration of the melt season on the ice sheet varies in different drainage basins with some basins melting progressively earlier over the study period. Some (but not all) of the basins also show a progressively-longer duration of melt. The short time of the study period (approximately 12 years) precludes an evaluation of statistically-significant trends. However the dataset provides valuable information on natural variability of IST, and on the ability of the MODIS instrument to capture changes in IST and melt conditions indifferent drainage basins of the ice sheet.

Enhanced near-surface ozone under heatwave conditions in a Mediterranean island.

PubMed

Pyrgou, Andri; Hadjinicolaou, Panos; Santamouris, Mat

2018-06-15

Near-surface ozone is enhanced under particular chemical reactions and physical processes. This study showed the seasonal variation of near-surface ozone in Nicosia, Cyprus and focused in summers when the highest ozone levels were noted using a seven year hourly dataset from 2007 to 2014. The originality of this study is that it examines how ozone levels changed under heatwave conditions (defined as 4 consecutive days with daily maximum temperature over 39 °C) with emphasis on specific air quality and meteorological parameters with respect to non-heatwave summer conditions. The influencing parameters had a medium-strong positive correlation of ozone with temperature, UVA and UVB at daytime which increased by about 35% under heatwave conditions. The analysis of the wind pattern showed a small decrease of wind speed during heatwaves leading to stagnant weather conditions, but also revealed a steady diurnal cycle of wind speed reaching a peak at noon, when the highest ozone levels were noted. The negative correlation of NOx budget with ozone was further increased under heatwave conditions leading to steeper lows of ozone in the morning. In summary, this research encourages further analysis into the persistent weather conditions prevalent during HWs stimulating ozone formation for higher temperatures.

Signal detection in global mean temperatures after "Paris": an uncertainty and sensitivity analysis

NASA Astrophysics Data System (ADS)

Visser, Hans; Dangendorf, Sönke; van Vuuren, Detlef P.; Bregman, Bram; Petersen, Arthur C.

2018-02-01

In December 2015, 195 countries agreed in Paris to hold the increase in global mean surface temperature (GMST) well below 2.0 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C. Since large financial flows will be needed to keep GMSTs below these targets, it is important to know how GMST has progressed since pre-industrial times. However, the Paris Agreement is not conclusive as regards methods to calculate it. Should trend progression be deduced from GCM simulations or from instrumental records by (statistical) trend methods? Which simulations or GMST datasets should be chosen, and which trend models? What is pre-industrial and, finally, are the Paris targets formulated for total warming, originating from both natural and anthropogenic forcing, or do they refer to anthropogenic warming only? To find answers to these questions we performed an uncertainty and sensitivity analysis where datasets and model choices have been varied. For all cases we evaluated trend progression along with uncertainty information. To do so, we analysed four trend approaches and applied these to the five leading observational GMST products. We find GMST progression to be largely independent of various trend model approaches. However, GMST progression is significantly influenced by the choice of GMST datasets. Uncertainties due to natural variability are largest in size. As a parallel path, we calculated GMST progression from an ensemble of 42 GCM simulations. Mean progression derived from GCM-based GMSTs appears to lie in the range of trend-dataset combinations. A difference between both approaches appears to be the width of uncertainty bands: GCM simulations show a much wider spread. Finally, we discuss various choices for pre-industrial baselines and the role of warming definitions. Based on these findings we propose an estimate for signal progression in GMSTs since pre-industrial.

The uncertainties and causes of the recent changes in global evapotranspiration from 1982 to 2010

NASA Astrophysics Data System (ADS)

Dong, Bo; Dai, Aiguo

2017-07-01

Recent studies have shown considerable changes in terrestrial evapotranspiration (ET) since the early 1980s, but the causes of these changes remain unclear. In this study, the relative contributions of external climate forcing and internal climate variability to the recent ET changes are examined. Three datasets of global terrestrial ET and the CMIP5 multi-model ensemble mean ET are analyzed, respectively, to quantify the apparent and externally-forced ET changes, while the unforced ET variations are estimated as the apparent ET minus the forced component. Large discrepancies of the ET estimates, in terms of their trend, variability, and temperature- and precipitation-dependence, are found among the three datasets. Results show that the forced global-mean ET exhibits an upward trend of 0.08 mm day-1 century-1 from 1982 to 2010. The forced ET also contains considerable multi-year to decadal variations during the latter half of the 20th century that are caused by volcanic aerosols. The spatial patterns and interannual variations of the forced ET are more closely linked to precipitation than temperature. After removing the forced component, the global-mean ET shows a trend ranging from -0.07 to 0.06 mm day-1 century-1 during 1982-2010 with varying spatial patterns among the three datasets. Furthermore, linkages between the unforced ET and internal climate modes are examined. Variations in Pacific sea surface temperatures (SSTs) are found to be consistently correlated with ET over many land areas among the ET datasets. The results suggest that there are large uncertainties in our current estimates of global terrestrial ET for the recent decades, and the greenhouse gas (GHG) and aerosol external forcings account for a large part of the apparent trend in global-mean terrestrial ET since 1982, but Pacific SST and other internal climate variability dominate recent ET variations and changes over most regions.

The ISLSCP initiative I global datasets: Surface boundary conditions and atmospheric forcings for land-atmosphere studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sellers, P.J.; Collatz, J.; Koster, R.

1996-09-01

A comprehensive series of global datasets for land-atmosphere models has been collected, formatted to a common grid, and released on a set of CD-ROMs. This paper describes the motivation for and the contents of the dataset. In June of 1992, an interdisciplinary earth science workshop was convened in Columbia, Maryland, to assess progress in land-atmosphere research, specifically in the areas of models, satellite data algorithms, and field experiments. At the workshop, representatives of the land-atmosphere modeling community defined a need for global datasets to prescribe boundary conditions, initialize state variables, and provide near-surface meteorological and radiative forcings for their models.more » The International Satellite Land Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment, worked with the Distributed Active Archive Center of the National Aeronautics and Space Administration Goddard Space Flight Center to bring the required datasets together in a usable format. The data have since been released on a collection of CD-ROMs. The datasets on the CD-ROMs are grouped under the following headings: vegetation; hydrology and soils; snow, ice, and oceans; radiation and clouds; and near-surface meteorology. All datasets cover the period 1987-88, and all but a few are spatially continuous over the earth`s land surface. All have been mapped to a common 1{degree} x 1{degree} equal-angle grid. The temporal frequency for most of the datasets is monthly. A few of the near-surface meteorological parameters are available both as six-hourly values and as monthly means. 26 refs., 8 figs., 2 tabs.« less

High-resolution data on the impact of warming on soil CO2 efflux from an Asian monsoon forest

PubMed Central

Liang, Naishen; Teramoto, Munemasa; Takagi, Masahiro; Zeng, Jiye

2017-01-01

This paper describes a project for evaluation of global warming’s impacts on soil carbon dynamics in Japanese forest ecosystems. We started a soil warming experiment in late 2008 in a 55-year-old evergreen broad-leaved forest at the boundary between the subtropical and warm-temperate biomes in southern Japan. We used infrared carbon-filament heat lamps to increase soil temperature by about 2.5 °C at a depth of 5 cm and continuously recorded CO2 emission from the soil surface using a multichannel automated chamber system. Here, we present details of the experimental processes and datasets for the CO2 emission rate, soil temperature, and soil moisture from control, trenched, and warmed trenched plots. The long term of the study and its high resolution make the datasets meaningful for use in or development of coupled climate-ecosystem models to tune their dynamic behaviour as well as to provide mean parameters for decomposition of soil organic carbon to support future predictions of soil carbon sequestration. PMID:28291228

Detection of Warming Effects Due to Industrialization: An Accumulated Intervention Model with an Application in Pohang, Korea.

NASA Astrophysics Data System (ADS)

Ryoo, S. B.; Moon, S. E.

1995-06-01

Modifications of surface air temperature caused by anthropogenic impacts have received much attention recently because of the heightened interest in climatic change. When an industrial area is constructed, resulting in a large-scale anthropogenic heat source, is it possible to detect the warming effect of the heat source? In this paper, the intensity of warming is estimated in the area of the source. A statistical model is suggested to estimate the warming caused by that anthropogenic heat source. The model used in this study is an accumulated intervention (AI) model that is applied to industrial heat perturbations that occurred in the area. To evaluate the AI model performance, the forecast experiment was carried out with an independent dataset. The data used in this study are the monthly mean temperatures at Pohang, Korea. The AI model was developed based on the data for the 38-year period from 1953 to 1990, and the forecast experiment was carried out with an independent dataset for the 2-year period from 1991 to 1992.

Derivation and evaluation of land surface temperature from the geostationary operational environmental satellite series

NASA Astrophysics Data System (ADS)

Fang, Li

The Geostationary Operational Environmental Satellites (GOES) have been continuously monitoring the earth surface since 1970, providing valuable and intensive data from a very broad range of wavelengths, day and night. The National Oceanic and Atmospheric Administration's (NOAA's) National Environmental Satellite, Data, and Information Service (NESDIS) is currently operating GOES-15 and GOES-13. The design of the GOES series is now heading to the 4 th generation. GOES-R, as a representative of the new generation of the GOES series, is scheduled to be launched in 2015 with higher spatial and temporal resolution images and full-time soundings. These frequent observations provided by GOES Image make them attractive for deriving information on the diurnal land surface temperature (LST) cycle and diurnal temperature range (DTR). These parameters are of great value for research on the Earth's diurnal variability and climate change. Accurate derivation of satellite-based LSTs from thermal infrared data has long been an interesting and challenging research area. To better support the research on climate change, the generation of consistent GOES LST products for both GOES-East and GOES-West from operational dataset as well as historical archive is in great demand. The derivation of GOES LST products and the evaluation of proposed retrieval methods are two major objectives of this study. Literature relevant to satellite-based LST retrieval techniques was reviewed. Specifically, the evolution of two LST algorithm families and LST retrieval methods for geostationary satellites were summarized in this dissertation. Literature relevant to the evaluation of satellite-based LSTs was also reviewed. All the existing methods are a valuable reference to develop the GOES LST product. The primary objective of this dissertation is the development of models for deriving consistent GOES LSTs with high spatial and high temporal coverage. Proper LST retrieval algorithms were studied according to the characteristics of the imager onboard the GOES series. For the GOES 8-11 and GOES R series with split window (SW) channels, a new temperature and emissivity separation (TES) approach was proposed for deriving LST and LSE simultaneously by using multiple-temporal satellite observations. Two split-window regression formulas were selected for this approach, and two satellite observations over the same geo-location within a certain time interval were utilized. This method is particularly applicable to geostationary satellite missions from which qualified multiple-temporal observations are available. For the GOES M(12)-Q series without SW channels, the dual-window LST algorithm was adopted to derive LST. Instead of using the conventional training method to generate coefficients for the LST regression algorithms, a machine training technique was introduced to automatically select the criteria and the boundary of the sub-ranges for generating algorithm coefficients under different conditions. A software package was developed to produce a brand new GOES LST product from both operational GOES measurements and historical archive. The system layers of the software and related system input and output were illustrated in this work. Comprehensive evaluation of GOES LST products was conducted by validating products against multiple ground-based LST observations, LST products from fine-resolution satellites (e.g. MODIS) and GSIP LST products. The key issues relevant to the cloud diffraction effect were studied as well. GOES measurements as well as ancillary data, including satellite and solar geometry, water vapor, cloud mask, land emissivity etc., were collected to generate GOES LST products. In addition, multiple in situ temperature measurements were collected to test the performance of the proposed GOES LST retrieval algorithms. The ground-based dataset included direct surface temperature measurements from the Atmospheric Radiation Measurement program (ARM), and indirect measurements (surface long-wave radiation observations) from the SURFace RADiation Budget (SURFRAD) Network. A simulated dataset was created to analyse the sensitivity of the proposed retrieval algorithms. In addition, the MODIS LST and GSIP LST products were adopted to cross-evaluate the accuracy of the GOES LST products. Evaluation results demonstrate that the proposed GOES LST system is capable of deriving consistent land surface temperatures with good retrieval precision. Consistent GOES LST products with high spatial/temporal coverage and reliable accuracy will better support detections and observations of meteorological over land surfaces.

Percentile-Based ETCCDI Temperature Extremes Indices for CMIP5 Model Output: New Results through Semiparametric Quantile Regression Approach

NASA Astrophysics Data System (ADS)

Li, L.; Yang, C.

2017-12-01

Climate extremes often manifest as rare events in terms of surface air temperature and precipitation with an annual reoccurrence period. In order to represent the manifold characteristics of climate extremes for monitoring and analysis, the Expert Team on Climate Change Detection and Indices (ETCCDI) had worked out a set of 27 core indices based on daily temperature and precipitation data, describing extreme weather and climate events on an annual basis. The CLIMDEX project (http://www.climdex.org) had produced public domain datasets of such indices for data from a variety of sources, including output from global climate models (GCM) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Among the 27 ETCCDI indices, there are six percentile-based temperature extremes indices that may fall into two groups: exceedance rates (ER) (TN10p, TN90p, TX10p and TX90p) and durations (CSDI and WSDI). Percentiles must be estimated prior to the calculation of the indices, and could more or less be biased by the adopted algorithm. Such biases will in turn be propagated to the final results of indices. The CLIMDEX used an empirical quantile estimator combined with a bootstrap resampling procedure to reduce the inhomogeneity in the annual series of the ER indices. However, there are still some problems remained in the CLIMDEX datasets, namely the overestimated climate variability due to unaccounted autocorrelation in the daily temperature data, seasonally varying biases and inconsistency between algorithms applied to the ER indices and to the duration indices. We now present new results of the six indices through a semiparametric quantile regression approach for the CMIP5 model output. By using the base-period data as a whole and taking seasonality and autocorrelation into account, this approach successfully addressed the aforementioned issues and came out with consistent results. The new datasets cover the historical and three projected (RCP2.6, RCP4.5 and RCP8.5) emission scenarios run a multimodel ensemble of 19 members. We analyze changes in the six indices on global and regional scales over the 21st century relative to either the base period 1961-1990 or the reference period 1981-2000, and compare the results with those based on the CLIMDEX datasets.

Correction and evaluation of thermal infrared data acquired with two different airborne systems at the Elbe estuary

NASA Astrophysics Data System (ADS)

Fricke, Katharina; Baschek, Björn; Jenal, Alexander; Kneer, Caspar; Weber, Immanuel; Bongartz, Jens; Wyrwa, Jens; Schöl, Andreas

2016-10-01

This study presents the results from a combined aerial survey performed with a hexacopter and a gyrocopter over a part of the Elbe estuary near Hamburg, Germany. The survey was conducted by the Federal Institute of Hydrology, Germany, and the Fraunhofer Application Center for Multimodal and Airborne Sensors as well as by a contracted engineering company with the aim to acquire spatial thermal infrared (TIR) data of the Hahnöfer Nebenelbe, a branch of the Elbe estuary. Additionally, RGB and NIR data was captured to facilitate the identification of water surfaces and intertidal mudflats. The temperature distribution of the Elbe estuary affects all biological processes and in consequence the oxygen content, which is a key parameter in water quality. The oxygen levels vary in space between the main fairway and side channels. So far, only point measurements are available for monitoring and calibration/validation of water quality models. To better represent this highly dynamic system with a high spatial and temporal variability, tidal streams, heating and cooling, diffusion and mixing processes, spatially distributed data from several points of time within the tidal cycle are necessary. The data acquisition took place during two tidal cycles over two subsequent days in the summer of 2015. While the piloted gyrocopter covered the whole Hahnöfer Nebenelbe seven times, the unmanned hexacopter covered a smaller section of the branch and tidal mudflats with a higher spatial and temporal resolution (16 coverages of the subarea). The gyrocopter data was acquired with a thermal imaging system and processed and georeferenced using the structure from motion algorithm with GPS information from the gyrocopter and optional ground control points. The hexacopter data was referenced based on ground control points and the GPS and position information of the acquisition system. Both datasets from the gyrocopter and the hexacopter are corrected for the effects of the atmosphere and emissivity of the water surface and compared to in situ measurements, taken during the data acquisition. Of particular interest is the effect of the observation angle on the brightness temperature acquired by the wide angle lenses on the platforms, which is up to 40° at the margins of the imagery. Here, both datasets show deviating temperatures, which are probably not due to actual temperature differences. We will discuss the position accuracy achieved over the water areas, the adaptation of atmospheric and emissivity correction to the observation angle and subsequent improvement of the temperature data. With two datasets of the same research area at different resolutions we will investigate the effects of the acquisition platforms, acquisition system and resolutions on the accuracy of the remotely sensed temperatures as well as their ability to represent temperature patterns of tidal currents and mixing processes.

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS

PubMed Central

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data. PMID:26090852

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

PubMed

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

A Gridded Daily Min/Max Temperature Dataset With 0.1° Resolution for the Yangtze River Valley and its Error Estimation

NASA Astrophysics Data System (ADS)

Xiong, Qiufen; Hu, Jianglin

2013-05-01

The minimum/maximum (Min/Max) temperature in the Yangtze River valley is decomposed into the climatic mean and anomaly component. A spatial interpolation is developed which combines the 3D thin-plate spline scheme for climatological mean and the 2D Barnes scheme for the anomaly component to create a daily Min/Max temperature dataset. The climatic mean field is obtained by the 3D thin-plate spline scheme because the relationship between the decreases in Min/Max temperature with elevation is robust and reliable on a long time-scale. The characteristics of the anomaly field tend to be related to elevation variation weakly, and the anomaly component is adequately analyzed by the 2D Barnes procedure, which is computationally efficient and readily tunable. With this hybridized interpolation method, a daily Min/Max temperature dataset that covers the domain from 99°E to 123°E and from 24°N to 36°N with 0.1° longitudinal and latitudinal resolution is obtained by utilizing daily Min/Max temperature data from three kinds of station observations, which are national reference climatological stations, the basic meteorological observing stations and the ordinary meteorological observing stations in 15 provinces and municipalities in the Yangtze River valley from 1971 to 2005. The error estimation of the gridded dataset is assessed by examining cross-validation statistics. The results show that the statistics of daily Min/Max temperature interpolation not only have high correlation coefficient (0.99) and interpolation efficiency (0.98), but also the mean bias error is 0.00 °C. For the maximum temperature, the root mean square error is 1.1 °C and the mean absolute error is 0.85 °C. For the minimum temperature, the root mean square error is 0.89 °C and the mean absolute error is 0.67 °C. Thus, the new dataset provides the distribution of Min/Max temperature over the Yangtze River valley with realistic, successive gridded data with 0.1° × 0.1° spatial resolution and daily temporal scale. The primary factors influencing the dataset precision are elevation and terrain complexity. In general, the gridded dataset has a relatively high precision in plains and flatlands and a relatively low precision in mountainous areas.

Properties of the surface snow in Princess Elizabeth Land, East Antarctica - climate and non-climate dependent variability of the surface mass balance and stable water isotopic composition

NASA Astrophysics Data System (ADS)

Vladimirova, D.; Ekaykin, A.; Lipenkov, V.; Popov, S. V.; Petit, J. R.; Masson-Delmotte, V.

2017-12-01

Glaciological and meteorological observations conducted during the past four decades in Princess Elizabeth Land, East Antarctica, are compiled. The database is used to investigate spatial patterns of surface snow isotopic composition and surface mass balance, including detailed information near subglacial lake Vostok. We show diverse relationships between snow isotopic composition and surface temperature. In the most inland part (elevation 3200-3400 m a.s.l.), surface snow isotopic composition varies independently from surface temperature, and is closely related to the distance to the open water source (with a slope of 0.98±0.17 ‰ per 100 km). Surface mass balance values are higher along the ice sheet slope, and relatively evenly distributed inland. The minimum values of snow isotopic composition and surface mass balance are identified in an area XX km southwestward from Vostok station. The spatial distribution of deuterium excess delineates regions influenced by the Indian Ocean and Pacific Ocean air masses, with Vostok area being situated close to their boundary. Anomalously high deuterium excess values are observed near Dome A, suggesting high kinetic fractionation for its moisture source, or specifically high post-deposition artifacts. The dataset is available for further studies such as the assessment of skills of general circulation or regional atmospheric models, and the search for the oldest ice.

Global River Water Temperature Modelling at Hyper-Resolution

NASA Astrophysics Data System (ADS)

Wanders, N.; van Vliet, M. T. H.; Wada, Y.; Van Beek, L. P.

2017-12-01

The temperature of river water plays a crucial role in many physical, chemical and biological aquatic processes. The influence of changing water temperatures is not only felt locally, but also has regional and downstream impacts. Sectors that might be affected by sudden or gradual changes in the water temperature are: energy production, industry and recreation. Although it is very important to have detailed information on this environmental variable, high-resolution simulations of water temperature on a large scale are currently lacking. Here we present a novel hyper-resolution water temperature dataset at the global scale. We developed the 1-D energy routing model WARM, to simulate river temperature for the period 1980-2014 at 10 km and 50 km resolution. The WARM model accounts for surface water abstraction, reservoirs, riverine flooding and formation of ice, therefore enabling a realistic representation of the water temperature. The water temperature simulations have been validated against 358 river monitoring stations globally for the period 1980 to 2014. The results indicate the increase in resolution significantly improves the simulation performance with a decrease in the water temperature RMSE from 3.5°C to 3.0°C and an increase in the mean correlation of the daily discharge simulations, from R=0.4 to 0.6. We find an average global increase in water temperature of 0.22°C per decade between 1960-2014, with increasing trends towards the end of the simulations period. Strong increasing trends in maxima in the Northern Hemisphere (0.62°C per decade) and minima in the Southern Hemisphere (0.45°C per decade). Finally, we show the impact of major heatwaves and drought events on the water temperature and water availability. The high resolution not only improves the model performance; it also positively impacts the relevancy of the simulation for local and regional scale studies and impact assessments. This new global water temperature dataset could help to develop decision-support system related to water quality with increasing precision and accuracy.

Global retrieval of soil moisture and vegetation properties using data-driven methods

NASA Astrophysics Data System (ADS)

Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann

2017-04-01

Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve soil moisture from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of soil moisture with the Soil Moisture and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS soil moisture retrievals as reference dataset for the training. The presentation will discuss several possibilities for both the input datasets and the datasets to be used as reference for the supervised learning phase. Regarding the input datasets, it will be shown that NNs take advantage of the synergy of SMOS data and data from other sensors such as the Advanced Scatterometer (ASCAT, active microwaves) and MODIS (visible and infra red). NNs have also been successfully used to construct long time series of soil moisture from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS soil moisture as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS soil moisture. Regarding the reference data to train the data-driven retrievals, we will show different possibilities depending on the application. Using actual in situ measurements is challenging at global scale due to the scarce distribution of sensors. In contrast, in situ measurements have been successfully used to retrieve SM at continental scale in North America, where the density of in situ measurement stations is high. Using global land surface models to train the NN constitute an interesting alternative to implement new remote sensing surface datasets. In addition, these datasets can be used to perform data assimilation into the model used as reference for the training. This approach has recently been tested at the European Centre for Medium-Range Weather Forecasts (ECMWF). Finally, retrievals using radiative transfer models can also be used as a reference SM dataset for the training phase. This approach was used to retrieve soil moisture from ASMR-E, as mentioned above, and also to implement the official European Space Agency (ESA) SMOS soil moisture product in Near-Real-Time. We will finish with a discussion of the retrieval of vegetation parameters from SMOS observations using data-driven methods.

The Effect of Large Scale Climate Oscillations on the Land Surface Phenology of the Northern Polar Regions and Central Asia

NASA Astrophysics Data System (ADS)

de Beurs, K.; Henebry, G. M.; Owsley, B.; Sokolik, I. N.

2016-12-01

Land surface phenology metrics allow for the summarization of long image time series into a set of annual observations that describe the vegetated growing season. These metrics have been shown to respond to both large scale climatic and anthropogenic impacts. In this study we assemble a time series (2001 - 2014) of Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-Adjusted Reflectance data and land surface temperature data at 0.05º spatial resolution. We then derive land surface phenology metrics focusing on the peak of the growing season by fitting quadratic regression models using NDVI and Accumulated Growing Degree-Days (AGDD) derived from land surface temperature. We link the annual information on the peak timing, the thermal time to peak and the maximum of the growing season with five of the most important large scale climate oscillations: NAO, AO, PDO, PNA and ENSO. We demonstrate several significant correlations between the climate oscillations and the land surface phenology peak metrics for a range of different bioclimatic regions in both dryland Central Asia and the northern Polar Regions. We will then link the correlation results with trends derived by the seasonal Mann-Kendall trend detection method applied to several satellite derived vegetation and albedo datasets.

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

NASA Technical Reports Server (NTRS)

Parinussa, Robert M.; de Jeu, Richard A. M.; van Der Schalie, Robin; Crow, Wade T.; Lei, Fangni; Holmes, Thomas R. H.

2016-01-01

Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the Rvalue technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the Rvalue technique. Results indicate an average increasing Rvalue of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore provides insights on the relative quality of soil moisture retrieved from AMSR-E's day- and night-time observations.

microclim: Global estimates of hourly microclimate based on long-term monthly climate averages

PubMed Central

Kearney, Michael R; Isaac, Andrew P; Porter, Warren P

2014-01-01

The mechanistic links between climate and the environmental sensitivities of organisms occur through the microclimatic conditions that organisms experience. Here we present a dataset of gridded hourly estimates of typical microclimatic conditions (air temperature, wind speed, relative humidity, solar radiation, sky radiation and substrate temperatures from the surface to 1 m depth) at high resolution (~15 km) for the globe. The estimates are for the middle day of each month, based on long-term average macroclimates, and include six shade levels and three generic substrates (soil, rock and sand) per pixel. These data are suitable for deriving biophysical estimates of the heat, water and activity budgets of terrestrial organisms. PMID:25977764

Microclim: Global estimates of hourly microclimate based on long-term monthly climate averages.

PubMed

Kearney, Michael R; Isaac, Andrew P; Porter, Warren P

2014-01-01

The mechanistic links between climate and the environmental sensitivities of organisms occur through the microclimatic conditions that organisms experience. Here we present a dataset of gridded hourly estimates of typical microclimatic conditions (air temperature, wind speed, relative humidity, solar radiation, sky radiation and substrate temperatures from the surface to 1 m depth) at high resolution (~15 km) for the globe. The estimates are for the middle day of each month, based on long-term average macroclimates, and include six shade levels and three generic substrates (soil, rock and sand) per pixel. These data are suitable for deriving biophysical estimates of the heat, water and activity budgets of terrestrial organisms.

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

NASA Technical Reports Server (NTRS)

Bell, Jordan R.; Case, Jonathan L.; Molthan, Andrew L.

2011-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the new SPoRT-MODIS GVF dataset on land surface models apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true for areas with lower GVF in the SPoRT model runs. These differences in the heating and evaporation rates produced subtle yet quantifiable differences in the simulated convective precipitation systems for the selected severe weather case examined.

Combined evaluation of optical and microwave satellite dataset for soil moisture deficit estimation

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Singh, Sudhir Kumar; Gupta, Manika; Gupta, Dileep Kumar; Kumar, Pradeep

2016-04-01

Soil moisture is a key variable responsible for water and energy exchanges from land surface to the atmosphere (Srivastava et al., 2014). On the other hand, Soil Moisture Deficit (or SMD) can help regulating the proper use of water at specified time to avoid any agricultural losses (Srivastava et al., 2013b) and could help in preventing natural disasters, e.g. flood and drought (Srivastava et al., 2013a). In this study, evaluation of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and soil moisture from Soil Moisture and Ocean Salinity (SMOS) satellites are attempted for prediction of Soil Moisture Deficit (SMD). Sophisticated algorithm like Adaptive Neuro Fuzzy Inference System (ANFIS) is used for prediction of SMD using the MODIS and SMOS dataset. The benchmark SMD estimated from Probability Distributed Model (PDM) over the Brue catchment, Southwest of England, U.K. is used for all the validation. The performances are assessed in terms of Nash Sutcliffe Efficiency, Root Mean Square Error and the percentage of bias between ANFIS simulated SMD and the benchmark. The performance statistics revealed a good agreement between benchmark and the ANFIS estimated SMD using the MODIS dataset. The assessment of the products with respect to this peculiar evidence is an important step for successful development of hydro-meteorological model and forecasting system. The analysis of the satellite products (viz. SMOS soil moisture and MODIS LST) towards SMD prediction is a crucial step for successful hydrological modelling, agriculture and water resource management, and can provide important assistance in policy and decision making. Keywords: Land Surface Temperature, MODIS, SMOS, Soil Moisture Deficit, Fuzzy Logic System References: Srivastava, P.K., Han, D., Ramirez, M.A., Islam, T., 2013a. Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. Journal of Hydrology 498, 292-304. Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Al-Shrafany, D., Islam, T., 2013b. Data fusion techniques for improving soil moisture deficit using SMOS satellite and WRF-NOAH land surface model. Water Resources Management 27, 5069-5087. Srivastava, P.K., Han, D., Rico-Ramirez, M.A., O'Neill, P., Islam, T., Gupta, M., 2014. Assessment of SMOS soil moisture retrieval parameters using tau-omega algorithms for soil moisture deficit estimation. Journal of Hydrology 519, 574-587.

Watershed Data Management (WDM) database for West Branch DuPage River streamflow simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2013

USGS Publications Warehouse

Bera, Maitreyee

2017-10-16

The U.S. Geological Survey (USGS), in cooperation with the DuPage County Stormwater Management Department, maintains a database of hourly meteorological and hydrologic data for use in a near real-time streamflow simulation system. This system is used in the management and operation of reservoirs and other flood-control structures in the West Branch DuPage River watershed in DuPage County, Illinois. The majority of the precipitation data are collected from a tipping-bucket rain-gage network located in and near DuPage County. The other meteorological data (air temperature, dewpoint temperature, wind speed, and solar radiation) are collected at Argonne National Laboratory in Argonne, Ill. Potential evapotranspiration is computed from the meteorological data using the computer program LXPET (Lamoreux Potential Evapotranspiration). The hydrologic data (water-surface elevation [stage] and discharge) are collected at U.S.Geological Survey streamflow-gaging stations in and around DuPage County. These data are stored in a Watershed Data Management (WDM) database.This report describes a version of the WDM database that is quality-assured and quality-controlled annually to ensure datasets are complete and accurate. This database is named WBDR13.WDM. It contains data from January 1, 2007, through September 30, 2013. Each precipitation dataset may have time periods of inaccurate data. This report describes the methods used to estimate the data for the periods of missing, erroneous, or snowfall-affected data and thereby improve the accuracy of these data. The other meteorological datasets are described in detail in Over and others (2010), and the hydrologic datasets in the database are fully described in the online USGS annual water data reports for Illinois (U.S. Geological Survey, 2016) and, therefore, are described in less detail than the precipitation datasets in this report.

Climatic warming in China during 1901–2015 based on an extended dataset of instrumental temperature records

DOE PAGES

Cao, Lijuan; Yan, Zhongwei; Zhao, Ping; ...

2017-05-26

Monthly mean instrumental surface air temperature (SAT) observations back to the nineteenth century in China are synthesized from different sources via specific quality-control, interpolation, and homogenization. Compared with the first homogenized long-term SAT dataset for China which contained 18 stations mainly located in the middle and eastern part of China, the present dataset includes homogenized monthly SAT series at 32 stations, with an extended coverage especially towards western China. Missing values are interpolated by using observations at nearby stations, including those from neighboring countries. Cross validation shows that the mean bias error (MBE) is generally small and falls between 0.45more » °C and –0.35 °C. Multiple homogenization methods and available metadata are applied to assess the consistency of the time series and to adjust inhomogeneity biases. The homogenized annual mean SAT series shows a range of trends between 1.1 °C and 4.0 °C/century in northeastern China, between 0.4 °C and 1.9 °C/century in southeastern China, and between 1.4 °C and 3.7 °C/century in western China to the west of 105 E (from the initial years of the stations to 2015). The unadjusted data include unusually warm records during the 1940s and hence tend to underestimate the warming trends at a number of stations. As a result, the mean SAT series for China based on the climate anomaly method shows a warming trend of 1.56 °C/century during 1901–2015, larger than those based on other currently available datasets.« less

Climatic warming in China during 1901–2015 based on an extended dataset of instrumental temperature records

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Lijuan; Yan, Zhongwei; Zhao, Ping

Monthly mean instrumental surface air temperature (SAT) observations back to the nineteenth century in China are synthesized from different sources via specific quality-control, interpolation, and homogenization. Compared with the first homogenized long-term SAT dataset for China which contained 18 stations mainly located in the middle and eastern part of China, the present dataset includes homogenized monthly SAT series at 32 stations, with an extended coverage especially towards western China. Missing values are interpolated by using observations at nearby stations, including those from neighboring countries. Cross validation shows that the mean bias error (MBE) is generally small and falls between 0.45more » °C and –0.35 °C. Multiple homogenization methods and available metadata are applied to assess the consistency of the time series and to adjust inhomogeneity biases. The homogenized annual mean SAT series shows a range of trends between 1.1 °C and 4.0 °C/century in northeastern China, between 0.4 °C and 1.9 °C/century in southeastern China, and between 1.4 °C and 3.7 °C/century in western China to the west of 105 E (from the initial years of the stations to 2015). The unadjusted data include unusually warm records during the 1940s and hence tend to underestimate the warming trends at a number of stations. As a result, the mean SAT series for China based on the climate anomaly method shows a warming trend of 1.56 °C/century during 1901–2015, larger than those based on other currently available datasets.« less

A tool to evaluate local biophysical effects on temperature due to land cover change transitions

NASA Astrophysics Data System (ADS)

Perugini, Lucia; Caporaso, Luca; Duveiller, Gregory; Cescatti, Alessandro; Abad-Viñas, Raul; Grassi, Giacomo; Quesada, Benjamin

2017-04-01

Land Cover Changes (LCC) affect local, regional and global climate through biophysical variations of the surface energy budget mediated by albedo, evapotranspiration, and roughness. Assessment of the full climate impacts of anthropogenic LCC are incomplete without considering biophysical effects, but the high level of uncertainties in quantifying their impacts to date have made it impractical to offer clear advice on which policy makers could act. To overcome this barrier, we provide a tool to evaluate the biophysical impact of a matrix of land cover transitions, following a tiered methodological approach similar to the one provided by the IPCC to estimate the biogeochemical effects, i.e. through three levels of methodological complexity, from Tier 1 (i.e. default method and factors) to Tier 3 (i.e. specific methods and factors). In particular, the tool provides guidance for quantitative assessment of changes in temperature following a land cover transition. The tool focuses on temperature for two main reasons (i) it is the main variable of interest for policy makers at local and regional level, and (ii) temperature is able to summarize the impact of radiative and non-radiative processes following LULCC. The potential changes in annual air temperature that can be expected from various land cover transitions are derived from a dedicated dataset constructed by the JRC in the framework of the LUC4C FP7 project. The inputs for the dataset are air temperature values derived from satellite Earth Observation data (MODIS) and land cover characterization from the ESA Climate Change Initiative product reclassified into their IPCC land use category equivalent. This data, originally at 0.05 degree of spatial resolution, is aggregated and analysed at regional level to provide guidance on the expected temperature impact following specific LCC transitions.

Comparison of ASTER Global Emissivity Database (ASTER-GED) With In-Situ Measurement In Italian Vulcanic Areas

NASA Astrophysics Data System (ADS)

Silvestri, M.; Musacchio, M.; Buongiorno, M. F.; Amici, S.; Piscini, A.

2015-12-01

LP DAAC released the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GED) datasets on April 2, 2014. The database was developed by the National Aeronautics and Space Administration's (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology. The database includes land surface emissivities derived from ASTER data acquired over the contiguous United States, Africa, Arabian Peninsula, Australia, Europe, and China. In this work we compare ground measurements of emissivity acquired by means of Micro-FTIR (Fourier Thermal Infrared spectrometer) instrument with the ASTER emissivity map extract from ASTER-GED and the emissivity obtained by using single ASTER data. Through this analysis we want to investigate differences existing between the ASTER-GED dataset (average from 2000 to 2008 seasoning independent) and fall in-situ emissivity measurement. Moreover the role of different spatial resolution characterizing ASTER and MODIS, 90mt and 1km respectively, by comparing them with in situ measurements. Possible differences can be due also to the different algorithms used for the emissivity estimation, Temperature and Emissivity Separation algorithm for ASTER TIR band( Gillespie et al, 1998) and the classification-based emissivity method (Snyder and al, 1998) for MODIS. In-situ emissivity measurements have been collected during dedicated fields campaign on Mt. Etna vulcano and Solfatara of Pozzuoli. Gillespie, A. R., Matsunaga, T., Rokugawa, S., & Hook, S. J. (1998). Temperature and emissivity separation from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36, 1113-1125. Snyder, W.C., Wan, Z., Zhang, Y., & Feng, Y.-Z. (1998). Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19, 2753-2574.

Putting oxygen and temperature thresholds of marine animals in context of environmental change in coastal seas: a regional perspective for the Scotian Shelf and Gulf of St. Lawrence

NASA Astrophysics Data System (ADS)

Brennan, Catherine E.; Blanchard, Hannah; Fennel, Katja

2014-05-01

We surveyed the literature in order to compile reported oxygen, temperature, salinity and depth preferences and thresholds of important marine species found in the Gulf of St. Lawrence and the Scotian Shelf regions of the northwest North Atlantic. We determined species importance based on the existence of a commercial fishery, a threatened or at risk status, or by meeting the following criteria: bycatch, baitfish, invasive, vagrant, important for ecosystem energy transfer, and predators and prey of the above species. Using the dataset compiled for the 53 regional fishes and macroinvertebrates, we rank species (including for different lifestages) by their maximum thermal limit, as well as by the lowest oxygen concentration tolerated before negative impacts (e.g. physiological stress), 50% mortality or 100% mortality are experienced. Additionally, we compare these thresholds to observed marine deoxygenation trends at multiple sites, and observed surface warming trends. This results in an assessment of which regional species are most vulnerable to future warming and oxygen depletion, and a first-order estimate of the consequences of thermal and oxygen stress on a highly productive marine shelf. If regional multi-decadal oxygen and temperature trends continue through the 21st century, many species will lose favorable oxygen conditions, experience oxygen-stress, or disappear due to insufficient oxygen. Future warming can additionally displace vulnerable species, though we note that large natural variability in environmental conditions may amplify or dampen the effects of anthropogenic surface warming trends. This dataset may be combined with regional ocean model predictions to map future species distributions.

Assessment of the Long Term Trends in Extreme Heat Events and the Associated Health Impacts in the United States

NASA Astrophysics Data System (ADS)

Bell, J.; Rennie, J.; Kunkel, K.; Herring, S.; Cullen, H. M.

2017-12-01

Land surface air temperature products have been essential for monitoring the evolution of the climate system. Before a temperature dataset is included in such reports, it is important that non-climatic influences be removed or changed so the dataset is considered homogenous. These inhomogeneities include changes in station location, instrumentation and observing practices. While many homogenized products exist on the monthly time scale, few daily products exist, due to the complication of removing breakpoints that are truly inhomogeneous rather than solely by chance (for example, sharp changes due to synoptic conditions). Recently, a sub monthly homogenized dataset has been developed using data and software provided by NOAA's National Centers for Environmental Information (NCEI). Homogeneous daily data are useful for identification and attribution of extreme heat events over a period of time. Projections of increasing temperatures are expected to result in corresponding increases in the frequency, duration, and intensity of extreme heat events. It is also established that extreme heat events can have significant public health impacts, including short-term increases in mortality and morbidity. In addition, it can exacerbate chronic health conditions in vulnerable populations, including renal and cardiovascular issues. To understand how heat events impact a specific population, it will be important to connect observations on the duration and intensity of extreme heat events with health impacts data including insurance claims and hospital admissions data. This presentation will explain the methodology to identify extreme heat events, provide a climatology of heat event onset, length and severity, and explore a case study of an anomalous heat event with available health data.

Large-scale circulation associated with moisture intrusions into the Arctic during winter

NASA Astrophysics Data System (ADS)

Woods, Cian; Caballero, Rodrigo; Svensson, Gunilla

2014-05-01

Observations during recent decades show that there is a greater near surface warming occurring in the Arctic, particularly during winter, than at lower latitudes. Understanding the mechanisms controlling surface temperature in the Arctic is therefore an important priority in climate research. The surface energy budget is a key proximate control on Arctic surface temperature. During winter, insolation is low or absent and the atmospheric boundary layer is typically very stable, limiting turbulent hear exchange, so that the surface energy budget is almost entirely governed by longwave radiation. The net surface longwave radiation (NetLW) at this time has a strikingly bimodal distribution: conditions oscillate between a 'radiatively clear' state with rapid surface heat loss and a "moist cloudy" state with NetLW ˜ 0 W m-2. Each state can persist for days or weeks at a time but transitions between them happen in a matter of hours. This distribution of NetLW has important implications for the Arctic climate, as even a small shift in the frequency of occupancy of each state would be enough to significantly affect the overall surface energy budget and thus winter sea ice thickness. The clear and cloudy states typically occur during periods of relatively high and low surface pressure respectively, suggesting a link with synoptic-scale dynamics. This suggestion is consistent with previous studies indicating that the formation of low-level and mid-level clouds over the Arctic Ocean is typically associated with cyclonic activity and passing frontal systems . More recent work has shown that intense filamentary moisture intrusion events are a common feature in the Arctic and can induce large episodic increases of longwave radiation into the surface. The poleward transport of water vapor across 70N during boreal winter is examined in the ERA-Interim reanalysis product and 16 of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models, focusing on intense moisture intrusion events. A total of 298 events are objectively identified between 1990 and 2010 in the reanalysis dataset, an average of 14 per season, accounting for 28% of the total poleward moisture transport across 70N. Composites of sea level pressure and potential temperature on the 2 potential vorticity unit surface during intrusions show a large-scale blocking pattern to the east of each basin, deflecting midlatitude cyclones and their associated moisture poleward. The interannual variability of intrusions is strongly correlated with variability in winter-mean surface downward longwave radiation and skin temperature averaged over the Arctic. The 16 CMIP5 models are validated with respect to the reanalysis dataset and a subset of 7 models is chosen as best representing intrusions. Intrusions in the representative concentration pathway 8.5 scenario (RCP8.5) from these 7 models are analyzed between 2060 and 2100. Positive trends in the moisture transported by intrusions are noted. The mechanisms behind these trends are examined in each of the models, dynamically and thermodynamically, with regard to the positioning of the storm track and climatological jets in a moistening atmosphere.

Arctic temperature and moisture trends during the past 2000 years - Progress from multiproxy-paleoclimate data compilations

NASA Astrophysics Data System (ADS)

Kaufman, Darrell; Routson, Cody; McKay, Nicholas; Beltrami, Hugo; Jaume-Santero, Fernando; Konecky, Bronwen; Saenger, Casey

2017-04-01

Instrumental climate data and climate-model projections show that Arctic-wide surface temperature and precipitation are positively correlated. Higher temperatures coincide with greater moisture by: (1) expanding the duration and source area for evaporation as sea ice retracts, (2) enhancing the poleward moisture transport, and (3) increasing the water-vapor content of the atmosphere. Higher temperature also influences evaporation rate, and therefore precipitation minus evaporation (P-E), the climate variable often sensed by paleo-hydroclimate proxies. Here, we test whether Arctic temperature and moisture also correlate on centennial timescales over the Common Era (CE). We use the new PAGES2k multiproxy-temperature dataset along with a first-pass compilation of moisture-sensitive proxy records to calculate century-scale composite timeseries, with a focus on longer records that extend back through the first millennium CE. We present a new Arctic borehole temperature reconstruction as a check on the magnitude of Little Ice Age cooling inferred from the proxy records, and we investigate the spatial pattern of centennial-scale variability. Similar to previous reconstructions, v2 of the PAGES2k proxy temperature dataset shows that, prior to the 20th century, mean annual Arctic-wide temperature decreased over the CE. The millennial-scale cooling trend is most prominent in proxy records from glacier ice, but is also registered in lake and marine sediment, and trees. In contrast, the composite of moisture-sensitive (primarily P-E) records does not exhibit a millennial-scale trend. Determining whether fluctuations in the mean state of Arctic temperature and moisture were in fact decoupled is hampered by the difficulty in detecting a significant trend within the relatively small number of spatially heterogeneous multi-proxy moisture-sensitive records. A decoupling of temperature and moisture would indicate that evaporation had a strong counterbalancing effect on precipitation and/or that shifting circulation patterns overwhelmed any multi-centennial-scale co-variability.

Temporal change and its spatial variety on land surface temperature and land use changes in the Red River Delta, Vietnam, using MODIS time-series imagery.

PubMed

Van Nguyen, On; Kawamura, Kensuke; Trong, Dung Phan; Gong, Zhe; Suwandana, Endan

2015-07-01

Temporal changes in the land surface temperature (LST) in urbanization areas are important for studying an urban heat island (UHI) and regional climate change. This study examined the LST trends under different land use categories in the Red River Delta, Vietnam, using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11A2) and land cover type product (MCD12Q1) for 11 years (2002-2012). Smoothened time-series MODIS LST data were reconstructed by the Harmonic Analysis of Time Series (HANTS) algorithm. The reconstructed LST (maximum and minimum temperatures) was assessed using the hourly air temperature dataset in two land-based meteorological stations provided by the National Climatic Data Center (NCDC). Significant correlation was obtained between MODIS LST and the air temperature for the daytime (R (2) = 0.73, root mean square error [RMSE] = 1.66 °C) and night time (R (2) = 0.84, RMSE = 1.79 °C). Statistical analysis also showed that LST trends vary strongly depending on the land cover type. Forest, wetland, and cropland had a slight tendency to decline, whereas cropland and urban had sharper increases. In urbanized areas, these increasing trends are even more obvious. This is undeniable evidence of the negative impact of urbanization on a surface urban heat island (SUHI) and global warming.

Downscaling of Remotely Sensed Land Surface Temperature with multi-sensor based products

NASA Astrophysics Data System (ADS)

Jeong, J.; Baik, J.; Choi, M.

2016-12-01

Remotely sensed satellite data provides a bird's eye view, which allows us to understand spatiotemporal behavior of hydrologic variables at global scale. Especially, geostationary satellite continuously observing specific regions is useful to monitor the fluctuations of hydrologic variables as well as meteorological factors. However, there are still problems regarding spatial resolution whether the fine scale land cover can be represented with the spatial resolution of the satellite sensor, especially in the area of complex topography. To solve these problems, many researchers have been trying to establish the relationship among various hydrological factors and combine images from multi-sensor to downscale land surface products. One of geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS), has Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI). MI performing the meteorological mission produce Rainfall Intensity (RI), Land Surface Temperature (LST), and many others every 15 minutes. Even though it has high temporal resolution, low spatial resolution of MI data is treated as major research problem in many studies. This study suggests a methodology to downscale 4 km LST datasets derived from MI in finer resolution (500m) by using GOCI datasets in Northeast Asia. Normalized Difference Vegetation Index (NDVI) recognized as variable which has significant relationship with LST are chosen to estimate LST in finer resolution. Each pixels of NDVI and LST are separated according to land cover provided from MODerate resolution Imaging Spectroradiometer (MODIS) to achieve more accurate relationship. Downscaled LST are compared with LST observed from Automated Synoptic Observing System (ASOS) for assessing its accuracy. The downscaled LST results of this study, coupled with advantage of geostationary satellite, can be applied to observe hydrologic process efficiently.

Rescuing biogeographic legacy data: The "Thor" Expedition, a historical oceanographic expedition to the Mediterranean Sea

PubMed Central

Mavraki, Dimitra; Fanini, Lucia; Tsompanou, Marilena; Gerovasileiou, Vasilis; Nikolopoulou, Stamatina; Chatzinikolaou, Eva; Plaitis, Wanda

2016-01-01

Abstract Background This article describes the digitization of a series of historical datasets based οn the reports of the 1908–1910 Danish Oceanographical Expeditions to the Mediterranean and adjacent seas. All station and sampling metadata as well as biodiversity data regarding calcareous rhodophytes, pelagic polychaetes, and fish (families Engraulidae and Clupeidae) obtained during these expeditions were digitized within the activities of the LifeWatchGreece Research Ιnfrastructure project and presented in the present paper. The aim was to safeguard public data availability by using an open access infrastructure, and to prevent potential loss of valuable historical data on the Mediterranean marine biodiversity. New information The datasets digitized here cover 2,043 samples taken at 567 stations during a time period from 1904 to 1930 in the Mediterranean and adjacent seas. The samples resulted in 1,588 occurrence records of pelagic polychaetes, fish (Clupeiformes) and calcareous algae (Rhodophyta). In addition, basic environmental data (e.g. sea surface temperature, salinity) as well as meterological conditions are included for most sampling events. In addition to the description of the digitized datasets, a detailed description of the problems encountered during the digitization of this historical dataset and a discussion on the value of such data are provided. PMID:28174510

Evaluation of Thermal State of Siberian Permafrost From Accumulated Surface Heat Flow Balance.

NASA Astrophysics Data System (ADS)

Sueyoshi, T.

2008-12-01

Permafrost exists as a response to the climatic condition and has significant longer response time than that of climate change itself. It is oftern reported the warming of permafrost in relation with recent warming. It is essential to look into the past trends of variation, since its response of to the climate change is partly determined by past condition. In this study, we use the "accumulated surface heat flow balance" as an index to discuss the year-to-year change of the thermal condition of the permafrost. This method aim to analyze the trend of the ground temperature change quantitatively, using relatively shallow-depth ground temperature data, up to several meters deep. It would be useful because deep boreholes are not always available at the field observation, while the shallow depth measurements is far easier to install. As an application of this method, we present a case of Siberian permafrost, using dataset "Russian Historical Soil Temperature Data" compiled by Zhang et al. (2001) and archived by NCAR/EOL. Some sites in this data are showing the sign of temperature rise, which should correspond to the permafrost degradation. Central Siberia is one of the key regions where a remarkable rise of ground temperature was observed recently. Our analysis provides historical information of thermal state in the region.

Comparison of land-surface humidity between observations and CMIP5 models

NASA Astrophysics Data System (ADS)

Dunn, Robert; Willett, Kate; Ciavarella, Andrew; Stott, Peter; Jones, Gareth

2017-04-01

We compare the latest observational land-surface humidity dataset, HadISDH, with the CMIP5 model archive spatially and temporally over the period 1973-2015. None of the CMIP5 models or experiments capture the observed temporal behaviour of the globally averaged relative or specific humidity over the entire study period. When using an atmosphere-only model, driven by observed sea-surface temperatures and radiative forcing changes, the behaviour of regional average temperature and specific humidity are better captured, but there is little improvement in the relative humidity. Comparing the observed and historical model climatologies show that the models are generally cooler everywhere, are drier and less saturated in the tropics and extra tropics, and have comparable moisture levels but are more saturated in the high latitudes. The spatial pattern of linear trends are relatively similar between the models and HadISDH for temperature and specific humidity, but there are large differences for relative humidity, with less moistening shown in the models over the Tropics, and very little at high atitudes. The observed temporal behaviour appears to be a robust climate feature rather than observational error. It has been previously documented and is theoretically consistent with faster warming rates over land compared to oceans. Thus, the poor replication in the models, especially in the atmosphere only model, leads to questions over future projections of impacts related to changes in surface relative humidity.

Re-approaching global iodine emissions: A novel parameterisation for sea-surface iodide concentrations using a machine learning approach

NASA Astrophysics Data System (ADS)

Sherwen, T.; Evans, M. J.; Chance, R.; Tinel, L.; Carpenter, L.

2017-12-01

Halogens (Cl, Br, I) in the troposphere have been shown to play a profound role in determining the concentrations of ozone and OH. Iodine, which is essentially oceanic in source, exerts its largest impacts on composition in both the marine boundary layer, and in the upper troposphere. This chemistry has only recently been implemented into global models and significant uncertainties remain, particularly regarding the magnitude of iodine emissions. Iodine emissions are dominated by the inorganic oxidation of iodide in the sea surface by ozone, which leads to release of gaseous inorganic iodine (HOI, I2). Critical for calculation of these fluxes is the sea-surface concentration of iodide, which is poorly constrained by observations. Previous parameterizations for sea-surface iodide concentration have focused on simple regressive relationships with sea surface temperature and another single oceanographic variables. This leads to differences in iodine fluxes of approximately a factor of two, and leads to substantial differences in the modelled impact of iodine on atmospheric composition. Here we use an expanded dataset of oceanic iodide observations, which incorporates new data that has been targeted at areas with poor coverage previously. A novel approach of multivariate machine learning techniques is applied to this expanded dataset to generate a model that yields improved estimates of the global sea surface iodide distribution. We then use a global chemical transport model (GEOS-Chem) to explore the impact of this new parameterisation on the atmospheric budget of iodine and its impact on tropospheric composition.

Sea surface temperature estimates for the mid-Piacenzian Indian Ocean—Ocean Drilling Program sites 709, 716, 722, 754, 757, 758, and 763

USGS Publications Warehouse

Robinson, Marci M.; Dowsett, Harry J.; Stoll, Danielle K.

2018-01-30

Despite the wealth of global paleoclimate data available for the warm period in the middle of the Piacenzian Stage of the Pliocene Epoch (about 3.3 to 3.0 million years ago [Ma]; Dowsett and others, 2013, and references therein), the Indian Ocean has remained a region of sparse geographic coverage in terms of microfossil analysis. In an effort to characterize the surface Indian Ocean during this interval, we examined the planktic foraminifera from Ocean Drilling Program (ODP) sites 709, 716, 722, 754, 757, 758, and 763, encompassing a wide range of oceanographic conditions. We quantitatively analyzed the data for sea surface temperature (SST) estimation using both the modern analog technique (MAT) and a factor analytic transfer function. The data will contribute to the U.S. Geological Survey (USGS) Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project’s global SST reconstruction and climate model SST boundary condition for the mid-Piacenzian and will become part of the PRISM verification dataset designed to ground-truth Pliocene climate model simulations (Dowsett and others, 2013).

A WRF sensitivity study for summer ozone and winter PM events in California

NASA Astrophysics Data System (ADS)

Zhao, Z.; Chen, J.; Mahmud, A.; Di, P.; Avise, J.; DaMassa, J.; Kaduwela, A. P.

2014-12-01

Elevated summer ozone and winter PM frequently occur in the San Joaquin Valley (SJV) and the South Coast Air Basin (SCAB) in California. Meteorological conditions, such as wind, temperature and planetary boundary layer height (PBLH) play crucial roles in these air pollution events. Therefore, accurate representation of these fields from a meteorological model is necessary to successfully reproduce these air pollution events in subsequent air quality model simulations. California's complex terrain and land-sea interface can make it challenging for meteorological models to replicate the atmospheric conditions over the SJV and SCAB during extreme pollution events. In this study, the performance of the Weather Research and Forecasting Model (WRF) over these two regions for a summer month (July 2012) and a winter month (January 2013) is evaluated with different model configurations and forcing. Different land surface schemes (Pleim-Xiu vs. hybrid scheme), the application of observational and soil nudging, two SST datasets (the Global Ocean Data Assimilation Experiment (GODAE) SST vs. the default SST from North American Regional Reanalysis (NARR) reanalysis), and two land use datasets (the National Land Cover Data (NLCD) 2006 40-category vs. USGS 24-category land use data) have been tested. Model evaluation will focus on both surface and vertical profiles for wind, temperature, relative humidity, as well as PBLH. Sensitivity of the Community Multi-scale Air Quality Model (CMAQ) results to different WRF configurations will also be presented and discussed.

Moderate-resolution sea surface temperature data for the nearshore North Pacific

USGS Publications Warehouse

Payne, Meredith C.; Reusser, Deborah A.; Lee, Henry; Brown, Cheryl A.

2011-01-01

Coastal sea surface temperature (SST) is an important environmental characteristic in determining the suitability of habitat for nearshore marine and estuarine organisms. This publication describes and provides access to an easy-to-use coastal SST dataset for ecologists, biogeographers, oceanographers, and other scientists conducting research on nearshore marine habitats or processes. The data cover the Temperate Northern Pacific Ocean as defined by the 'Marine Ecosystems of the World' (MEOW) biogeographic schema developed by The Nature Conservancy. The spatial resolution of the SST data is 4-km grid cells within 20 km of the shore. The data span a 29-year period - from September 1981 to December 2009. These SST data were derived from Advanced Very High Resolution Radiometer (AVHRR) instrument measurements compiled into monthly means as part of the Pathfinder versions 5.0 and 5.1 (PFSST V50 and V51) Project. The processing methods used to transform the data from their native Hierarchical Data Format Scientific Data Set (HDF SDS) to georeferenced, spatial datasets capable of being read into geographic information systems (GIS) software are explained. In addition, links are provided to examples of scripts involved in the data processing steps. The scripts were written in the Python programming language, which is supported by ESRI's ArcGIS version 9 or later. The processed data files are also provided in text (.csv) and Access 2003 Database (.mdb) formats. All data except the raster files include attributes identifying realm, province, and ecoregion as defined by the MEOW classification schema.

Examination of snowmelt over Western Himalayas using remote sensing data

NASA Astrophysics Data System (ADS)

Tiwari, Sarita; Kar, Sarat C.; Bhatla, R.

2016-07-01

Snowmelt variability in the Western Himalayas has been examined using remotely sensed snow water equivalent (SWE) and snow-covered area (SCA) datasets. It is seen that climatological snowfall and snowmelt amount varies in the Himalayan region from west to east and from month to month. Maximum snowmelt occurs at the elevation zone between 4500 and 5000 m. As the spring and summer approach and snowmelt begins, a large amount of snow melts in May. Strength and weaknesses of temperature-based snowmelt models have been analyzed for this region by computing the snowmelt factor or the degree-day factor (DDF). It is seen that average DDF in the Himalayas is more in April and less in July. During spring and summer months, melting rate is higher in the areas that have height above 2500 m. The region that lies between 4500 and 5000 m elevation zones contributes toward more snowmelt with higher melting rate. Snowmelt models have been developed to estimate interannual variations of monthly snowmelt amount using the DDF, observed SWE, and surface air temperature from reanalysis datasets. In order to further improve the estimate snowmelt, regression between observed and modeled snowmelt has been carried out and revised DDF values have been computed. It is found that both the models do not capture the interannual variability of snowmelt in April. The skill of the model is moderate in May and June, but the skill is relatively better in July. In order to explain this skill, interannual variability (IAV) of surface air temperature has been examined. Compared to July, in April, the IAV of temperature is large indicating that a climatological value of DDF is not sufficient to explain the snowmelt rate in April. Snow area and snow amount depletion curves over Himalayas indicate that in a small area at high altitude, snow is still observed with large SWE whereas over most of the region, all the snow has melted.

Evaluation of CMIP5 Ability to Reproduce 20th Century Regional Trends in Surface Air Temperature and Precipitation over CONUS

NASA Astrophysics Data System (ADS)

Lee, J.; Waliser, D. E.; Lee, H.; Loikith, P. C.; Kunkel, K.

2017-12-01

Monitoring temporal changes in key climate variables, such as surface air temperature and precipitation, is an integral part of the ongoing efforts of the United States National Climate Assessment (NCA). Climate models participating in CMIP5 provide future trends for four different emissions scenarios. In order to have confidence in the future projections of surface air temperature and precipitation, it is crucial to evaluate the ability of CMIP5 models to reproduce observed trends for three different time periods (1895-1939, 1940-1979, and 1980-2005). Towards this goal, trends in surface air temperature and precipitation obtained from the NOAA nClimGrid 5 km gridded station observation-based product are compared during all three time periods to the 206 CMIP5 historical simulations from 48 unique GCMs and their multi-model ensemble (MME) for NCA-defined climate regions during summer (JJA) and winter (DJF). This evaluation quantitatively examines the biases of simulated trends of the spatially averaged temperature and precipitation in the NCA climate regions. The CMIP5 MME reproduces historical surface air temperature trends for JJA for all time period and all regions, except the Northern Great Plains from 1895-1939 and Southeast during 1980-2005. Likewise, for DJF, the MME reproduces historical surface air temperature trends across all time periods over all regions except the Southeast from 1895-1939 and the Midwest during 1940-1979. The Regional Climate Model Evaluation System (RCMES), an analysis tool which supports the NCA by providing access to data and tools for regional climate model validation, facilitates the comparisons between the models and observation. The RCMES Toolkit is designed to assist in the analysis of climate variables and the procedure of the evaluation of climate projection models to support the decision-making processes. This tool is used in conjunction with the above analysis and results will be presented to demonstrate its capability to access observation and model datasets, calculate evaluation metrics, and visualize the results. Several other examples of the RCMES capabilities can be found at https://rcmes.jpl.nasa.gov.

Potential utility of three-dimensional temperature and salinity fields estimated from satellite altimetry and Argo data for improving mesoscale reproducibility in regional ocean modeling

NASA Astrophysics Data System (ADS)

Kanki, R.; Uchiyama, Y.; Miyazaki, D.; Takano, A.; Miyazawa, Y.; Yamazaki, H.

2014-12-01

Mesoscale oceanic structure and variability are required to be reproduced as accurately as possible in realistic regional ocean modeling. Uchiyama et al. (2012) demonstrated with a submesoscale eddy-resolving JCOPE2-ROMS downscaling oceanic modeling system that the mesoscale reproducibility of the Kuroshio meandering along Japan is significantly improved by introducing a simple restoration to data which we call "TS nudging" (a.k.a. robust diagnosis) where the prognostic temperature and salinity fields are weakly nudged four-dimensionally towards the assimilative JCOPE2 reanalysis (Miyazawa et al., 2009). However, there is not always a reliable reanalysis for oceanic downscaling in an arbitrary region and at an arbitrary time, and therefore alternative dataset should be prepared. Takano et al. (2009) proposed an empirical method to estimate mesoscale 3-D thermal structure from the near real-time AVISO altimetry data along with the ARGO float data based on the two-layer model of Goni et al. (1996). In the present study, we consider the TS data derived from this method as a candidate. We thus conduct a synoptic forward modeling of the Kuroshio using the JCOPE2-ROMS downscaling system to explore potential utility of this empirical TS dataset (hereinafter TUM-TS) by carrying out two runs with the T-S nudging towards 1) the JCOPE2-TS and 2) TUM-TS fields.　An example of the comparison between the two ROMS test runs is shown in the attached figure showing the annually averaged surface EKE. Both of TUM-TS and JCOPE2-TS are found to help reproducing the mesoscale variance of the Koroshio and its extension as well as its mean paths, surface KE and EKE reasonably well. Therefore, the AVISO-ARGO derived empirical 3-D TS estimation is potentially exploitable for the dataset to conduct the T-S nudging to reproduce mesoscale oceanic structure.

Pronounced centennial-scale Atlantic Ocean climate variability correlated with Western Hemisphere hydroclimate.

PubMed

Thirumalai, Kaustubh; Quinn, Terrence M; Okumura, Yuko; Richey, Julie N; Partin, Judson W; Poore, Richard Z; Moreno-Chamarro, Eduardo

2018-01-26

Surface-ocean circulation in the northern Atlantic Ocean influences Northern Hemisphere climate. Century-scale circulation variability in the Atlantic Ocean, however, is poorly constrained due to insufficiently-resolved paleoceanographic records. Here we present a replicated reconstruction of sea-surface temperature and salinity from a site sensitive to North Atlantic circulation in the Gulf of Mexico which reveals pronounced centennial-scale variability over the late Holocene. We find significant correlations on these timescales between salinity changes in the Atlantic, a diagnostic parameter of circulation, and widespread precipitation anomalies using three approaches: multiproxy synthesis, observational datasets, and a transient simulation. Our results demonstrate links between centennial changes in northern Atlantic surface-circulation and hydroclimate changes in the adjacent continents over the late Holocene. Notably, our findings reveal that weakened surface-circulation in the Atlantic Ocean was concomitant with well-documented rainfall anomalies in the Western Hemisphere during the Little Ice Age.

Pronounced centennial-scale Atlantic Ocean climate variability correlated with Western Hemisphere hydroclimate

USGS Publications Warehouse

Thirumalai, Kaustubh; Quinn, Terrence M.; Okumura, Yuko; Richey, Julie; Partin, Judson W.; Poore, Richard Z.; Moreno-Chamarro, Eduardo

2018-01-01

Surface-ocean circulation in the northern Atlantic Ocean influences Northern Hemisphere climate. Century-scale circulation variability in the Atlantic Ocean, however, is poorly constrained due to insufficiently-resolved paleoceanographic records. Here we present a replicated reconstruction of sea-surface temperature and salinity from a site sensitive to North Atlantic circulation in the Gulf of Mexico which reveals pronounced centennial-scale variability over the late Holocene. We find significant correlations on these timescales between salinity changes in the Atlantic, a diagnostic parameter of circulation, and widespread precipitation anomalies using three approaches: multiproxy synthesis, observational datasets, and a transient simulation. Our results demonstrate links between centennial changes in northern Atlantic surface-circulation and hydroclimate changes in the adjacent continents over the late Holocene. Notably, our findings reveal that weakened surface-circulation in the Atlantic Ocean was concomitant with well-documented rainfall anomalies in the Western Hemisphere during the Little Ice Age.

Comparative Perspectives on Recent Trends in Land Surface Dynamics in the Grasslands of North and South America

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Valle De Carvalho E Oliveira, P.; Zheng, B.; de Beurs, K.; Owsley, B.

2015-12-01

In our current era of intensive earth observation the time is ripe to shift away from studies relying on single sensors or single products to the synergistic use of multiple sensors and products at complementary spatial, temporal, and spectral scales. The use of multiple time series can not only reveal hotspots of change in land surface dynamics, but can indicate plausible proximate causes of the changes and suggest their possible consequences. Here we explore recent trends in the land surface dynamics of exemplary semi-arid grasslands in the western hemisphere, including the shortgrass prairie of eastern Colorado and New Mexico, the sandhills prairie of Nebraska, the "savana gramineo-lenhosa" variety of cerrado in central Brazil, and the pampas of Argentina. Observational datasets include (1) NBAR-based vegetation indices, land surface temperature, and evapotranspiration from MODIS, (2) air temperature, water vapor, and vegetation optical depth from AMSR-E and AMSR2, (3) surface air temperature, water vapor, and relative humidity from AIRS, and (4) surface shortwave, longwave, and total net flux from CERES. The spatial resolutions of these nested data include 500 m, 1000 m, 0.05 degree, 25 km, and 1 degree. We apply the nonparametric Seasonal Kendall trend test to each time series independently to identify areas of significant change. We then examine polygons of co-occurrence of significant change in two or more types of products using the surface radiation and energy budgets as guides to interpret the multiple changes. Changes occurring across broad areas are more likely to be of climatic origin; whereas, changes that are abrupt in space and time and of limited area are more likely anthropogenic. Results illustrate the utility of considering multiple remote sensing products as complementary views of land surface dynamics.

Analysis of the relationship between the monthly temperatures and weather types in Iberian Peninsula

NASA Astrophysics Data System (ADS)

Peña Angulo, Dhais; Trigo, Ricardo; Nicola, Cortesi; José Carlos, González-Hidalgo

2016-04-01

In this study, the relationship between the atmospheric circulation and weather types and the monthly average maximum and minimum temperatures in the Iberian Peninsula is modeled (period 1950-2010). The temperature data used were obtained from a high spatial resolution (10km x 10km) dataset (MOTEDAS dataset, Gonzalez-Hidalgo et al., 2015a). In addition, a dataset of Portuguese temperatures was used (obtained from the Portuguese Institute of Sea and Atmosphere). The weather type classification used was the one developed by Jenkinson and Collison, which was adapted for the Iberian Peninsula by Trigo and DaCamara (2000), using Sea Level Pressure data from NCAR/NCEP Reanalysis dataset (period 1951-2010). The analysis of the behaviour of monthly temperatures based on the weather types was carried out using a stepwise regression procedure of type forward to estimate temperatures in each cell of the considered grid, for each month, and for both maximum and minimum monthly average temperatures. The model selects the weather types that best estimate the temperatures. From the validation model it was obtained the error distribution in the time (months) and space (Iberian Peninsula). The results show that best estimations are obtained for minimum temperatures, during the winter months and in coastal areas. González-Hidalgo J.C., Peña-Angulo D., Brunetti M., Cortesi, C. (2015a): MOTEDAS: a new monthly temperature database for mainland Spain and the trend in temperature (1951-2010). International Journal of Climatology 31, 715-731. DOI: 10.1002/joc.4298

Impacts of Land Cover Changes on Climate over China

NASA Astrophysics Data System (ADS)

Chen, L.; Frauenfeld, O. W.

2014-12-01

Land cover changes can influence regional climate through modifying the surface energy balance and water fluxes, and can also affect climate at large scales via changes in atmospheric general circulation. With rapid population growth and economic development, China has experienced significant land cover changes, such as deforestation, grassland degradation, and farmland expansion. In this study, the Community Earth System Model (CESM) is used to investigate the climate impacts of anthropogenic land cover changes over China. To isolate the climatic effects of land cover change, we focus on the CAM and CLM models, with prescribed climatological sea surface temperature and sea ice cover. Two experiments were performed, one with current vegetation and the other with potential vegetation. Current vegetation conditions were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations, and potential vegetation over China was obtained from Ramankutty and Foley's global potential vegetation dataset. Impacts of land cover changes on surface air temperature and precipitation are assessed based on the difference of the two experiments. Results suggest that land cover changes have a cold-season cooling effect in a large region of China, but a warming effect in summer. These temperature changes can be reconciled with albedo forcing and evapotranspiration. Moreover, impacts on atmospheric circulation and the Asian Monsoon is also discussed.

Calibration approach and plan for the sea and land surface temperature radiometer

NASA Astrophysics Data System (ADS)

Smith, David L.; Nightingale, Tim J.; Mortimer, Hugh; Middleton, Kevin; Edeson, Ruben; Cox, Caroline V.; Mutlow, Chris T.; Maddison, Brian J.; Coppo, Peter

2014-01-01

The sea and land surface temperature radiometer (SLSTR) to be flown on the European Space Agency's (ESA) Sentinel-3 mission is a multichannel scanning radiometer that will continue the 21 year dataset of the along-track scanning radiometer (ATSR) series. As its name implies, measurements from SLSTR will be used to retrieve global sea surface temperatures to an uncertainty of <0.3 K traced to international standards. To achieve, these low uncertainties require an end-to-end instrument calibration strategy that includes prelaunch calibration at subsystem and instrument level, on-board calibration systems, and sustained postlaunch activities. The authors describe the preparations for the prelaunch calibration activities, including the spectral response, the instrument level alignment tests, and the solar and infrared radiometric calibrations. A purpose built calibration rig has been designed and built at the Rutherford Appleton Laboratory space department (RAL Space) that will accommodate the SLSTR instrument, the infrared calibration sources, and the alignment equipment. The calibration rig has been commissioned and results of these tests will be presented. Finally, the authors will present the planning for the on-orbit monitoring and calibration activities to ensure that the calibration is maintained. These activities include vicarious calibration techniques that have been developed through previous missions and the deployment of ship-borne radiometers.

Estimating Morning Change in Land Surface Temperature from MODIS Day/Night Observations: Applications for Surface Energy Balance Modeling.

PubMed

Hain, Christopher R; Anderson, Martha C

2017-10-16

Observations of land surface temperature (LST) are crucial for the monitoring of surface energy fluxes from satellite. Methods that require high temporal resolution LST observations (e.g., from geostationary orbit) can be difficult to apply globally because several geostationary sensors are required to attain near-global coverage (60°N to 60°S). While these LST observations are available from polar-orbiting sensors, providing global coverage at higher spatial resolutions, the temporal sampling (twice daily observations) can pose significant limitations. For example, the Atmosphere Land Exchange Inverse (ALEXI) surface energy balance model, used for monitoring evapotranspiration and drought, requires an observation of the morning change in LST - a quantity not directly observable from polar-orbiting sensors. Therefore, we have developed and evaluated a data-mining approach to estimate the mid-morning rise in LST from a single sensor (2 observations per day) of LST from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua platform. In general, the data-mining approach produced estimates with low relative error (5 to 10%) and statistically significant correlations when compared against geostationary observations. This approach will facilitate global, near real-time applications of ALEXI at higher spatial and temporal coverage from a single sensor than currently achievable with current geostationary datasets.

Seabird drift as a proxy to estimate surface currents in the western Mediterranean?

NASA Astrophysics Data System (ADS)

Gomez-Navarro, Laura; Sánchez-Román, Antonio; Pascual, Ananda; Fablet, Ronan; Hernandez-Carrasco, Ismael; Mason, Evan; Arcos, José Manuel; Oro, Daniel

2017-04-01

Seabird trajectories can be used as proxies to investigate the dynamics of marine systems and their spatiotemporal evolution. Previous studies have mainly been based on analyses of long range flights, where birds are travelling at high velocities over long time periods. Such data have been used to study wind patterns, and areas of avian feeding and foraging have also been used to study oceanic fronts. Here we focus on "slow moving" periods (which we associate to when birds appear to be drifting on the sea surface), in order to investigate bird drift as a proxy for sea surface currents in the western Mediterranean Sea. We analyse trajectories corresponding to "slow moving" periods recorded by GPSs attached to individuals of the species Calonectris diomedea ( Scopoli's shearwater) from mid August to mid September 2012. The trajectories are compared with sea level anomaly (SLA), sea surface temperature (SST), Finite Size Lyapunov Exponents (FSLE), wind fields, and the outputs from an automated sea-surface-height based eddy tracker. The SLA and SST datasets were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS) with a spatial resolution of 1/8 ̊ and 1/100 ̊ respectively while the FSLEs were computed from the SLA dataset. Finally, the wind data comes from the outputs of the CCMPv2 numerical model. This model has a global coverage with a spatial resolution of 1/4 ̊. Interesting relationships between the trajectories and SLA fields are found. According to the angle between the SLA gradient and the trajectories of birds, we classify drifts into three scenarios: perpendicular, parallel and other, which are associated with different driving forces. The first scenario implies that bird drift is driven by geostrophic sea surface currents. The second we associate with wind drag as the main driving force. This is validated through the wind dataset. Moreover, from the SST, FSLEs and the eddy tracker, we obtain supplementary information on the presence of oceanic structures (such as eddies or fronts), not observed in the SLA field due to its limited spatial and temporal resolutions. Therefore, this data helps to explain some of the third case scenario trajectories.

Mapping Global Ocean Surface Albedo from Satellite Observations: Models, Algorithms, and Datasets

NASA Astrophysics Data System (ADS)

Li, X.; Fan, X.; Yan, H.; Li, A.; Wang, M.; Qu, Y.

2018-04-01

Ocean surface albedo (OSA) is one of the important parameters in surface radiation budget (SRB). It is usually considered as a controlling factor of the heat exchange among the atmosphere and ocean. The temporal and spatial dynamics of OSA determine the energy absorption of upper level ocean water, and have influences on the oceanic currents, atmospheric circulations, and transportation of material and energy of hydrosphere. Therefore, various parameterizations and models have been developed for describing the dynamics of OSA. However, it has been demonstrated that the currently available OSA datasets cannot full fill the requirement of global climate change studies. In this study, we present a literature review on mapping global OSA from satellite observations. The models (parameterizations, the coupled ocean-atmosphere radiative transfer (COART), and the three component ocean water albedo (TCOWA)), algorithms (the estimation method based on reanalysis data, and the direct-estimation algorithm), and datasets (the cloud, albedo and radiation (CLARA) surface albedo product, dataset derived by the TCOWA model, and the global land surface satellite (GLASS) phase-2 surface broadband albedo product) of OSA have been discussed, separately.

The Effects of Temperature and Salinity on Mg Incorporation in Planktonic Foraminifera Globigerinoides ruber (white): Results from a Global Sediment Trap Mg/Ca Database

NASA Astrophysics Data System (ADS)

Gray, W. R.; Weldeab, S.; Lea, D. W.

2015-12-01

Mg/Ca in Globigerinoides ruber is arguably the most important proxy for sea surface temperature (SST) in tropical and sub tropical regions, and as such guides our understanding of past climatic change in these regions. However, the sensitivity of Mg/Ca to salinity is debated; while analysis of foraminifera grown in cultures generally indicates a sensitivity of 3 - 6% per salinity unit, core-top studies have suggested a much higher sensitivity of between 15 - 27% per salinity unit, bringing the utility of Mg/Ca as a SST proxy into dispute. Sediment traps circumvent the issues of dissolution and post-depositional calcite precipitation that hamper core-top calibration studies, whilst allowing the analysis of foraminifera that have calcified under natural conditions within a well constrained period of time. We collated previously published sediment trap/plankton tow G. ruber (white) Mg/Ca data, and generated new Mg/Ca data from a sediment trap located in the highly-saline tropical North Atlantic, close to West Africa. Calcification temperature and salinity were calculated for the time interval represented by each trap/tow sample using World Ocean Atlas 2013 data. The resulting dataset comprises >240 Mg/Ca measurements (in the size fraction 150 - 350 µm), that span a temperature range of 18 - 28 °C and 33.6 - 36.7 PSU. Multiple regression of the dataset reveals a temperature sensitivity of 7 ± 0.4% per °C (p < 2.2*10-16) and a salinity sensitivity of 4 ± 1% per salinity unit (p = 2*10-5). Application of this calibration has significant implications for both the magnitude and timing of glacial-interglacial temperature changes when variations in salinity are accounted for.

Accelerated warming at high elevations: a review of the current evidence and proposals for future research (Invited)

NASA Astrophysics Data System (ADS)

Pepin, N. C.

2013-12-01

Arctic amplification, whereby enhanced warming is evident at high latitudes, is well accepted amongst the scientific community. Increased warming at high elevations is more controversial and is often given the more vague term 'elevational dependency'. The way in which different approaches (mountain surface data, radiosondes, satellite data and models) often yield different results is discussed, along with the differences between these approaches. Analyses of surface data differ in the stations chosen for comparison, the time period, elevational range, and methods of trend identification. An analysis of global datasets using over a thousand stations (GHCN, CRU) and defining change by the most common method of calculating the linear gradient of a best fit line (linear regression) shows no simple relationship between warming rate and elevation. There are however feedback mechanisms in the mountain environment (e.g. cryospheric change, water vapor and treelines) which, although they may enhance warming at certain elevations, are fairly poorly understood. Warming rates are also shown to be influenced by factors in the mountain environment other than elevation, including topography (aspect, slope, topographic exposure) as well as mean annual temperature, but the relative influences of such controls have yet to be disentangled from those that show a more simple elevationally-dependent signal. Mountain summits and exposed ridge sites are shown to show least variability in warming rates, rising up above a sea of noise. Radiosondes and satellite data are further removed from changes on the ground (surface temperatures) and studies using such data tend to be rather divorced from the mountain environment and need calibration/comparison with surface datasets. Reanalyses such as NCEP/NCAR and ERA, although having good spatial coverage, tend to suffer from the same problems. Following a discussion of differences between all these approaches, a plan to develop an integrated global approach to this issue will be discussed.

Global temperature definition affects achievement of long-term climate goals

NASA Astrophysics Data System (ADS)

Richardson, Mark; Cowtan, Kevin; Millar, Richard J.

2018-05-01

The Paris Agreement on climate change aims to limit ‘global average temperature’ rise to ‘well below 2 °C’ but reported temperature depends on choices about how to blend air and water temperature data, handle changes in sea ice and account for regions with missing data. Here we use CMIP5 climate model simulations to estimate how these choices affect reported warming and carbon budgets consistent with the Paris Agreement. By the 2090s, under a low-emissions scenario, modelled global near-surface air temperature rise is 15% higher (5%–95% range 6%–21%) than that estimated by an approach similar to the HadCRUT4 observational record. The difference reduces to 8% with global data coverage, or 4% with additional removal of a bias associated with changing sea-ice cover. Comparison of observational datasets with different data sources or infilling techniques supports our model results regarding incomplete coverage. From high-emission simulations, we find that a HadCRUT4 like definition means higher carbon budgets and later exceedance of temperature thresholds, relative to global near-surface air temperature. 2 °C warming is delayed by seven years on average, to 2048 (2035–2060), and CO2 emissions budget for a >50% chance of <2 °C warming increases by 67 GtC (246 GtCO2).

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

NASA Technical Reports Server (NTRS)

Epperson, David L.; Davis, Jerry M.; Bloomfield, Peter; Karl, Thomas R.; Mcnab, Alan L.; Gallo, Kevin P.

1995-01-01

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Evaluation of terrestrial photogrammetric point clouds derived from thermal imagery

NASA Astrophysics Data System (ADS)

Metcalf, Jeremy P.; Olsen, Richard C.

2016-05-01

Computer vision and photogrammetric techniques have been widely applied to digital imagery producing high density 3D point clouds. Using thermal imagery as input, the same techniques can be applied to infrared data to produce point clouds in 3D space, providing surface temperature information. The work presented here is an evaluation of the accuracy of 3D reconstruction of point clouds produced using thermal imagery. An urban scene was imaged over an area at the Naval Postgraduate School, Monterey, CA, viewing from above as with an airborne system. Terrestrial thermal and RGB imagery were collected from a rooftop overlooking the site using a FLIR SC8200 MWIR camera and a Canon T1i DSLR. In order to spatially align each dataset, ground control points were placed throughout the study area using Trimble R10 GNSS receivers operating in RTK mode. Each image dataset is processed to produce a dense point cloud for 3D evaluation.

Topographical effects of climate dataset and their impacts on the estimation of regional net primary productivity

NASA Astrophysics Data System (ADS)

Sun, L. Qing; Feng, Feng X.

2014-11-01

In this study, we first built and compared two different climate datasets for Wuling mountainous area in 2010, one of which considered topographical effects during the ANUSPLIN interpolation was referred as terrain-based climate dataset, while the other one did not was called ordinary climate dataset. Then, we quantified the topographical effects of climatic inputs on NPP estimation by inputting two different climate datasets to the same ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS), to evaluate the importance of considering relief when estimating NPP. Finally, we found the primary contributing variables to the topographical effects through a series of experiments given an overall accuracy of the model output for NPP. The results showed that: (1) The terrain-based climate dataset presented more reliable topographic information and had closer agreements with the station dataset than the ordinary climate dataset at successive time series of 365 days in terms of the daily mean values. (2) On average, ordinary climate dataset underestimated NPP by 12.5% compared with terrain-based climate dataset over the whole study area. (3) The primary climate variables contributing to the topographical effects of climatic inputs for Wuling mountainous area were temperatures, which suggest that it is necessary to correct temperature differences for estimating NPP accurately in such a complex terrain.

A Simple Technique for Creating Regional Composites of Sea Surface Temperature from MODIS for Use in Operational Mesoscale NWP

NASA Technical Reports Server (NTRS)

Knievel, Jason C.; Rife, Daran L.; Grim, Joseph A.; Hahmann, Andrea N.; Hacker, Joshua P.; Ge, Ming; Fisher, Henry H.

2010-01-01

This paper describes a simple technique for creating regional, high-resolution, daytime and nighttime composites of sea surface temperature (SST) for use in operational numerical weather prediction (NWP). The composites are based on observations from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra. The data used typically are available nearly in real time, are applicable anywhere on the globe, and are capable of roughly representing the diurnal cycle in SST. The composites resolution is much higher than that of many other standard SST products used for operational NWP, including the low- and high-resolution Real-Time Global (RTG) analyses. The difference in resolution is key because several studies have shown that highly resolved SSTs are important for driving the air sea interactions that shape patterns of static stability, vertical and horizontal wind shear, and divergence in the planetary boundary layer. The MODIS-based composites are compared to in situ observations from buoys and other platforms operated by the National Data Buoy Center (NDBC) off the coasts of New England, the mid-Atlantic, and Florida. Mean differences, mean absolute differences, and root-mean-square differences between the composites and the NDBC observations are all within tenths of a degree of those calculated between RTG analyses and the NDBC observations. This is true whether or not one accounts for the mean offset between the skin temperatures of the MODIS dataset and the bulk temperatures of the NDBC observations and RTG analyses. Near the coast, the MODIS-based composites tend to agree more with NDBC observations than do the RTG analyses. The opposite is true away from the coast. All of these differences in point-wise comparisons among the SST datasets are small compared to the 61.08C accuracy of the NDBC SST sensors. Because skin-temperature variations from land to water so strongly affect the development and life cycle of the sea breeze, this phenomenon was chosen for demonstrating the use of the MODIS-based composite in an NWP model. A simulated sea breeze in the vicinity of New York City and Long Island shows a small, net, but far from universal improvement when MODIS-based composites are used in place of RTG analyses. The timing of the sea breeze s arrival is more accurate at some stations, and the near-surface temperature, wind, and humidity within the breeze are more realistic.

A high space-time resolution dataset linking meteorological forcing and hydro-sedimentary response in a mesoscale Mediterranean catchment (Auzon) of the Ardèche region, France

NASA Astrophysics Data System (ADS)

Nord, Guillaume; Boudevillain, Brice; Berne, Alexis; Branger, Flora; Braud, Isabelle; Dramais, Guillaume; Gérard, Simon; Le Coz, Jérôme; Legoût, Cédric; Molinié, Gilles; Van Baelen, Joel; Vandervaere, Jean-Pierre; Andrieu, Julien; Aubert, Coralie; Calianno, Martin; Delrieu, Guy; Grazioli, Jacopo; Hachani, Sahar; Horner, Ivan; Huza, Jessica; Le Boursicaud, Raphaël; Raupach, Timothy H.; Teuling, Adriaan J.; Uber, Magdalena; Vincendon, Béatrice; Wijbrans, Annette

2017-03-01

A comprehensive hydrometeorological dataset is presented spanning the period 1 January 2011-31 December 2014 to improve the understanding of the hydrological processes leading to flash floods and the relation between rainfall, runoff, erosion and sediment transport in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. Badlands are present in the Auzon catchment and well connected to high-gradient channels of bedrock rivers which promotes the transfer of suspended solids downstream. The number of observed variables, the various sensors involved (both in situ and remote) and the space-time resolution ( ˜ km2, ˜ min) of this comprehensive dataset make it a unique contribution to research communities focused on hydrometeorology, surface hydrology and erosion. Given that rainfall is highly variable in space and time in this region, the observation system enables assessment of the hydrological response to rainfall fields. Indeed, (i) rainfall data are provided by rain gauges (both a research network of 21 rain gauges with a 5 min time step and an operational network of 10 rain gauges with a 5 min or 1 h time step), S-band Doppler dual-polarization radars (1 km2, 5 min resolution), disdrometers (16 sensors working at 30 s or 1 min time step) and Micro Rain Radars (5 sensors, 100 m height resolution). Additionally, during the special observation period (SOP-1) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). (ii) Other meteorological data are taken from the operational surface weather observation stations of Météo-France (including 2 m air temperature, atmospheric pressure, 2 m relative humidity, 10 m wind speed and direction, global radiation) at the hourly time resolution (six stations in the region of interest). (iii) The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations estimate water discharge at a 2-10 min time resolution. Two of these stations also measure additional physico-chemical variables (turbidity, temperature, conductivity) and water samples are collected automatically during floods, allowing further geochemical characterization of water and suspended solids. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 sensors installed in the intermittent hydrographic network continuously measures water level and water temperature in headwater subcatchments (from 0.17 to 116 km2) at a time resolution of 2-5 min. A network of soil moisture sensors enables the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, concomitant observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. Finally, this dataset is considered appropriate for understanding the rainfall variability in time and space at fine scales, improving areal rainfall estimations and progressing in distributed hydrological and erosion modelling. DOI of the referenced dataset: doi:10.6096/MISTRALS-HyMeX.1438.

NATIONAL HYDROGRAPHY DATASET

EPA Science Inventory

Resource Purpose:The National Hydrography Dataset (NHD) is a comprehensive set of digital spatial data that contains information about surface water features such as lakes, ponds, streams, rivers, springs and wells. Within the NHD, surface water features are combined to fo...

Partitioning Evapotranspiration in Semiarid Grassland and Shrubland Ecosystems Using Diurnal Surface Temperature Variation

NASA Technical Reports Server (NTRS)

Moran, M. Susan; Scott, Russell L.; Keefer, Timothy O.; Paige, Ginger B.; Emmerich, William E.; Cosh, Michael H.; O'Neill, Peggy E.

2007-01-01

The encroachment of woody plants in grasslands across the Western U.S. will affect soil water availability by altering the contributions of evaporation (E) and transpiration (T) to total evapotranspiration (ET). To study this phenomenon, a network of flux stations is in place to measure ET in grass- and shrub-dominated ecosystems throughout the Western U.S. A method is described and tested here to partition the daily measurements of ET into E and T based on diurnal surface temperature variations of the soil and standard energy balance theory. The difference between the mid-afternoon and pre-dawn soil surface temperature, termed Apparent Thermal Inertia (I(sub A)), was used to identify days when E was negligible, and thus, ET=T. For other days, a three-step procedure based on energy balance equations was used to estimate Qe contributions of daily E and T to total daily ET. The method was tested at Walnut Gulch Experimental Watershed in southeast Arizona based on Bowen ratio estimates of ET and continuous measurements of surface temperature with an infrared thermometer (IRT) from 2004- 2005, and a second dataset of Bowen ratio, IRT and stem-flow gage measurements in 2003. Results showed that reasonable estimates of daily T were obtained for a multi-year period with ease of operation and minimal cost. With known season-long daily T, E and ET, it is possible to determine the soil water availability associated with grass- and shrub-dominated sites and better understand the hydrologic impact of regional woody plant encroachment.

Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

NASA Astrophysics Data System (ADS)

Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

2016-06-01

The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

Modeling Lunar Borehole Temperature in order to Reconstruct Historical Total Solar Irradiance and Estimate Surface Temperature in Permanently Shadowed Regions

NASA Astrophysics Data System (ADS)

Wen, G.; Cahalan, R. F.; Miyahara, H.; Ohmura, A.

2007-12-01

The Moon is an ideal place to reconstruct historical total solar irradiance (TSI). With undisturbed lunar surface albedo and the very low thermal diffusivity of lunar regolith, changes in solar input lead to changes in lunar surface temperature that diffuse downward to be recorded in the temperature profile in the near-surface layer. Using regolith thermal properties from Apollo, we model the heat transfer in the regolith layer, and compare modeled surface temperature to Apollo observations to check model performance. Using as alternative input scenarios two reconstructed TSI time series from 1610 to 2000 (Lean, 2000; Wang, Lean, and Sheeley 2005), we conclude that the two scenarios can be distinguished by detectable differences in regolith temperature, with the peak difference of about 10 mK occuring at a depth of about 10 m (Miyahara et al., 2007). The possibility that water ice exists in permanently shadowed areas near the lunar poles (Nozette et al., 1997; Spudis et al, 1998), makes it of interest to estimate surface temperature in such dark regions. "Turning off" the Sun in our time dependent model, we found it would take several hundred years for the surface temperature to drop from ~~100K immediately after sunset down to a nearly constant equilibrium temperature of about 24~~38 K, with the range determined by the range of possible input from Earth, from 0 W/m2 without Earth visible, up to about 0.1 W/m2 at maximum Earth phase. A simple equilibrium model (e.g., Huang 2007) is inappropriate to relate the Apollo-observed nighttime temperature to Earth's radiation budget, given the long multi- centennial time scale needed for equilibration of the lunar surface layer after sunset. Although our results provide the key mechanisms for reconstructing historical TSI, further research is required to account for topography of lunar surfaces, and new measurements of regolith thermal properties will also be needed once a new base of operations is established. References Huang, S., (2007), Surface Temperatures at the Nearside of the Moon as a Record of the Radiation Budget of Earth's Climate System, Advances in Space Research, doi:10.1016/j.asr.2007.04.093. Lean, J., Geophys. Res. Lett., (2000), 27(16), 2425-2428. Miyahara, H., G. Wen, R. F. Cahalan, and A. Ohmura, (2007), Deriving Historical Total Solar Irradiance from Lunar Borehole Temperatures, submitted to Geophy. Res. Lett. Nozette, S., E. M. Shoemaker, P. D. Spudis, and C. L. Lichtenberg, The possibility of ice on the Moon, Science, 278, 144-145, 1997. Spudis, P.D., T. Cook, M. Robinson, B. Bussey, and B. Fessler, Topography of the southe polar region from Clementine stereo imaging, New views of the Moon, Integrated remotely sensed, geophysical, and sample datasets, Lunar Planet. Inst., [CD-ROM], abstract 6010, 1998. Wang, Y. M., J. L. Lean and N. R. Sheeley (2005), Astrophys. J., 625, 522-538.

Potential distribution dataset of honeybees in Indian Ocean Islands: Case study of Zanzibar Island.

PubMed

Mwalusepo, Sizah; Muli, Eliud; Nkoba, Kiatoko; Nguku, Everlyn; Kilonzo, Joseph; Abdel-Rahman, Elfatih M; Landmann, Tobias; Fakih, Asha; Raina, Suresh

2017-10-01

Honeybees ( Apis mellifera ) are principal insect pollinators, whose worldwide distribution and abundance is known to largely depend on climatic conditions. However, the presence records dataset on potential distribution of honeybees in Indian Ocean Islands remain less documented. Presence records in shape format and probability of occurrence of honeybees with different temperature change scenarios is provided in this article across Zanzibar Island. Maximum entropy (Maxent) package was used to analyse the potential distribution of honeybees. The dataset provides information on the current and future distribution of the honey bees in Zanzibar Island. The dataset is of great importance for improving stakeholders understanding of the role of temperature change on the spatial distribution of honeybees.

Diurnal Ensemble Surface Meteorology Statistics

EPA Pesticide Factsheets

Excel file containing diurnal ensemble statistics of 2-m temperature, 2-m mixing ratio and 10-m wind speed. This Excel file contains figures for Figure 2 in the paper and worksheets containing all statistics for the 14 members of the ensemble and a base simulation.This dataset is associated with the following publication:Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Jin -Ho

Amazon rainfall is subject to year-to-year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the Sea Surface Temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational datasets were used to support the Atlantic influence on Amazon rainfall. Furthermore, it is found that multiple global climate models do reproduce the Atlantic-Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.

Temperature and dust profiles of Mars' atmosphere derived from Mars Climate Sounder (Mars Reconnaissance Orbiter)

NASA Astrophysics Data System (ADS)

Teanby, N. A.; Irwin, P. G.; Howett, C.; Calcutt, S. B.; Lolachi, R.; Bowles, N. E.; Taylor, F. W.; Schofield, J. T.; Kleinboehl, A.; McCleese, D. J.

2007-12-01

Mars Climate Sounder (MCS) on board NASA's Mars Reconnaissance Orbiter (MRO) primarily operates as a limb sounding infrared radiometer. The small field of view and limb scanning mode allow retrieval of atmospheric temperature and dust properties from the surface up to approximately 80km with 5km vertical resolution. The polar orbit of MRO gives coverage of all latitudes at 3pm and 3am Mars local-time. The ability of MCS to sounds these altitudes at high spatial and temporal resolution gives a unique dataset with which to test our understanding of the Martian atmosphere. It also complements and extends upon previous climatalogical datasets (for example TES). Measured mid-infrared radiances from MCS were analysed using the correlated-k approximation with Oxford's NEMESIS retrieval software. The correlated-k approximation was compared with a line-by-line model to confirm its accuracy under Martian atmospheric conditions. Dust properties were taken from analysis of TES data by Wolff and Clancy (2003). We present profiles of temperature and dust for data covering September to December 2006. During this period Mars' north pole was experiencing summer and the south pole was in winter. Preliminary results show that high altitude warming over the southern winter pole is greater than that predicted by models. Our results will be compared to numerical models of the Martian atmosphere and the implications discussed.

Vertical Profiles Of Temperature And Dust Derived From Mars Climate Sounder

NASA Astrophysics Data System (ADS)

Teanby, Nicholas; Irwin, P. G.; Howett, C.; Calcutt, S.; Lolachi, R.; Bowles, N.; Taylor, F.; Schofield, J. T.; Kleinboehl, A.; McCleese, D. J.

2007-10-01

Mars Climate Sounder (MCS) on board NASA's Mars Reconnaissance Orbiter (MRO) primarily operates as a limb sounding infrared radiometer. The small field of view and limb scanning mode allow retrieval of temperature and dust properties from the surface up to approximately 80km with 5km vertical resolution. The polar orbit of MRO gives coverage of all latitudes at 3pm and 3am local time. The ability of MCS to sounds these altitudes at high spatial and temporal resolution gives a unique dataset with which to test our understanding of the Martian atmosphere. It also complements and extends upon previous climatalogical datasets (for example TES). Measured mid-infrared radiances from MCS were analysed using the correlated-k approximation with Oxford's NEMESIS retrieval software. The correlated-k approximation was compared with a line-by-line model to confirm its accuracy under Martian atmospheric conditions. Dust properties were taken from analysis of TES data by Wolff and Clancy (2003). We present profiles of temperature and dust for data covering September to December 2006. During this period Mars' north pole was experiencing summer and the south pole was in winter. Preliminary results show that high altitude warming over the southern winter pole is greater than that predicted by models. Our results will be compared to numerical models of the Martian atmosphere and the implications discussed.

Simulating Virtual Terminal Area Weather Data Bases for Use in the Wake Vortex Avoidance System (Wake VAS) Prediction Algorithm

NASA Technical Reports Server (NTRS)

Kaplan, Michael L.; Lin, Yuh-Lang

2004-01-01

During the research project, sounding datasets were generated for the region surrounding 9 major airports, including Dallas, TX, Boston, MA, New York, NY, Chicago, IL, St. Louis, MO, Atlanta, GA, Miami, FL, San Francico, CA, and Los Angeles, CA. The numerical simulation of winter and summer environments during which no instrument flight rule impact was occurring at these 9 terminals was performed using the most contemporary version of the Terminal Area PBL Prediction System (TAPPS) model nested from 36 km to 6 km to 1 km horizontal resolution and very detailed vertical resolution in the planetary boundary layer. The soundings from the 1 km model were archived at 30 minute time intervals for a 24 hour period and the vertical dependent variables as well as derived quantities, i.e., 3-dimensional wind components, temperatures, pressures, mixing ratios, turbulence kinetic energy and eddy dissipation rates were then interpolated to 5 m vertical resolution up to 1000 m elevation above ground level. After partial validation against field experiment datasets for Dallas as well as larger scale and much coarser resolution observations at the other 8 airports, these sounding datasets were sent to NASA for use in the Virtual Air Space and Modeling program. The application of these datasets being to determine representative airport weather environments to diagnose the response of simulated wake vortices to realistic atmospheric environments. These virtual datasets are based on large scale observed atmospheric initial conditions that are dynamically interpolated in space and time. The 1 km nested-grid simulated datasets providing a very coarse and highly smoothed representation of airport environment meteorological conditions. Details concerning the airport surface forcing are virtually absent from these simulated datasets although the observed background atmospheric processes have been compared to the simulated fields and the fields were found to accurately replicate the flows surrounding the airport where coarse verification data were available as well as where airport scale datasets were available.

Influence of Anthropogenic Climate Change on Planetary Wave Resonance and Extreme Weather Events.

PubMed

Mann, Michael E; Rahmstorf, Stefan; Kornhuber, Kai; Steinman, Byron A; Miller, Sonya K; Coumou, Dim

2017-03-27

Persistent episodes of extreme weather in the Northern Hemisphere summer have been shown to be associated with the presence of high-amplitude quasi-stationary atmospheric Rossby waves within a particular wavelength range (zonal wavenumber 6-8). The underlying mechanistic relationship involves the phenomenon of quasi-resonant amplification (QRA) of synoptic-scale waves with that wavenumber range becoming trapped within an effective mid-latitude atmospheric waveguide. Recent work suggests an increase in recent decades in the occurrence of QRA-favorable conditions and associated extreme weather, possibly linked to amplified Arctic warming and thus a climate change influence. Here, we isolate a specific fingerprint in the zonal mean surface temperature profile that is associated with QRA-favorable conditions. State-of-the-art ("CMIP5") historical climate model simulations subject to anthropogenic forcing display an increase in the projection of this fingerprint that is mirrored in multiple observational surface temperature datasets. Both the models and observations suggest this signal has only recently emerged from the background noise of natural variability.

Refining multi-model projections of temperature extremes by evaluation against land-atmosphere coupling diagnostics

NASA Astrophysics Data System (ADS)

Sippel, Sebastian; Zscheischler, Jakob; Mahecha, Miguel D.; Orth, Rene; Reichstein, Markus; Vogel, Martha; Seneviratne, Sonia I.

2017-05-01

The Earth's land surface and the atmosphere are strongly interlinked through the exchange of energy and matter. This coupled behaviour causes various land-atmosphere feedbacks, and an insufficient understanding of these feedbacks contributes to uncertain global climate model projections. For example, a crucial role of the land surface in exacerbating summer heat waves in midlatitude regions has been identified empirically for high-impact heat waves, but individual climate models differ widely in their respective representation of land-atmosphere coupling. Here, we compile an ensemble of 54 combinations of observations-based temperature (T) and evapotranspiration (ET) benchmarking datasets and investigate coincidences of T anomalies with ET anomalies as a proxy for land-atmosphere interactions during periods of anomalously warm temperatures. First, we demonstrate that a large fraction of state-of-the-art climate models from the Coupled Model Intercomparison Project (CMIP5) archive produces systematically too frequent coincidences of high T anomalies with negative ET anomalies in midlatitude regions during the warm season and in several tropical regions year-round. These coincidences (high T, low ET) are closely related to the representation of temperature variability and extremes across the multi-model ensemble. Second, we derive a land-coupling constraint based on the spread of the T-ET datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour that is compatible with these benchmark estimates. The constrained multi-model simulations exhibit more realistic temperature extremes of reduced magnitude in present climate in regions where models show substantial spread in T-ET coupling, i.e. biases in the model ensemble are consistently reduced. Also the multi-model simulations for the coming decades display decreased absolute temperature extremes in the constrained ensemble. On the other hand, the differences between projected and present-day climate extremes are affected to a lesser extent by the applied constraint, i.e. projected changes are reduced locally by around 0.5 to 1 °C - but this remains a local effect in regions that are highly sensitive to land-atmosphere coupling. In summary, our approach offers a physically consistent, diagnostic-based avenue to evaluate multi-model ensembles and subsequently reduce model biases in simulated and projected extreme temperatures.

Sensitivity of Crop Gross Primary Production Simulations to In-situ and Reanalysis Meteorological Data

NASA Astrophysics Data System (ADS)

Jin, C.; Xiao, X.; Wagle, P.

2014-12-01

Accurate estimation of crop Gross Primary Production (GPP) is important for food securityand terrestrial carbon cycle. Numerous publications have reported the potential of the satellite-based Production Efficiency Models (PEMs) to estimate GPP driven by in-situ climate data. Simulations of the PEMs often require surface reanalysis climate data as inputs, for example, the North America Regional Reanalysis datasets (NARR). These reanalysis datasets showed certain biases from the in-situ climate datasets. Thus, sensitivity analysis of the PEMs to the climate inputs is needed before their application at the regional scale. This study used the satellite-based Vegetation Photosynthesis Model (VPM), which is driven by solar radiation (R), air temperature (T), and the satellite-based vegetation indices, to quantify the causes and degree of uncertainties in crop GPP estimates due to different meteorological inputs at the 8-day interval (in-situ AmeriFlux data and NARR surface reanalysis data). The NARR radiation (RNARR) explained over 95% of the variability in in-situ RAF and TAF measured from AmeriFlux. The bais of TNARR was relatively small. However, RNARR had a systematical positive bias of ~3.5 MJ m-2day-1 from RAF. A simple adjustment based on the spatial statistic between RNARR and RAF produced relatively accurate radiation data for all crop site-years by reducing RMSE from 4 to 1.7 MJ m-2day-1. The VPM-based GPP estimates with three climate datasets (i.e., in-situ, and NARR before and after adjustment, GPPVPM,AF, GPPVPM,NARR, and GPPVPM,adjNARR) showed good agreements with the seasonal dynamics of crop GPP derived from the flux towers (GPPAF). The GPPVPM,AF differed from GPPAF by 2% for maize, and -8% to -12% for soybean on the 8-day interval. The positive bias of RNARR resulted in an overestimation of GPPVPM,NARR at both maize and soybean systems. However, GPPVPM,adjNARR significantly reduced the uncertainties of the maize GPP from 25% to 2%. The results from this study revealed that the errors of the NARR surface reanalysis data introduced significant uncertainties of the PEMs-based GPP estimates. Therefore, it is important to develop more accurate radiation datasets at the regional and global scales to estimate gross and net primary production of terrestrial ecosystems at the regional and global scales.

Maine Geological Survey Borehole Temperature Profiles

DOE Data Explorer

Marvinney, Robert

2013-11-06

This dataset includes temperature profiles from 30 boreholes throughout Maine that were selected for their depth, location, and lithologies encountered. Depths range from about 300 feet to 2,200 feet. Most of the boreholes selected for measurement were completed in granite because this lithology can be assumed to be nearly homogeneous over the depth of the borehole. Boreholes were also selected to address gaps in existing geothermal datasets. Temperature profiles were collected in October and November, 2012.

A normalisation framework for (hyper-)spectral imagery

NASA Astrophysics Data System (ADS)

Grumpe, Arne; Zirin, Vladimir; Wöhler, Christian

2015-06-01

It is well known that the topography has an influence on the observed reflectance spectra. This influence is not compensated by spectral ratios, i.e. the effect is wavelength dependent. In this work, we present a complete normalisation framework. The surface temperature is estimated based on the measured surface reflectance. To normalise the spectral reflectance with respect to a standard illumination geometry, spatially varying reflectance parameters are estimated based on a non-linear reflectance model. The reflectance parameter estimation has one free parameter, i.e. a low-pass function, which sets the scale of the spatial-variance, i.e. the lateral resolution of the reflectance parameter maps. Since the local surface topography has a major influence on the measured reflectance, often neglected shading information is extracted from the spectral imagery and an existing topography model is refined to image resolution. All methods are demonstrated on the Moon Mineralogy Mapper dataset. Additionally, two empirical methods are introduced that deal with observed systematic reflectance changes in co-registered images acquired at different phase angles. These effects, however, may also be caused by the sensor temperature, due to its correlation with the phase angle. Surface temperatures above 300 K are detected and are very similar to a reference method. The proposed method, however, seems more robust in case of absorptions visible in the reflectance spectrum near 2000 nm. By introducing a low-pass into the computation of the reflectance parameters, the reflectance behaviour of the surfaces may be derived at different scales. This allows for an iterative refinement of the local surface topography using shape from shading and the computation reflectance parameters. The inferred parameters are derived from all available co-registered images and do not show significant influence of the local surface topography. The results of the empirical correction show that both proposed methods greatly reduce the influence of different phase angles or sensor temperatures.

Hydrology Research with the North American Land Data Assimilation System (NLDAS) Datasets at the NASA GES DISC Using Giovanni

NASA Technical Reports Server (NTRS)

Mocko, David M.; Rui, Hualan; Acker, James G.

2013-01-01

The North American Land Data Assimilation System (NLDAS) is a collaboration project between NASA/GSFC, NOAA, Princeton Univ., and the Univ. of Washington. NLDAS has created a surface meteorology dataset using the best-available observations and reanalyses the backbone of this dataset is a gridded precipitation analysis from rain gauges. This dataset is used to drive four separate land-surface models (LSMs) to produce datasets of soil moisture, snow, runoff, and surface fluxes. NLDAS datasets are available hourly and extend from Jan 1979 to near real-time with a typical 4-day lag. The datasets are available at 1/8th-degree over CONUS and portions of Canada and Mexico from 25-53 North. The datasets have been extensively evaluated against observations, and are also used as part of a drought monitor. NLDAS datasets are available from the NASA GES DISC and can be accessed via ftp, GDS, Mirador, and Giovanni. GES DISC news articles were published showing figures from the heat wave of 2011, Hurricane Irene, Tropical Storm Lee, and the low-snow winter of 2011-2012. For this presentation, Giovanni-generated figures using NLDAS data from the derecho across the U.S. Midwest and Mid-Atlantic will be presented. Also, similar figures will be presented from the landfall of Hurricane Isaac and the before-and-after drought conditions of the path of the tropical moisture into the central states of the U.S. Updates on future products and datasets from the NLDAS project will also be introduced.

Research Applications of Data from Arctic Ocean Drifting Platforms: The Arctic Buoy Program and the Environmental Working Group CD's.

NASA Astrophysics Data System (ADS)

Moritz, R. E.; Rigor, I.

2006-12-01

ABSTRACT: The Arctic Buoy Program was initiated in 1978 to measure surface air pressure, surface temperature and sea-ice motion in the Arctic Ocean, on the space and time scales of synoptic weather systems, and to make the data available for research, forecasting and operations. The program, subsequently renamed the International Arctic Buoy Programme (IABP), has endured and expanded over the past 28 years. A hallmark of the IABP is the production, dissemination and archival of research-quality datasets and analyses. These datasets have been used by the authors of over 500 papers on meteorolgy, sea-ice physics, oceanography, air-sea interactions, climate, remote sensing and other topics. Elements of the IABP are described briefly, including measurements, analysis, data dissemination and data archival. Selected highlights of the research applications are reviewed, including ice dynamics, ocean-ice modeling, low-frequency variability of Arctic air-sea-ice circulation, and recent changes in the age, thickness and extent of Arctic Sea-ice. The extended temporal coverage of the data disseminated on the Environmental Working Group CD's is important for interpreting results in the context of climate.

Radiation Climatology of the Greenland Ice Sheet Derived from Greenland Climate Network Data

NASA Technical Reports Server (NTRS)

Steffen, Konrad; Box, Jason

2003-01-01

The magnitude of shortwave and longwave dative fluxes are critical to surface energy balance variations over the Greenland ice sheet, affecting many aspects of its climate, including melt rates, the nature of low-level temperature inversions, the katabatic wind regime and buoyant stability of the atmosphere. Nevertheless, reliable measurements of the radiative fluxes over the ice sheet are few in number, and have been of limited duration and areal distribution (e.g. Ambach, 1960; 1963, Konzelmann et al., 1994, Harding et al., 1995, Van den Broeke, 1996). Hourly GC-Net radiation flux measurements spanning 1995-2001 period have been used to produce a monthly dataset of surface radiation balance components. The measurements are distributed widely across Greenland and incorporate multiple sensors

New Insights in Tropospheric Ozone and its Variability

NASA Technical Reports Server (NTRS)

Oman, Luke D.; Douglass, Anne R.; Ziemke, Jerry R.; Rodriquez, Jose M.

2011-01-01

We have produced time-slice simulations using the Goddard Earth Observing System Version 5 (GEOS-5) coupled to a comprehensive stratospheric and tropospheric chemical mechanism. These simulations are forced with observed sea surface temperatures over the past 25 years and use constant specified surface emissions, thereby providing a measure of the dynamically controlled ozone response. We examine the model performance in simulating tropospheric ozone and its variability. Here we show targeted comparisons results from our simulations with a multi-decadal tropical tropospheric column ozone dataset obtained from satellite observations of total column ozone. We use SHADOZ ozonesondes to gain insight into the observed vertical response and compare with the simulated vertical structure. This work includes but is not limited to ENSO related variability.

The Surface Radiation Budget over Oceans and Continents.

NASA Astrophysics Data System (ADS)

Garratt, J. R.; Prata, A. J.; Rotstayn, L. D.; McAvaney, B. J.; Cusack, S.

1998-08-01

An updated evaluation of the surface radiation budget in climate models (1994-96 versions; seven datasets available, with and without aerosols) and in two new satellite-based global datasets (with aerosols) is presented. All nine datasets capture the broad mean monthly zonal variations in the flux components and in the net radiation, with maximum differences of some 100 W m2 occurring in the downwelling fluxes at specific latitudes. Using long-term surface observations, both from land stations and the Pacific warm pool (with typical uncertainties in the annual values varying between ±5 and 20 W m2), excess net radiation (RN) and downwelling shortwave flux density (So) are found in all datasets, consistent with results from earlier studies [for global land, excesses of 15%-20% (12 W m2) in RN and about 12% (20 W m2) in So]. For the nine datasets combined, the spread in annual fluxes is significant: for RN, it is 15 (50) W m2 over global land (Pacific warm pool) in an observed annual mean of 65 (135) W m2; for So, it is 25 (60) W m2 over land (warm pool) in an annual mean of 176 (197) W m2.The effects of aerosols are included in three of the authors' datasets, based on simple aerosol climatologies and assumptions regarding aerosol optical properties. They offer guidance on the broad impact of aerosols on climate, suggesting that the inclusion of aerosols in models would reduce the annual So by 15-20 W m2 over land and 5-10 W m2 over the oceans. Model differences in cloud cover contribute to differences in So between datasets; for global land, this is most clearly demonstrated through the effects of cloud cover on the surface shortwave cloud forcing. The tendency for most datasets to underestimate cloudiness, particularly over global land, and possibly to underestimate atmospheric water vapor absorption, probably contributes to the excess downwelling shortwave flux at the surface.

Influence of rock strength variations on interpretation of thermochronologic data

NASA Astrophysics Data System (ADS)

Flowers, Rebecca; Ehlers, Todd

2017-04-01

Low temperature thermochronologic datasets are the primary means for estimating the timing, magnitude, and rates of erosion over extended (10s to 100s of Ma) timescales. Typically, abrupt shifts in cooling rates recorded by thermochronologic data are interpreted as changes in erosion rates caused by shifts in uplift rates, drainage patterns, or climate. However, recent work shows that different rock types vary in strength and erodibility by as much as several orders of magnitude, therefore implying that lithology should be an important control on how landscapes change through time and the thermochronometer record of erosion histories. Attention in the surface processes community has begun to focus on rock strength as a critical control on short-term (Ka to Ma) landscape evolution, but there has been less consideration of the influence of this factor on landscapes over longer intervals. If intrinsic lithologic variability can strongly modify erosion rates without changes in external factors, this result would have important implications for how thermochronologic datasets are interpreted. Here we evaluate the importance of rock strength for interpreting thermochronologic datasets by examining erosion rates and total denudation magnitudes across sedimentary rock-crystalline basement rock interfaces. We particularly focus on the 'Great Unconformity', a global stratigraphic surface between Phanerozoic sedimentary rocks and Precambrian crystalline basement, which based on rock strength studies marks a dramatic rock erodibility contrast across which erosion rates should decelerate. In the Rocky Mountain basement uplifts of the western U.S., thermochronologic data and geologic observations indicate that erosion rates were high during latest Cretaceous-early Tertiary denudation of the sedimentary cover (3-4 km over 10 m.y., 300-400 m/m.y.) but dramatically decelerated when less erodible basement rocks were encountered (0.1-0.5 km over 55 m.y., 2-9 m/m.y.). Similarly, the western Canadian shield underwent multiple Phanerozoic episodes of substantial (1-4 km) sedimentary rock burial and erosion, but total Phanerozoic erosion of the crystalline basement below the Great Unconformity was no more than a few hundred meters. We use published low temperature thermochronologic dates, the LandLab landscape evolution model, and 1D thermokinematic and erosion (Pecube) models to assess whether the observed deceleration of erosion can be explained by measured variations in rock strength alone. We use these results to consider the extent to which rock strength can change the cooling history recorded by thermochronologic datasets.

Dataset used to improve liquid water absorption models in the microwave

DOE Data Explorer

Turner, David

2015-12-14

Two datasets, one a compilation of laboratory data and one a compilation from three field sites, are provided here. These datasets provide measurements of the real and imaginary refractive indices and absorption as a function of cloud temperature. These datasets were used in the development of the new liquid water absorption model that was published in Turner et al. 2015.

The creation of future daily gridded datasets of precipitation and temperature with a spatial weather generator, Cyprus 2020-2050

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Pashiardis, Stelios; Lange, Manfred

2014-05-01

High-resolution gridded daily datasets are essential for natural resource management and the analysis of climate changes and their effects. This study aimed to create gridded datasets of daily precipitation and daily minimum and maximum temperature, for the future (2020-2050). The horizontal resolution of the developed datasets is 1 x 1 km2, covering the area under control of the Republic of Cyprus (5.760 km2). The study is divided into two parts. The first consists of the evaluation of the performance of different interpolation techniques for daily rainfall and temperature data (1980-2010) for the creation of the gridded datasets. Rainfall data recorded at 145 stations and temperature data from 34 stations were used. For precipitation, inverse distance weighting (IDW) performs best for local events, while a combination of step-wise geographically weighted regression and IDW proves to be the best method for large scale events. For minimum and maximum temperature, a combination of step-wise linear multiple regression and thin plate splines is recognized as the best method. Six Regional Climate Models (RCMs) for the A1B SRES emission scenario from the EU ENSEMBLE project database were selected as sources for future climate projections. The RCMs were evaluated for their capacity to simulate Cyprus climatology for the period 1980-2010. Data for the period 2020-2050 from the three best performing RCMs were downscaled, using the change factors approach, at the location of observational stations. Daily time series were created with a stochastic rainfall and temperature generator. The RainSim V3 software (Burton et al., 2008) was used to generate spatial-temporal coherent rainfall fields. The temperature generator was developed in R and modeled temperature as a weakly stationary process with the daily mean and standard deviation conditioned on the wet and dry state of the day (Richardson, 1981). Finally gridded datasets depicting projected future climate conditions were created with the identified best interpolation methods. The difference between the input and simulated mean daily rainfall, averaged over all the stations, was 0.03 mm (2.2%), while the error related to the number of dry days was 2 (0.6%). For mean daily minimum temperature the error was 0.005 ºC (0.04%), while for maximum temperature it was 0.01 ºC (0.04%). Overall, the weather generators were found to be reliable instruments for the downscaling of precipitation and temperature. The resulting datasets indicate a decrease of the mean annual rainfall over the study area between 5 and 70 mm (1-15%) for 2020-2050, relative to 1980-2010. Average annual minimum and maximum temperature over the Republic of Cyprus are projected to increase between 1.2 and 1.5 ºC. The dataset is currently used to compute agricultural production and water use indicators, as part of the AGWATER project (AEIFORIA/GEORGO/0311(BIE)/06), co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation. Burton, A., Kilsby, C.G., Fowler, H.J., Cowpertwait, P.S.P., and O'Connell, P.E.: RainSim: A spatial-temporal stochastic rainfall modelling system. Environ. Model. Software 23, 1356-1369, 2008 Richardson, C.W.: Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res. 17, 182-190, 1981.

Status and Preliminary Evaluation for Chinese Re-Analysis Datasets

NASA Astrophysics Data System (ADS)

bin, zhao; chunxiang, shi; tianbao, zhao; dong, si; jingwei, liu

2016-04-01

Based on operational T639L60 spectral model, combined with Hybird_GSI assimilation system by using meteorological observations including radiosondes, buoyes, satellites el al., a set of Chinese Re-Analysis (CRA) datasets is developing by Chinese National Meteorological Information Center (NMIC) of Chinese Meteorological Administration (CMA). The datasets are run at 30km (0.28°latitude / longitude) resolution which holds higher resolution than most of the existing reanalysis dataset. The reanalysis is done in an effort to enhance the accuracy of historical synoptic analysis and aid to find out detailed investigation of various weather and climate systems. The current status of reanalysis is in a stage of preliminary experimental analysis. One-year forecast data during Jun 2013 and May 2014 has been simulated and used in synoptic and climate evaluation. We first examine the model prediction ability with the new assimilation system, and find out that it represents significant improvement in Northern and Southern hemisphere, due to addition of new satellite data, compared with operational T639L60 model, the effect of upper-level prediction is improved obviously and overall prediction stability is enhanced. In climatological analysis, compared with ERA-40, NCEP/NCAR and NCEP/DOE reanalyses, the results show that surface temperature simulates a bit lower in land and higher over ocean, 850-hPa specific humidity reflects weakened anomaly and the zonal wind value anomaly is focus on equatorial tropics. Meanwhile, the reanalysis dataset shows good ability for various climate index, such as subtropical high index, ESMI (East-Asia subtropical Summer Monsoon Index) et al., especially for the Indian and western North Pacific monsoon index. Latter we will further improve the assimilation system and dynamical simulating performance, and obtain 40-years (1979-2018) reanalysis datasets. It will provide a more comprehensive analysis for synoptic and climate diagnosis.

Preprocessed Consortium for Neuropsychiatric Phenomics dataset.

PubMed

Gorgolewski, Krzysztof J; Durnez, Joke; Poldrack, Russell A

2017-01-01

Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.

Refinements to SSiB with an Emphasis on Snow-Physics: Evaluation and Validation Using GSWP and Valdai Data

NASA Technical Reports Server (NTRS)

Mocko, David M.; Sud, Y. C.

2000-01-01

Refinements to the snow-physics scheme of SSiB (Simplified Simple Biosphere Model) are described and evaluated. The upgrades include a partial redesign of the conceptual architecture to better simulate the diurnal temperature of the snow surface. For a deep snowpack, there are two separate prognostic temperature snow layers - the top layer responds to diurnal fluctuations in the surface forcing, while the deep layer exhibits a slowly varying response. In addition, the use of a very deep soil temperature and a treatment of snow aging with its influence on snow density is parameterized and evaluated. The upgraded snow scheme produces better timing of snow melt in GSWP-style simulations using ISLSCP Initiative I data for 1987-1988 in the Russian Wheat Belt region. To simulate more realistic runoff in regions with high orographic variability, additional improvements are made to SSiB's soil hydrology. These improvements include an orography-based surface runoff scheme as well as interaction with a water table below SSiB's three soil layers. The addition of these parameterizations further help to simulate more realistic runoff and accompanying prognostic soil moisture fields in the GSWP-style simulations. In intercomparisons of the performance of the new snow-physics SSiB with its earlier versions using an 18-year single-site dataset from Valdai Russia, the version of SSiB described in this paper again produces the earliest onset of snow melt. Soil moisture and deep soil temperatures also compare favorably with observations.

Indian Ocean corals reveal crucial role of World War II bias for twentieth century warming estimates.

PubMed

Pfeiffer, M; Zinke, J; Dullo, W-C; Garbe-Schönberg, D; Latif, M; Weber, M E

2017-10-31

The western Indian Ocean has been warming faster than any other tropical ocean during the 20 th century, and is the largest contributor to the global mean sea surface temperature (SST) rise. However, the temporal pattern of Indian Ocean warming is poorly constrained and depends on the historical SST product. As all SST products are derived from the International Comprehensive Ocean-Atmosphere dataset (ICOADS), it is challenging to evaluate which product is superior. Here, we present a new, independent SST reconstruction from a set of Porites coral geochemical records from the western Indian Ocean. Our coral reconstruction shows that the World War II bias in the historical sea surface temperature record is the main reason for the differences between the SST products, and affects western Indian Ocean and global mean temperature trends. The 20 th century Indian Ocean warming pattern portrayed by the corals is consistent with the SST product from the Hadley Centre (HadSST3), and suggests that the latter should be used in climate studies that include Indian Ocean SSTs. Our data shows that multi-core coral temperature reconstructions help to evaluate the SST products. Proxy records can provide estimates of 20 th century SST that are truly independent from the ICOADS data base.

REM-3D Reference Datasets: Reconciling large and diverse compilations of travel-time observations

NASA Astrophysics Data System (ADS)

Moulik, P.; Lekic, V.; Romanowicz, B. A.

2017-12-01

A three-dimensional Reference Earth model (REM-3D) should ideally represent the consensus view of long-wavelength heterogeneity in the Earth's mantle through the joint modeling of large and diverse seismological datasets. This requires reconciliation of datasets obtained using various methodologies and identification of consistent features. The goal of REM-3D datasets is to provide a quality-controlled and comprehensive set of seismic observations that would not only enable construction of REM-3D, but also allow identification of outliers and assist in more detailed studies of heterogeneity. The community response to data solicitation has been enthusiastic with several groups across the world contributing recent measurements of normal modes, (fundamental mode and overtone) surface waves, and body waves. We present results from ongoing work with body and surface wave datasets analyzed in consultation with a Reference Dataset Working Group. We have formulated procedures for reconciling travel-time datasets that include: (1) quality control for salvaging missing metadata; (2) identification of and reasons for discrepant measurements; (3) homogenization of coverage through the construction of summary rays; and (4) inversions of structure at various wavelengths to evaluate inter-dataset consistency. In consultation with the Reference Dataset Working Group, we retrieved the station and earthquake metadata in several legacy compilations and codified several guidelines that would facilitate easy storage and reproducibility. We find strong agreement between the dispersion measurements of fundamental-mode Rayleigh waves, particularly when made using supervised techniques. The agreement deteriorates substantially in surface-wave overtones, for which discrepancies vary with frequency and overtone number. A half-cycle band of discrepancies is attributed to reversed instrument polarities at a limited number of stations, which are not reflected in the instrument response history. By assessing inter-dataset consistency across similar paths, we quantify travel-time measurement errors for both surface and body waves. Finally, we discuss challenges associated with combining high frequency ( 1 Hz) and long period (10-20s) body-wave measurements into the REM-3D reference dataset.

First laboratory high-temperature emissivity measurements of Venus analog measurements in the near-infrared atmospheric windows

NASA Astrophysics Data System (ADS)

Helbert, J.; Maturilli, A.; Ferrari, S.; Dyar, M. D.; Smrekar, S. E.

2014-12-01

The permanent cloud cover of Venus prohibits observation of the surface with traditional imaging techniques over most of the visible spectral range. Venus' CO2 atmosphere is transparent exclusively in small spectral windows near 1 μm. The Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) team on the European Space Agency Venus-Express mission have recently used these windows successfully to map the southern hemisphere from orbit. VIRTIS is showing variations in surface brightness, which can be interpreted as variations in surface emissivity. Deriving surface composition from these variations is a challenging task. Comparison with laboratory analogue spectra are complicated by the fact that Venus has an average surface temperature of 730K. Mineral crystal structures and their resultant spectral signatures are notably affected by temperature, therefore any interpretations based on room temperature laboratory spectra database can be misleading. In order to support the interpretation of near-infrared data from Venus we have started an extensive measurement campaign at the Planetary Emissivity Laboratory (PEL, Institute of Planetary Research of the German Aerospace Center, Berlin). The PEL facility, which is unique in the world, allows emission measurements covering the 1 to 2 μm wavelength range at sample temperatures of up to 770K. Conciliating the expected emissivity variation between felsic and mafic minerals with Venera and VEGA geochemical data we have started with a set of five analog samples. This set includes basalt, gneiss, granodiorite, anorthosite and hematite, thus covering the range of mineralogies. Preliminary results show significant spectral contrast, thus allowing different samples to be distinguished with only 5 spectral points and validating the use of thermal emissivity for investigating composition. This unique new dataset from PEL not only allows interpretation of the Venus Express VIRTIS data but also provide a baseline for considering new instrument designs for future Venus missions.

Forcing Mechanisms for the Variations of Near-surface Temperature Lapse Rates along the Himalayas, Tibetan Plateau (HTP) and Their Surroundings

NASA Astrophysics Data System (ADS)

Kattel, D. B.; Yao, T.; Ullah, K.; Islam, G. M. T.

2016-12-01

This study investigates the monthly characteristics of near-surface temperature lapse rates (TLRs) (i.e., governed by surface energy balance) based on the 176 stations 30-year (1980 to 2010) dataset covering a wide range of topography, climatic regime and relief (4801 m) in the HTP and its surroundings. Empirical analysis based on techniques in thermodynamics and hydrostatic system were used to obtain the results. Steepest TLRs in summer is due to strong dry convection and shallowest in winter is due to inversion effect is the general pattern of TLR that reported in previous studies in other mountainous region. Result of this study reports a contrast variation of TLRs from general patterns, and suggest distinct forcing mechanisms in an annual cycle. Shallower lapse rate occurs in summer throughout the regions is due to strong heat exchange process within the boundary layer, corresponding to the warm and moist atmospheric conditions. There is a systematic differences of TLRs in winter between the northern and southern slopes the Himalayas. Steeper TLRs in winter on the northern slopes is due to intense cooling at higher elevations, corresponding to the continental dry and cold air surges, and considerable snow-temperature feedback. The differences in elevation and topography, as well as the distinct variation of turbulent heating and cooling, explain the contrast TLRs (shallower) values in winter on the southern slopes. Distinct diurnal variations of TLRs and its magnitudes between alpine, dry, humid and coastal regions is due to the variations of adiabatic mixing during the daytime in the boundary layer i.e., associated with the variations in net radiations, elevation, surface roughness and sea surface temperature. The findings of this study is useful to determine the temperature range for accurately modelling in various field such as hydrology, glaciology, ecology, forestry, agriculture, as well as inevitable for climate downscaling in complex mountainous terrain.

Analyzed DTS Data, Guelph, ON Canada

DOE Data Explorer

Coleman, Thomas

2015-07-01

Analyzed DTS datasets from active heat injection experiments in Guelph, ON Canada is included. A .pdf file of images including borehole temperature distributions, temperature difference distributions, temperature profiles, and flow interpretations is included as the primary analyzed dataset. Analyzed data used to create the .pdf images are included as a matlab data file that contains the following 5 types of data: 1) Borehole Temperature (matrix of temperature data collected in the borehole), 2) Borehole Temperature Difference (matrix of temperature difference above ambient for each test), 3) Borehole Time (time in both min and sec since the start of a DTS test), 4) Borehole Depth (channel depth locations for the DTS measurements), 5) Temperature Profiles (ambient, active, active off early time, active off late time, and injection).

Data assimilation and model evaluation experiment datasets

NASA Technical Reports Server (NTRS)

Lai, Chung-Cheng A.; Qian, Wen; Glenn, Scott M.

1994-01-01

The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.

Mapping 2000 2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

NASA Technical Reports Server (NTRS)

Wang, Panshi; Huang, Chengquan; Brown De Colstoun, Eric C.

2017-01-01

Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.

New microphysical volcanic forcing datasets for the Agung, El Chichon and Pinatubo eruptions

NASA Astrophysics Data System (ADS)

Dhomse, Sandip; Mann, Graham; Marshall, Lauren; Carslaw, Kenneth; Chipperfield, Martyn; Bellouin, Nicolas; Morgenstern, Olaf; Johnson, Colin; O'Connor, Fiona

2017-04-01

Major tropical volcanic eruptions inject huge amounts of SO2 directly into the stratosphere, and create a long-lasting perturbation to the stratospheric aerosol. The abruptly elevated aerosol has strong climate impacts, principally surface cooling via scattering incoming solar radiation. The enhanced tropical stratospheric aerosol can also absorb outgoing long wave radiation causing a warming of the stratosphere and subsequent complex composition-dynamics responses (e.g. Dhomse et al., 2015). In this presentation we apply the composition-climate model UM-UKCA with interactive stratospheric chemistry and aerosol microphysics (Dhomse et al., 2014) to assess the enhancement to the stratospheric aerosol and associated radiative forcings from the three largest tropical eruptions in the last 60 years: Mt Agung (February 1963), El Chichon (April 1982) and Mt. Pinatubo (June 1991). Accurately characterising the forcing signature from these major eruptions is important for attribution of recent climate change and volcanic effects have been identified as a key requirement for robust attribution of multi-decadal surface temperature trends (e.g. Marotzke and Forster, 2015). Aligning with the design of the ISA-MIP co-ordinated multi-model "Historical Eruption SO2 Emissions Assessment" (HErSEA), we have carried out 3-member ensemble of simulations with each of upper, low and mid-point best estimates for SO2 and injection height for each eruption. We evaluate simulated aerosol properties (e.g. extinction, AOD, effective radius, particle size distribution) against a range of satellite and in-situ observational datasets and assess stratospheric heating against temperature anomalies are compared against reanalysis and other datasets. References: Dhomse SS, Chipperfield MP, Feng W, Hossaini R, Mann GW, Santee ML (2015) Revisiting the hemispheric asymmetry in midlatitude ozone changes following the Mount Pinatubo eruption: A 3-D model study, Geophysical Research Letters, 42, pp.3038-3047. doi: 10.1002/2015GL063052 Dhomse SS, Emmerson KM, Mann GW, Bellouin N, Carslaw KS, Chipperfield MP, Hommel R, Abraham NL, Telford P, Braesicke P, Dalvi M, Johnson CE, O'Connor F, Morgenstern O, Pyle JA, Deshler T, Zawodny JM, Thomason LW (2014) Aerosol microphysics simulations of the Mt.˜Pinatubo eruption with the UM-UKCA composition-climate model, Atmospheric Chemistry and Physics, 14, pp.11221-11246. doi: 10.5194/acp-14-11221-2014 Marotzke J; Forster PM (2015) Forcing, feedback and internal variability in global temperature trends, Nature, 517, pp.565-570. doi: 10.1038/nature14117

Development of Super-Ensemble techniques for ocean analyses: the Mediterranean Sea case

NASA Astrophysics Data System (ADS)

Pistoia, Jenny; Pinardi, Nadia; Oddo, Paolo; Collins, Matthew; Korres, Gerasimos; Drillet, Yann

2017-04-01

Short-term ocean analyses for Sea Surface Temperature SST in the Mediterranean Sea can be improved by a statistical post-processing technique, called super-ensemble. This technique consists in a multi-linear regression algorithm applied to a Multi-Physics Multi-Model Super-Ensemble (MMSE) dataset, a collection of different operational forecasting analyses together with ad-hoc simulations produced by modifying selected numerical model parameterizations. A new linear regression algorithm based on Empirical Orthogonal Function filtering techniques is capable to prevent overfitting problems, even if best performances are achieved when we add correlation to the super-ensemble structure using a simple spatial filter applied after the linear regression. Our outcomes show that super-ensemble performances depend on the selection of an unbiased operator and the length of the learning period, but the quality of the generating MMSE dataset has the largest impact on the MMSE analysis Root Mean Square Error (RMSE) evaluated with respect to observed satellite SST. Lower RMSE analysis estimates result from the following choices: 15 days training period, an overconfident MMSE dataset (a subset with the higher quality ensemble members), and the least square algorithm being filtered a posteriori.

High-resolution mapping and modelling of surface albedo in Norwegian boreal forests: from remotely sensed data to predictions

NASA Astrophysics Data System (ADS)

Cherubini, Francesco; Hu, Xiangping; Vezhapparambu, Sajith; Stromman, Anders

2017-04-01

Surface albedo, a key parameter of the Earth's climate system, has high variability in space, time, and land cover and its parameterization is among the most important variables in climate models. The lack of extensive estimates for model improvement is one of the main limitations for accurately quantifying the influence of surface albedo changes on the planetary radiation balance. We use multi-year satellite retrievals of MODIS surface albedo (MCD43A3), high resolution land cover maps, and meteorological records to characterize albedo variations in Norway across latitude, seasons, land cover type, and topography. We then use this dataset to elaborate semi-empirical models to predict albedo values as a function of tree species, age, volume and climate variables like temperature and snow water equivalents (SWE). Given the complexity of the dataset and model formulation, we apply an innovative non-linear programming approach simultaneously coupled with linear un-mixing. The MODIS albedo products are at a resolution of about 500 m and 8 days. The land cover maps provide vegetation structure information on relative abundance of tree species, age, and biomass volumes at 16 m resolution (for both deciduous and coniferous species). Daily observations of meteorological information on air temperature and SWE are produced at 1 km resolution from interpolation of meteorological weather stations in Norway. These datasets have different resolution and projection, and are harmonized by identifying, for each MODIS pixel, the intersecting land cover polygons and the percentage area of the MODIS pixel represented by each land cover type. We then filter the subplots according to the following criteria: i) at least 96% of the total pixel area is covered by a single land cover class (either forest or cropland); ii) if forest area, at least 98% of the forest area is covered by spruce, deciduous or pine. Forested pixels are then categorized as spruce, deciduous, or pine dominant if the fraction of the respective tree species is greater than 75%. Results show averages of albedo estimates for forests and cropland depicting spatial (along a latitudinal gradient) and temporal (daily, monthly, and seasonal) variations across Norway. As the case study region is a country with heterogeneous topography, we also study the sensitivity of the albedo estimates to the slope and aspect of the terrain. The mathematical programming approach uses a variety of functional forms, constraints and variables, leading to many different model outputs. There are several models with relatively high performances, allowing for a flexibility in the model selection, with different model variants suitable for different situations. This approach produces albedo predictions at the same resolution of the land cover dataset (16 m, notably higher than the MODIS estimates), can incorporate changes in climate conditions, and is robust to cross-validation between different locations. By integrating satellite measurements and high-resolution vegetation maps, we can thus produce semi-empirical models that can predict albedo values for boreal forests using a variety of input variables representing climate and/or vegetation structure. Further research can explore the possible advantages of its implementation in land surface schemes over existing approaches.

Controls of air temperature variability over an Alpine Glacier

NASA Astrophysics Data System (ADS)

Shaw, Thomas; Brock, Ben; Ayala, Álvaro; Rutter, Nick

2016-04-01

Near surface air temperature (Ta) is one of the most important controls on energy exchange between a glacier surface and the overlying atmosphere. However, not enough detail is known about the controls on Ta across a glacier due to sparse data availability. Recent work has provided insights into variability of Ta along glacier centre-lines in different parts of the world, yet there is still a limited understanding of off-centreline variability in Ta and how best to estimate it from distant off-glacier locations. We present a new dataset of distributed 2m Ta records for the Tsanteleina Glacier in Northwest Italy from July-September, 2015. Data provide detailed information of lateral (across-glacier) and centre-line variations in Ta, with ~20,000 hourly observations from 17 locations. The suitability of different vertical temperature gradients (VTGs) in estimating air temperature is considered under a range of meteorological conditions and from different forcing locations. A key finding is that local VTGs account for a lot of Ta variability under a broad range of climatic conditions. However, across-glacier variability is found to be significant, particularly for high ambient temperatures and for localised topographic depressions. The relationship of spatial Ta patterns with regional-scale reanalysis data and alternative Ta estimation methodologies are also presented. This work improves the knowledge of local scale Ta variations and their importance to melt modelling.

Crowdsourcing urban air temperatures through smartphone battery temperatures in São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Droste, Arjan; Pape, Jan-Jaap; Overeem, Aart; Leijnse, Hidde; Steeneveld, Gert-Jan; Van Delden, Aarnout; Uijlenhoet, Remko

2017-04-01

Crowdsourcing as a method to obtain and apply vast datasets is rapidly becoming prominent in meteorology, especially for urban areas where traditional measurements are scarce. Earlier studies showed that smartphone battery temperature readings allow for estimating the daily and city-wide air temperature via a straightforward heat transfer model. This study advances these model estimations by studying spatially and temporally smaller scales. The accuracy of temperature retrievals as a function of the number of battery readings is also studied. An extensive dataset of over 10 million battery temperature readings is available for São Paulo (Brazil), for estimating hourly and daily air temperatures. The air temperature estimates are validated with air temperature measurements from a WMO station, an Urban Fluxnet site, and crowdsourced data from 7 hobby meteorologists' private weather stations. On a daily basis temperature estimates are good, and we show they improve by optimizing model parameters for neighbourhood scales as categorized in Local Climate Zones. Temperature differences between Local Climate Zones can be distinguished from smartphone battery temperatures. When validating the model for hourly temperature estimates, initial results are poor, but are vastly improved by using a diurnally varying parameter function in the heat transfer model rather than one fixed value for the entire day. The obtained results show the potential of large crowdsourced datasets in meteorological studies, and the value of smartphones as a measuring platform when routine observations are lacking.

An ensemble approach to reconstructing 20th century climate trends in data-sparse regions of the tropical Pacific using young fossil corals

NASA Astrophysics Data System (ADS)

Hitt, N. T.; Cobb, K. M.; Sayani, H. R.; Grothe, P. R.; Atwood, A. R.; O'Connor, G.; Chen, T.; Hagos, M. M.; Deocampo, D.; Edwards, R. L.; Cheng, H.; Lu, Y.; Thompson, D. M.

2016-12-01

Sea-surface temperature (SST) variability in the central tropical Pacific drives global-scale responses through atmospheric teleconnections, so the response of this region to anthropogenic forcing has important implications for regional climate responses in many areas. However, quantification of anthropogenic SST trends in the central tropical Pacific is complicated by the fact that instrumental SST observations in this region are extremely limited prior to 1950, with trends of opposite sign observed across the various gridded instrumental datasets (Deser et al., 2010). Researchers have turned to multi-century coral records to reconstruct ocean temperatures through time, but the paucity of such records prohibits the generation of uncertainty estimates. In this study, we use a large collection of U/Th-dated fossil corals that to investigate a new ensemble approach to reconstructing temperature from the Central Pacific over the late 20th century. Here we combine monthly-resolved d18O and Sr/Ca from 8 5-14 year long coral records from Christmas Island (2°N, 157°W) to quantify temperature and hydrological trends in this region from 1930 to present. We compare our fossil coral ensemble reconstruction to a long modern coral core from this site that extends back to 1940, as well as to gridded SST datasets. We also provide the first well-replicated coral d18O and Sr/Ca records across both the 1997/98 and 2015/2016 El Nino events, comparing the strength of these two events in the context of long-term temperature trends observed in our longer reconstruction. We conclude that the fossil coral ensemble approach provides a robust means of reconstructing 20th century climate trends. Deser et al., 2010, GRL, doi: 10.1029/2010GL043321

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

NASA Technical Reports Server (NTRS)

Bell, Jordan R.; Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.

2012-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). Portions of the Northern Plains states experienced substantial increases in convective available potential energy as a result of the higher SPoRT/MODIS GVFs. These differences produced subtle yet quantifiable differences in the simulated convective precipitation systems for this event.

Improvement of Meteorological Inputs for TexAQS-II Air Quality Simulations

NASA Astrophysics Data System (ADS)

Ngan, F.; Byun, D.; Kim, H.; Cheng, F.; Kim, S.; Lee, D.

2008-12-01

An air quality forecasting system (UH-AQF) for Eastern Texas, which is in operation by the Institute for Multidimensional Air Quality Studies (IMAQS) at the University of Houston, uses the Fifth-Generation PSU/NCAR Mesoscale Model MM5 model as the meteorological driver for modeling air quality with the Community Multiscale Air Quality (CMAQ) model. While the forecasting system was successfully used for the planning and implementation of various measurement activities, evaluations of the forecasting results revealed a few systematic problems in the numerical simulations. From comparison with observations, we observe some times over-prediction of northerly winds caused by inaccurate synoptic inputs and other times too strong southerly winds caused by local sea breeze development. Discrepancies in maximum and minimum temperature are also seen for certain days. Precipitation events, as well as clouds, are simulated at the incorrect locations and times occasionally. Model simulatednrealistic thunderstorms are simulated, causing sometimes cause unrealistically strong outflows. To understand physical and chemical processes influencing air quality measures, a proper description of real world meteorological conditions is essential. The objective of this study is to generate better meteorological inputs than the AQF results to support the chemistry modeling. We utilized existing objective analysis and nudging tools in the MM5 system to develop the MUltiscale Nest-down Data Assimilation System (MUNDAS), which incorporates extensive meteorological observations available in the simulated domain for the retrospective simulation of the TexAQS-II period. With the re-simulated meteorological input, we are able to better predict ozone events during TexAQS-II period. In addition, base datasets in MM5 such as land use/land cover, vegetation fraction, soil type and sea surface temperature are updated by satellite data to represent the surface features more accurately. They are key physical parameters inputs affecting transfer of heat, momentum and soil moisture in land-surface process in MM5. Using base the accurate input datasets, we are able to have improved see the differences of predictions of ground temperatures, winds and even thunderstorm activities within boundary layer.

Computer assisted screening, correction, and analysis of historical weather measurements

NASA Astrophysics Data System (ADS)

Burnette, Dorian J.; Stahle, David W.

2013-04-01

A computer program, Historical Observation Tools (HOB Tools), has been developed to facilitate many of the calculations used by historical climatologists to develop instrumental and documentary temperature and precipitation datasets and makes them readily accessible to other researchers. The primitive methodology used by the early weather observers makes the application of standard techniques difficult. HOB Tools provides a step-by-step framework to visually and statistically assess, adjust, and reconstruct historical temperature and precipitation datasets. These routines include the ability to check for undocumented discontinuities, adjust temperature data for poor thermometer exposures and diurnal averaging, and assess and adjust daily precipitation data for undercount. This paper provides an overview of the Visual Basic.NET program and a demonstration of how it can assist in the development of extended temperature and precipitation datasets using modern and early instrumental measurements from the United States.

Climatological assessment of spatiotemporal trends in observational monthly snowfall totals and extremes over the Canadian Great Lakes Basin

NASA Astrophysics Data System (ADS)

Baijnath, Janine; Duguay, Claude; Sushama, Laxmi; Huziy, Oleksandr

2017-04-01

The Laurentian Great Lakes Basin (GLB) is susceptible to snowfall events that derive from extratropical cyclones and heavy lake effect snowfall (HLES). The former is generated by quasigeostropic forcing from positive temperature or vorticity advection associated with low-pressure centres. HLES is produced by planetary boundary layer (PBL) convection that is initiated as a result of cold and dry continental air mass advecting over relatively warm lakes and generating turbulent moisture and heat fluxes into the PBL. HLES events can have disastrous impacts on local communities such as the November 2014 Buffalo storm that caused 13 fatalities. Albeit the many HLES studies, most are focused on specific case study events with a discernible under examination of climatological HLES trend analyses for the Canadian GLB. The research objectives are to first determine the historical, climatological trends in monthly snowfall totals and to examine potential surface and atmospheric variables driving the resultant changes in HLES. The second aims to analyze the historical extremes in snowfall by assessing the intensity, frequency, and duration of snowfall within the domain of interest. Spatiotemporal snowfall and precipitation trends are computed for the 1982 to 2015 period using Daymet (Version 3) monthly gridded observational datasets from the Oak Ridge National Laboratory. The North American Regional Reanalysis (NARR), NOAA Optimum Interpolation Sea Surface Temperature (OISST), and the Canadian Ice Service (CIS) datasets are also used for evaluating trends in HLES driving variables such as air temperature, lake surface temperature (LST), ice cover concentration, omega, and vertical temperature gradient (VTGlst-850). Climatological trends in monthly snowfall totals show a significant decrease along the Ontario snowbelt of Lake Superior, Lake Huron and Georgian Bay at the 90 percent confidence level. These results are attributed to significant warming in LST, significant decrease in ice cover fraction, and an increase in VTGlst-850, which enhances evaporation into the lower PBL. It is suggested that inefficient moisture recycling and increase moisture storage in warmer air masses inhibits the development of HLES. The 99th percentile of snowfall events within the GLB suggests an extreme snowfall value equal to or exceeding 15 cm per day. Spatiotemporal snowfall patterns indicate that mostly lake effect processes and not extratropical cyclones drive the high intensity, frequency, and duration of these extreme events over the GLB. Furthermore, the Canadian snowbelt region of Lake Huron and Lake Superior exhibit different spatiotemporal trends in snowfall extremes but, even within a particular snowbelt region, trends in extreme snowfall are not spatially coherent. It is suggested that geographic location of the lakes, topography, lake bathymetry, and lake orientation can influence local and large scale surface-atmosphere variables.

Antarctic Temperature Extremes from MODIS Land Surface Temperatures: New Processing Methods Reveal Data Quality Puzzles

NASA Astrophysics Data System (ADS)

Grant, G.; Gallaher, D. W.

2017-12-01

New methods for processing massive remotely sensed datasets are used to evaluate Antarctic land surface temperature (LST) extremes. Data from the MODIS/Terra sensor (Collection 6) provides a twice-daily look at Antarctic LSTs over a 17 year period, at a higher spatiotemporal resolution than past studies. Using a data condensation process that creates databases of anomalous values, our processes create statistical images of Antarctic LSTs. In general, the results find few significant trends in extremes; however, they do reveal a puzzling picture of inconsistent cloud detection and possible systemic errors, perhaps due to viewing geometry. Cloud discrimination shows a distinct jump in clear-sky detections starting in 2011, and LSTs around the South Pole exhibit a circular cooling pattern, which may also be related to cloud contamination. Possible root causes are discussed. Ongoing investigations seek to determine whether the results are a natural phenomenon or, as seems likely, the results of sensor degradation or processing artefacts. If the unusual LST patterns or cloud detection discontinuities are natural, they point to new, interesting processes on the Antarctic continent. If the data artefacts are artificial, MODIS LST users should be alerted to the potential issues.

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation: A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

NASA Astrophysics Data System (ADS)

Yang, Junhua; Ji, Zhenming; Chen, Deliang; Kang, Shichang; Fu, Congshen; Duan, Keqin; Shen, Miaogen

2018-06-01

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level (surface-sensitive) channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets. Here, we used an improved land use and leaf area index (LAI) dataset in the WRF-3DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels (e.g., channel 3), the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

A data centred method to estimate and map changes in the full distribution of daily surface temperature

NASA Astrophysics Data System (ADS)

Chapman, Sandra; Stainforth, David; Watkins, Nicholas

2016-04-01

Characterizing how our climate is changing includes local information which can inform adaptation planning decisions. This requires quantifying the geographical patterns in changes at specific quantiles or thresholds in distributions of variables such as daily surface temperature. Here we focus on these local changes and on a model independent method to transform daily observations into patterns of local climate change. Our method [1] is a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. This deconstruction facilitates an assessment of how fast different quantiles of the distributions are changing. This involves both determining which quantiles and geographical locations show the greatest change but also, those at which any change is highly uncertain. For temperature, changes in the distribution itself can yield robust results [2]. We demonstrate how the fundamental timescales of anthropogenic climate change limit the identification of societally relevant aspects of changes. We show that it is nevertheless possible to extract, solely from observations, some confident quantified assessments of change at certain thresholds and locations [3]. We demonstrate this approach using E-OBS gridded data [4] timeseries of local daily surface temperature from specific locations across Europe over the last 60 years. [1] Chapman, S. C., D. A. Stainforth, N. W. Watkins, On estimating long term local climate trends, Phil. Trans. Royal Soc., A,371 20120287 (2013) [2] Stainforth, D. A. S. C. Chapman, N. W. Watkins, Mapping climate change in European temperature distributions, ERL 8, 034031 (2013) [3] Chapman, S. C., Stainforth, D. A., Watkins, N. W. Limits to the quantification of local climate change, ERL 10, 094018 (2015) [4] Haylock M. R. et al ., A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, (2008)

Analysing and correcting the differences between multi-source and multi-scale spatial remote sensing observations.

PubMed

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

PubMed Central

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation. PMID:25405760

Ground-based Remote Sensing for Quantifying Subsurface and Surface Co-variability to Scale Arctic Ecosystem Functioning

NASA Astrophysics Data System (ADS)

Oktem, R.; Wainwright, H. M.; Curtis, J. B.; Dafflon, B.; Peterson, J.; Ulrich, C.; Hubbard, S. S.; Torn, M. S.

2016-12-01

Predicting carbon cycling in Arctic requires quantifying tightly coupled surface and subsurface processes including permafrost, hydrology, vegetation and soil biogeochemistry. The challenge has been a lack of means to remotely sense key ecosystem properties in high resolution and over large areas. A particular challenge has been characterizing soil properties that are known to be highly heterogeneous. In this study, we exploit tightly-coupled above/belowground ecosystem functioning (e.g., the correlations among soil moisture, vegetation and carbon fluxes) to estimate subsurface and other key properties over large areas. To test this concept, we have installed a ground-based remote sensing platform - a track-mounted tram system - along a 70 m transect in the ice-wedge polygonal tundra near Barrow, Alaska. The tram carries a suite of near-surface remote sensing sensors, including sonic depth, thermal IR, NDVI and multispectral sensors. Joint analysis with multiple ground-based measurements (soil temperature, active layer soil moisture, and carbon fluxes) was performed to quantify correlations and the dynamics of above/belowground processes at unprecedented resolution, both temporally and spatially. We analyzed the datasets with particular focus on correlating key subsurface and ecosystem properties with surface properties that can be measured by satellite/airborne remote sensing over a large area. Our results provided several new insights about system behavior and also opens the door for new characterization approaches. We documented that: (1) soil temperature (at >5 cm depth; critical for permafrost thaw) was decoupled from soil surface temperature and was influenced strongly by soil moisture, (2) NDVI and greenness index were highly correlated with both soil moisture and gross primary productivity (based on chamber flux data), and (3) surface deformation (which can be measured by InSAR) was a good proxy for thaw depth dynamics at non-inundated locations.

Evaluating global reanalysis datasets for provision of boundary conditions in regional climate modelling

NASA Astrophysics Data System (ADS)

Moalafhi, Ditiro B.; Evans, Jason P.; Sharma, Ashish

2016-11-01

Regional climate modelling studies often begin by downscaling a reanalysis dataset in order to simulate the observed climate, allowing the investigation of regional climate processes and quantification of the errors associated with the regional model. To date choice of reanalysis to perform such downscaling has been made based either on convenience or on performance of the reanalyses within the regional domain for relevant variables such as near-surface air temperature and precipitation. However, the only information passed from the reanalysis to the regional model are the atmospheric temperature, moisture and winds at the location of the boundaries of the regional domain. Here we present a methodology to evaluate reanalyses derived lateral boundary conditions for an example domain over southern Africa using satellite data. This study focusses on atmospheric temperature and moisture which are easily available. Five commonly used global reanalyses (NCEP1, NCEP2, ERA-I, 20CRv2, and MERRA) are evaluated against the Atmospheric Infrared Sounder satellite temperature and relative humidity over boundaries of two domains centred on southern Africa for the years 2003-2012 inclusive. The study reveals that MERRA is the most suitable for climate mean with NCEP1 the next most suitable. For climate variability, ERA-I is the best followed by MERRA. Overall, MERRA is preferred for generating lateral boundary conditions for this domain, followed by ERA-I. While a "better" LBC specification is not the sole precursor to an improved downscaling outcome, any reduction in uncertainty associated with the specification of LBCs is a step in the right direction.

Impact of missing data on the efficiency of homogenisation: experiments with ACMANTv3

NASA Astrophysics Data System (ADS)

Domonkos, Peter; Coll, John

2018-04-01

The impact of missing data on the efficiency of homogenisation with ACMANTv3 is examined with simulated monthly surface air temperature test datasets. The homogeneous database is derived from an earlier benchmarking of daily temperature data in the USA, and then outliers and inhomogeneities (IHs) are randomly inserted into the time series. Three inhomogeneous datasets are generated and used, one with relatively few and small IHs, another one with IHs of medium frequency and size, and a third one with large and frequent IHs. All of the inserted IHs are changes to the means. Most of the IHs are single sudden shifts or pair of shifts resulting in platform-shaped biases. Each test dataset consists of 158 time series of 100 years length, and their mean spatial correlation is 0.68-0.88. For examining the impacts of missing data, seven experiments are performed, in which 18 series are left complete, while variable quantities (10-70%) of the data of the other 140 series are removed. The results show that data gaps have a greater impact on the monthly root mean squared error (RMSE) than the annual RMSE and trend bias. When data with a large ratio of gaps is homogenised, the reduction of the upper 5% of the monthly RMSE is the least successful, but even there, the efficiency remains positive. In terms of reducing the annual RMSE and trend bias, the efficiency is 54-91%. The inclusion of short and incomplete series with sufficient spatial correlation in all cases improves the efficiency of homogenisation with ACMANTv3.

Unlocking the secrets of Venus surface mineralogy from orbit

NASA Astrophysics Data System (ADS)

Helbert, J.; Maturilli, A.; Ferrari, S.; Dyar, M. D.; Mueller, N. T.; Smrekar, S. E.; Koulen, J.

2016-12-01

The surface composition of a planet is a key to understand its interior and evolution. Proper interpretations of Venus surface observations in the near-infrared require a dedicated laboratory effort. The atmosphere of Venus dictates which spectral bands on the surface can be observed. This places severe constraints on the ability to identify rock-forming minerals. To complicate matters further, we cannot observe reflectance, as would be the standard at 1 mm. Observations are obtained on the night side where the thermal emission of the surface is measured directly. Finally, high surface temperatures are known to affect band positions of mineral spectra as expected from crystal field theory. Over the last year we have started at the Planetary Spectroscopy Laboratory (PSL) at DLR in Berlin, Germany to systematically build a spectral library for rocks and minerals under Venus thermal conditions. Using funding from the European Union as part of the EuroPlanet consortium we extended the spectral coverage for high temperature measurements down to 0.7 micron. The spectral library will be key in understanding and modeling differences in emissivity between ambient and Venus conditions, potentially enabling calibration transfer between datasets. We can show that the expected emissivity variation between felsic and mafic minerals would be observable even with the limited number of surface windows available. Furthermore the absolute emissivity derived from our laboratory measurements at Venus temperature match in situ reflectivity data from the Venera 9 and 10 landing sites in the same bands. Based on experience gained from using the VIRTIS instrument on Venus Express to observe the surface of Venus and the new high temperature laboratory experiments, we have developed the multi-spectral Venus Emissivity Mapper (VEM) to study the surface of Venus. VEM imposes minimal requirements on the spacecraft and mission design and can therefore be added to any future Venus mission. Ideally, the VEM instrument will be combined with a high-resolution radar mapper to provide accurate topographic information, as it will be the case for the proposed NASA Discovery VERITAS mission or the ESA EnVision M5 proposal.

Diagnosing causes of cloud parameterization deficiencies using ARM measurements over SGP site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, W.; Liu, Y.; Betts, A. K.

2010-03-15

Decade-long continuous surface-based measurements at Great Southern Plains (SGP) collected by the US Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility are first used to evaluate the three major reanalyses (i.e., ERA-Interim, NCEP/NCAR Reanalysis I and NCEP/DOE Reanalysis II) to identify model biases in simulating surface shortwave cloud forcing and total cloud fraction. The results show large systematic lower biases in the modeled surface shortwave cloud forcing and cloud fraction from all the three reanalysis datasets. Then we focus on diagnosing the causes of these model biases using the Active Remote Sensing of Clouds (ARSCL) products (e.g., verticalmore » distribution of cloud fraction, cloud-base and cloud-top heights, and cloud optical depth) and meteorological measurements (temperature, humidity and stability). Efforts are made to couple cloud properties with boundary processes in the diagnosis.« less

Evaluation and attribution of vegetation contribution to seasonal climate predictability

NASA Astrophysics Data System (ADS)

Catalano, Franco; Alessandri, Andrea; De Felice, Matteo

2015-04-01

The land surface model of EC-Earth has been modified to include dependence of vegetation densities on the Leaf Area Index (LAI), based on the Lambert-Beer formulation. Effective vegetation fractional coverage can now vary at seasonal and interannual time-scales and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. The modified model is used to perform a real predictability seasonal hindcast experiment. LAI is prescribed using a recent observational dataset based on the third generation GIMMS and MODIS satellite data. Hindcast setup is: 7 months forecast length, 2 start dates (1st May and 1st November), 10 members, 28 years (1982-2009). The effect of the realistic LAI prescribed from observation is evaluated with respect to a control experiment where LAI does not vary. Hindcast results demonstrate that a realistic representation of vegetation significantly improves the forecasts of temperature and precipitation. The sensitivity is particularly large for temperature during boreal winter over central North America and Central Asia. This may be attributed in particular to the effect of the high vegetation component on the snow cover. Summer forecasts are improved in particular for precipitation over Europe, Sahel, North America, West Russia and Nordeste. Correlation improvements depends on the links between targets (temperature and precipitation) and drivers (surface heat fluxes, albedo, soil moisture, evapotranspiration, moisture divergence) which varies from region to region.

Modeling green infrastructure land use changes on future air ...

EPA Pesticide Factsheets

Green infrastructure can be a cost-effective approach for reducing stormwater runoff and improving water quality as a result, but it could also bring co-benefits for air quality: less impervious surfaces and more vegetation can decrease the urban heat island effect, and also result in more removal of air pollutants via dry deposition with increased vegetative surfaces. Cooler surface temperatures can also decrease ozone formation through the increases of NOx titration; however, cooler surface temperatures also lower the height of the boundary layer resulting in more concentrated pollutants within the same volume of air, especially for primary emitted pollutants (e.g. NOx, CO, primary particulate matter). To better understand how green infrastructure impacts air quality, the interactions between all of these processes must be considered collectively. In this study, we use a comprehensive coupled meteorology-air quality model (WRF-CMAQ) to simulate the influence of planned land use changes that include green infrastructure in Kansas City (KC) on regional meteorology and air quality. Current and future land use data was provided by the Mid-America Regional Council for 2012 and 2040 (projected land use due to population growth, city planning and green infrastructure implementation). These land use datasets were incorporated into the WRF-CMAQ modeling system allowing the modeling system to propagate the changes in vegetation and impervious surface coverage on meteoro

The forcing of southwestern Asia teleconnections by low-frequency sea surface temperature variability during boreal winter

USGS Publications Warehouse

Hoell, Andrew; Funk, Christopher C.; Mathew Barlow,

2015-01-01

Southwestern Asia, defined here as the domain bounded by 20°–40°N and 40°–70°E, which includes the nations of Iraq, Iran, Afghanistan, and Pakistan, is a water-stressed and semiarid region that receives roughly 75% of its annual rainfall during November–April. The November–April climate of southwestern Asia is strongly influenced by tropical Indo-Pacific variability on intraseasonal and interannual time scales, much of which can be attributed to sea surface temperature (SST) variations. The influences of lower-frequency SST variability on southwestern Asia climate during November–April Pacific decadal SST (PDSST) variability and the long-term trend in SST (LTSST) is examined. The U.S. Climate Variability and Predictability Program (CLIVAR) Drought Working Group forced global atmospheric climate models with PDSST and LTSST patterns, identified using empirical orthogonal functions, to show the steady atmospheric response to these modes of decadal to multidecadal SST variability. During November–April, LTSST forces an anticyclone over southwestern Asia, which results in reduced precipitation and increases in surface temperature. The precipitation and tropospheric circulation influences of LTSST are corroborated by independent observed precipitation and circulation datasets during 1901–2004. The decadal variations of southwestern Asia precipitation may be forced by PDSST variability, with two of the three models indicating that the cold phase of PDSST forces an anticyclone and precipitation reductions. However, there are intermodel circulation variations to PDSST that influence subregional precipitation patterns over the Middle East, southwestern Asia, and subtropical Asia. Changes in wintertime temperature and precipitation over southwestern Asia forced by LTSST and PDSST imply important changes to the land surface hydrology during the spring and summer.

A Climate-Data Record (CDR) of the "Clear Sky" Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Comiso, J. C.; DiGirolamo, N. E.; Shuman, C. A.

2011-01-01

To quantify the ice-surface temperature (IST) we are developing a climate-data record (CDR) of monthly IST of the Greenland ice sheet, from 1982 to the present using Advanced Very High Resolution Radiometer (AVHRR) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data at 5-km resolution. "Clear-sky" surface temperature increases have been measured from the early 1980s to the early 2000s in the Arctic using AVHRR data, showing increases ranging from 0.57-0.02 (Wang and Key, 2005) to 0.72 0.10 deg C per decade (Comiso, 2006). Arctic warming has implications for ice-sheet mass balance because much of the periphery of the ice sheet is near 0 deg C in the melt season and is thus vulnerable to more extensive melting (Hanna et al., 2008). The algorithm used for this work has a long history of measuring IST in the Arctic with AVHRR (Key and Haefliger, 1992). The data are currently available from 1981 to 2004 in the AVHRR Polar Pathfinder (APP) dataset (Fowler et al., 2000). J. Key1NOAA modified the AVHRR algorithm for use with MODIS (Hall et al., 2004). The MODIS algorithm is now being processed over Greenland. Issues being addressed in the production of the CDR are: time-series bias caused by cloud cover, and cross-calibration between AVHRR and MODIS instruments. Because of uncertainties, time series of satellite ISTs do not necessarily correspond with actual surface temperatures. The CDR will be validated by comparing results with in-situ (see Koenig and Hall, in press) and automatic-weather station data (e.g., Shuman et al., 2001).

Comparison of in-situ measurements and satellite-derived surface emissivity over Italian volcanic areas

NASA Astrophysics Data System (ADS)

Silvestri, Malvina; Musacchio, Massimo; Cammarano, Diego; Fabrizia Buongiorno, Maria; Amici, Stefania; Piscini, Alessandro

2016-04-01

In this work we compare ground measurements of emissivity collected during dedicated fields campaign on Mt. Etna and Solfatara of Pozzuoli volcanoes and acquired by means of Micro-FTIR (Fourier Thermal Infrared spectrometer) instrument with the emissivity obtained by using single ASTER data (Advanced Spaceborne Thermal Emission and Reflection Radiometer, ASTER 05) and the ASTER emissivity map extract from ASTER Global Emissivity Database (GED), released by LP DAAC on April 2, 2014. The database was developed by the National Aeronautics and Space Administration's (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology. The database includes land surface emissivity derived from ASTER data acquired over the contiguous United States, Africa, Arabian Peninsula, Australia, Europe, and China. Through this analysis we want to investigate the differences existing between the ASTER-GED dataset (average from 2000 to 2008 seasoning independent) and fall in-situ emissivity measurement. Moreover the role of different spatial resolution characterizing ASTER and MODIS, 90mt and 1km respectively, by comparing them with in situ measurements, is analyzed. Possible differences can be due also to the different algorithms used for the emissivity estimation, Temperature and Emissivity Separation algorithm for ASTER TIR band( Gillespie et al, 1998) and the classification-based emissivity method (Snyder and al, 1998) for MODIS. Finally land surface temperature products generated using ASTER-GED and ASTER 05 emissivity are also analyzed. Gillespie, A. R., Matsunaga, T., Rokugawa, S., & Hook, S. J. (1998). Temperature and emissivity separation from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36, 1113-1125. Snyder, W.C., Wan, Z., Zhang, Y., & Feng, Y.-Z. (1998). Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19, 2753-2574.

Climate variability related to the 11 year solar cycle as represented in different spectral solar irradiance reconstructions

NASA Astrophysics Data System (ADS)

Kruschke, Tim; Kunze, Markus; Misios, Stergios; Matthes, Katja; Langematz, Ulrike; Tourpali, Kleareti

2016-04-01

Advanced spectral solar irradiance (SSI) reconstructions differ significantly from each other in terms of the mean solar spectrum, that is the spectral distribution of energy, and solar cycle variability. Largest uncertainties - relative to mean irradiance - are found for the ultraviolet range of the spectrum, a spectral region highly important for radiative heating and chemistry in the stratosphere and troposphere. This study systematically analyzes the effects of employing different SSI reconstructions in long-term (40 years) chemistry-climate model (CCM) simulations to estimate related uncertainties of the atmospheric response. These analyses are highly relevant for the next round of CCM studies as well as climate models within the CMIP6 exercise. The simulations are conducted by means of two state-of-the-art CCMs - CESM1(WACCM) and EMAC - run in "atmosphere-only"-mode. These models are quite different with respect to the complexity of the implemented radiation and chemistry schemes. CESM1(WACCM) features a chemistry module with considerably higher spectral resolution of the photolysis scheme while EMAC employs a radiation code with notably higher spectral resolution. For all simulations, concentrations of greenhouse gases and ozone depleting substances, as well as observed sea surface temperatures (SST) are set to average conditions representative for the year 2000 (for SSTs: mean of decade centered over year 2000) to exclude anthropogenic influences and differences due to variable SST forcing. Only the SSI forcing differs for the various simulations. Four different forcing datasets are used: NRLSSI1 (used as a reference in all previous climate modeling intercomparisons, i.e. CMIP5, CCMVal, CCMI), NRLSSI2, SATIRE-S, and the SSI forcing dataset recommended for the CMIP6 exercise. For each dataset, a solar maximum and minimum timeslice is integrated, respectively. The results of these simulations - eight in total - are compared to each other with respect to their shortwave heating rate differences (additionally collated with line-by-line calculations using libradtran), differences in the photolysis rates, as well as atmospheric circulation features (temperature, zonal wind, geopotential height, etc.). It is shown that atmospheric responses to the different SSI datasets differ significantly from each other. This is a result from direct radiative effects as well as indirect effects induced by ozone feedbacks. Differences originating from using different SSI datasets for the same level of solar activity are in the same order of magnitude as those associated with the 11 year solar cycle within a specific dataset. However, the climate signals related to the solar cycle are quite comparable across datasets.

Towards an estimation of water masses formation areas from SMOS-based TS diagrams

NASA Astrophysics Data System (ADS)

Klockmann, Marlene; Sabia, Roberto; Fernandez-Prieto, Diego; Donlon, Craig; Font, Jordi

2014-05-01

Temperature-Salinity (TS) diagrams emphasize the mutual variability of ocean temperature and salinity values, relating them to the corresponding density. Canonically used in oceanography, they provide a means to characterize and trace ocean water masses. In [1], a first attempt to estimate surface-layer TS diagrams based on satellite measurements has been performed, profiting from the recent availability of spaceborne salinity data. In fact, the Soil Moisture and Ocean Salinity (SMOS, [2]) and the Aquarius/SAC-D [3] satellite missions allow to study the dynamical patterns of Sea Surface Salinity (SSS) for the first time on a global scale. In [4], given SMOS and Aquarius salinity estimates, and by also using Sea Surface Temperature (SST) from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA, [5]) effort, experimental satellite-based TS diagrams have been routinely derived for the year 2011. They have been compared with those computed from ARGO-buoys interpolated fields, referring to a customised partition of the global ocean into seven regions, according to the water masses classification of [6]. In [7], moreover, besides using TS diagrams as a diagnostic tool to evaluate the temporal variation of SST and SSS (and their corresponding density) as estimated by satellite measurements, the emphasis was on the interpretation of the geographical deviations with respect to the ARGO baseline (aiming at distinguishing between the SSS retrieval errors and the additional information contained in the satellite data with respect to ARGO). In order to relate these mismatches to identifiable oceanographic structures and processes, additional satellite datasets of ocean currents, evaporation/precipitation fluxes, and wind speed have been super-imposed. Currently, the main focus of the study deals with the exploitation of these TS diagrams as a prognostic tool to derive water masses formation areas. Firstly, following the approach described in [8], the surface density flux (i.e., the change in density induced by surface heat and freshwater fluxes) is computed, characterizing how the buoyancy of a water parcel is being transformed, by increasing or decreasing its density. Afterwards, integrating over a certain time/space and deriving with respect to density, the formation (in Sv) of water masses themselves can be computed, pinpointing the range of SST and SSS in the TS diagrams where a specific water mass is formed. A geographical representation of these points, ultimately, allows to provide a relevant temporal series of the spatial extent of the water masses formation areas (in the specific test zones chosen). This can be then extended over challenging ocean regions, also evaluating the sensitivity of the performances to the datasets used. With this approach, known water masses can be identified and their formation traced in time and space. Longer time series will give further insights by helping to identify inter-annual water mass formation variability and trends in the TS/geographical domains. Future work aims at exploring additional datasets and at connecting the surface information to the vertical structure and to buoyancy-driven ocean circulation processes. References [1] Sabia, R., J. Ballabrera, G. Lagerloef, E. Bayler, M. Talone, Y. Chao, C. Donlon, D. Fernández-Prieto, J. Font, "Derivation of an Experimental Satellite-based T-S Diagram", In Proceedings of IGARSS '12 , Munich, Germany, pp. 5760-5763, 2012. [2] Font, J., A. Camps, A. Borges, M. Martín-Neira, J. Boutin, N. Reul, Y. H. Kerr, A. Hahne, and S. Mecklenburg, "SMOS: The challenging sea surface salinity measurement from space," Proceedings of the IEEE, vol. 98, pp. 649-665, 2010. [3] Le Vine, D.M.; Lagerloef, G.S.E.; Torrusio, S.E.; "Aquarius and Remote Sensing of Sea Surface Salinity from Space," Proceedings of the IEEE , vol.98, no.5, pp.688-703, May 2010, doi: 10.1109/JPROC.2010.2040550. [4] Sabia, R., M. Klockmann, D. Fernández-Prieto, C. Donlon, E. Bayler, J. Font, G. Lagerloef, "Satellite-based T/S Diagrams and Surface Ocean Water Masses", SMOS-Aquarius Science Workshop, Brest, France, April 2013. [5] Donlon, C. J., M. Martin, J. D. Stark, J. Roberts-Jones, E. Fiedler and W. Wimmer, "The Operational Sea Surface Temperature and Sea Ice analysis (OSTIA)", Remote Sensing of the Environment. doi: 10.1016/j.rse.2010.10.017 2011. [6] Emery, W. J., "Water Types and Water Masses", Ocean Circulation, Elsevier science, pp 1556-1567, 2003. [7] Sabia, R., M. Klockmann, C. Donlon, D. Fernández-Prieto, M. Talone, J. Ballabrera, "Satellite-based T-S Diagrams: a prospective diagnostic tool to trace ocean water masses", Living Planet Symposium 2013, Edinburgh, UK, September 2013. [8] Speer, K., E. Tzipermann, "Rates of Water Mass Transformation in the North Atlantic", Journal of Physical Oceanography, 22, 93 - 104, 1992.

The 3D Reference Earth Model: Status and Preliminary Results

NASA Astrophysics Data System (ADS)

Moulik, P.; Lekic, V.; Romanowicz, B. A.

2017-12-01

In the 20th century, seismologists constructed models of how average physical properties (e.g. density, rigidity, compressibility, anisotropy) vary with depth in the Earth's interior. These one-dimensional (1D) reference Earth models (e.g. PREM) have proven indispensable in earthquake location, imaging of interior structure, understanding material properties under extreme conditions, and as a reference in other fields, such as particle physics and astronomy. Over the past three decades, new datasets motivated more sophisticated efforts that yielded models of how properties vary both laterally and with depth in the Earth's interior. Though these three-dimensional (3D) models exhibit compelling similarities at large scales, differences in the methodology, representation of structure, and dataset upon which they are based, have prevented the creation of 3D community reference models. As part of the REM-3D project, we are compiling and reconciling reference seismic datasets of body wave travel-time measurements, fundamental mode and overtone surface wave dispersion measurements, and normal mode frequencies and splitting functions. These reference datasets are being inverted for a long-wavelength, 3D reference Earth model that describes the robust long-wavelength features of mantle heterogeneity. As a community reference model with fully quantified uncertainties and tradeoffs and an associated publically available dataset, REM-3D will facilitate Earth imaging studies, earthquake characterization, inferences on temperature and composition in the deep interior, and be of improved utility to emerging scientific endeavors, such as neutrino geoscience. Here, we summarize progress made in the construction of the reference long period dataset and present a preliminary version of REM-3D in the upper-mantle. In order to determine the level of detail warranted for inclusion in REM-3D, we analyze the spectrum of discrepancies between models inverted with different subsets of the reference dataset. This procedure allows us to evaluate the extent of consistency in imaging heterogeneity at various depths and between spatial scales.

Comparing soil moisture anomalies from multiple independent sources over different regions across the globe

NASA Astrophysics Data System (ADS)

Cammalleri, Carmelo; Vogt, Jürgen V.; Bisselink, Bernard; de Roo, Ad

2017-12-01

Agricultural drought events can affect large regions across the world, implying the need for a suitable global tool for an accurate monitoring of this phenomenon. Soil moisture anomalies are considered a good metric to capture the occurrence of agricultural drought events, and they have become an important component of several operational drought monitoring systems. In the framework of the JRC Global Drought Observatory (GDO, http://edo.jrc.ec.europa.eu/gdo/), the suitability of three datasets as possible representations of root zone soil moisture anomalies has been evaluated: (1) the soil moisture from the Lisflood distributed hydrological model (namely LIS), (2) the remotely sensed Land Surface Temperature data from the MODIS satellite (namely LST), and (3) the ESA Climate Change Initiative combined passive/active microwave skin soil moisture dataset (namely CCI). Due to the independency of these three datasets, the triple collocation (TC) technique has been applied, aiming at quantifying the likely error associated with each dataset in comparison to the unknown true status of the system. TC analysis was performed on five macro-regions (namely North America, Europe, India, southern Africa and Australia) detected as suitable for the experiment, providing insight into the mutual relationship between these datasets as well as an assessment of the accuracy of each method. Even if no definitive statement on the spatial distribution of errors can be provided, a clear outcome of the TC analysis is the good performance of the remote sensing datasets, especially CCI, over dry regions such as Australia and southern Africa, whereas the outputs of LIS seem to be more reliable over areas that are well monitored through meteorological ground station networks, such as North America and Europe. In a global drought monitoring system, the results of the error analysis are used to design a weighted-average ensemble system that exploits the advantages of each dataset.

Web-based Reanalysis Intercomparison Tools (WRIT): Comparing Reanalyses and Observational data.

NASA Astrophysics Data System (ADS)

Compo, G. P.; Smith, C. A.; Hooper, D. K.

2014-12-01

While atmospheric reanalysis datasets are widely used in climate science, many technical issues hinder comparing them to each other and to observations. The reanalysis fields are stored in diverse file architectures, data formats, and resolutions, with metadata, such as variable name and units, that also differ. Individual users have to download the fields, convert them to a common format, store them locally, change variable names, re-grid if needed, and convert units. Comparing reanalyses with observational datasets is difficult for similar reasons. Even if a dataset can be read via Open-source Project for a Network Data Access Protocol (OPeNDAP) or a similar protocol, most of this work is still needed. All of these tasks take time, effort, and money. To overcome some of the obstacles in reanalysis intercomparison, our group at the Cooperative Institute for Research in the Environmental Sciences (CIRES) at the University of Colorado and affiliated colleagues at National Oceanic and Atmospheric Administration's (NOAA's) Earth System Research Laboratory Physical Sciences Division (ESRL/PSD) have created a set of Web-based Reanalysis Intercomparison Tools (WRIT) at http://www.esrl.noaa.gov/psd/data/writ/. WRIT allows users to easily plot and compare reanalysis and observational datasets, and to test hypotheses. Currently, there are tools to plot monthly mean maps and vertical cross-sections, timeseries, and trajectories for standard pressure level and surface variables. Users can refine dates, statistics, and plotting options. Reanalysis datasets currently available include the NCEP/NCAR R1, NCEP/DOE R2, MERRA, ERA-Interim, NCEP CFSR and the 20CR. Observational datasets include those containing precipitation (e.g. GPCP), temperature (e.g. GHCNCAMS), winds (e.g. WASWinds), precipitable water (e.g. NASA NVAP), SLP (HadSLP2), and SST (NOAA ERSST). WRIT also facilitates the mission of the Reanalyses.org website as a convenient toolkit for studying the reanalysis datasets.

The sensitivity of the atmospheric branch of the global water cycle to temperature fluctuations at synoptic to decadal time-scales in different satellite- and model-based products

NASA Astrophysics Data System (ADS)

Nogueira, Miguel

2018-02-01

Spectral analysis of global-mean precipitation, P, evaporation, E, precipitable water, W, and surface temperature, Ts, revealed significant variability from sub-daily to multi-decadal time-scales, superposed on high-amplitude diurnal and yearly peaks. Two distinct regimes emerged from a transition in the spectral exponents, β. The weather regime covering time-scales < 10 days with β ≥ 1; and the macroweather regime extending from a few months to a few decades with 0 <β <1. Additionally, the spectra showed a generally good statistical agreement amongst several different model- and satellite-based datasets. Detrended cross-correlation analysis (DCCA) revealed three important results which are robust across all datasets: (1) Clausius-Clapeyron (C-C) relationship is the dominant mechanism of W non-periodic variability at multi-year time-scales; (2) C-C is not the dominant control of W, P or E non-periodic variability at time-scales below about 6 months, where the weather regime is approached and other mechanisms become important; (3) C-C is not a dominant control for P or E over land throughout the entire time-scale range considered. Furthermore, it is suggested that the atmosphere and oceans start to act as a single coupled system at time-scales > 1-2 years, while at time-scales < 6 months they are not the dominant drivers of each other. For global-ocean and full-globe averages, ρDCCA showed large spread of the C-C importance for P and E variability amongst different datasets at multi-year time-scales, ranging from negligible (< 0.3) to high ( 0.6-0.8) values. Hence, state-of-the-art climate datasets have significant uncertainties in the representation of macroweather precipitation and evaporation variability and its governing mechanisms.

Putative archaeal viruses from the mesopelagic ocean.

PubMed

Vik, Dean R; Roux, Simon; Brum, Jennifer R; Bolduc, Ben; Emerson, Joanne B; Padilla, Cory C; Stewart, Frank J; Sullivan, Matthew B

2017-01-01

Oceanic viruses that infect bacteria, or phages, are known to modulate host diversity, metabolisms, and biogeochemical cycling, while the viruses that infect marine Archaea remain understudied despite the critical ecosystem roles played by their hosts. Here we introduce "MArVD", for Metagenomic Archaeal Virus Detector, an annotation tool designed to identify putative archaeal virus contigs in metagenomic datasets. MArVD is made publicly available through the online iVirus analytical platform. Benchmarking analysis of MArVD showed it to be >99% accurate and 100% sensitive in identifying the 127 known archaeal viruses among the 12,499 viruses in the VirSorter curated dataset. Application of MArVD to 10 viral metagenomes from two depth profiles in the Eastern Tropical North Pacific (ETNP) oxygen minimum zone revealed 43 new putative archaeal virus genomes and large genome fragments ranging in size from 10 to 31 kb. Network-based classifications, which were consistent with marker gene phylogenies where available, suggested that these putative archaeal virus contigs represented six novel candidate genera. Ecological analyses, via fragment recruitment and ordination, revealed that the diversity and relative abundances of these putative archaeal viruses were correlated with oxygen concentration and temperature along two OMZ-spanning depth profiles, presumably due to structuring of the host Archaea community. Peak viral diversity and abundances were found in surface waters, where Thermoplasmata 16S rRNA genes are prevalent, suggesting these archaea as hosts in the surface habitats. Together these findings provide a baseline for identifying archaeal viruses in sequence datasets, and an initial picture of the ecology of such viruses in non-extreme environments.

SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919-2014.

PubMed

Zeng, Lili; Wang, Dongxiao; Chen, Ju; Wang, Weiqiang; Chen, Rongyu

2016-04-26

In addition to the oceanographic data available for the South China Sea (SCS) from the World Ocean Database (WOD) and Array for Real-time Geostrophic Oceanography (Argo) floats, a suite of observations has been made by the South China Sea Institute of Oceanology (SCSIO) starting from the 1970s. Here, we assemble a SCS Physical Oceanographic Dataset (SCSPOD14) based on 51,392 validated temperature and salinity profiles collected from these three datasets for the period 1919-2014. A gridded dataset of climatological monthly mean temperature, salinity, and mixed and isothermal layer depth derived from an objective analysis of profiles is also presented. Comparisons with the World Ocean Atlas (WOA) and IFREMER/LOS Mixed Layer Depth Climatology confirm the reliability of the new dataset. This unique dataset offers an invaluable baseline perspective on the thermodynamic processes, spatial and temporal variability of water masses, and basin-scale and mesoscale oceanic structures in the SCS. We anticipate improvements and regular updates to this product as more observations become available from existing and future in situ networks.

SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919–2014

PubMed Central

Zeng, Lili; Wang, Dongxiao; Chen, Ju; Wang, Weiqiang; Chen, Rongyu

2016-01-01

In addition to the oceanographic data available for the South China Sea (SCS) from the World Ocean Database (WOD) and Array for Real-time Geostrophic Oceanography (Argo) floats, a suite of observations has been made by the South China Sea Institute of Oceanology (SCSIO) starting from the 1970s. Here, we assemble a SCS Physical Oceanographic Dataset (SCSPOD14) based on 51,392 validated temperature and salinity profiles collected from these three datasets for the period 1919–2014. A gridded dataset of climatological monthly mean temperature, salinity, and mixed and isothermal layer depth derived from an objective analysis of profiles is also presented. Comparisons with the World Ocean Atlas (WOA) and IFREMER/LOS Mixed Layer Depth Climatology confirm the reliability of the new dataset. This unique dataset offers an invaluable baseline perspective on the thermodynamic processes, spatial and temporal variability of water masses, and basin-scale and mesoscale oceanic structures in the SCS. We anticipate improvements and regular updates to this product as more observations become available from existing and future in situ networks. PMID:27116565

Estimating aboveground biomass in the boreal forests of the Yukon River Basin, Alaska

NASA Astrophysics Data System (ADS)

Ji, L.; Wylie, B. K.; Nossov, D.; Peterson, B.; Waldrop, M. P.; McFarland, J.; Alexander, H. D.; Mack, M. C.; Rover, J. A.; Chen, X.

2011-12-01

Quantification of aboveground biomass (AGB) in Alaska's boreal forests is essential to accurately evaluate terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. However, regional AGB datasets with spatially detailed information (<500 m) are not available for this extensive and remote area. Our goal was to map AGB at 30-m resolution for the boreal forests in the Yukon River Basin of Alaska using recent Landsat data and ground measurements. We collected field data in the Yukon River Basin from 2008 to 2010. Ground measurements included diameter at breast height (DBH) or basal diameter (BD) for live and dead trees and shrubs (>1 m tall), which were converted to plot-level AGB using allometric equations. We acquired Landsat Enhanced Thematic Mapper Plus (ETM+) images from the Web Enabled Landsat Data (WELD) that provides multi-date composites of top-of-atmosphere reflectance and brightness temperature for Alaska. From the WELD images, we generated a three-year (2008 - 2010) image composite for the Yukon River Basin using a series of compositing criteria including non-saturation, non-cloudiness, maximal normalize difference vegetation index (NDVI), and maximal brightness temperature. Airborne lidar datasets were acquired for two sub-regions in the central basin in 2009, which were converted to vegetation height datasets using the bare-earth digital surface model (DSM) and the first-return DSM. We created a multiple regression model in which the response variable was the field-observed AGB and the predictor variables were Landsat-derived reflectance, brightness temperature, and spectral vegetation indices including NDVI, soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), and normalized difference water index (NDWI). Principal component analysis was incorporated in the regression model to remedy the multicollinearity problems caused by high correlations between predictor variables. The model fitted the observed data well with an R-square of 0.62, mean absolute error of 29.1 Mg/ha, and mean bias error of 3.9 Mg/ha. By applying this model to the Landsat mosaic, we generated a 30-m AGB map for the boreal forests in the Yukon River Basin. Validation of the Landsat-derived AGB using the lidar dataset indicated a significant correlation between the AGB estimates and the lidar-derived canopy height. The production of a basin-wide boreal forest AGB dataset will provide an important biophysical parameter for the modeling and investigation of Alaska's ecosystems.

Updating sea spray aerosol emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2

EPA Pesticide Factsheets

The uploaded data consists of the BRACE Na aerosol observations paired with CMAQ model output, the updated model's parameterization of sea salt aerosol emission size distribution, and the model's parameterization of the sea salt emission factor as a function of sea surface temperature. This dataset is associated with the following publication:Gantt , B., J. Kelly , and J. Bash. Updating sea spray aerosol emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 8: 3733-3746, (2015).

The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware

USGS Publications Warehouse

Vanderhoof, Melanie; Distler, Hayley; Lang, Megan W.; Alexander, Laurie C.

2018-01-01

The dependence of downstream waters on upstream ecosystems necessitates an improved understanding of watershed-scale hydrological interactions including connections between wetlands and streams. An evaluation of such connections is challenging when, (1) accurate and complete datasets of wetland and stream locations are often not available and (2) natural variability in surface-water extent influences the frequency and duration of wetland/stream connectivity. The Upper Choptank River watershed on the Delmarva Peninsula in eastern Maryland and Delaware is dominated by a high density of small, forested wetlands. In this analysis, wetland/stream surface water connections were quantified using multiple wetland and stream datasets, including headwater streams and depressions mapped from a lidar-derived digital elevation model. Surface-water extent was mapped across the watershed for spring 2015 using Landsat-8, Radarsat-2 and Worldview-3 imagery. The frequency of wetland/stream connections increased as a more complete and accurate stream dataset was used and surface-water extent was included, in particular when the spatial resolution of the imagery was finer (i.e., <10 m). Depending on the datasets used, 12–60% of wetlands by count (21–93% of wetlands by area) experienced surface-water interactions with streams during spring 2015. This translated into a range of 50–94% of the watershed contributing direct surface water runoff to streamflow. This finding suggests that our interpretation of the frequency and duration of wetland/stream connections will be influenced not only by the spatial and temporal characteristics of wetlands, streams and potential flowpaths, but also by the completeness, accuracy and resolution of input datasets.

GOSAT TIR radiometric validation toward simultaneous GHG column and profile observation

NASA Astrophysics Data System (ADS)

Kataoka, F.; Knuteson, R. O.; Kuze, A.; Shiomi, K.; Suto, H.; Saitoh, N.

2015-12-01

The Greenhouse gases Observing SATellite (GOSAT) was launched on January 2009 and continues its operation for more than six years. The thermal and near infrared sensor for carbon observation Fourier-Transform Spectrometer (TANSO-FTS) onboard GOSAT measures greenhouse gases (GHG), such as CO2 and CH4, with wide and high resolution spectra from shortwave infrared (SWIR) to thermal infrared (TIR). This instrument has the advantage of being able to measure simultaneously the same field of view in different spectral ranges. The combination of column-GHG form SWIR band and vertical profile-GHG from TIR band provide better understanding and distribution of GHG, especially in troposphere. This work describes the radiometric validation and sensitivity analysis of TANSO-FTS TIR spectra, especially CO2, atmospheric window and CH4 channels with forward calculation. In this evaluation, we used accurate in-situ dataset of the HIPPO (HIAPER Pole-to-Pole Observation) airplane observation data and GOSAT vicarious calibration and validation campaign data in Railroad Valley, NV. The HIPPO aircraft campaign had taken accurate atmospheric vertical profile dataset (T, RH, O3, CO2, CH4, N2O, CO) approximately pole-to-pole from the surface to the tropopause over the ocean. We implemented these dataset for forward calculation and made the spectral correction model with respect to wavenumber and internal calibration blackbody temperature The GOSAT vicarious calibration campaign have conducted every year since 2009 near summer solstice in Railroad Valley, where high-temperature desert site. In this campaign, we have measured temperature and humidity by a radiosonde and CO2, CH4 and O3 profile by the AJAX airplane at the time of the GOSAT overpass. Sometimes, the GHG profiles over the Railroad Valley show the air mass advection in mid-troposphere depending on upper wind. These advections bring the different concentration of GHG in lower and upper troposphere. Using these cases, we made sensitivity analysis of TANSO-FTS TIR band in troposphere changing in-situ GHG profiles.

Validation of satellite-retrieved MBL cloud properties using DOE ARM AMF measurements at the Azores

NASA Astrophysics Data System (ADS)

Xi, B.; Dong, X.; Minnis, P.; Sun-Mack, S.

2013-05-01

Marine Boundary Layer (MBL) cloud properties derived for the Clouds and the Earth's Radiant Energy System (CERES) Project using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with observations taken at the Atmospheric Radiation Measurement (ARM) AMF AZORES site from June 2009 through December 2010. Retrievals from ARM surface-based data were averaged over a 1-hour interval centered at the time of each satellite overpass, and the CERES-MODIS Ed4 cloud properties were averaged within a 30-km x 30-km box centered on the ARM AZORES site. Two datasets were analyzed: all of the single-layered unbroken decks (SL) and those cases without temperature inversions. The CERES-MODIS cloud top/base heights were determined from cloud top/base temperature by using a lapse rate method normalized to the 24-h mean surface air temperature. The preliminary results show: for all SL MBL at daytime, they are, on average, 0.148 km (cloud top) and 0.087 km (cloud base) higher than the ARM radar-lidar observed cloud top and base, respectively. At nighttime, they are 0.446 km (cloud top) and 0.334 km (cloud base). For those cases without temperature inversions, the comparisons are close to their SL counterparts. For cloud temperatures, the MODIS-derived cloud-top and -base temperatures are 1.6 K lower and 0.4 K higher than the surface values with correlations of 0.92 during daytime. At nighttime, the differences are slightly larger and correlations are lower than daytime comparisons. Variations in the height difference are mainly caused by uncertainties in the surface air temperatures and lapse rates. Based on a total of 61 daytime and 87 nighttime samples (ALL SL cases), the temperature inversion layers occur about 72% during daytime and 83% during nighttime. The difference of surface-observed lapse rate and the satellite derived lapse rate can be 1.6 K/km for daytime and 3.3K/km for nighttime. From these lapse rates, we can further analyze the surface air temperature difference that used to calculate these lapse rate, which are ~3K difference between surface-observed and the satellite derived during the daytime and 5.1 K during nighttime. Further studies of the cause of the temperature inversions that may help the cloud heights retrievals by satellite. The preliminary comparisons in MBL microphysical properties have shown that the averaged CERES-MODIS derived MBL cloud-droplet effective radius is only 1.5 μm larger than ARM retrieval (13.2 μm), and LWP values are also very close to each other (112 vs. 124 gm-2) with a relative large difference in optical depth (10.6 vs. 14.4).

Consistency of Estimated Global Water Cycle Variations Over the Satellite Era

NASA Technical Reports Server (NTRS)

Robertson, F. R.; Bosilovich, M. G.; Roberts, J. B.; Reichle, R. H.; Adler, R.; Ricciardulli, L.; Berg, W.; Huffman, G. J.

2013-01-01

Motivated by the question of whether recent indications of decadal climate variability and a possible "climate shift" may have affected the global water balance, we examine evaporation minus precipitation (E-P) variability integrated over the global oceans and global land from three points of view-remotely sensed retrievals / objective analyses over the oceans, reanalysis vertically-integrated moisture convergence (MFC) over land, and land surface models forced with observations-based precipitation, radiation and near-surface meteorology. Because monthly variations in area-averaged atmospheric moisture storage are small and the global integral of moisture convergence must approach zero, area-integrated E-P over ocean should essentially equal precipitation minus evapotranspiration (P-ET) over land (after adjusting for ocean and land areas). Our analysis reveals considerable uncertainty in the decadal variations of ocean evaporation when integrated to global scales. This is due to differences among datasets in 10m wind speed and near-surface atmospheric specific humidity (2m qa) used in bulk aerodynamic retrievals. Precipitation variations, all relying substantially on passive microwave retrievals over ocean, still have uncertainties in decadal variability, but not to the degree present with ocean evaporation estimates. Reanalysis MFC and P-ET over land from several observationally forced diagnostic and land surface models agree best on interannual variations. However, upward MFC (i.e. P-ET) reanalysis trends are likely related in part to observing system changes affecting atmospheric assimilation models. While some evidence for a low-frequency E-P maximum near 2000 is found, consistent with a recent apparent pause in sea-surface temperature (SST) rise, uncertainties in the datasets used here remain significant. Prospects for further reducing uncertainties are discussed. The results are interpreted in the context of recent climate variability (Pacific Decadal Oscillation, Atlantic Meridional Overturning), and efforts to distinguish these modes from longer-term trends.

Evaluation of the Precision of Satellite-Derived Sea Surface Temperature Fields

NASA Astrophysics Data System (ADS)

Wu, F.; Cornillon, P. C.; Guan, L.

2016-02-01

A great deal of attention has been focused on the temporal accuracy of satellite-derived sea surface temperature (SST) fields with little attention being given to their spatial precision. Specifically, the primary measure of the quality of SST fields has been the bias and variance of selected values minus co-located (in space and time) in situ values. Contributing values, determined by the location of the in situ values and the necessity that the satellite-derived values be cloud free, are generally widely separated in space and time hence provide little information related to the pixel-to-pixel uncertainty in the retrievals. But the main contribution to the uncertainty in satellite-derived SST retrievals relates to atmospheric contamination and because the spatial scales of atmospheric features are, in general, large compared with the pixel separation of modern infra-red sensors, the pixel-to-pixel uncertainty is often smaller than the accuracy determined from in situ match-ups. This makes selection of satellite-derived datasets for the study of submesoscale processes, for which the spatial structure of the upper ocean is significant, problematic. In this presentation we present a methodology to characterize the spatial precision of satellite-derived SST fields. The method is based on an examination of the high wavenumber tail of the 2-D spectrum of SST fields in the Sargasso Sea, a low energy region of the ocean close to the track of the MV Oleander, a container ship making weekly roundtrips between New York and Bermuda, with engine intake temperatures sampled every 75 m along track. Important spectral characteristics are the point at which the satellite-derived spectra separate from the Oleander spectra and the spectral slope following separation. In this presentation a number of high resolution 375 m to 10 km SST datasets are evaluated based on this approach.

Evaluation of different methods to estimate daily reference evapotranspiration in ungauged basins in Southern Brazil

NASA Astrophysics Data System (ADS)

Ribeiro Fontoura, Jessica; Allasia, Daniel; Herbstrith Froemming, Gabriel; Freitas Ferreira, Pedro; Tassi, Rutineia

2016-04-01

Evapotranspiration is a key process of hydrological cycle and a sole term that links land surface water balance and land surface energy balance. Due to the higher information requirements of the Penman-Monteith method and the existing data uncertainty, simplified empirical methods for calculating potential and actual evapotranspiration are widely used in hydrological models. This is especially important in Brazil, where the monitoring of meteorological data is precarious. In this study were compared different methods for estimating evapotranspiration for Rio Grande do Sul, the Southernmost State of Brazil, aiming to suggest alternatives to the recommended method (Penman-Monteith-FAO 56) for estimate daily reference evapotranspiration (ETo) when meteorological data is missing or not available. The input dataset included daily and hourly-observed data from conventional and automatic weather stations respectively maintained by the National Weather Institute of Brazil (INMET) from the period of 1 January 2007 to 31 January 2010. Dataset included maximum temperature (Tmax, °C), minimum temperature (Tmin, °C), mean relative humidity (%), wind speed at 2 m height (u2, m s-1), daily solar radiation (Rs, MJ m- 2) and atmospheric pressure (kPa) that were grouped at daily time-step. Was tested the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method (PM) at its full form, against PM assuming missing several variables not normally available in Brazil in order to calculate daily reference ETo. Missing variables were estimated as suggested in FAO56 publication or from climatological means. Furthermore, PM was also compared against the following simplified empirical methods: Hargreaves-Samani, Priestley-Taylor, Mccloud, McGuiness-Bordne, Romanenko, Radiation-Temperature, Tanner-Pelton. The statistical analysis indicates that even if just Tmin and Tmax are available, it is better to use PM estimating missing variables from syntetic data than simplified empirical methods evaluated except for Tanner-Pelton and Priestley-Taylor.

Temperature measurements at IODP 337 Expedition, off Shimokita, NE Japan.

NASA Astrophysics Data System (ADS)

Yamada, Y.; Sanada, Y.; Moe, K.; Kubo, Y.; Inagaki, F.

2014-12-01

Precise estimation of underground temperature is a challenging issue, since direct measurements require drill holes that disturb the original underground environment. During IODP 337 expedition, we have obtained in-situ temperature datasets for several times by using geophysical logging tools. A common procedure to estimate the undisturbed maximum underground temperature is by approximating that the 'build-up' pattern of measured values in the borehole should reach to the equilibrium temperature. At the Shimokita site, this was 63.7 oC at a depth of 2466 m. We have much more measurement dataset and all of these were used to analyze detailed in-site temperatures at various depths. The result shows a non-linear temperature profile to the depth and this may be reflected by the thermal properties of the surrounding rocks.

Multisource Estimation of Long-term Global Terrestrial Surface Radiation

NASA Astrophysics Data System (ADS)

Peng, L.; Sheffield, J.

2017-12-01

Land surface net radiation is the essential energy source at the earth's surface. It determines the surface energy budget and its partitioning, drives the hydrological cycle by providing available energy, and offers heat, light, and energy for biological processes. Individual components in net radiation have changed historically due to natural and anthropogenic climate change and land use change. Decadal variations in radiation such as global dimming or brightening have important implications for hydrological and carbon cycles. In order to assess the trends and variability of net radiation and evapotranspiration, there is a need for accurate estimates of long-term terrestrial surface radiation. While large progress in measuring top of atmosphere energy budget has been made, huge discrepancies exist among ground observations, satellite retrievals, and reanalysis fields of surface radiation, due to the lack of observational networks, the difficulty in measuring from space, and the uncertainty in algorithm parameters. To overcome the weakness of single source datasets, we propose a multi-source merging approach to fully utilize and combine multiple datasets of radiation components separately, as they are complementary in space and time. First, we conduct diagnostic analysis of multiple satellite and reanalysis datasets based on in-situ measurements such as Global Energy Balance Archive (GEBA), existing validation studies, and other information such as network density and consistency with other meteorological variables. Then, we calculate the optimal weighted average of multiple datasets by minimizing the variance of error between in-situ measurements and other observations. Finally, we quantify the uncertainties in the estimates of surface net radiation and employ physical constraints based on the surface energy balance to reduce these uncertainties. The final dataset is evaluated in terms of the long-term variability and its attribution to changes in individual components. The goal of this study is to provide a merged observational benchmark for large-scale diagnostic analyses, remote sensing and land surface modeling.

The effects of sea surface temperature anomalies on oceanic coral reef systems in the southwestern tropical Atlantic

NASA Astrophysics Data System (ADS)

Ferreira, B. P.; Costa, M. B. S. F.; Coxey, M. S.; Gaspar, A. L. B.; Veleda, D.; Araujo, M.

2013-06-01

In 2010, high sea surface temperatures that were recorded in several parts of the world and caused coral bleaching and coral mortality were also recorded in the southwest Atlantic Ocean, between latitudes 0°S and 8°S. This paper reports on coral bleaching and diseases in Rocas Atoll and Fernando de Noronha archipelago and examines their relationship with sea surface temperature (SST) anomalies recorded by PIRATA buoys located at 8°S30°W, 0°S35°W, and 0°S23°W. Adjusted satellite data were used to derive SST climatological means at buoy sites and to derive anomalies at reef sites. The whole region was affected by the elevated temperature anomaly that persisted through 2010, reaching 1.67 °C above average at reef sites and 1.83 °C above average at buoys sites. A significant positive relationship was found between the percentage of coral bleaching that was observed on reef formations and the corresponding HotSpot SST anomaly recorded by both satellite and buoys. These results indicate that the warming observed in the ocean waters was followed by a warming at the reefs. The percentage of bleached corals persisting after the subsidence of the thermal stress, and disease prevalence increased through 2010, after two periods of thermal stress. The in situ temperature anomaly observed during the 2009-2010 El Niño event was equivalent to the anomaly observed during the 1997-1998 El Niño event, explaining similar bleaching intensity. Continued monitoring efforts are necessary to further assess the relationship between bleaching severity and PIRATA SST anomalies and improve the use of this new dataset in future regional bleaching predictions.

Global surface displacement data for assessing variability of displacement at a point on a fault

USGS Publications Warehouse

Hecker, Suzanne; Sickler, Robert; Feigelson, Leah; Abrahamson, Norman; Hassett, Will; Rosa, Carla; Sanquini, Ann

2014-01-01

This report presents a global dataset of site-specific surface-displacement data on faults. We have compiled estimates of successive displacements attributed to individual earthquakes, mainly paleoearthquakes, at sites where two or more events have been documented, as a basis for analyzing inter-event variability in surface displacement on continental faults. An earlier version of this composite dataset was used in a recent study relating the variability of surface displacement at a point to the magnitude-frequency distribution of earthquakes on faults, and to hazard from fault rupture (Hecker and others, 2013). The purpose of this follow-on report is to provide potential data users with an updated comprehensive dataset, largely complete through 2010 for studies in English-language publications, as well as in some unpublished reports and abstract volumes.

A comparison of all-weather land surface temperature products

NASA Astrophysics Data System (ADS)

Martins, Joao; Trigo, Isabel F.; Ghilain, Nicolas; Goettche, Frank-M.; Ermida, Sofia; Olesen, Folke-S.; Gellens-Meulenberghs, Françoise; Arboleda, Alirio

2017-04-01

The Satellite Application Facility on Land Surface Analysis (LSA-SAF, http://landsaf.ipma.pt) has been providing land surface temperature (LST) estimates using SEVIRI/MSG on an operational basis since 2006. The LSA-SAF service has since been extended to provide a wide range of satellite-based quantities over land surfaces, such as emissivity, albedo, radiative fluxes, vegetation state, evapotranspiration, and fire-related variables. Being based on infra-red measurements, the SEVIRI/MSG LST product is limited to clear-sky pixels only. Several all-weather LST products have been proposed by the scientific community either based on microwave observations or using Soil-Vegetation-Atmosphere Transfer models to fill the gaps caused by clouds. The goal of this work is to provide a nearly gap-free operational all-weather LST product and compare these approaches. In order to estimate evapotranspiration and turbulent energy fluxes, the LSA-SAF solves the surface energy budget for each SEVIRI pixel, taking into account the physical and physiological processes occurring in vegetation canopies. This task is accomplished with an adapted SVAT model, which adopts some formulations and parameters of the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) model operated at the European Center for Medium-range Weather Forecasts (ECMWF), and using: 1) radiative inputs also derived by LSA-SAF, which includes surface albedo, down-welling fluxes and fire radiative power; 2) a land-surface characterization obtained by combining the ECOCLIMAP database with both LSA-SAF vegetation products and the H(ydrology)-SAF snow mask; 3) meteorological fields from ECMWF forecasts interpolated to SEVIRI pixels, and 4) soil moisture derived by the H-SAF and LST from LSA-SAF. A byproduct of the SVAT model is surface skin temperature, which is needed to close the surface energy balance. The model skin temperature corresponds to the radiative temperature of the interface between soil and atmosphere, which is assumed to have no heat storage. The modelled skin temperatures are in fair agreement with LST directly estimated from SEVIRI observations. However, in contrast to LST retrievals from SEVIRI/MSG (or other infrared sensors) the SVAT model solves the energy budget equation under all-sky conditions. The SVAT surface skin temperature is then used to fill gaps in LST fields caused by clouds. Since under cloudy conditions the direct incoming solar radiation is greatly reduced, thermal balance at the surface is more easily achieved and directional effects are also less important. Therefore, a better performance of the model skin temperature may be expected. In contrast, under clear skies the satellite LST showed to be more reliable, since the SVAT model shows biases in the daily amplitude of the skin temperature. In the context of the GlobTemperature project (http://www.globtemperature.info/), all-weather LST datasets using AMSR-E microwave radiances were produced, which are compared here to the SVAT-based LST. Both products were validated against in situ data - particularly from Gobabeb & Farm Heimat (Namibia), and Évora (Portugal) - to show that under cloudy conditions the agreement between in-situ LST and modelled skin temperature is acceptable. Compared to the SVAT-based LST, AMSR-E LST is closer to satellite observations (level 2 product); the complementarity of the two approaches is assessed.

Stable isotopes in the atmospheric marine boundary layer water vapour over the Atlantic Ocean, 2012–2015

PubMed Central

Benetti, Marion; Steen-Larsen, Hans Christian; Reverdin, Gilles; Sveinbjörnsdóttir, Árný Erla; Aloisi, Giovanni; Berkelhammer, Max B.; Bourlès, Bernard; Bourras, Denis; de Coetlogon, Gaëlle; Cosgrove, Ann; Faber, Anne-Katrine; Grelet, Jacques; Hansen, Steffen Bo; Johnson, Rod; Legoff, Hervé; Martin, Nicolas; Peters, Andrew J.; Popp, Trevor James; Reynaud, Thierry; Winther, Malte

2017-01-01

The water vapour isotopic composition (1H216O, H218O and 1H2H16O) of the Atlantic marine boundary layer has been measured from 5 research vessels between 2012 and 2015. Using laser spectroscopy analysers, measurements have been carried out continuously on samples collected 10–20 meter above sea level. All the datasets have been carefully calibrated against the international VSMOW-SLAP scale following the same protocol to build a homogeneous dataset covering the Atlantic Ocean between 4°S to 63°N. In addition, standard meteorological variables have been measured continuously, including sea surface temperatures using calibrated Thermo-Salinograph for most cruises. All calibrated observations are provided with 15-minute resolution. We also provide 6-hourly data to allow easier comparisons with simulations from the isotope-enabled Global Circulation Models. In addition, backwards trajectories from the HYSPLIT model are supplied every 6-hours for the position of our measurements. PMID:28094798

ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation

NASA Astrophysics Data System (ADS)

Guimberteau, Matthieu; Zhu, Dan; Maignan, Fabienne; Huang, Ye; Yue, Chao; Dantec-Nédélec, Sarah; Ottlé, Catherine; Jornet-Puig, Albert; Bastos, Ana; Laurent, Pierre; Goll, Daniel; Bowring, Simon; Chang, Jinfeng; Guenet, Bertrand; Tifafi, Marwa; Peng, Shushi; Krinner, Gerhard; Ducharne, Agnès; Wang, Fuxing; Wang, Tao; Wang, Xuhui; Wang, Yilong; Yin, Zun; Lauerwald, Ronny; Joetzjer, Emilie; Qiu, Chunjing; Kim, Hyungjun; Ciais, Philippe

2018-01-01

The high-latitude regions of the Northern Hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance - those of the permanently frozen soils (permafrost), and of the great expanse of boreal forest - are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO2 fluxes, in addition to a recently developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input datasets, are extensively evaluated against (i) temperature gradients between the atmosphere and deep soils, (ii) the hydrological components comprising the water balance of the largest high-latitude basins, and (iii) CO2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.

Investigation of decadal-scale divergence in tree-ring density chronologies

NASA Astrophysics Data System (ADS)

Vaccaro, A.; Emile-Geay, J.; Anchukaitis, K. J.; Wang, J.

2013-12-01

Tree-ring data from certain forest sites at northern circumpolar latitudes and from some at higher elevation show an anomalous decrease in temperature-sensitivity of tree growth starting in the mid-20th century. This phenomenon, known as the ';divergence problem' (DP), leads to tree-ring reconstructions that underestimate the warming trend exhibited by instrumental measurements over recent decades (e.g. D'Arrigo et al. 2008). In a study conducted in 1998, Briffa et al. discovered a type of divergence wherein latewood density (MXD) chronologies from an early manifestation of the Schweingruber tree-ring dataset showed strong interannual correlation to summer temperature measurements, but increasing divergence between the decadal-scale trends of the tree-rings and temperature records during the second half of the 20th century. This low-frequency divergence suggests that although tree-rings may accurately trace year-to-year changes in temperature, they might not capture longer-term warming trends, making them unsuitable for reconstructions of long-term climate variations. There is reason to believe, however, that the divergence found by Briffa (1998) is at least partly due to detrending or related statistical issues (Esper et al. 2009). Herein, we will investigate the distribution of this decadal-scale ';Briffa-style' divergence to see if it is confined to the earlier chronologies in the Schweingruber dataset or if it is persistent throughout more recent tree-ring data as well. Following the methodology of previous DP investigations (e.g. Briffa et al. 1998), we will draw comparisons between a network of MXD data and instrumental temperature records over an early period (1850-1960) and a recent period (1961-2000) to detect decadal-scale divergence in recent decades. We will apply the Mann et al. 2009 (M09) style of RegEM reconstruction to the M09 dataset, with and without controlling for divergence, and also to a new tree-ring database assembled using strict, objective criteria, including most of the updated Schweingruber network. Other climate field reconstruction (CFR) methods as described by Wang et al. (2013) will be used on our new tree-ring network to check for robustness. The tree-ring data will be independently compared to instrumental temperature series derived from the GHCN-monthly, HadCRUT4, and the M09 infilled HadCRUT3v temperature datasets for cross-validation. Implications for large-scale temperature reconstructions of the Common Era will be discussed. Briffa, K.R., F. H. Schweingruber, P.D. Jones, T.J. Osborn, S.G. Shiyatov, and E.A. Vaganov (1998),Reduced sensitivity of recent tree-growth to temperature at high northern latitudes, Nature, 391, 678-682. D'Arrigo, R., R. Wilson, B. Liepert, and P. Cherubini (2008), On the ';Divergence Problem' in Northern Forests: A review of the tree-ring evidence and possible causes, GAPC, 60, 289-305. Esper, J. and D. Frank (2009), Divergence pitfalls in tree-ring research, Climate Change, 94, 261-266. Mann, M.E., Z. Zhang, M.K. Hughes, R.S. Bradley, S.K. Miller, S. Rutherford, and F. Ni (2009), Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia, PNAS, 105 (36) 13252-13257. Wang, J., J. Emile-Geay, D. Guillot, and J. E. Smerdon (2013), Evaluating climate field reconstruction techniques using improved emulations of real-world conditions. CPD, 9, 3015-3060.

Correlation Dimension Estimates of Global and Local Temperature Data.

NASA Astrophysics Data System (ADS)

Wang, Qiang

1995-11-01

The author has attempted to detect the presence of low-dimensional deterministic chaos in temperature data by estimating the correlation dimension with the Hill estimate that has been recently developed by Mikosch and Wang. There is no convincing evidence of low dimensionality with either global dataset (Southern Hemisphere monthly average temperatures from 1858 to 1984) or local temperature dataset (daily minimums at Auckland, New Zealand). Any apparent reduction in the dimension estimates appears to be due large1y, if not entirely, to effects of statistical bias, but neither is it a purely random stochastic process. The dimension of the climatic attractor may be significantly larger than 10.

Impact of Rice Paddy Areas Decrease on Local Climate over Taiwan

NASA Astrophysics Data System (ADS)

Lo, M. H.; Wen, W. H.; Chen, C. C.

2014-12-01

Agricultural irrigation practice is one of the important anthropogenic processes in the land surface modeling. Irrigation can decrease local surface temperature with alternating surface energy partitioning. Rice paddy is the major food crop in Asian monsoon region and rice is grown under flooded conditions during the growing season; hence, the rice paddy can be considered as an open water body, which has more impacts on the surface energy budget than other cropland does. In this study, we explore how the rice paddy area changes affect Taiwan's regional climate from both observational data and numerical modeling exercise. The Weather Research and Forecasting (WRF) model is utilized to explore impacts of rice paddy area changes on the regional climate, and energy and water budget changes. In addition, temperature datasets from six automatic weather stations in the northern Taiwan and two stations in the southern Taiwan are analyzed in this study to explore how the Daily Temperature Range (DTR) changes with the decreased rice paddy areas. Previous studies show that due to the urban heat island effect, aerosol direct and indirect effects, and global warming, the DTR has decreased in the past 4 decades observed from most of the weather stations around Taiwan. However, the declined rice paddy area may increase the DTR with higher Bowen ratio during the daytime. Preliminary results show that DTR is decreased in weather stations near the urban area, but increased in weather stations near fallow areas in the past 20 years. It shows that different land use changes may have opposite impacts on local and regional climate.

'The surface management system' (SuMS) database: a surface-based database to aid cortical surface reconstruction, visualization and analysis

NASA Technical Reports Server (NTRS)

Dickson, J.; Drury, H.; Van Essen, D. C.

2001-01-01

Surface reconstructions of the cerebral cortex are increasingly widely used in the analysis and visualization of cortical structure, function and connectivity. From a neuroinformatics perspective, dealing with surface-related data poses a number of challenges. These include the multiplicity of configurations in which surfaces are routinely viewed (e.g. inflated maps, spheres and flat maps), plus the diversity of experimental data that can be represented on any given surface. To address these challenges, we have developed a surface management system (SuMS) that allows automated storage and retrieval of complex surface-related datasets. SuMS provides a systematic framework for the classification, storage and retrieval of many types of surface-related data and associated volume data. Within this classification framework, it serves as a version-control system capable of handling large numbers of surface and volume datasets. With built-in database management system support, SuMS provides rapid search and retrieval capabilities across all the datasets, while also incorporating multiple security levels to regulate access. SuMS is implemented in Java and can be accessed via a Web interface (WebSuMS) or using downloaded client software. Thus, SuMS is well positioned to act as a multiplatform, multi-user 'surface request broker' for the neuroscience community.

Can air temperatures be used to project influences of climate change on stream temperatures?

NASA Astrophysics Data System (ADS)

Arismendi, I.; Safeeq, M.; Dunham, J.; Johnson, S. L.

2013-12-01

The lack of available in situ stream temperature records at broad spatiotemporal scales have been recognized as a major limiting factor in the understanding of thermal behavior of stream and river systems. This has motivated the promotion of a wide variety of models that use surrogates for stream temperatures including a regression approach that uses air temperature as the predictor variable. We investigate the long-term performance of widely used linear and non-linear regression models between air and stream temperatures to project the latter in future climate scenarios. Specifically, we examine the temporal variability of the parameters that define each of these models in long-term stream and air temperature datasets representing relatively natural and highly human-influenced streams. We selected 25 sites with long-term records that monitored year-round daily measurements of stream temperature (daily mean) in the western United States (California, Oregon, Idaho, Washington, and Alaska). Surface air temperature data from each site was not available. Therefore, we calculated daily mean surface air temperature for each site in contiguous US from a 1/16-degree resolution gridded surface temperature data. Our findings highlight several limitations that are endemic to linear or nonlinear regressions that have been applied in many recent attempts to project future stream temperatures based on air temperature. Our results also show that applications over longer time periods, as well as extrapolation of model predictions to project future stream temperatures are unlikely to be reliable. Although we did not analyze a broad range of stream types at a continental or global extent, our analysis of stream temperatures within the set of streams considered herein was more than sufficient to illustrate a number of specific limitations associated with statistical projections of stream temperature based on air temperature. Radar plots of Nash-Sutcliffe efficiency (NSE) values for the two correlation models in regulated (n=14; lower panel) and unregulated (n=11; upper panel) streams. Solid lines represent average × SD of the NSE estimated for different time periods every 5-year. Dotted line at each plot indicates a NSE = 0.7. Symbols outside of the dotted line at each plot represent a satisfactory level of accuracy of the model

Multiangular L-band Datasets for Soil Moisture and Sea Surface Salinity Retrieval Measured by Airborne HUT-2D Synthetic Aperture Radiometer

NASA Astrophysics Data System (ADS)

Kainulainen, J.; Rautiainen, K.; Seppänen, J.; Hallikainen, M.

2009-04-01

SMOS is the European Space Agency's next Earth Explorer satellite due for launch in 2009. It aims for global monitoring of soil moisture and ocean salinity utilizing a new technology concept for remote sensing: two-dimensional aperture synthesis radiometry. The payload of SMOS is Microwave Imaging Radiometer by Aperture Synthesis, or MIRAS. It is a passive instrument that uses 72 individual L-band receivers for measuring the brightness temperature of the Earth. From each acquisition, i.e. integration time or snapshot, MIRAS provides two-dimensional brightness temperature of the scene in the instrument's field of view. Thus, consecutive snapshots provide multiangular measurements of the target once the instrument passes over it. Depending on the position of the target in instrument's swath, the brightness temperature of the target at incidence angles from zero up to 50 degrees can be measured with one overpass. To support the development MIRAS instrument, its calibration, and soil moisture and sea surface salinity retrieval algorithm development, Helsinki University of Technology (TKK) has designed, manufactured and tested a radiometer which operates at L-band and utilizes the same two-dimensional methodology of interferometery and aperture synthesis as MIRAS does. This airborne instrument, called HUT-2D, was designed to be used on board the University's research aircraft. It provides multiangular measurements of the target in its field of view, which spans up to 30 degrees off the boresight of the instrument, which is pointed to the nadir. The number of independent measurements of each target point depends on the flight speed and altitude. In addition to the Spanish Airborne MIRAS demonstrator (AMIRAS), HUT-2D is the only European airborne synthetic aperture radiometer. This paper presents the datasets and measurement campaigns, which have been carried out using the HUT-2D radiometer and are available for the scientific community. In April 2007 HUT-2D participated in to the first scientific measurement campaign. This campaign consisted of a single flight over the Gulf of Finland simultaneously with R/V Aranda's (Finnish Marine Research Institute) ground truth collection. The vessel measured e.g. sea surface salinity and sea temperature along the test lines measured with the radiometer system. During the autumn of 2007 HUT-2D participated in the CoSMOS-2007 campaign, in which three datasets from the Finnish coastal area were measured in order to demonstrate sea salinity retrieval. The campaign consisted of two two-hour measurement flights over an expected salinity gradient with HUT-2D and the Danish conventional radiometer EMIRAD. For the reference data, sea surface temperature and salinity were measured along the gradient line from a vessel. The third flight included different maneuvers, such as wing-wags, circles, and clover leafs, over the Gulf of Finland. During the same autumn, HUT-2D was used to measure datasets in northern Finland for soil moisture retrieval purposes. The flight consisted of measurement flights over test areas in Sodankylä, and Pallas. These test sites were equipped with weather stations of Finnish Meteorological Institute. Also soil moisture samples were collected at the sites. During the transition flights (approx. 800 km) from southern Finland to these test sites HUT-2D measured continuously, however, ground reference data for soil moisture was not collected beyond a few weather stations overpassed. Land classification maps for the transit flights are available. The most significant measurement campaign of HUT-2D so far was carried out during the spring of 2008. This 6-week campaign consisted of measurements of soil moisture test sites in Germany (Danube Catchment Area, DCA) and Spain (Valencia Anchor Station, VAS). The campaign at the DCA site consisted of four two-hour flights over the selected test lines in the Danube river catchment area, which is actively used for soil moisture studies. The VAC site consisted of 10 x 10 kilometers area also used for soil moisture studies. This area was mapped with HUT-2D in four different days.

Updated population metadata for United States historical climatology network stations

USGS Publications Warehouse

Owen, T.W.; Gallo, K.P.

2000-01-01

The United States Historical Climatology Network (HCN) serial temperature dataset is comprised of 1221 high-quality, long-term climate observing stations. The HCN dataset is available in several versions, one of which includes population-based temperature modifications to adjust urban temperatures for the "heat-island" effect. Unfortunately, the decennial population metadata file is not complete as missing values are present for 17.6% of the 12 210 population values associated with the 1221 individual stations during the 1900-90 interval. Retrospective grid-based populations. Within a fixed distance of an HCN station, were estimated through the use of a gridded population density dataset and historically available U.S. Census county data. The grid-based populations for the HCN stations provide values derived from a consistent methodology compared to the current HCN populations that can vary as definitions of the area associated with a city change over time. The use of grid-based populations may minimally be appropriate to augment populations for HCN climate stations that lack any population data, and are recommended when consistent and complete population data are required. The recommended urban temperature adjustments based on the HCN and grid-based methods of estimating station population can be significantly different for individual stations within the HCN dataset.

Observational evidence of seasonality in the timing of loop current eddy separation

NASA Astrophysics Data System (ADS)

Hall, Cody A.; Leben, Robert R.

2016-12-01

Observational datasets, reports and analyses over the time period from 1978 through 1992 are reviewed to derive pre-altimetry Loop Current (LC) eddy separation dates. The reanalysis identified 20 separation events in the 15-year record. Separation dates are estimated to be accurate to approximately ± 1.5 months and sufficient to detect statistically significant LC eddy separation seasonality, which was not the case for previously published records because of the misidentification of separation events and their timing. The reanalysis indicates that previously reported LC eddy separation dates, determined for the time period before the advent of continuous altimetric monitoring in the early 1990s, are inaccurate because of extensive reliance on satellite sea surface temperature (SST) imagery. Automated LC tracking techniques are used to derive LC eddy separation dates in three different altimetry-based sea surface height (SSH) datasets over the time period from 1993 through 2012. A total of 28-30 LC eddy separation events were identified in the 20-year record. Variations in the number and dates of eddy separation events are attributed to the different mean sea surfaces and objective-analysis smoothing procedures used to produce the SSH datasets. Significance tests on various altimetry and pre-altimetry/altimetry combined date lists consistently show that the seasonal distribution of separation events is not uniform at the 95% confidence level. Randomization tests further show that the seasonal peak in LC eddy separation events in August and September is highly unlikely to have occurred by chance. The other seasonal peak in February and March is less significant, but possibly indicates two seasons of enhanced probability of eddy separation centered near the spring and fall equinoxes. This is further quantified by objectively dividing the seasonal distribution into two seasons using circular statistical techniques and a k-means clustering algorithm. The estimated spring and fall centers are March 2nd and August 23rd, respectively, with season boundaries in May and December.

Concerted Earth Observation and Prediction of Water and Energy Cycles in the Third Pole Environment (CEOP-TPE)

NASA Astrophysics Data System (ADS)

Su, Bob; Ma, Yaoming; Menenti, Massimo; Wen, Jun; Sobrino, Jose; He, Yanbo; Li, Zhao-Liang; Tang, Bohui; Sneeuw, Nico; Zhong, Lei; Zeng, Yijian; van der Veld, Rogier; Chen, Xuelong; Zheng, Donghai; Huang, Ying; Lv, Shaoning; Wang, Lichun

2016-08-01

The achievements made in Dragon III in 2014-2016 are listed below:1. Maintaining the Tibetan Plateau Soil Moisture and Soil Temperature Observatory (Tibet-Obs) [1-3] and developing a method and data product by blending SM product over Tibetan Plateau and evaluating other available SM products [4].2. Developing a new algorithm for representing the effective soil temperature in microwave radiometry [5-7].3. Developing data sets to study the regional and plateau scale land-atmosphere interactions in TPE [8-11].4. Identifying and developing improved land surface processes [12-15].5. Developing a method for the quantification of water cycle components based on earth observation data and a comparison to reanalysis data [16-17].6. Investigating and revealing the mechanism of surface and tropospheric heatings on the Tibetan plateau [18].7. Proposing a validation framework for the generationof climate data records [19].8. Graduating seven young scientists with their doctorates during the last two years of Dragon III programme.9. Making the datasets and algorithms accessible to the scientific community.

Influence of Anthropogenic Climate Change on Planetary Wave Resonance and Extreme Weather Events

PubMed Central

Mann, Michael E.; Rahmstorf, Stefan; Kornhuber, Kai; Steinman, Byron A.; Miller, Sonya K.; Coumou, Dim

2017-01-01

Persistent episodes of extreme weather in the Northern Hemisphere summer have been shown to be associated with the presence of high-amplitude quasi-stationary atmospheric Rossby waves within a particular wavelength range (zonal wavenumber 6–8). The underlying mechanistic relationship involves the phenomenon of quasi-resonant amplification (QRA) of synoptic-scale waves with that wavenumber range becoming trapped within an effective mid-latitude atmospheric waveguide. Recent work suggests an increase in recent decades in the occurrence of QRA-favorable conditions and associated extreme weather, possibly linked to amplified Arctic warming and thus a climate change influence. Here, we isolate a specific fingerprint in the zonal mean surface temperature profile that is associated with QRA-favorable conditions. State-of-the-art (“CMIP5”) historical climate model simulations subject to anthropogenic forcing display an increase in the projection of this fingerprint that is mirrored in multiple observational surface temperature datasets. Both the models and observations suggest this signal has only recently emerged from the background noise of natural variability. PMID:28345645

Linear extension rates and fluctuations of trace metals in Porites sp. from around Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Amir, Liyana; Mohamed, Che Abd Rahim

2018-04-01

Coral cores were collected from P. Payar, Port Dickson, P. Redang and P. Tioman. The length of cores represented data spanning from year 2009 - 2015. Satellite sea surface temperatures from year 2009 - 2015 were obtained from the Reynolds and Smith dataset. Sr/Ca concentrations were measured from the coral powder taken at 1mm intervals along the vertical growth axis. Sea Surface Temperature (SST) was significantly higher during year 2010 in all four locations and linear extension was observed to have declined in year 2010 compared to year 2009 in cores from both sites. This decline coincides with the higher SST observed in year 2010 as a result of the El Niño event. Correlation analysis showed that Sr/Ca ratios in cores from all sites have a significant inverse relationship with SST. Analysis of the trace metals such as Pb, Ba, Cr and Cu produced results that were within the reported range in coral skeleton. Concentrations were significantly higher in Port Dickson and the lowest in P. Redang. These findings could be due to differences in terrestrial input at respective reef sites.

Tropospheric characteristics over sea ice during N-ICE2015

NASA Astrophysics Data System (ADS)

Kayser, Markus; Maturilli, Marion; Graham, Robert; Hudson, Stephen; Cohen, Lana; Rinke, Annette; Kim, Joo-Hong; Park, Sang-Jong; Moon, Woosok; Granskog, Mats

2017-04-01

Over recent years, the Arctic Ocean region has shifted towards a younger and thinner sea-ice regime. The Norwegian young sea ICE (N-ICE2015) expedition was designed to investigate the atmosphere-snow-ice-ocean interactions in this new ice regime north of Svalbard. Here we analyze upper-air measurements made by radiosondes launched twice daily together with surface meteorology observations during N-ICE2015 from January to June 2015. We study the multiple cyclonic events observed during N-ICE2015 with respect to changes in the vertical thermodynamic structure, sudden increases in moisture content and temperature, temperature inversions and boundary layer dynamics. The influence of synoptic cyclones is strongest under polar night conditions, when radiative cooling is most effective and the moisture content is low. We find that transitions between the radiatively clear and opaque state are the largest drivers of changes to temperature inversion and stability characteristics in the boundary layer during winter. In spring radiative fluxes warm the surface leading to lifted temperature inversions and a statically unstable boundary layer. The unique N-ICE2015 dataset is used for case studies investigating changes in the vertical structure of the atmosphere under varying synoptic conditions. The goal is to deepen our understanding of synoptic interactions within the Arctic climate system, to improve model performance, as well as to identify gaps in instrumentation, which precludes further investigations.

Improved Decadal Climate Prediction in the North Atlantic using EnOI-Assimilated Initial Condition

NASA Astrophysics Data System (ADS)

Li, Q.; Xin, X.; Wei, M.; Zhou, W.

2017-12-01

Decadal prediction experiments of Beijing Climate Center climate system model version 1.1(BCC-CSM1.1) participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation (SODA) reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature (SST) of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation (EnOI). Then a suite of decadal re-forecasts launched annually over the period 1961-2005 is carried out with simulated ocean temperature restored to the assimilated ocean data. Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal Oscillation (AMO), which is consistent with the better skill of Atlantic meridional overturning circulation (AMOC) predicted by the re-forecasts. We conclude that the EnOI assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.

On the relationship between the stratospheric quasi-biennial oscillation and the tropospheric Southern oscillation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, J.S.

1992-05-01

Two quasi-periodic oscillations in the tropical atmosphere with similar oscillation period-the stratospheric quasi-biennial and the Southern oscillations-and the relationship between these two oscillations are examined using the Principal Oscillation Pattern (POP) analysis technique. The POP analysis of the equatorial stratospheric dataset provides a compact description of the QBO. The oscillation features identified by the POP analysis, namely, the spatial structure, the characteristic times of the oscillation, and the asymmetry in downward propagation, are almost identical to those found by earlier studies using more conventional analyses. The simultaneous POP analysis of the equatorial zonal surface wind and sea surface temperature indicatesmore » a well-defined cyclic behavior of the SO. In contrast to the very regular QBO, the SO appears to be much more noisy with intermittent quiet phases. A spectral analysis of the complex POP coefficient time series and the SO index reveals a negligible correlation between the two processes. A POP analysis of the combined equatorial dataset of stratospheric wind, zonal surface wind, and SST also indicates no relation between the QBO and the SO. Two independent modes are identified, one of them completely describing the QBO and the other representing the entire SO. No linear relationship is found between the two modes either in space or in time. It is concluded that the SO and the QBO are two independent processes in the tropical atmosphere with similar time scales. 26 refs., 17 figs.« less

Connection between the Silk Road Pattern in July and the following January temperature over East Asia

NASA Astrophysics Data System (ADS)

He, Shengping; Liu, Yang; Wang, Huijun

2017-04-01

This study investigates a cross-seasonal influence of the Silk Road Pattern (SRP) in July and discusses the related mechanism. Both the reanalysis and observational datasets indicate that the July SRP is closely related to the following January temperature over East Asia during 1958/59-2001/02. Linear regression results reveal that, following a higher-than-normal SRP index in July, the Siberian high, Aleutian low, Urals high, East Asian trough, and meridional shear of the East Asian jet intensify significantly in January. Such atmospheric circulation anomalies are favorable for northerly wind anomalies over East Asia, leading to more southward advection of cold air and causing a decrease in temperature. Further analysis indicates that the North Pacific sea surface temperature anomalies (SSTAs) might play a critical role in storing the anomalous signal of the July SRP. The significant SSTAs related to the July SRP weaken in October and November, re-emerge in December, and strengthen in the following January. Such an SSTA pattern in January can induce a surface anomalous cyclone over North Pacific and lead to dominant convergence anomalies over northwestern Pacific. Correspondingly, significant divergence anomalies appear, collocated in the upper-level troposphere in situ. Due to the advection of vorticity by divergent wind, which can be regarded as a wave source, a stationary Rossby wave originates from North Pacific and propagates eastward to East Asia, leading to temperature anomalies through its influence on the large-scale atmospheric circulation.

Met Éireann high resolution reanalysis for Ireland

NASA Astrophysics Data System (ADS)

Gleeson, Emily; Whelan, Eoin; Hanley, John

2017-03-01

The Irish Meteorological Service, Met Éireann, has carried out a 35-year very high resolution (2.5 km horizontal grid) regional climate reanalysis for Ireland using the ALADIN-HIRLAM numerical weather prediction system. This article provides an overview of the reanalysis, called MÉRA, as well as a preliminary analysis of surface parameters including screen level temperature, 10 m wind speeds, mean sea-level pressure (MSLP), soil temperatures, soil moisture and 24 h rainfall accumulations. The quality of the 3-D variational data assimilation used in the reanalysis is also assessed. Preliminary analysis shows that it takes almost 12 months to spin up the deep soil in terms of moisture, justifying the choice of running year-long spin up periods. Overall, the model performed consistently over the time period. Small biases were found in screen-level temperatures (less than -0.5 °C), MSLP (within 0.5 hPa) and 10 m wind speed (up to 0.5 m s-1) Soil temperatures are well represented by the model. 24 h accumulations of precipitation generally exhibit a small positive bias of ˜ 1 mm per day and negative biases over mountains due to a mismatch between the model orography and the geography of the region. MÉRA outperforms the ERA-Interim reanalysis, particularly in terms of standard deviations in screen-level temperatures and surface winds. This dataset is the first of its kind for Ireland that will be made publically available during spring 2017.

Moderate-resolution sea surface temperature data and seasonal pattern analysis for the Arctic Ocean ecoregions

USGS Publications Warehouse

Payne, Meredith C.; Reusser, Deborah A.; Lee, Henry

2012-01-01

Sea surface temperature (SST) is an important environmental characteristic in determining the suitability and sustainability of habitats for marine organisms. In particular, the fate of the Arctic Ocean, which provides critical habitat to commercially important fish, is in question. This poses an intriguing problem for future research of Arctic environments - one that will require examination of long-term SST records. This publication describes and provides access to an easy-to-use Arctic SST dataset for ecologists, biogeographers, oceanographers, and other scientists conducting research on habitats and/or processes in the Arctic Ocean. The data cover the Arctic ecoregions as defined by the "Marine Ecoregions of the World" (MEOW) biogeographic schema developed by The Nature Conservancy as well as the region to the north from approximately 46°N to about 88°N (constrained by the season and data coverage). The data span a 29-year period from September 1981 to December 2009. These SST data were derived from Advanced Very High Resolution Radiometer (AVHRR) instrument measurements that had been compiled into monthly means at 4-kilometer grid cell spatial resolution. The processed data files are available in ArcGIS geospatial datasets (raster and point shapefiles) and also are provided in text (.csv) format. All data except the raster files include attributes identifying latitude/longitude coordinates, and realm, province, and ecoregion as defined by the MEOW classification schema. A seasonal analysis of these Arctic ecoregions reveals a wide range of SSTs experienced throughout the Arctic, both over the course of an annual cycle and within each month of that cycle. Sea ice distribution plays a major role in SST regulation in all Arctic ecoregions.

Convergence and Divergence in a Multi-Model Ensemble of Terrestrial Ecosystem Models in North America

NASA Astrophysics Data System (ADS)

Dungan, J. L.; Wang, W.; Hashimoto, H.; Michaelis, A.; Milesi, C.; Ichii, K.; Nemani, R. R.

2009-12-01

In support of NACP, we are conducting an ensemble modeling exercise using the Terrestrial Observation and Prediction System (TOPS) to evaluate uncertainties among ecosystem models, satellite datasets, and in-situ measurements. The models used in the experiment include public-domain versions of Biome-BGC, LPJ, TOPS-BGC, and CASA, driven by a consistent set of climate fields for North America at 8km resolution and daily/monthly time steps over the period of 1982-2006. The reference datasets include MODIS Gross Primary Production (GPP) and Net Primary Production (NPP) products, Fluxnet measurements, and other observational data. The simulation results and the reference datasets are consistently processed and systematically compared in the climate (temperature-precipitation) space; in particular, an alternative to the Taylor diagram is developed to facilitate model-data intercomparisons in multi-dimensional space. The key findings of this study indicate that: the simulated GPP/NPP fluxes are in general agreement with observations over forests, but are biased low (underestimated) over non-forest types; large uncertainties of biomass and soil carbon stocks are found among the models (and reference datasets), often induced by seemingly “small” differences in model parameters and implementation details; the simulated Net Ecosystem Production (NEP) mainly responds to non-respiratory disturbances (e.g. fire) in the models and therefore is difficult to compare with flux data; and the seasonality and interannual variability of NEP varies significantly among models and reference datasets. These findings highlight the problem inherent in relying on only one modeling approach to map surface carbon fluxes and emphasize the pressing necessity of expanded and enhanced monitoring systems to narrow critical structural and parametrical uncertainties among ecosystem models.

Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing

NASA Astrophysics Data System (ADS)

Twigt, Daniel J.; de Goede, Erik D.; Schrama, Ernst J. O.; Gerritsen, Herman

2007-10-01

The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications.

The Application of Satellite-Derived, High-Resolution Land Use/Land Cover Data to Improve Urban Air Quality Model Forecasts

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Lapenta, W. M.; Crosson, W. L.; Estes, M. G., Jr.; Limaye, A.; Kahn, M.

2006-01-01

Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.

Peat conditions mapping using MODIS time series

NASA Astrophysics Data System (ADS)

Poggio, Laura; Gimona, Alessandro; Bruneau, Patricia; Johnson, Sally; McBride, Andrew; Artz, Rebekka

2016-04-01

Large areas of Scotland are covered in peatlands, providing an important sink of carbon in their near natural state but act as a potential source of gaseous and dissolved carbon emission if not in good conditions. Data on the condition of most peatlands in Scotland are, however, scarce and largely confined to sites under nature protection designations, often biased towards sites in better condition. The best information available at present is derived from labour intensive field-based monitoring of relatively few designated sites (Common Standard Monitoring Dataset). In order to provide a national dataset of peat conditions, the available point information from the CSM data was modelled with morphological features and information derived from MODIS sensor. In particular we used time series of indices describing vegetation greenness (Enhanced Vegetation Index), water availability (Normalised Water Difference index), Land Surface Temperature and vegetation productivity (Gross Primary productivity). A scorpan-kriging approach was used, in particular using Generalised Additive Models for the description of the trend. The model provided the probability of a site to be in favourable conditions and the uncertainty of the predictions was taken into account. The internal validation (leave-one-out) provided a mis-classification error of around 0.25. The derived dataset was then used, among others, in the decision making process for the selection of sites for restoration.

Plastic-covered agriculture forces the regional climate to change

NASA Astrophysics Data System (ADS)

Yang, D.; Chen, J.; Chen, X.; Cao, X.

2016-12-01

The practice of plastic-covered agriculture as a solution to moderate the dilemma of global food shortage, meanwhile, brings great pressure to the local environment. This research was conducted to reveal the impacts of plastic-covered agritulture on regional climate change by experimenting in a plastic greenhouse (PG) dominated area - Weifang district, Shandong province, China. Based on a new plastic greenhouse index (PGI) proposed in this study, we reconstructed the spatial distribution of PG across 1995-2015 in the study area. With that, land surface temperature (LST) dataset combined with surface evapotranspiration, surface reflectance and precipitation data, was applied to the probe of PG's climatic impacts. Results showed that PG, in the study area, has experienced a striking spatial expansion during the past 20 years, and more important, the expansion correlated strongly to the local climate change. It showed that the annual precipitation, in the study area, decreased during these years, which constrasts to a slightly increasing trend of the adjacent districts without PG construction. In addition, resulting from the greenhouse effect, PG area presented a harsher increase of surface temperature compared to the non-PG areas. Our study also telled that the evapotranspiration of PG area has been largely cutted down ascribing to the gas tightness of plastic materials, showing a decline around 40%. This indicates a way that the development of plastic-covered agriculture may contribute to the change of the local climate.

Examining Environmental Gradients with satellite data in permafrost regions - the current state of the ESA GlobPermafrost initative

NASA Astrophysics Data System (ADS)

Grosse, G.; Bartsch, A.; Kääb, A.; Westermann, S.; Strozzi, T.; Wiesmann, A.; Duguay, C. R.; Seifert, F. M.; Obu, J.; Nitze, I.; Heim, B.; Haas, A.; Widhalm, B.

2017-12-01

Permafrost cannot be directly detected from space, but many surface features of permafrost terrains and typical periglacial landforms are observable with a variety of EO sensors ranging from very high to medium resolution at various wavelengths. In addition, landscape dynamics associated with permafrost changes and geophysical variables relevant for characterizing the state of permafrost, such as land surface temperature or freeze-thaw state can be observed with spaceborne Earth Observation. Suitable regions to examine environmental gradients across the Arctic have been defined in a community white paper (Bartsch et al. 2014, hdl:10013/epic.45648.d001). These transects have been revised and adjusted within the DUE GlobPermafrost initiative of the European Space Agency. The ESA DUE GlobPermafrost project develops, validates and implements Earth Observation (EO) products to support research communities and international organisations in their work on better understanding permafrost characteristics and dynamics. Prototype product cases will cover different aspects of permafrost by integrating in situ measurements of subsurface and surface properties, Earth Observation, and modelling to provide a better understanding of permafrost today. The project will extend local process and permafrost monitoring to broader spatial domains, support permafrost distribution modelling, and help to implement permafrost landscape and feature mapping in a GIS framework. It will also complement active layer and thermal observing networks. Both lowland (latitudinal) and mountain (altitudinal) permafrost issues are addressed. The status of the Permafrost Information System and first results will be presented. Prototypes of GlobPermafrost datasets include: Modelled mean annual ground temperature by use of land surface temperature and snow water equivalent from satellites Land surface characterization including shrub height, land cover and parameters related to surface roughness Trends from Landsat time-series over selected transects For selected sites: subsidence, ground fast lake ice, land surface features and rock glacier monitoring

SMOS near-real-time soil moisture product: processor overview and first validation results

NASA Astrophysics Data System (ADS)

Rodríguez-Fernández, Nemesio J.; Muñoz Sabater, Joaquin; Richaume, Philippe; de Rosnay, Patricia; Kerr, Yann H.; Albergel, Clement; Drusch, Matthias; Mecklenburg, Susanne

2017-10-01

Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (˜ 915 km) than that of the L2 SM dataset (˜ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of < 0.05 m3 m-3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m-3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

Surface Hydrological Processes of Rock Glaciated Basins in the San Juan Mountains, Colorado

NASA Astrophysics Data System (ADS)

Mateo, E. I.

2017-12-01

Glaciers in the western United States have been examined in terms of their summer meltwater contributions to regional hydrological systems. In the San Juan Mountains of Colorado where glaciers do not and cannot exist due to a rising zero-degree isotherm, rock glaciers take the place of valley glaciers during the summer runoff period. Most of the rock glaciers in Colorado are located on a northerly slope aspect, however, there are multiple in the southwest region of the state that occur on different aspects. This study asked how slope aspect and rising air temperatures influenced the hydrological processes of streams below rock glaciers in the San Juan Mountains during the 2016 summer season. This project focused on three basins, Yankee Boy basin, Blue Lakes basin, and Mill Creek basin, which are adjacent to each other and share a common peak, Gilpin Peak. Findings of this one-season study showed that air temperature significantly influenced stream discharge below each rock glacier. Discharge and air temperature patterns indicate a possible air temperature threshold during late summer when rock glacier melt increased at a greater rate. The results also suggest that slope aspect of rock glacier basins influences stream discharge, but temperature and precipitation are likely larger components of the melt regimes. The continuation of data collection during the 2017 summer season has allowed for more detailed analysis of the relationship between air temperature and rock glacier melt. This continual expansion of the original dataset is crucial for understanding the hydrological processes of surface runoff below rock glaciers.

Historical gridded reconstruction of potential evapotranspiration for the UK

NASA Astrophysics Data System (ADS)

Tanguy, Maliko; Prudhomme, Christel; Smith, Katie; Hannaford, Jamie

2018-06-01

Potential evapotranspiration (PET) is a necessary input data for most hydrological models and is often needed at a daily time step. An accurate estimation of PET requires many input climate variables which are, in most cases, not available prior to the 1960s for the UK, nor indeed most parts of the world. Therefore, when applying hydrological models to earlier periods, modellers have to rely on PET estimations derived from simplified methods. Given that only monthly observed temperature data is readily available for the late 19th and early 20th century at a national scale for the UK, the objective of this work was to derive the best possible UK-wide gridded PET dataset from the limited data available.To that end, firstly, a combination of (i) seven temperature-based PET equations, (ii) four different calibration approaches and (iii) seven input temperature data were evaluated. For this evaluation, a gridded daily PET product based on the physically based Penman-Monteith equation (the CHESS PET dataset) was used, the rationale being that this provides a reliable ground truth PET dataset for evaluation purposes, given that no directly observed, distributed PET datasets exist. The performance of the models was also compared to a naïve method, which is defined as the simplest possible estimation of PET in the absence of any available climate data. The naïve method used in this study is the CHESS PET daily long-term average (the period from 1961 to 1990 was chosen), or CHESS-PET daily climatology.The analysis revealed that the type of calibration and the input temperature dataset had only a minor effect on the accuracy of the PET estimations at catchment scale. From the seven equations tested, only the calibrated version of the McGuinness-Bordne equation was able to outperform the naïve method and was therefore used to derive the gridded, reconstructed dataset. The equation was calibrated using 43 catchments across Great Britain.The dataset produced is a 5 km gridded PET dataset for the period 1891 to 2015, using the Met Office 5 km monthly gridded temperature data available for that time period as input data for the PET equation. The dataset includes daily and monthly PET grids and is complemented with a suite of mapped performance metrics to help users assess the quality of the data spatially.This dataset is expected to be particularly valuable as input to hydrological models for any catchment in the UK. The data can be accessed at https://doi.org/10.5285/17b9c4f7-1c30-4b6f-b2fe-f7780159939c.

Developing a Global Network of River Reaches in Preparation of SWOT

NASA Astrophysics Data System (ADS)

Lion, C.; Pavelsky, T.; Allen, G. H.; Beighley, E.; Schumann, G.; Durand, M. T.

2016-12-01

In 2020, the Surface Water and Ocean Topography satellite (SWOT), a joint mission of NASA/CNES/CSA/UK will be launched. One of its major products will be the measurements of continental water surfaces, including the width, height, and slope of rivers and the surface area and elevations of lakes. The mission will improve the monitoring of continental water and also our understanding of the interactions between different hydrologic reservoirs. For rivers, SWOT measurements of slope will be carried out over predefined river reaches. As such, an a priori dataset for rivers is needed in order to facilitate analysis of the raw SWOT data. The information required to produce this dataset includes measurements of river width, elevation, slope, planform, river network topology, and flow accumulation. To produce this product, we have linked two existing global datasets: the Global River Widths from Landsat (GRWL) database, which contains river centerline locations, widths, and a braiding index derived from Landsat imagery, and a modified version of the HydroSHEDS hydrologically corrected digital elevation product, which contains heights and flow accumulation measurements for streams at 3 arcseconds spatial resolution. Merging these two datasets requires considerable care. The difficulties, among others, lie in the difference of resolution: 30m versus 3 arseconds, and the age of the datasets: 2000 versus 2010 (some rivers have moved, the braided sections are different). As such, we have developed custom software to merge the two datasets, taking into account the spatial proximity of river channels in the two datasets and ensuring that flow accumulation in the final dataset always increases downstream. Here, we present our results for the globe.

Decreases in beetle body size linked to climate change and warming temperatures.

PubMed

Tseng, Michelle; Kaur, Katrina M; Soleimani Pari, Sina; Sarai, Karnjit; Chan, Denessa; Yao, Christine H; Porto, Paula; Toor, Anmol; Toor, Harpawantaj S; Fograscher, Katrina

2018-05-01

Body size is a fundamental ecological trait and is correlated with population dynamics, community structure and function, and ecosystem fluxes. Laboratory data from broad taxonomic groups suggest that a widespread response to a warming world may be an overall decrease in organism body size. However, given the myriad of biotic and abiotic factors that can also influence organism body size in the wild, it is unclear whether results from these laboratory assays hold in nature. Here we use datasets spanning 30 to 100 years to examine whether the body size of wild-caught beetles has changed over time, whether body size changes are correlated with increased temperatures, and we frame these results using predictions derived from a quantitative review of laboratory responses of 22 beetle species to temperature. We found that 95% of laboratory-reared beetles decreased in size with increased rearing temperature, with larger-bodied species shrinking disproportionately more than smaller-bodied beetles. In addition, the museum datasets revealed that larger-bodied beetle species have decreased in size over time, that mean beetle body size explains much of the interspecific variation in beetle responses to temperature, and that long-term beetle size changes are explained by increases in autumn temperature and decreases in spring temperature in this region. Our data demonstrate that the relationship between body size and temperature of wild-caught beetles matches relatively well with results from laboratory studies, and that variation in this relationship is largely explained by interspecific variation in mean beetle body size. This long-term beetle dataset is one of the most comprehensive arthropod body size datasets compiled to date, it improves predictions regarding the shrinking of organisms with global climate change, and together with the meta-analysis data, call for new hypotheses to explain why larger-bodied organisms may be more sensitive to temperature. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

Climate Impacts of Fire-Induced Land-Surface Changes

NASA Astrophysics Data System (ADS)

Liu, Y.; Hao, X.; Qu, J. J.

2017-12-01

One of the consequences of wildfires is the changes in land-surface properties such as removal of vegetation. This will change local and regional climate through modifying the land-air heat and water fluxes. This study investigates mechanism by developing and a parameterization of fire-induced land-surface property changes and applying it to modeling of the climate impacts of large wildfires in the United States. Satellite remote sensing was used to quantitatively evaluate the land-surface changes from large fires provided from the Monitoring Trends in Burning Severity (MTBS) dataset. It was found that the changes in land-surface properties induced by fires are very complex, depending on vegetation type and coverage, climate type, season and time after fires. The changes in LAI are remarkable only if the actual values meet a threshold. Large albedo changes occur in winter for fires in cool climate regions. The signs are opposite between the first post-fire year and the following years. Summer day-time temperature increases after fires, while nigh-time temperature changes in various patterns. The changes are larger in forested lands than shrub / grassland lands. In the parameterization scheme, the detected post-fire changes are decomposed into trends using natural exponential functions and fluctuations of periodic variations with the amplitudes also determined by natural exponential functions. The final algorithm is a combination of the trends, periods, and amplitude functions. This scheme is used with Earth system models to simulate the local and regional climate effects of wildfires.

Evidence of the Atlantic Multidecadal Oscillation driving multi-decadal variability of summertime surface air quality in the eastern United States: Implications for air quality management in the coming decades

NASA Astrophysics Data System (ADS)

Shen, L.; Mickley, L. J.

2016-12-01

Atlantic sea surface temperatures have a significant influence on the summertime meteorology and air quality in the eastern United States. In this study, we investigate the effect of the Atlantic Multidecadal Oscillation (AMO) on two key air pollutants, surface ozone and PM2.5, over the eastern United States. The shift of AMO from cold to warm phase increases surface air temperatures by 0.5 K across the East and reduces precipitation, resulting in a warmer and drier summer. By applying observed, present-day relationships between these pollutants and meteorological variables to a variety of observations and historical reanalysis datasets, we calculate the impacts of AMO on U.S. air quality. Our study reveals a multidecadal variability in mean summertime (JJA) maximum daily 8-hour (MDA8) ozone and surface PM2.5 concentrations in the eastern United States. In one-half cycle ( 30 years) of the AMO from negative to positive phase with constant anthropogenic emissions, JJA MDA8 ozone concentrations increase by 1-3 ppbv in the Northeast and 2-5 ppbv in the Great Plains; JJA PM2.5 concentrations increase by 0.8-1.2 μg m-3 in the Northeast and Southeast. The resulting impact on mortality rates is 4000 excess deaths per half cycle of AMO. We suggest that a complete picture of air quality management in coming decades requires consideration of the AMO influence.

Evaluating the effects of historical land cover change on summertime weather and climate in New Jersey

NASA Astrophysics Data System (ADS)

Wichansky, Paul Stuart

The 19th-century agrarian landscape of New Jersey (NJ) and the surrounding region has been extensively transformed to the present-day land cover by urbanization, reforestation, and localized areas of deforestation. This study used a mesoscale atmospheric numerical model to investigate the sensitivity of the warm season climate of NJ to these land cover changes. Reconstructed 1880s-era and present-day land cover datasets were used as surface boundary conditions for a set of simulations performed with the Regional Atmospheric Modeling System (RAMS). Three-member ensembles with historical and present-day land cover were compared to examine the sensitivity of surface air and dewpoint temperatures, rainfall, the individual components of the surface energy budget, horizontal and vertical winds, and the vertical profiles of temperature and humidity to these land cover changes. Mean temperatures for the present-day landscape were 0.3-0.6°C warmer than for the historical landscape over a considerable portion of NJ and the surrounding region, with daily maximum temperatures at least 1.0°C warmer over some of the highly urbanized locations. Reforested regions in the present-day landscape, however, showed a slight cooling. Surface warming was generally associated with repartitioning of net radiation from latent to sensible heat flux, and conversely for cooling. Reduced evapotranspiration from much of the present-day land surface led to dewpoint temperature decreases of 0.3-0.6°C. While urbanization was accompanied by strong surface albedo decreases and increases in net shortwave radiation, reforestation and potential changes in forest composition have generally increased albedos and also enhanced landscape heterogeneity. The increased deciduousness of forests may have further reduced net downward longwave radiation. These land cover changes have modified boundary-layer dynamics by increasing low-level convergence and upper-level divergence in the interior of NJ, especially where sensible heat fluxes have increased for the present-day landscape, hence enhancing uplift in the mid-troposphere. The mesoscale circulations that developed in the present-day ensemble were also more effective at lifting available moisture to higher levels of the boundary layer, lowering dewpoints near the surface but increasing them aloft. Likewise, the sea breeze in coastal areas of NJ in the present-day ensemble had stronger uplift during the afternoon and enhanced moisture transport to higher levels.

Eco-hydrological Wireless Sensor Network and upscaling method research in the Heihe River Basin, China

NASA Astrophysics Data System (ADS)

Jin, Rui; kang, Jian

2017-04-01

Wireless Sensor Networks are recognized as one of most important near-surface components of GEOSS (Global Earth Observation System of Systems), with flourish development of low-cost, robust and integrated data loggers and sensors. A nested eco-hydrological wireless sensor network (EHWSN) was installed in the up- and middle-reaches of the Heihe River Basin, operated to obtain multi-scale observation of soil moisture, soil temperature and land surface temperature from 2012 till now. The spatial distribution of EHWSN was optimally designed based on the geo-statistical theory, with the aim to capture the spatial variations and temporal dynamics of soil moisture and soil temperature, and to produce ground truth at grid scale for validating the related remote sensing products and model simulation in the heterogeneous land surface. In terms of upscaling research, we have developed a set of method to aggregate multi-point WSN observations to grid scale ( 1km), including regression kriging estimation to utilize multi-resource remote sensing auxiliary information, block kriging with homogeneous measurement errors, and bayesian-based upscaling algorithm that utilizes MODIS-derived apparent thermal inertia. All the EHWSN observation are organized as datasets to be freely published at http://westdc.westgis.ac.cn/hiwater. EHWSN integrates distributed observation nodes to achieve an automated, intelligent and remote-controllable network that provides superior integrated, standardized and automated observation capabilities for hydrological and ecological processes research at the basin scale.

Comparison of rotational temperature derived from ground-based OH airglow observations with TIMED/SABER to evaluate the Einstein Coefficients

NASA Astrophysics Data System (ADS)

Liu, W.; Xu, J.; Smith, A. K.; Yuan, W.

2017-12-01

Ground-based observations of the OH(9-4, 8-3, 6-2, 5-1, 3-0) band airglows over Xinglong, China (40°24'N, 117°35'E) from December 2011 to 2014 are used to calculate rotational temperatures. The temperatures are calculated using five commonly used Einstein coefficient datasets. The kinetic temperature from TIMED/SABER is completely independent of the OH rotational temperature. SABER temperatures are weighted vertically by weighting functions calculated for each emitting vibrational state from two SABER OH volume emission rate profiles. By comparing the ground-based OH rotational temperature with SABER's, five Einstein coefficient datasets are evaluated. The results show that temporal variations of the rotational temperatures are well correlated with SABER's; the linear correlation coefficients are higher than 0.72, but the slopes of the fit between the SABER and rotational temperatures are not equal to 1. The rotational temperatures calculated using each set of Einstein coefficients produce a different bias with respect to SABER; these are evaluated over each of vibrational levels to assess the best match. It is concluded that rotational temperatures determined using any of the available Einstein coefficient datasets have systematic errors. However, of the five sets of coefficients, the rotational temperature derived with the Langhoff et al.'s (1986) set is most consistent with SABER. In order to get a set of optimal Einstein coefficients for rotational temperature derivation, we derive the relative values from ground-based OH spectra and SABER temperatures statistically using three year data. The use of a standard set of Einstein coefficients will be beneficial for comparing rotational temperatures observed at different sites.

Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, S.; Kim, H.; Choi, M.; Kim, K.

2016-12-01

Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

Options to Improve Rain Snow Parameterization in Surface Based Models

NASA Astrophysics Data System (ADS)

Feiccabrino, J. M.

2017-12-01

Precipitation phase determination is of upmost importance in a number of surface based hydrological, ecological, and safety models. However, precipitation phase at Earth's surface is a result of cloud and atmospheric properties not measured by surface weather stations. Nonetheless, they can be inferred from the available surface datum. This study uses 681,620 weather observations with air temperatures between -3 and 5°C and identified precipitation occurring at the time of the observation to determine simple, yet accurate, thresholds for precipitation phase determination schemes (PPDS). This dataset represents 38% and 42% of precipitation observations over a 16 year period for 85 Swedish, and 84 Norwegian weather stations. The misclassified precipitation (error) from PPDS using AT, dew-point temperature (DT) and wet-bulb temperature (WB) thresholds were compared using a single threshold PPDS. The Norwegian observations between -3 and 5°C resulted in 11.64%, 11.21%, and 8.42% error for DT (-0.2°C), AT (1.2°C), and WB (0.3°C) thresholds respectively. Individual station thresholds had a range of -0.7 to 1.2°C, -1.2 to 0.9°C, and -0.1 to 2.5°C for WB, DP, and AT respectively. To address threshold variance while decreasing error, weather stations were grouped into nine landscape categories; windward (WW) ocean, WW coast, WW fjord, WW hill, WW mountain, leeward (LW) mountain, LW hill, LW rolling hills, and LW coast. Landscape classification was based on location relative to the Scandinavian Mountains, and the % water or range of elevation within 15KM. Within landscapes, stations share similar land atmosphere exchanges which differ from other landscapes. These differences change optimal thresholds for PPDS between landscapes. Also tested were threshold temperature affects based on assumed atmospheric differences for the following observation groups; 1.) occurring before and after an air mass boundary, 2.) with different water temperatures and/or NAO phases, 3.) with snow cover, 4.) coupled with higher elevation stations and 5.) with different cloud heights. For example, in Norway, as the unsaturated layer depth beneath clouds increased, AT thresholds warmed. Cloud height adjusted AT thresholds reduced error by 5% before threshold adjustments for landscapes.

Least Square Approach for Estimating of Land Surface Temperature from LANDSAT-8 Satellite Data Using Radiative Transfer Equation

NASA Astrophysics Data System (ADS)

Jouybari-Moghaddam, Y.; Saradjian, M. R.; Forati, A. M.

2017-09-01

Land Surface Temperature (LST) is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE). However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11) and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI) and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE) is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data.

Assessing the relationship between surface urban heat islands and landscape patterns across climatic zones in China.

PubMed

Yang, Qiquan; Huang, Xin; Li, Jiayi

2017-08-24

The urban heat island (UHI) effect exerts a great influence on the Earth's environment and human health and has been the subject of considerable attention. Landscape patterns are among the most important factors relevant to surface UHIs (SUHIs); however, the relationship between SUHIs and landscape patterns is poorly understood over large areas. In this study, the surface UHI intensity (SUHII) is defined as the temperature difference between urban and suburban areas, and the landscape patterns are quantified by the urban-suburban differences in several typical landscape metrics (ΔLMs). Temperature and land-cover classification datasets based on satellite observations were applied to analyze the relationship between SUHII and ΔLMs in 332 cities/city agglomerations distributed in different climatic zones of China. The results indicate that SUHII and its correlations with ΔLMs are profoundly influenced by seasonal, diurnal, and climatic factors. The impacts of different land-cover types on SUHIs are different, and the landscape patterns of the built-up and vegetation (including forest, grassland, and cultivated land) classes have the most significant effects on SUHIs. The results of this study will help us to gain a deeper understanding of the relationship between the SUHI effect and landscape patterns.

What do we learn about the impact of extreme hydrological events on tropical wetlands from the synergistic use of altimetry from Sentinel-3/SARAL-Altika and L-Band radiometry from SMOS/SMAP ?

NASA Astrophysics Data System (ADS)

Al Bitar, Ahmad; Parrens, Marie; Frappart, Frederic; Cauduro Dias de Paiva, Rodrigo; Papa, Fabrice; Kerr, Yann

2017-04-01

What do we learn about the impact of extreme hydrological events on tropical wetlands from the synergistic use of altimetry from Sentinel-3/SARAL-Altika and L-Band radiometry from SMOS/SMAP ? The question of the contribution of the tropical basins to the carbon and water cycle remains an open question in the science community. The tropical basins are highly impact by the wetlands dynamics but the also the link with extreme events like El-Nino are yet to be clarified. The main reason to this uncertainty is that the monitoring of inland water surfaces via remote sensing over tropical areas is a difficult task because of impact of vegetation and cloud cover. The most common solution is to use microwave remote sensing. In this study we combine the use of L-band microwave brightness temperatures and altimetric data from SARAL/ALTIKA and Sentinel-3 to derive water storage maps at relatively high (7days) temporal frequency. This study concerns the Amazon and Congo basin. The water fraction in inland are estimated by inversing a first order radiative model is used to derive surface water over land from the brightness temperature measured by ESA SMOS and SMAP mission at coarse resolution (25 km x 25 km) and 7-days frequency. The product is compared to the static land cover map such as ESA CCI and the International Geosphere-Biosphere Program (IGBP) and also dynamic maps from GIEMS and SWAPS products. Water storage is then obtained by combining the altimetric data from SARAL/ALTIKA and Sentinel-3 to the water surface fraction using an hypsometric approach. The water surfaces and water storage products are then compared to precipitation data from GPM TRMM datasets and river discharge data from field data. The amplitudes and time shifts of the signals is compared based on the sub-basin definition from Hydroshed database. The dataset is then divided into years of strong and weak El-Nino signal and the anomaly is between the two dataset is compared. The results show a strong influence of EL-Nino on the time shift of the different components showing that the hydrological regime of wetlands is highly impacted by these extreme events with a differentiated impact when compared to precipitation. Since the wetlands have particular impacts on the dynamics of the water and carbon cycle of the tropical basins, the results suggest that the current approach using future more accurate SWOT mission data can help better understand the physical processes in these basins.

A Multilayer Dataset of SSM/I-Derived Global Ocean Surface Turbulent Fluxes

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Shie, Chung-Lin; Atlas, Robert M.; Ardizzone, Joe; Nelkin, Eric; Einaud, Franco (Technical Monitor)

2001-01-01

A dataset including daily- and monthly-mean turbulent fluxes (momentum, latent heat, and sensible heat) and some relevant parameters over global oceans, derived from the Special Sensor Microwave/Imager (SSM/I) data, for the period July 1987-December 1994 and the 1988-94 annual and monthly-mean climatologies of the same variables is created. It has a spatial resolution of 2.0deg x 2.5deg latitude-longitude. The retrieved surface air humidity is found to be generally accurate as compared to the collocated radiosonde observations over global oceans. The retrieved wind stress and latent heat flux show useful accuracy as verified against research quality measurements of ship and buoy in the western equatorial Pacific. The 1988-94 seasonal-mean wind stress and latent heat flux show reasonable patterns related to seasonal variations of the atmospheric general circulation. The patterns of 1990-93 annual-mean turbulent fluxes and input variables are generally in good agreement with one of the best global analyzed flux datasets that based on COADS (comprehensive ocean-atmosphere data set) with corrections on wind speeds and covered the same period. The retrieved wind speed is generally within +/-1 m/s of the COADS-based, but is stronger by approx. 1-2 m/s in the northern extratropical oceans. The discrepancy is suggested to be mainly due to higher COADS-modified wind speeds resulting from underestimation of anemometer heights. Compared to the COADS-based, the retrieved latent heat flux and sea-air humidity difference are generally larger with significant differences in the trade wind zones and the ocean south of 40degS (up to approx. 40-60 W/sq m and approx. 1-1.5 g/kg). The discrepancy is believed to be mainly caused by higher COADS-based surface air humidity arising from the overestimation of dew point temperatures and from the extrapolation of observed high humidity southward into data-void regions south of 40degS. The retrieved sensible heat flux is generally within +/-5 W/sq m of UWM/COADS, except for some areas in the extratropical oceans, where the differences in wind speed have large impact on the difference in sensible heat flux. The dataset of SSM/I-derived turbulent fluxes is useful for climate studies, forcing of ocean models, and validation of coupled ocean-atmosphere global models.

A High Space-Time Resolution Dataset Linking Meteorological Forcing and Hydro-Sedimentary Response in a Mesoscale Mediterranean Catchment (Auzon) of the Ardèche Region, France

NASA Astrophysics Data System (ADS)

Nord, G.; Braud, I.; Boudevillain, B.; Gérard, S.; Molinié, G.; Vandervaere, J. P.; Huza, J.; Le Coz, J.; Dramais, G.; Legout, C.; Berne, A.; Grazioli, J.; Raupach, T.; Van Baelen, J.; Wijbrans, A.; Delrieu, G.; Andrieu, J.; Caliano, M.; Aubert, C.; Teuling, R.; Le Boursicaud, R.; Branger, F.; Vincendon, B.; Horner, I.

2014-12-01

A comprehensive hydrometeorological dataset is presented spanning the period 1 Jan 2011-31 Dec 2014 to improve the understanding and simulation of the hydrological processes leading to flash floods in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. The specificity of the dataset is its high space-time resolution, especially concerning rainfall and the hydrological response which is particularly adapted to the highly spatially variable rainfall events that may occur in this region. This type of dataset is rare in scientific literature because of the quantity and type of sensors for meteorology and surface hydrology. Rainfall data include continuous precipitation measured by rain-gages (5 min time step for the research network of 21 rain-gages and 1h time step for the operational network of 9 rain-gages), S-band Doppler dual-polarization radar (1 km2, 5 min resolution), and disdrometers (11 sensors working at 1 min time step). During the special observation period (SOP-1) and enhanced observation period (Sep-Dec 2012, Sep-Dec 2013) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). Meteorological data are taken from the operational surface weather observation stations of Meteo France at the hourly time resolution (6 stations in the region of interest). The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations measure water discharge and additional physico-chemical variables at a 2-10 min time resolution. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 gauges continuously measures water level and temperature in headwater subcatchments at a time resolution of 2-5 min. A network of soil moisture sensors enable the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, opportunistic observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. The data are appropriate for understanding rainfall variability, improving areal rainfall estimations and progress in distributed hydrological modelling.

A High Space-Time Resolution Dataset Linking Meteorological Forcing and Hydro-Sedimentary Response in a Mesoscale Mediterranean Catchment (Auzon) of the Ardèche Region, France

NASA Astrophysics Data System (ADS)

Nord, G.; Braud, I.; Boudevillain, B.; Gérard, S.; Molinié, G.; Vandervaere, J. P.; Huza, J.; Le Coz, J.; Dramais, G.; Legout, C.; Berne, A.; Grazioli, J.; Raupach, T.; Van Baelen, J.; Wijbrans, A.; Delrieu, G.; Andrieu, J.; Caliano, M.; Aubert, C.; Teuling, R.; Le Boursicaud, R.; Branger, F.; Vincendon, B.; Horner, I.

2015-12-01

A comprehensive hydrometeorological dataset is presented spanning the period 1 Jan 2011-31 Dec 2014 to improve the understanding and simulation of the hydrological processes leading to flash floods in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. The specificity of the dataset is its high space-time resolution, especially concerning rainfall and the hydrological response which is particularly adapted to the highly spatially variable rainfall events that may occur in this region. This type of dataset is rare in scientific literature because of the quantity and type of sensors for meteorology and surface hydrology. Rainfall data include continuous precipitation measured by rain-gages (5 min time step for the research network of 21 rain-gages and 1h time step for the operational network of 9 rain-gages), S-band Doppler dual-polarization radar (1 km2, 5 min resolution), and disdrometers (11 sensors working at 1 min time step). During the special observation period (SOP-1) and enhanced observation period (Sep-Dec 2012, Sep-Dec 2013) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). Meteorological data are taken from the operational surface weather observation stations of Meteo France at the hourly time resolution (6 stations in the region of interest). The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations measure water discharge and additional physico-chemical variables at a 2-10 min time resolution. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 gauges continuously measures water level and temperature in headwater subcatchments at a time resolution of 2-5 min. A network of soil moisture sensors enable the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, opportunistic observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. The data are appropriate for understanding rainfall variability, improving areal rainfall estimations and progress in distributed hydrological modelling.

Erosion processes in molassic cliffs: the role of the rock surface temperature and atmospheric conditions

NASA Astrophysics Data System (ADS)

Carrea, Dario; Abellán, Antonio; Guerin, Antoine; Jaboyedoff, Michel; Voumard, Jérémie

2014-05-01

The morphology of the Swiss Plateau is modeled by numerous steep cliffs of Molasse. These cliffs are mainly composed of sub-horizontal alternated layers of sandstone, shale and conglomerates deposed in the Alps foreland basin during the Tertiary period. These Molasse cliffs are affected by erosion processes inducing numerous rockfall events. Thus, it is relevant to understand how different external factors influence Molasse erosion rates. In this study, we focus on analyzing temperature variation during a winter season. As pilot study area we selected a cliff which is formed by a sub-horizontal alternation of outcropping sandstone and shale. The westward facing test site (La Cornalle, Vaud, Switzerland), which is a lateral scarp of a slow moving landslide area, is currently affected by intense erosion. Regarding data acquisition, we monitored both in-situ rock and air temperatures at 15 minutes time-step since October 2013: (1) on the one hand we measured Ground Surface Temperature (GST) at near-surface (0.1 meter depth) using a GST mini-datalogger M-Log5W-Rock model; (2) On the other hand we monitored atmospheric conditions using a weather station (Davis Vantage pro2 plus) collecting numerous parameters (i.e. temperature, irradiation, rain, wind speed, etc.). Furthermore, the area was also seasonally monitored by Ground-Based (GB) LiDAR since 2010 and monthly monitored since September 2013. In order to understand how atmospheric conditions (such as freeze and thaw effect) influence the erosion of the cliff, we modeled the temperature diffusion through the rock mass. To this end, we applied heat diffusion and radiation equation using a 1D temperature profile, obtaining as a result both temperature variations at different depths together with the location of the 0°C isotherm. Our model was calibrated during a given training set using both in-situ rock temperatures and atmospheric conditions. We then carried out a comparison with the rockfall events derived from the 3D GB-LiDAR datasets in order to quantify the erosion rates and to correlate it with atmospheric conditions, aiming to analyze which parameters influence Molasse erosion process.

Inland Water Temperature: An Ideal Indicator for the National Climate Assessment

NASA Astrophysics Data System (ADS)

Hook, S. J.; Lenters, J. D.; O'Reilly, C.; Healey, N. C.

2014-12-01

NASA is a significant contributor to the U.S. National Climate Assessment (NCA), which is a central component of the 2012-2022 U.S. Global Change Research Program Strategic Plan. The NCA has identified the need for indicators that provide a clear, concise way of communicating to NCA audiences about not only the status and trends of physical drivers of the climate system, but also the ecological and socioeconomic impacts, vulnerabilities, and responses to those drivers. We are using thermal infrared satellite data in conjunction with in situ measurements to produce water temperatures for all the large inland water bodies in North America for potential use as an indicator for the NCA. Recent studies have revealed significant warming of inland waters throughout the world. The observed rate of warming is - in many cases - greater than that of the ambient air temperature. These rapid, unprecedented changes in inland water temperatures have profound implications for lake hydrodynamics, productivity, and biotic communities. Scientists are just beginning to understand the global extent, regional patterns, physical mechanisms, and ecological consequences of lake warming. As part of our earlier studies we have collected thermal infrared satellite data from those satellite sensors that provide long-term and frequent spaceborne thermal infrared measurements of inland waters including ATSR, AVHRR, and MODIS and used these to examine trends in water surface temperature for approximately 100 of the largest inland water bodies in the world. We are now extending this work to generate temperature time-series of all North American inland water bodies that are sufficiently large to be studied using 1km resolution satellite data for the last 3 decades. These data are then being related to changes in the surface air temperature and compared with regional trends in water surface temperature derived from CMIP5/IPCC model simulations/projections to better predict future temperature changes. This information is also being used to develop ecologically relevant indicators that are meaningful to the general public, and useful for the National Climate Assessment teams. We will discuss the available datasets and processing methodologies together with their potential use for climate assessment.

Feasibility Study of LANDSAT-8 Imagery for Retrieving Sea Surface Temperature (case Study Persian Gulf)

NASA Astrophysics Data System (ADS)

Bayat, F.; Hasanlou, M.

2016-06-01

Sea surface temperature (SST) is one of the critical parameters in marine meteorology and oceanography. The SST datasets are incorporated as conditions for ocean and atmosphere models. The SST needs to be investigated for various scientific phenomenon such as salinity, potential fishing zone, sea level rise, upwelling, eddies, cyclone predictions. On the other hands, high spatial resolution SST maps can illustrate eddies and sea surface currents. Also, near real time producing of SST map is suitable for weather forecasting and fishery applications. Therefore satellite remote sensing with wide coverage of data acquisition capability can use as real time tools for producing SST dataset. Satellite sensor such as AVHRR, MODIS and SeaWIFS are capable of extracting brightness values at different thermal spectral bands. These brightness temperatures are the sole input for the SST retrieval algorithms. Recently, Landsat-8 successfully launched and accessible with two instruments on-board: (1) the Operational Land Imager (OLI) with nine spectral bands in the visual, near infrared, and the shortwave infrared spectral regions; and (2) the Thermal Infrared Sensor (TIRS) with two spectral bands in the long wavelength infrared. The two TIRS bands were selected to enable the atmospheric correction of the thermal data using a split window algorithm (SWA). The TIRS instrument is one of the major payloads aboard this satellite which can observe the sea surface by using the split-window thermal infrared channels (CH10: 10.6 μm to 11.2 μm; CH11: 11.5 μm to 12.5 μm) at a resolution of 30 m. The TIRS sensors have three main advantages comparing with other previous sensors. First, the TIRS has two thermal bands in the atmospheric window that provide a new SST retrieval opportunity using the widely used split-window (SW) algorithm rather than the single channel method. Second, the spectral filters of TIRS two bands present narrower bandwidth than that of the thermal band on board on previous Landsat sensors. Third, TIRS is one of the best space born and high spatial resolution with 30 m. in this regards, Landsat-8 can use the Split-Window (SW) algorithm for retrieving SST dataset. Although several SWs have been developed to use with other sensors, some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this paper is to develop a SW, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment. In this research, that has been done for modelling SST using thermal Landsat 8-imagery of the Persian Gulf. Therefore, by incorporating contemporary in situ data and SST map estimated from other sensors like MODIS, we examine our proposed method with coefficient of determination (R2) and root mean square error (RMSE) on check point to model SST retrieval for Landsat-8 imagery. Extracted results for implementing different SW's clearly shows superiority of utilized method by R2 = 0.95 and RMSE = 0.24.

Multi-model analysis of the Atlantic influence on Southern Amazon rainfall

DOE PAGES

Yoon, Jin -Ho

2015-12-07

Amazon rainfall is subject to year-to-year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the Sea Surface Temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational datasets were used to support the Atlantic influence on Amazon rainfall. Furthermore, it is found that multiple global climate models do reproduce the Atlantic-Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.

CPLFD-GDPT5: high-resolution gridded daily precipitation and temperature dataset for two largest Polish river basins

NASA Astrophysics Data System (ADS)

Berezowski, T.; Szcześniak, M.; Kardel, I.; Michałowski, R.; Okruszko, T.; Mezghani, A.; Piniewski, M.

2015-12-01

The CHASE-PL Forcing Data-Gridded Daily Precipitation and Temperature Dataset-5 km (CPLFD-GDPT5) consists of 1951-2013 daily minimum and maximum air temperatures and precipitation totals interpolated onto a 5 km grid based on daily meteorological observations from Institute of Meteorology and Water Management (IMGW-PIB; Polish stations), Deutscher Wetterdienst (DWD, German and Czech stations), ECAD and NOAA-NCDC (Slovak, Ukrainian and Belarus stations). The main purpose for constructing this product was the need for long-term aerial precipitation and temperature data for earth-system modelling, especially hydrological modelling. The spatial coverage is the union of Vistula and Odra basin and Polish territory. The number of available meteorological stations for precipitation and temperature varies in time from about 100 for temperature and 300 for precipitation in 1950 up to about 180 for temperature and 700 for precipitation in 1990. The precipitation dataset was corrected for snowfall and rainfall under-catch with the Richter method. The interpolation methods were: kriging with elevation as external drift for temperatures and indicator kriging combined with universal kriging for precipitation. The kriging cross-validation revealed low root mean squared errors expressed as a fraction of standard deviation (SD): 0.54 and 0.47 for minimum and maximum temperature, respectively and 0.79 for precipitation. The correlation scores were 0.84 for minimum temperatures, 0.88 for maximum temperatures and 0.65 for precipitation. The CPLFD-GDPT5 product is consistent with 1971-2000 climatic data published by IMGW-PIB. We also confirm good skill of the product for hydrological modelling by performing an application using the Soil and Water Assessment Tool (SWAT) in the Vistula and Odra basins. Link to the dataset: http://data.3tu.nl/repository/uuid:e939aec0-bdd1-440f-bd1e-c49ff10d0a07

Evaluation of Methods to Estimate the Surface Downwelling Longwave Flux during Arctic Winter

NASA Technical Reports Server (NTRS)

Chiacchio, Marc; Francis, Jennifer; Stackhouse, Paul, Jr.

2002-01-01

Surface longwave radiation fluxes dominate the energy budget of nighttime polar regions, yet little is known about the relative accuracy of existing satellite-based techniques to estimate this parameter. We compare eight methods to estimate the downwelling longwave radiation flux and to validate their performance with measurements from two field programs in thc Arctic: the Coordinated Eastern Arctic Experiment (CEAREX ) conducted in the Barents Sea during the autumn and winter of 1988, and the Lead Experiment performed in the Beaufort Sea in the spring of 1992. Five of the eight methods were developed for satellite-derived quantities, and three are simple parameterizations based on surface observations. All of the algorithms require information about cloud fraction, which is provided from the NASA-NOAA Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) polar pathfinder dataset (Path-P): some techniques ingest temperature and moisture profiles (also from Path-P): one-half of the methods assume that clouds are opaque and have a constant geometric thickness of 50 hPa, and three include no thickness information whatsoever. With a somewhat limited validation dataset, the following primary conclusions result: (1) all methods exhibit approximately the same correlations with measurements and rms differences, but the biases range from -34 W sq m (16% of the mean) to nearly 0; (2) the error analysis described here indicates that the assumption of a 50-hPa cloud thickness is too thin by a factor of 2 on average in polar nighttime conditions; (3) cloud-overlap techniques. which effectively increase mean cloud thickness, significantly improve the results; (4) simple Arctic-specific parameterizations performed poorly, probably because they were developed with surface-observed cloud fractions; and (5) the single algorithm that includes an estimate of cloud thickness exhibits the smallest differences from observations.

Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

NASA Technical Reports Server (NTRS)

Kala, Jatin; Decker, Mark; Exbrayat, Jean-Francois; Pitman, Andy J.; Carouge, Claire; Evans, Jason P.; Abramowitz, Gab; Mocko, David

2013-01-01

Leaf-area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. We investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI data-set is generated using the MODIS LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980-2008) are carried out at 25 km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly-varying LAI data-sets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from -90 to 60 %. PFTs with high absolute LAI and low inter-annual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, whilst those with lower absolute LAI and higher inter-annual variability, such as croplands, were more sensitive. We show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of the terrestrial carbon fluxes, especially for PFTs with high inter-annual variability. Our study highlights that the accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence this will become critical in quantifying the uncertainty in future changes in primary production.

Analysis of Ultra High Resolution Sea Surface Temperature Level 4 Datasets

NASA Technical Reports Server (NTRS)

Wagner, Grant

2011-01-01

Sea surface temperature (SST) studies are often focused on improving accuracy, or understanding and quantifying uncertainties in the measurement, as SST is a leading indicator of climate change and represents the longest time series of any ocean variable observed from space. Over the past several decades SST has been studied with the use of satellite data. This allows a larger area to be studied with much more frequent measurements being taken than direct measurements collected aboard ship or buoys. The Group for High Resolution Sea Surface Temperature (GHRSST) is an international project that distributes satellite derived sea surface temperatures (SST) data from multiple platforms and sensors. The goal of the project is to distribute these SSTs for operational uses such as ocean model assimilation and decision support applications, as well as support fundamental SST research and climate studies. Examples of near real time applications include hurricane and fisheries studies and numerical weather forecasting. The JPL group has produced a new 1 km daily global Level 4 SST product, the Multiscale Ultrahigh Resolution (MUR), that blends SST data from 3 distinct NASA radiometers: the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Very High Resolution Radiometer (AVHRR), and the Advanced Microwave Scanning Radiometer ? Earth Observing System(AMSRE). This new product requires further validation and accuracy assessment, especially in coastal regions.We examined the accuracy of the new MUR SST product by comparing the high resolution version and a lower resolution version that has been smoothed to 19 km (but still gridded to 1 km). Both versions were compared to the same data set of in situ buoy temperature measurements with a focus on study regions of the oceans surrounding North and Central America as well as two smaller regions around the Gulf Stream and California coast. Ocean fronts exhibit high temperature gradients (Roden, 1976), and thus satellite data of SST can be used in the detection of these fronts. In this case, accuracy is less of a concern because the primary focus is on the spatial derivative of SST. We calculated the gradients for both versions of the MUR data set and did statistical comparisons focusing on the same regions.

Hydrography change detection: the usefulness of surface channels derived From LiDAR DEMs for updating mapped hydrography

USGS Publications Warehouse

Poppenga, Sandra K.; Gesch, Dean B.; Worstell, Bruce B.

2013-01-01

The 1:24,000-scale high-resolution National Hydrography Dataset (NHD) mapped hydrography flow lines require regular updating because land surface conditions that affect surface channel drainage change over time. Historically, NHD flow lines were created by digitizing surface water information from aerial photography and paper maps. Using these same methods to update nationwide NHD flow lines is costly and inefficient; furthermore, these methods result in hydrography that lacks the horizontal and vertical accuracy needed for fully integrated datasets useful for mapping and scientific investigations. Effective methods for improving mapped hydrography employ change detection analysis of surface channels derived from light detection and ranging (LiDAR) digital elevation models (DEMs) and NHD flow lines. In this article, we describe the usefulness of surface channels derived from LiDAR DEMs for hydrography change detection to derive spatially accurate and time-relevant mapped hydrography. The methods employ analyses of horizontal and vertical differences between LiDAR-derived surface channels and NHD flow lines to define candidate locations of hydrography change. These methods alleviate the need to analyze and update the nationwide NHD for time relevant hydrography, and provide an avenue for updating the dataset where change has occurred.

Ten-year global distribution of downwelling longwave radiation

NASA Astrophysics Data System (ADS)

Pavlakis, K. G.; Hatzidimitriou, D.; Matsoukas, C.; Drakakis, E.; Hatzianastassiou, N.; Vardavas, I.

2003-10-01

Downwelling longwave fluxes, DLFs, have been derived for each month over a ten year period (1984-1993), on a global scale with a resolution of 2.5° × 2.5°. The fluxes were computed using a deterministic model for atmospheric radiation transfer, along with satellite and reanalysis data for the key atmospheric input parameters, i.e. cloud properties, and specific humidity and temperature profiles. The cloud climatologies were taken from the latest released and improved International Satellite Climatology Project D2 series. Specific humidity and temperature vertical profiles were taken from three different reanalysis datasets; NCEP/NCAR, GEOS, and ECMWF (acronyms explained in main text). DLFs were computed for each reanalysis dataset, with differences reaching values as high as 30 Wm-2 in specific regions, particularly over high altitude areas and deserts. However, globally, the agreement is good, with the rms of the difference between the DLFs derived from the different reanalysis datasets ranging from 5 to 7 Wm-2. The results are presented as geographical distributions and as time series of hemispheric and global averages. The DLF time series based on the different reanalysis datasets show similar seasonal and inter-annual variations, and similar anomalies related to the 86/87 El Niño and 89/90 La Niña events. The global ten-year average of the DLF was found to be between 342.2 Wm-2 and 344.3 Wm-2, depending on the dataset. We also conducted a detailed sensitivity analysis of the calculated DLFs to the key input data. Plots are given that can be used to obtain a quick assessment of the sensitivity of the DLF to each of the three key climatic quantities, for specific climatic conditions corresponding to different regions of the globe. Our model downwelling fluxes are validated against available data from ground-based stations distributed over the globe, as given by the Baseline Surface Radiation Network. There is a negative bias of the model fluxes when compared against BSRN fluxes, ranging from -7 to -9 Wm-2, mostly caused by low cloud amount differences between the station and satellite measurements, particularly in cold climates. Finally, we compare our model results with those of other deterministic models and general circulation models.

Ten-year global distribution of downwelling longwave radiation

NASA Astrophysics Data System (ADS)

Pavlakis, K. G.; Hatzidimitriou, D.; Matsoukas, C.; Drakakis, E.; Hatzianastassiou, N.; Vardavas, I.

2004-01-01

Downwelling longwave fluxes, DLFs, have been derived for each month over a ten year period (1984-1993), on a global scale with a spatial resolution of 2.5x2.5 degrees and a monthly temporal resolution. The fluxes were computed using a deterministic model for atmospheric radiation transfer, along with satellite and reanalysis data for the key atmospheric input parameters, i.e. cloud properties, and specific humidity and temperature profiles. The cloud climatologies were taken from the latest released and improved International Satellite Climatology Project D2 series. Specific humidity and temperature vertical profiles were taken from three different reanalysis datasets; NCEP/NCAR, GEOS, and ECMWF (acronyms explained in main text). DLFs were computed for each reanalysis dataset, with differences reaching values as high as 30 Wm-2 in specific regions, particularly over high altitude areas and deserts. However, globally, the agreement is good, with the rms of the difference between the DLFs derived from the different reanalysis datasets ranging from 5 to 7 Wm-2. The results are presented as geographical distributions and as time series of hemispheric and global averages. The DLF time series based on the different reanalysis datasets show similar seasonal and inter-annual variations, and similar anomalies related to the 86/87 El Niño and 89/90 La Niña events. The global ten-year average of the DLF was found to be between 342.2 Wm-2 and 344.3 Wm-2, depending on the dataset. We also conducted a detailed sensitivity analysis of the calculated DLFs to the key input data. Plots are given that can be used to obtain a quick assessment of the sensitivity of the DLF to each of the three key climatic quantities, for specific climatic conditions corresponding to different regions of the globe. Our model downwelling fluxes are validated against available data from ground-based stations distributed over the globe, as given by the Baseline Surface Radiation Network. There is a negative bias of the model fluxes when compared against BSRN fluxes, ranging from -7 to -9 Wm-2, mostly caused by low cloud amount differences between the station and satellite measurements, particularly in cold climates. Finally, we compare our model results with those of other deterministic models and general circulation models.

Reconciling Conflicting Geologic and Thermochronologic Interpretations Via Multiple Apatite Thermochronometers (AHe, AFT, and 4He/3He): 6 Ma Incision of the Westernmost Grand Canyon

NASA Astrophysics Data System (ADS)

Winn, C.; Karlstrom, K. E.; Shuster, D. L.; Kelley, S.; Fox, M.

2017-12-01

The application of low-temperature apatite thermochronology to the incision history of the Grand Canyon has led to conflicting hypotheses of either a 70 Ma ("old") or <6 Ma ("young") Grand Canyon. This controversy is best captured in the westernmost segment of the Grand Canyon, where several lines of evidence favor a "young" Canyon: 1) North-derived Paleocene Hindu Fanglomerate was deposited across the present track of the Canyon; 2) The Separation Point basalt (19 Ma) is stranded between high relief tributaries and the main stem of the Colorado River; 3) Relief generation in tributaries and on plateaus adjacent to the Canyon took place after 17 Ma; and 4) The late Miocene-Pliocene Muddy Creek Formation shows that no far-traveled materials entered the Grand Wash Trough until after 6 Ma. Some interpretations of apatite thermochronology data conflict with these lines of evidence and indicate a much older ( 70 Ma) westernmost Grand Canyon. We reconcile this conflict by applying apatite (U-Th)/He ages (AHe), 4He/3He thermochronometry, and apatite fission track ages and lengths (AFT) to the same sample at a key location. Using HeFTy, t-T paths that predict these data show cooling from ˜100 °C to 40-60 °C at 70-50 Ma, long-term residence at 40-60 °C from 50-10 Ma, and cooling to surface temperatures after 10 Ma, indicating young incision. New AFT (5) and AHe (3) datasets are also presented here. When datasets are examined separately, AHe data show t-T paths that cool to surface temperatures during the Laramide, consistent with an "old" Canyon. When multiple methods are applied, t-T paths instead show young incision. This inconsistency demonstrates the age of the Grand Canyon controversy. Here we reconcile the difference in t-T paths by adjusting model parameters to account for uncertainty in the rate of radiation damage annealing in apatite during burial heating and the resulting variations in He retentivity. In this area, peak burial conditions during the Laramide were likely insufficient to fully anneal radiation damage that accumulated during prolonged near-surface residence prior to burial. We conclude that application of multiple thermochronometers from common rocks reconciles conflicting thermochronologic interpretations and these data are best explained by a "young" westernmost Grand Canyon.

The construction of a Central Netherlands temperature

NASA Astrophysics Data System (ADS)

van der Schrier, G.; van Ulden, A.; van Oldenborgh, G. J.

2011-05-01

The Central Netherlands Temperature (CNT) is a monthly daily mean temperature series constructed from homogenized time series from the centre of the Netherlands. The purpose of this series is to offer a homogeneous time series representative of a larger area in order to study large-scale temperature changes. It will also facilitate a comparison with climate models, which resolve similar scales. From 1906 onwards, temperature measurements in the Netherlands have been sufficiently standardized to construct a high-quality series. Long time series have been constructed by merging nearby stations and using the overlap to calibrate the differences. These long time series and a few time series of only a few decades in length have been subjected to a homogeneity analysis in which significant breaks and artificial trends have been corrected. Many of the detected breaks correspond to changes in the observations that are documented in the station metadata. This version of the CNT, to which we attach the version number 1.1, is constructed as the unweighted average of four stations (De Bilt, Winterswijk/Hupsel, Oudenbosch/Gilze-Rijen and Gemert/Volkel) with the stations Eindhoven and Deelen added from 1951 and 1958 onwards, respectively. The global gridded datasets used for detecting and attributing climate change are based on raw observational data. Although some homogeneity adjustments are made, these are not based on knowledge of local circumstances but only on statistical evidence. Despite this handicap, and the fact that these datasets use grid boxes that are far larger then the area associated with that of the Central Netherlands Temperature, the temperature interpolated to the CNT region shows a warming trend that is broadly consistent with the CNT trend in all of these datasets. The actual trends differ from the CNT trend up to 30 %, which highlights the need to base future global gridded temperature datasets on homogenized time series.

Evaluation of Ten Methods for Initializing a Land Surface Model

NASA Technical Reports Server (NTRS)

Rodell, M.; Houser, P. R.; Berg, A. A.; Famiglietti, J. S.

2005-01-01

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth"s water cycle and climate variability. NASA"s Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type).

A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description, Validation, and Case Study

NASA Technical Reports Server (NTRS)

Claverie, Martin; Matthews, Jessica L.; Vermote, Eric F.; Justice, Christopher O.

2016-01-01

In- land surface models, which are used to evaluate the role of vegetation in the context ofglobal climate change and variability, LAI and FAPAR play a key role, specifically with respect to thecarbon and water cycles. The AVHRR-based LAIFAPAR dataset offers daily temporal resolution,an improvement over previous products. This climate data record is based on a carefully calibratedand corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitablefor climate studies. It spans from mid-1981 to the present. Further, this operational dataset is availablein near real-time allowing use for monitoring purposes. The algorithm relies on artificial neuralnetworks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparisonwith MODIS products and in situ data show the dataset is consistent and reliable with overalluncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect isobserved in the broadleaf forest biomes with high LAI (greater than 4.5) and FAPAR (greater than 0.8) values.

Consensuses and discrepancies of basin-scale ocean heat content changes in different ocean analyses

NASA Astrophysics Data System (ADS)

Wang, Gongjie; Cheng, Lijing; Abraham, John; Li, Chongyin

2018-04-01

Inconsistent global/basin ocean heat content (OHC) changes were found in different ocean subsurface temperature analyses, especially in recent studies related to the slowdown in global surface temperature rise. This finding challenges the reliability of the ocean subsurface temperature analyses and motivates a more comprehensive inter-comparison between the analyses. Here we compare the OHC changes in three ocean analyses (Ishii, EN4 and IAP) to investigate the uncertainty in OHC in four major ocean basins from decadal to multi-decadal scales. First, all products show an increase of OHC since 1970 in each ocean basin revealing a robust warming, although the warming rates are not identical. The geographical patterns, the key modes and the vertical structure of OHC changes are consistent among the three datasets, implying that the main OHC variabilities can be robustly represented. However, large discrepancies are found in the percentage of basinal ocean heating related to the global ocean, with the largest differences in the Pacific and Southern Ocean. Meanwhile, we find a large discrepancy of ocean heat storage in different layers, especially within 300-700 m in the Pacific and Southern Oceans. Furthermore, the near surface analysis of Ishii and IAP are consistent with sea surface temperature (SST) products, but EN4 is found to underestimate the long-term trend. Compared with ocean heat storage derived from the atmospheric budget equation, all products show consistent seasonal cycles of OHC in the upper 1500 m especially during 2008 to 2012. Overall, our analyses further the understanding of the observed OHC variations, and we recommend a careful quantification of errors in the ocean analyses.

NASA Cold Land Processes Experiment (CLPX 2002/03): Atmospheric analyses datasets

Treesearch

Glen E. Liston; Daniel L. Birkenheuer; Christopher A. Hiemstra; Donald W. Cline; Kelly Elder

2008-01-01

This paper describes the Local Analysis and Prediction System (LAPS) and the 20-km horizontal grid version of the Rapid Update Cycle (RUC20) atmospheric analyses datasets, which are available as part of the Cold Land Processes Field Experiment (CLPX) data archive. The LAPS dataset contains spatially and temporally continuous atmospheric and surface variables over...

Dynamical and thermodynamical coupling between the North Atlantic subtropical high and the marine boundary layer clouds in boreal summer

NASA Astrophysics Data System (ADS)

Wei, Wei; Li, Wenhong; Deng, Yi; Yang, Song; Jiang, Jonathan H.; Huang, Lei; Liu, W. Timothy

2018-04-01

This study investigates dynamical and thermodynamical coupling between the North Atlantic subtropical high (NASH), marine boundary layer (MBL) clouds, and the local sea surface temperatures (SSTs) over the North Atlantic in boreal summer for 1984-2009 using NCEP/DOE Reanalysis 2 dataset, various cloud data, and the Hadley Centre sea surface temperature. On interannual timescales, the summer mean subtropical MBL clouds to the southeast of the NASH is actively coupled with the NASH and local SSTs: a stronger (weaker) NASH is often accompanied with an increase (a decrease) of MBL clouds and abnormally cooler (warmer) SSTs along the southeast flank of the NASH. To understand the physical processes between the NASH and the MBL clouds, the authors conduct a data diagnostic analysis and implement a numerical modeling investigation using an idealized anomalous atmospheric general circulation model (AGCM). Results suggest that significant northeasterly anomalies in the southeast flank of the NASH associated with an intensified NASH tend to induce stronger cold advection and coastal upwelling in the MBL cloud region, reducing the boundary surface temperature. Meanwhile, warm advection associated with the easterly anomalies from the African continent leads to warming over the MBL cloud region at 700 hPa. Such warming and the surface cooling increase the atmospheric static stability, favoring growth of the MBL clouds. The anomalous diabatic cooling associated with the growth of the MBL clouds dynamically excites an anomalous anticyclone to its north and contributes to strengthening of the NASH circulation in its southeast flank. The dynamical and thermodynamical couplings and their associated variations in the NASH, MBL clouds, and SSTs constitute an important aspect of the summer climate variability over the North Atlantic.

Influence of a forest canopy on velocity and temperature profiles under synoptic conditions

NASA Astrophysics Data System (ADS)

Pattantyus, A.; Hocut, C. M.; Wang, Y.; Creegan, E.; Krishnamurthy, R.; Otarola-Bust, S.; Leo, L. S.; Fernando, H. J. S.

2017-12-01

Numerous field campaigns have found the importance of surface conditions on boundary layer evolution. Specifically, soil properties were found to control surface fluxes of heat, moisture, and momentum that significantly modulated the atmospheric boundary layer (ABL) over flat and sparsely vegetated surfaces. There have been increasing numbers of studies related to canopy impacts on the boundary layer, such as CHATS, however few canopy studies over complex terrain have been performed with limited instrumentation. The recent Perdigão campaign greatly augmented the previous datasets available by instrumenting a unique, parallel ridge mountain in Perdigão, Portugal in unprecedented spatial and temporal resolution using traditional mast mounted sensors, instrumented aerial platforms, and remote sensing instrumentation. To aid the canopy studies, the Army Research Laboratory deployed sonic anemometers within the canopy transecting the ridges perpendicularly and placed five additional heavily instrumented meteorological masts on the northeast facing slope to investigate detailed slope flows. At each of these towers, there was an average of six levels of temperature, relative humidity, and wind sensors located above & below the canopy height which allowed a detailed study of the sub-canopy layer. In addition to the towers, two scanning Doppler LiDARs were oriented such that they performed synchronized dual Doppler virtual tower scans, extending from the canopy interface to several hundred meters above. Synoptically forced periods were analyzed to examine: the ABL structure of temperature, moisture, wind, and turbulent kinetic energy. Of particular interest are the shear layer at the canopy interface, recirculation events, as well as ejection and sweep events within the canopy and how these modify surface fluxes along the slopes.

Direct observation of electrothermal instability structures on intensely Ohmically heated aluminum with current flowing in a surface skin layer

NASA Astrophysics Data System (ADS)

Awe, Thomas

2017-10-01

Implosions on the Z Facility assemble high-energy-density plasmas for radiation effects and ICF experiments, but achievable stagnation pressures and temperatures are degraded by the Magneto-Rayleigh-Taylor (MRT) instability. While the beryllium liners (tubes) used in Magnetized Liner Inertial Fusion (MagLIF) experiments are astonishingly smooth (10 to 50 nm RMS roughness), they also contain distributed micron-scale resistive inclusions, and large MRT amplitudes are observed. Early in the implosion, an electrothermal instability (ETI) may provide a perturbation which greatly exceeds the initial surface roughness of the liner. Resistive inhomogeneities drive nonuniform current density and Joule heating, resulting in locally higher temperature, and thus still higher resistivity. Such unstable temperature and pressure growth produce density perturbations which seed MRT. For MagLIF liners, ETI seeding of MRT has been inferred by evaluating late-time MRT, but a direct observation of ETI is not made. ETI is directly observed on the surface of 1.0-mm-diameter solid Al rods pulsed to 1 MA in 100 ns via high resolution gated optical imaging (2 ns temporal and 3 micron spatial resolution). Aluminum 6061 alloy rods, with micron-scale resistive inclusions, consistently first demonstrate overheating from distinct, 10-micron-scale, sub-eV spots, which 5-10 ns later merge into azimuthally stretched elliptical spots and discrete strata (40-100 microns wide by 10 microns tall). Axial plasma filaments form shortly thereafter. Surface plasma can be suppressed for rods coated with dielectric, enabling extended study of the evolution of stratified ETI structures, and experimental inference of ETI growth rates. This fundamentally new and highly 3-dimensional dataset informs ETI physics, including when the ETI seed of MRT may be initiated.

Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990-2009).

PubMed

Sharma, Richa; Ghosh, Aniruddha; Joshi, Pawan Kumar

2013-04-01

Urbanisation is a ubiquitous phenomenon with greater prominence in developing nations. Urban expansion involves land conversions from vegetated moisture-rich to impervious moisture-deficient land surfaces. The urban land transformations alter biophysical parameters in a mode that promotes development of heat islands and degrades environmental health. This study elaborates relationships among various environmental variables using remote sensing dataset to study spatio-temporal footprint of urbanisation in Surat city. Landsat Thematic Mapper satellite data were used in conjugation with geo-spatial techniques to study urbanisation and correlation among various satellite-derived biophysical parameters, [Normalised Difference Vegetation Index, Normalised Difference Built-up Index, Normalised Difference Water Index, Normalised Difference Bareness Index, Modified NDWI and land surface temperature (LST)]. Land use land cover was prepared using hierarchical decision tree classification with an accuracy of 90.4 % (kappa = 0.88) for 1990 and 85 % (kappa = 0.81) for 2009. It was found that the city has expanded over 42.75 km(2) within a decade, and these changes resulted in elevated surface temperatures. For example, transformation from vegetation to built-up has resulted in 5.5 ± 2.6 °C increase in land surface temperature, vegetation to fallow 6.7 ± 3 °C, fallow to built-up is 3.5 ± 2.9 °C and built-up to dense built-up is 5.3 ± 2.8 °C. Directional profiling for LST was done to study spatial patterns of LST in and around Surat city. Emergence of two new LST peaks for 2009 was observed in N-S and NE-SW profiles.

Status update: is smoke on your mind? Using social media to assess smoke exposure

NASA Astrophysics Data System (ADS)

Ford, Bonne; Burke, Moira; Lassman, William; Pfister, Gabriele; Pierce, Jeffrey R.

2017-06-01

Exposure to wildland fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health endpoints to wildland fire smoke requires determining the locations, concentrations, and durations of smoke events. Most current methods for assessing these smoke events (ground-based measurements, satellite observations, and chemical transport modeling) are limited temporally, spatially, and/or by their level of accuracy. In this work, we explore using daily social media posts from Facebook regarding smoke, haze, and air quality to assess population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook dataset to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), daily (24 h) average surface particulate matter measurements, and model-simulated (WRF-Chem) surface concentrations. After adding population-weighted spatial smoothing to the Facebook data, this dataset is well correlated (R2 generally above 0.5) with the other methods in smoke-impacted regions. The Facebook dataset is better correlated with surface measurements of PM2. 5 at a majority of monitoring sites (163 of 293 sites) than the satellite observations and our model simulation. We also present an example case for Washington state in 2015, for which we combine this Facebook dataset with MODIS observations and WRF-Chem-simulated PM2. 5 in a regression model. We show that the addition of the Facebook data improves the regression model's ability to predict surface concentrations. This high correlation of the Facebook data with surface monitors and our Washington state example suggests that this social-media-based proxy can be used to estimate smoke exposure in locations without direct ground-based particulate matter measurements.

A Geophysical Characterization & Monitoring Strategy for Determining Hydrologic Processes in the Hyporheic Corridor at the Hanford 300-Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Slater, Lee; Day-Lewis, Frederick; Lane, John

2011-08-31

The primary objective of this research was to advance the prediction of solute transport between the Uranium contaminated Hanford aquifer and the Columbia River at the Hanford 300 Area by improving understanding of how fluctuations in river stage, combined with subsurface heterogeneity, impart spatiotemporal complexity to solute exchange along the Columbia River corridor. Our work explored the use of continuous waterborne electrical imaging (CWEI), in conjunction with fiber-optic distributed temperature sensor (FO-DTS) and time-lapse resistivity monitoring, to improve the conceptual model for how groundwater/surface water exchange regulates uranium transport. We also investigated how resistivity and induced polarization can be usedmore » to generate spatially rich estimates of the variation in depth to the Hanford-Ringold (H-R) contact between the river and the 300 Area Integrated Field Research Challenge (IFRC) site. Inversion of the CWEI datasets (a data rich survey containing {approx}60,000 measurements) provided predictions of the distributions of electrical resistivity and polarizability, from which the spatial complexity of the primary hydrogeologic units along the river corridor was reconstructed. Variation in the depth to the interface between the overlying coarse-grained, high permeability Hanford Formation and the underlying finer-grained, less permeable Ringold Formation, an important contact that limits vertical migration of contaminants, has been resolved along {approx}3 km of the river corridor centered on the IFRC site in the Hanford 300 Area. Spatial variability in the thickness of the Hanford Formation captured in the CWEI datasets indicates that previous studies based on borehole projections and drive-point and multi-level sampling likely overestimate the contributing area for uranium exchange within the Columbia River at the Hanford 300 Area. Resistivity and induced polarization imaging between the river and the 300 Area IFRC further imaged spatial variability in the depth to the Hanford-Ringold inland over a critical region where borehole information is absent, identifying evidence for a continuous depression in the H-R contact between the IFRC and the river corridor. Strong natural contrasts in temperature and specific conductance of river water compared to groundwater at this site, along with periodic river stage fluctuations driven by dam operations, were exploited to yield new insights into the dynamics of groundwater-surface water interaction. Whereas FO-DTS datasets have provided meter-scale measurements of focused groundwater discharge at the riverbed along the corridor, continuous resistivity monitoring has non-invasively imaged spatiotemporal variation in the resistivity inland driven by river stage fluctuations. Time series and time-frequency analysis of FO-DTS and 3D resistivity datasets has provided insights into the role of forcing variables, primarily daily dam operations, in regulating the occurrence of focused exchange at the riverbed and its extension inland. High amplitudes in the DTS and 3D resistivity signals for long periods that dominate the stage time series identify regions along the corridor where stage-driven exchange is preferentially focused. Our work has demonstrated how time-series analysis of both time-lapse resistivity and DTS datasets, in conjunction with resistivity/IP imaging of lithology, can improve understanding of groundwater-surface water exchange along river corridors, offering unique opportunities to connect stage-driven groundwater discharge observed with DTS on the riverbed to stage-driven groundwater and solute fluctuations captured with resistivity inland.« less

FINAL PROJECT REPORT: A Geophysical Characterization & Monitoring Strategy for Determining Hydrologic Processes in the Hyporheic Corridor at the Hanford 300-Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee Slater

2011-08-15

The primary objective of this research was to advance the prediction of solute transport between the Uranium contaminated Hanford aquifer and the Columbia River at the Hanford 300 Area by improving understanding of how fluctuations in river stage, combined with subsurface heterogeneity, impart spatiotemporal complexity to solute exchange along the Columbia River corridor. Our work explored the use of continuous waterborne electrical imaging (CWEI), in conjunction with fiber-optic distributed temperature sensor (FO-DTS) and time-lapse resistivity monitoring, to improve the conceptual model for how groundwater/surface water exchange regulates uranium transport. We also investigated how resistivity and induced polarization can be usedmore » to generate spatially rich estimates of the variation in depth to the Hanford-Ringold (H-R) contact between the river and the 300 Area Integrated Field Research Challenge (IFRC) site. Inversion of the CWEI datasets (a data rich survey containing ~60,000 measurements) provided predictions of the distributions of electrical resistivity and polarizability, from which the spatial complexity of the primary hydrogeologic units along the river corridor was reconstructed. Variation in the depth to the interface between the overlying coarse-grained, high permeability Hanford Formation and the underlying finer-grained, less permeable Ringold Formation, an important contact that limits vertical migration of contaminants, has been resolved along ~3 km of the river corridor centered on the IFRC site in the Hanford 300 Area. Spatial variability in the thickness of the Hanford Formation captured in the CWEI datasets indicates that previous studies based on borehole projections and drive-point and multi-level sampling likely overestimate the contributing area for uranium exchange within the Columbia River at the Hanford 300 Area. Resistivity and induced polarization imaging between the river and the 300 Area IFRC further imaged spatial variability in the depth to the Hanford-Ringold inland over a critical region where borehole information is absent, identifying evidence for a continuous depression in the H-R contact between the IFRC and the river corridor. Strong natural contrasts in temperature and specific conductance of river water compared to groundwater at this site, along with periodic river stage fluctuations driven by dam operations, were exploited to yield new insights into the dynamics of groundwater-surface water interaction. Whereas FO-DTS datasets have provided meter-scale measurements of focused groundwater discharge at the riverbed along the corridor, continuous resistivity monitoring has non-invasively imaged spatiotemporal variation in the resistivity inland driven by river stage fluctuations. Time series and time-frequency analysis of FO-DTS and 3D resistivity datasets has provided insights into the role of forcing variables, primarily daily dam operations, in regulating the occurrence of focused exchange at the riverbed and its extension inland. High amplitudes in the DTS and 3D resistivity signals for long periods that dominate the stage time series identify regions along the corridor where stage-driven exchange is preferentially focused. Our work has demonstrated how time-series analysis of both time-lapse resistivity and DTS datasets, in conjunction with resistivity/IP imaging of lithology, can improve understanding of groundwater-surface water exchange along river corridors, offering unique opportunities to connect stage-driven groundwater discharge observed with DTS on the riverbed to stage-driven groundwater and solute fluctuations captured with resistivity inland.« less

NHDPlusHR: A national geospatial framework for surface-water information

USGS Publications Warehouse

Viger, Roland; Rea, Alan H.; Simley, Jeffrey D.; Hanson, Karen M.

2016-01-01

The U.S. Geological Survey is developing a new geospatial hydrographic framework for the United States, called the National Hydrography Dataset Plus High Resolution (NHDPlusHR), that integrates a diversity of the best-available information, robustly supports ongoing dataset improvements, enables hydrographic generalization to derive alternate representations of the network while maintaining feature identity, and supports modern scientific computing and Internet accessibility needs. This framework is based on the High Resolution National Hydrography Dataset, the Watershed Boundaries Dataset, and elevation from the 3-D Elevation Program, and will provide an authoritative, high precision, and attribute-rich geospatial framework for surface-water information for the United States. Using this common geospatial framework will provide a consistent basis for indexing water information in the United States, eliminate redundancy, and harmonize access to, and exchange of water information.

The Statistical Differences Between the Gridded Temperature Datasets, and its Implications for Stochastic Modelling

NASA Astrophysics Data System (ADS)

Fredriksen, H. B.; Løvsletten, O.; Rypdal, M.; Rypdal, K.

2014-12-01

Several research groups around the world collect instrumental temperature data and combine them in different ways to obtain global gridded temperature fields. The three most well known datasets are HadCRUT4 produced by the Climatic Research Unit and the Met Office Hadley Centre in UK, one produced by NASA GISS, and one produced by NOAA. Recently Berkeley Earth has also developed a gridded dataset. All these four will be compared in our analysis. The statistical properties we will focus on are the standard deviation and the Hurst exponent. These two parameters are sufficient to describe the temperatures as long-range memory stochastic processes; the standard deviation describes the general fluctuation level, while the Hurst exponent relates the strength of the long-term variability to the strength of the short-term variability. A higher Hurst exponent means that the slow variations are stronger compared to the fast, and that the autocovariance function will have a stronger tail. Hence the Hurst exponent gives us information about the persistence or memory of the process. We make use of these data to show that data averaged over a larger area exhibit higher Hurst exponents and lower variance than data averaged over a smaller area, which provides information about the relationship between temporal and spatial correlations of the temperature fluctuations. Interpolation in space has some similarities with averaging over space, although interpolation is more weighted towards the measurement locations. We demonstrate that the degree of spatial interpolation used can explain some differences observed between the variances and memory exponents computed from the various datasets.

The isolation of the temperature effect on branched GDGT distribution in an elevation transect of the Eastern Cordillera, Colombia

NASA Astrophysics Data System (ADS)

Anderson, V. J.; Shanahan, T. M.; Saylor, J.; Horton, B. K.

2012-12-01

Recently, the distribution of branched GDGT's (glycerol dialkyl glycerol tetraethers) has been proposed as a proxy for temperature and pH in soils via the MBT/CBT index, and has been used to reconstruct past temperature variations in a number of settings ranging from marine sediments to loess deposits and paleosols. However, empirical calibrations of the MBT/CBT index against temperature show significant scatter, leading to uncertainties as large as ±2 degrees C . In this study we seek to add to and improve upon the existing soil calibration using a new set of samples spanning a large elevation (and temperature) gradient in the Eastern Cordillera of Colombia. At each site we buried temperature loggers to constrain the diurnal and seasonal temperature experienced by each soil sample. Located only 5 degrees north of the equator, our sites experience a very small seasonal temperature variation - most sites display an annual range of less than 4 degrees C. In addition, the pH of all of the soils is almost invariant across the transect, with the vast majority of samples having pH's between 4 and 5. This dataset represents a "best-case" scenario - small variations in seasonal temperature, pH, and well-constrained instrumental data - which allow us to examine the brGDGT-temperature relationship in the absence of major confounding factors such as seasonality and soil chemistry. Interestingly, the relationship between temperature and the MBT/CBT index is not improved using this dataset, suggesting that these factors are not the cause of the anomalous scatter in the calibration dataset. However, we find that using other parameterizations for the regression equation instead of the MBT and CBT indices, the errors in our temperature estimates are significantly reduced.

Operational use of open satellite data for marine water quality monitoring

NASA Astrophysics Data System (ADS)

Symeonidis, Panagiotis; Vakkas, Theodoros

2017-09-01

The purpose of this study was to develop an operational platform for marine water quality monitoring using near real time satellite data. The developed platform utilizes free and open satellite data available from different data sources like COPERNICUS, the European Earth Observation Initiative, or NASA, from different satellites and instruments. The quality of the marine environment is operationally evaluated using parameters like chlorophyll-a concentration, water color and Sea Surface Temperature (SST). For each parameter, there are more than one dataset available, from different data sources or satellites, to allow users to select the most appropriate dataset for their area or time of interest. The above datasets are automatically downloaded from the data provider's services and ingested to the central, spatial engine. The spatial data platform uses the Postgresql database with the PostGIS extension for spatial data storage and Geoserver for the provision of the spatial data services. The system provides daily, 10 days and monthly maps and time series of the above parameters. The information is provided using a web client which is based on the GET SDI PORTAL, an easy to use and feature rich geospatial visualization and analysis platform. The users can examine the temporal variation of the parameters using a simple time animation tool. In addition, with just one click on the map, the system provides an interactive time series chart for any of the parameters of the available datasets. The platform can be offered as Software as a Service (SaaS) to any area in the Mediterranean region.

Best Practices for International Collaboration and Applications of Interoperability within a NASA Data Center

NASA Astrophysics Data System (ADS)

Moroni, D. F.; Armstrong, E. M.; Tauer, E.; Hausman, J.; Huang, T.; Thompson, C. K.; Chung, N.

2013-12-01

The Physical Oceanographic Distributed Active Archive Center (PO.DAAC) is one of 12 data centers sponsored by NASA's Earth Science Data and Information System (ESDIS) project. The PO.DAAC is tasked with archival and distribution of NASA Earth science missions specific to physical oceanography, many of which have interdisciplinary applications for weather forecasting/monitoring, ocean biology, ocean modeling, and climate studies. PO.DAAC has a 20-year history of cross-project and international collaborations with partners in Europe, Japan, Australia, and the UK. Domestically, the PO.DAAC has successfully established lasting partners with non-NASA institutions and projects including the National Oceanic and Atmospheric Administration (NOAA), United States Navy, Remote Sensing Systems, and Unidata. A key component of these partnerships is PO.DAAC's direct involvement with international working groups and science teams, such as the Group for High Resolution Sea Surface Temperature (GHRSST), International Ocean Vector Winds Science Team (IOVWST), Ocean Surface Topography Science Team (OSTST), and the Committee on Earth Observing Satellites (CEOS). To help bolster new and existing collaborations, the PO.DAAC has established a standardized approach to its internal Data Management and Archiving System (DMAS), utilizing a Data Dictionary to provide the baseline standard for entry and capture of dataset and granule metadata. Furthermore, the PO.DAAC has established an end-to-end Dataset Lifecycle Policy, built upon both internal and external recommendations of best practices toward data stewardship. Together, DMAS, the Data Dictionary, and the Dataset Lifecycle Policy provide the infrastructure to enable standardized data and metadata to be fully ingested and harvested to facilitate interoperability and compatibility across data access protocols, tools, and services. The Dataset Lifecycle Policy provides the checks and balances to help ensure all incoming HDF and netCDF-based datasets meet minimum compliance requirements with the Lawrence Livermore National Laboratory's actively maintained Climate and Forecast (CF) conventions with additional goals toward metadata standards provided by the Attribute Convention for Dataset Discovery (ACDD), the International Organization for Standardization (ISO) 19100-series, and the Federal Geographic Data Committee (FGDC). By default, DMAS ensures all datasets are compliant with NASA's Global Change Master Directory (GCMD) and NASA's Reverb data discovery clearinghouse (also known as ECHO). For data access, PO.DAAC offers several widely-used technologies, including File Transfer Protocol (FTP), Open-source Project for a Network Data Access Protocol (OPeNDAP), and Thematic Realtime Environmental Distributed Data Services (THREDDS). These access technologies are available directly to users or through PO.DAAC's web interfaces, specifically the High-level Tool for Interactive Data Extraction (HiTIDE), Live Access Server (LAS), and PO.DAAC's set of search, image, and Consolidated Web Services (CWS). Lastly, PO.DAAC's newly introduced, standards-based CWS provide singular endpoints for search, imaging, and extraction capabilities, respectively, across L2/L3/L4 datasets. Altogether, these tools, services and policies serve to provide flexible, interoperable functionality for both users and data providers.

Coral mass spawning predicted by rapid seasonal rise in ocean temperature

PubMed Central

Maynard, Jeffrey A.; Edwards, Alasdair J.; Guest, James R.; Rahbek, Carsten

2016-01-01

Coral spawning times have been linked to multiple environmental factors; however, to what extent these factors act as generalized cues across multiple species and large spatial scales is unknown. We used a unique dataset of coral spawning from 34 reefs in the Indian and Pacific Oceans to test if month of spawning and peak spawning month in assemblages of Acropora spp. can be predicted by sea surface temperature (SST), photosynthetically available radiation, wind speed, current speed, rainfall or sunset time. Contrary to the classic view that high mean SST initiates coral spawning, we found rapid increases in SST to be the best predictor in both cases (month of spawning: R2 = 0.73, peak: R2 = 0.62). Our findings suggest that a rapid increase in SST provides the dominant proximate cue for coral mass spawning over large geographical scales. We hypothesize that coral spawning is ultimately timed to ensure optimal fertilization success. PMID:27170709

Temperature, Geochemistry, and Gravity Data of the Tularosa Basin

DOE Data Explorer

Nash, Greg

2017-06-16

This submission contains multiple excel spreadsheets and associated written reports. The datasets area are representative of shallow temperature, geochemistry, and other well logging observations made across WSMR (white sands missile range); located to the west of the Tularosa Basin but still within the study area. Written reports accompany some of the datasets, and they provide ample description of the methodology and results obtained from these studies. Gravity data is also included, as point data in a shapefile, along with a written report describing that particular study.

An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field, India

NASA Astrophysics Data System (ADS)

Roy, Priyom; Guha, Arindam; Kumar, K. Vinod

2015-07-01

Radiant temperature images from thermal remote sensing sensors are used to delineate surface coal fires, by deriving a cut-off temperature to separate coal-fire from non-fire pixels. Temperature contrast of coal fire and background elements (rocks and vegetation etc.) controls this cut-off temperature. This contrast varies across the coal field, as it is influenced by variability of associated rock types, proportion of vegetation cover and intensity of coal fires etc. We have delineated coal fires from background, based on separation in data clusters in maximum v/s mean radiant temperature (13th band of ASTER and 10th band of Landsat-8) scatter-plot, derived using randomly distributed homogeneous pixel-blocks (9 × 9 pixels for ASTER and 27 × 27 pixels for Landsat-8), covering the entire coal bearing geological formation. It is seen that, for both the datasets, overall temperature variability of background and fires can be addressed using this regional cut-off. However, the summer time ASTER data could not delineate fire pixels for one specific mine (Bhulanbararee) as opposed to the winter time Landsat-8 data. The contrast of radiant temperature of fire and background terrain elements, specific to this mine, is different from the regional contrast of fire and background, during summer. This is due to the higher solar heating of background rocky outcrops, thus, reducing their temperature contrast with fire. The specific cut-off temperature determined for this mine, to extract this fire, differs from the regional cut-off. This is derived by reducing the pixel-block size of the temperature data. It is seen that, summer-time ASTER image is useful for fire detection but required additional processing to determine a local threshold, along with the regional threshold to capture all the fires. However, the winter Landsat-8 data was better for fire detection with a regional threshold.

Evaluating Coral Health in La Parguera, Puerto Rico, and Southeastern Florida: Comparison of Satellite-Based Sea Surface Temperature to In Situ Observations

NASA Astrophysics Data System (ADS)

Gomez, A. M.; McDonald, K. C.; Shein, K. A.; Devries, S. L.; Armstrong, R.; Carlo, M.

2017-12-01

The third global coral bleaching event, which began in mid-2014, is a major environmental stressor that has been causing significant documented damage to coral reefs in all tropical ocean basins. This worldwide phenomenon is the longest and largest coral bleaching event on record and now finally appears to be ending. During this event, some coral colonies proved to be more resilient to increased ocean temperatures while others bleached severely. This research investigates the spatial and temporal variability of bleaching stress on coral reefs in La Parguera, Puerto Rico, and Southeastern Florida to help further understand the role of temperature and light in coral bleaching. We examine the microclimate within two coral reef systems, using in situ collections of temperature and light data from data loggers deployed throughout Cayo Enrique and Cayo Mario in La Parguera, and Lauderdale-By-The-Sea in FLorida. The in situ measurements are compared to NOAA Coral Reef Watch's 5-km sea surface temperature data as well as to the associated Light Stress Damage Product. Research outcomes include statistical analyses of in situ measurements with satellite datasets supporting enhanced interpretation of satellite-based SST and light products, and ecological niche modeling to assess where corals could potentially survive under future climate conditions. Additional understanding of the microclimate encompassing coral reefs and improved satellite SST and light data will ultimately help coral reef ecosystem managers and policy makers in prioritizing resources toward the monitoring and protection of coral reef ecosystems.

Analysis of the Meteorology Associated with the 1998 NASA Glenn Twin Otter Icing Flights

NASA Technical Reports Server (NTRS)

Bernstein, Ben C.

2000-01-01

This document contains a basic analysis of the meteorology associated with the NASA Glenn Twin Otter icing encounters between December 1997 and March 1998. The purpose of this analysis is to provide a meteorological context for the aircraft data collected during these flights. For each case, the following data elements are presented: (1) A brief overview of the Twin Otter encounter, including locations, liquid water contents, temperatures and microphysical makeup of the clouds and precipitation aloft, (2) Upper-air charts, providing hand-analyzed locations of lows, troughs, ridges, saturated/unsaturated air, temperatures, warm/cold advection, and jet streams, (3) Balloon-borne soundings, providing vertical profiles of temperature, moisture and winds, (4) Infrared and visible satellite data, providing cloud locations and cloud top temperature, (5) 3-hourly surface charts, providing hand-analyzed locations of lows, highs, fronts, precipitation (including type) and cloud cover, (6) Hourly, regional radar mosaics, providing fine resolution of the locations of precipitation (including intensity and type), pilot reports of icing (including intensity and type), surface observations of precipitation type and Twin Otter tracks for a one hour window centered on the time of the radar data, and (7) Hourly plots of icing pilot reports, providing the icing intensity, icing type, icing altitudes and aircraft type. Outages occurred in nearly every dataset at some point. All relevant data that was available is presented here. All times are in UTC and all heights are in feet above mean sea level (MSL).

Temporal and Spatial Variability of the Ras Al-Hadd Jet/Front in the Northwest Arabian Sea

NASA Astrophysics Data System (ADS)

Al Shaqsi, Hilal Mohamed Said

Thirteen years of 1.1 km resolution daily satellites remote sensing sea surface temperature datasets (2002-2014), sea surface winds, sea surface height, Argo floats, daily three-hour interval wind datasets, and hourly records of oceanography physical parameters from mooring current meters were processed and analyzed to investigate the dynamics, temporal and spatial variability of the Ras Al-Hadd Jet off the northwest Arabian Sea. Cayula and Cornillon single image edge detection algorithm was used to detect these thermal fronts. The Ras Al-Hadd thermal front was found to have two seasonal peaks. The first peak occurred during the intensified southwest monsoon period (July/August), while the second peak was clearly observed during the transitional period or the Post-Southwest monsoon (September-October). Interannual and intraseasonal variability showed the occurrence of the Ras Al-Hadd thermal fronts in the northwest Arabian Sea. The southwest monsoon winds, the Somalia Current, the East Arabian Current, and the warmer high salinity waters from the Sea of Oman are the main factors influencing the creation of the Ras Al-Hadd Jet. Based on direct observations, current velocity in the Cape Ras Al-Hadd Jet exceeded 120 cms-1, and the wind speed was over 12 ms-1 during the southwest monsoon seasons. The mean width and the mean length of the Jet were approximately 40 km and 260 km, respectively. Neither the winter monsoon, nor the Pre-Southwest monsoon seasons showed signs of the Ras Al-Hadd Jet or fronts in the northwest Arabian Sea.

Extensive validation of CM SAF surface radiation products over Europe.

PubMed

Urraca, Ruben; Gracia-Amillo, Ana M; Koubli, Elena; Huld, Thomas; Trentmann, Jörg; Riihelä, Aku; Lindfors, Anders V; Palmer, Diane; Gottschalg, Ralph; Antonanzas-Torres, Fernando

2017-09-15

This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to 2015. The products used have been developed by the Satellite Application Facility on Climate Monitoring (CM SAF) and are one geostationary climate dataset (SARAH-JRC), one polar-orbiting climate dataset (CLARA-A2) and one geostationary operational product. Further, the ERA-Interim reanalysis is also included in the comparison. The main objective is to determine the quality level of the daily means of CM SAF datasets, identifying their limitations, as well as analyzing the different factors that can interfere in the adequate validation of the products. The quality of the pyranometer was the most critical source of uncertainty identified. In this respect, the use of records from Second Class pyranometers and silicon-based photodiodes increased the absolute error and the bias, as well as the dispersion of both metrics, preventing an adequate validation of the daily means. The best spatial estimates for the three datasets were obtained in Central Europe with a Mean Absolute Deviation (MAD) within 8-13 W/m 2 , whereas the MAD always increased at high-latitudes, snow-covered surfaces, high mountain ranges and coastal areas. Overall, the SARAH-JRC's accuracy was demonstrated over a dense network of stations making it the most consistent dataset for climate monitoring applications. The operational dataset was comparable to SARAH-JRC in Central Europe, but lacked of the temporal stability of climate datasets, while CLARA-A2 did not achieve the same level of accuracy despite predictions obtained showed high uniformity with a small negative bias. The ERA-Interim reanalysis shows the by-far largest deviations from the surface reference measurements.

Advances in Using Fiber-Optic Distributed Temperature Sensing to Identify the Mixing of Waters

NASA Astrophysics Data System (ADS)

Briggs, M. A.; Day-Lewis, F. D.; Rosenberry, D. O.; Harvey, J. W.; Lane, J. W., Jr.; Hare, D. K.; Boutt, D. F.; Voytek, E. B.; Buckley, S.

2014-12-01

Fiber-optic distributed temperature sensing (FO-DTS) provides thermal data through space and time along linear cables. When installed along a streambed, FO-DTS can capture the influence of upwelling groundwater (GW) as thermal anomalies. The planning of labor-intensive physical measurements can make use of FO-DTS data to target areas of focused GW discharge that can disproportionately affect surface-water (SW) quality and temperature. Typical longitudinal FO-DTS spatial resolution ranges 0.25 to1.0 m, and cannot resolve small-scale water-column mixing or sub-surface diurnal fluctuations. However, configurations where the cable is wrapped around rods can improve the effective vertical resolution to sub-centimeter scales, and the pipes can be actively heated to induce a thermal tracer. Longitudinal streambed and high-resolution vertical arrays were deployed at the upper Delaware River (PA, USA) and the Quashnet River (MA, USA) for aquatic habitat studies. The resultant datasets exemplify the varied uses of FO-DTS. Cold anomalies found along the Delaware River steambed coincide with zones of known mussel populations, and high-resolution vertical array data showed relatively stable in-channel thermal refugia. Cold anomalies at the Quashnet River identified in 2013 were found to persist in 2014, and seepage measurements and water samples at these locations showed high GW flux with distinctive chemistry. Cable location is paramount to seepage identification, particularly in faster flowing deep streams such as the Quashnet and Delaware Rivers where steambed FO-DTS identified many seepage zones with no surface expression. The temporal characterization of seepage dynamics are unique to FO-DTS. However, data from Tidmarsh Farms, a cranberry bog restoration site in MA, USA indicate that in slower flowing shallow steams GW inflow affects surface temperature; therefore infrared imaging can provide seepage location information similar to FO-DTS with substantially less effort.

Weekly glacier flow estimation from dense satellite time series using adapted optical flow technology

NASA Astrophysics Data System (ADS)

Altena, Bas; Kääb, Andreas

2017-06-01

Contemporary optical remote sensing satellites or constellations of satellites can acquire imagery at sub-weekly or even daily timescales. Thus, these systems facilitate the potential for within-season velocity estimation of glacier surfaces. State-of-the-art techniques for displacement estimation are based on matching image pairs and are thus constrained by the need of significant displacement and/or preservation of the surface over time. Consequently, such approaches cannot benefit entirely from the increasing satellite revisit times. Here, we explore an approach that is fundamentally different from image correlation or similar techniques and exploits the concept of optical flow. Our goal is to assess if this concept could overcome above current limitations of image matching and thus give new insights in glacier flow dynamics. We implement two different methods of optical flow, and test these on the SPOT5 Take5 dataset over Kronebreen, Svalbard and over Kaskawulsh Glacier, Yukon. For Kaskawulsh Glacier we are able to extract seasonal velocity variation, that temporally coincide with events of increased air temperatures. Furthermore, even for the cloudy dataset of Kronebreen, we were able to extract spatio-temporal trajectories which correlate well with measured GPS flow paths. Because the underlying concept is simple and computationally efficient due to data-reduction, our methodology can easily be used for exploratory regional studies of several glaciers or estimation of small and slow flowing glaciers.

Evaluation of LIS-based Soil Moisture and Evapotranspiration in the Korean Peninsula

NASA Astrophysics Data System (ADS)

Jung, H. C.; Kang, D. H.; Kim, E. J.; Yoon, Y.; Kumar, S.; Peters-Lidard, C. D.; Baeck, S. H.; Hwang, E.; Chae, H.

2017-12-01

K-water is the South Korean national water agency. It is the government-funded private agency for water resource development that provides both civil and industrial water in S. Korea. K-water is interested in exploring how earth remote sensing and modeling can help their tasks. In this context, the NASA Land Information System (LIS) is implemented to simulate land surface processes in the Korean Peninsula. The Noah land surface model with Multi-Parameterization, version 3.6 (Noah-MP) is used to reproduce the water budget variables on a 1 km spatial resolution grid with a daily temporal resolution. The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) datasets is used to force the system. The rainfall data are spatially downscaled from high resolution WorldClim precipitation climatology. The other meteorological inputs (i.e. air temperature, humidity, pressure, winds, radiation) are also downscaled by statistical methods (i.e. lapse-rate, slope-aspect). Additional model experiments are conducted with local rainfall datasets and soil maps to replace the downscaled MERRA-2 precipitation field and the hybrid STATSGO/FAO soil texture, respectively. For the evaluation of model performance, daily soil moisture and evapotranspiration measurements at several stations are compared to the LIS-based outputs. This study demonstrates that application of NASA's LIS can enhance drought and flood prediction capabilities in South Asia and Korea.

Use of satellite images to determine surface-water cover during the flood event of September 13, 2013, in Lyons and western Longmont, Colorado

USGS Publications Warehouse

Cole, Christopher J.; Friesen, Beverly A.; Wilson, Earl M.; Wilds, Stanley R.; Noble, Suzanne M.

2015-01-01

This surface-water cover dataset was created as a timely representation of post-flood ground conditions to support response efforts. This dataset and all processed imagery and derived products were uploaded to the USGS Hazards Data Distribution System (HDDS) website (http://hddsexplorer.usgs.gov/uplift/hdds/) for distribution to those responding to the flood event.

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

NASA Technical Reports Server (NTRS)

Wang, Weile; Nemani, Ramakrishna R.; Michaelis, Andrew; Hashimoto, Hirofumi; Dungan, Jennifer L.; Thrasher, Bridget L.; Dixon, Keith W.

2016-01-01

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

The GNSS Reflectometry Response to the Ocean Surface

NASA Astrophysics Data System (ADS)

Chang, Paul; Jelenak, Zorana; Soisuvarn, Seubson; Said, Faozi

2016-04-01

Global Navigation Satellite System - Reflectometry (GNSS-R) exploits signals of opportunity from the Global Navigation Satellite System (GNSS). GNSS transmitters continuously transmit navigation signals at L-band toward the earth's surface. The scattered power reflected off the earth's surface can be sensed by specially designed GNSS-R receivers. The reflected signal can then be used to glean information about the surface of the earth, such as ocean surface roughness, snow depth, sea ice extent, and soil moisture. The use of GNSS-R for ocean wind retrievals was first demonstrated from aircraft. On July 8 2014, the TechDemoSat-1 satellite (TDS-1) was launched by Surrey Satellite Technology, Ltd as a technology risk reduction mission into sun-synchronous orbit. This paper investigates the GNSS-R measurements collected by the Space GNSS Receiver-Remote Sensing Instrument (SGR-ReSI) on board the TDS-1 satellite. The sensitivity of the SGR-ReSI measurements to the ocean surface winds and waves are characterized. The effects of sea surface temperature, wind direction, and rain are also investigated. The SGR-ReSI measurements exhibited sensitivity through the entire range of wind speeds sampled in this dataset, up to 35 m/s. A significant dependence on the larger waves was observed for winds < 6 m/s. Additionally, an interesting dependence on SST was observed where the slope of the SGR-ReSI measurements is positive for winds < 5 m/s and reverses for winds > 5 m/s. There appeared to be very little wind direction signal, and investigation of the rain impacts found no apparent sensitivity in the data. These results are shown through the analysis of global statistics and examination of a few case studies. This released SGR-ReSI dataset provided the first opportunity to comprehensively investigate the sensitivity of satellite-based GNSS-R measurements to various ocean surface parameters. The upcoming NASA's Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation will utilize a similar receiver to SGI-ReSI and thus this data provides valuable pre-launch knowledge for the CYGNSS mission.

LOGISMOS-B for primates: primate cortical surface reconstruction and thickness measurement

NASA Astrophysics Data System (ADS)

Oguz, Ipek; Styner, Martin; Sanchez, Mar; Shi, Yundi; Sonka, Milan

2015-03-01

Cortical thickness and surface area are important morphological measures with implications for many psychiatric and neurological conditions. Automated segmentation and reconstruction of the cortical surface from 3D MRI scans is challenging due to the variable anatomy of the cortex and its highly complex geometry. While many methods exist for this task in the context of the human brain, these methods are typically not readily applicable to the primate brain. We propose an innovative approach based on our recently proposed human cortical reconstruction algorithm, LOGISMOS-B, and the Laplace-based thickness measurement method. Quantitative evaluation of our approach was performed based on a dataset of T1- and T2-weighted MRI scans from 12-month-old macaques where labeling by our anatomical experts was used as independent standard. In this dataset, LOGISMOS-B has an average signed surface error of 0.01 +/- 0.03mm and an unsigned surface error of 0.42 +/- 0.03mm over the whole brain. Excluding the rather problematic temporal pole region further improves unsigned surface distance to 0.34 +/- 0.03mm. This high level of accuracy reached by our algorithm even in this challenging developmental dataset illustrates its robustness and its potential for primate brain studies.

A Compilation of Spatial Datasets to Support a Preliminary Assessment of Pesticides and Pesticide Use on Tribal Lands in Oklahoma

USGS Publications Warehouse

Mashburn, Shana L.; Winton, Kimberly T.

2010-01-01

This CD-ROM contains spatial datasets that describe natural and anthropogenic features and county-level estimates of agricultural pesticide use and pesticide data for surface-water, groundwater, and biological specimens in the state of Oklahoma. County-level estimates of pesticide use were compiled from the Pesticide National Synthesis Project of the U.S. Geological Survey, National Water-Quality Assessment Program. Pesticide data for surface water, groundwater, and biological specimens were compiled from U.S. Geological Survey National Water Information System database. These spatial datasets that describe natural and manmade features were compiled from several agencies and contain information collected by the U.S. Geological Survey. The U.S. Geological Survey datasets were not collected specifically for this compilation, but were previously collected for projects with various objectives. The spatial datasets were created by different agencies from sources with varied quality. As a result, features common to multiple layers may not overlay exactly. Users should check the metadata to determine proper use of these spatial datasets. These data were not checked for accuracy or completeness. If a question of accuracy or completeness arise, the user should contact the originator cited in the metadata.

Testing Snow Melt Algorithms in High Relief Topography Using Calibrated Enhanced-Resolution Brightness Temperatures, Hunza River Basin, Pakistan

NASA Astrophysics Data System (ADS)

Ramage, J. M.; Brodzik, M. J.; Hardman, M.; Troy, T. J.

2017-12-01

Snow is a vital part of the terrestrial hydrological cycle, a crucial resource for people and ecosystems. In mountainous regions snow is extensive, variable, and challenging to document. Snow melt timing and duration are important factors affecting the transfer of snow mass to soil moisture and runoff. Passive microwave brightness temperature (Tb) changes at 36 and 18 GHz are a sensitive way to detect snow melt onset due to their sensitivity to the abrupt change in emissivity. They are widely used on large icefields and high latitude watersheds. The coarse resolution ( 25 km) of historically available data has precluded effective use in high relief, heterogeneous regions, and gaps between swaths also create temporal data gaps at lower latitudes. New enhanced resolution data products generated from a scatterometer image reconstruction for radiometer (rSIR) technique are available at the original frequencies. We use these Calibrated Enhanced-resolution Brightness (CETB) Temperatures Earth System Data Records (ESDR) to evaluate existing snow melt detection algorithms that have been used in other environments, including the cross polarized gradient ratio (XPGR) and the diurnal amplitude variations (DAV) approaches. We use the 36/37 GHz (3.125 km resolution) and 18/19 GHz (6.25 km resolution) vertically and horizontally polarized datasets from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Radiometer for EOS (AMSR-E) and evaluate them for use in this high relief environment. The new data are used to assess glacier and snow melt records in the Hunza River Basin [area 13,000 sq. km, located at 36N, 74E], a tributary to the Upper Indus Basin, Pakistan. We compare the melt timing results visually and quantitatively to the corresponding EASE-Grid 2.0 25-km dataset, SRTM topography, and surface temperatures from station and reanalysis data. The new dataset is coarser than the topography, but is able to differentiate signals of melt/refreeze timing for different altitudes and land cover in this remote area with significant hazards from snow melt and glacier discharge. The improved spatial resolution, enhanced to 3-6 km, and retaining twice daily observations is a key improvement to fully analyze snowpack melt characteristics in remote mountainous regions.

Temperature calibration and phylogenetically distinct distributions for freshwater alkenones: Evidence from northern Alaskan lakes

NASA Astrophysics Data System (ADS)

Longo, William M.; Theroux, Susanna; Giblin, Anne E.; Zheng, Yinsui; Dillon, James T.; Huang, Yongsong

2016-05-01

Alkenones are a class of unsaturated long-chain ketone biomarkers that have been used to reconstruct sea surface temperature and, more recently, continental temperature, by way of alkenone unsaturation indices (e.g. U37K and U37K‧). Alkenones are frequently found in brackish and saline lakes, however species effects confound temperature reconstructions when multiple alkenone-producing species with different temperature responses are present. Interestingly, available genetic data indicate that numerous freshwater lakes host a distinct phylotype of alkenone-producing haptophyte algae (the Group I or Greenland phylotype), providing evidence that species effects may be diminished in freshwater lakes. These findings encourage further investigation of alkenone paleotemperature proxies in freshwater systems. Here, we investigated lakes from northern Alaska (n = 35) and show that alkenones commonly occurred in freshwater lakes, where they featured distinct distributions, characterized by dominant C37:4 alkenones and a series of tri-unsaturated alkenone isomers. The distributions were characteristic of Group I-type alkenone distributions previously identified in Greenland and North America. Our analysis of suspended particulate matter from Toolik Lake (68° 38‧N, 149° 36‧W) yielded the first in situ freshwater U37K calibration (U37K = 0.021 * T - 0.68; r2 = 0.85; n = 52; RMSE = ±1.37 °C). We explored the environmental significance of the tri-unsaturated isomers using our northern Alaskan lakes dataset in conjunction with new data from haptophyte cultures and Canadian surface sediments. Our results show that these temperature-sensitive isomers are biomarkers for the Group I phylotype and indicators of multiple-species effects. Together, these findings highlight freshwater lakes as valuable targets for continental alkenone-based paleotemperature reconstructions and demonstrate the significance of the recently discovered tri-unsaturated isomers.

An explanation for the 18O excess in Noelaerhabdaceae coccolith calcite

NASA Astrophysics Data System (ADS)

Hermoso, M.; Minoletti, F.; Aloisi, G.; Bonifacie, M.; McClelland, H. L. O.; Labourdette, N.; Renforth, P.; Chaduteau, C.; Rickaby, R. E. M.

2016-09-01

Coccoliths have dominated the sedimentary archive in the pelagic environment since the Jurassic. The biominerals produced by the coccolithophores are ideally placed to infer sea surface temperatures from their oxygen isotopic composition, as calcification in this photosynthetic algal group only occurs in the sunlit surface waters. In the present study, we dissect the isotopic mechanisms contributing to the "vital effect", which overprints the oceanic temperatures recorded in coccolith calcite. Applying the passive diffusion model of carbon acquisition by the marine phytoplankton widely used in biogeochemical and palaeoceanographic studies, our results suggest that the oxygen isotope offsets from inorganic calcite in fast dividing species Emiliania huxleyi and Gephyrocapsa oceanica originates from the legacy of assimilated 18O-rich CO2 that induces transient isotopic disequilibrium to the internal dissolved inorganic carbon (DIC) pool. The extent to which this intracellular isotopic disequilibrium is recorded in coccolith calcite (1.5 to +3‰ over a 10 to 25 °C temperature range) is set by the degree of isotopic re-equilibration between CO2 and water molecules before intracellular mineralisation. We show that the extent of re-equilibration is, in turn, set by temperature through both physiological (dynamics of the utilisation of the DIC pool) and thermodynamic (completeness of the re-equilibration of the relative 18O-rich CO2 influx) processes. At the highest temperature, less ambient aqueous CO2 is present for algal growth, and the consequence of carbon limitation is exacerbation of the oxygen isotope vital effect, obliterating the temperature signal. This culture dataset further demonstrates that the vital effect is variable for a given species/morphotype, and depends on the intricate relationship between the environment and the physiology of biomineralising algae.

Comparison of stratospheric temperature profiles from a ground-based microwave radiometer with lidar, radiosonde and satellite data

NASA Astrophysics Data System (ADS)

Navas-Guzmán, Francisco; Kämpfer, Niklaus; Haefele, Alexander; Keckhut, Philippe; Hauchecorne, Alain

2015-04-01

The importance of the knowledge of the temperature structure in the atmosphere has been widely recognized. Temperature is a key parameter for dynamical, chemical and radiative processes in the atmosphere. The cooling of the stratosphere is an indicator for climate change as it provides evidence of natural and anthropogenic climate forcing just like surface warming ( [1] and references therein). However, our understanding of the observed stratospheric temperature trend and our ability to test simulations of the stratospheric response to emissions of greenhouse gases and ozone depleting substances remains limited. Stratospheric long-term datasets are sparse and obtained trends differ from one another [1]. Therefore it is important that in the future such datasets are generated. Different techniques allow to measure stratospheric temperature profiles as radiosonde, lidar or satellite. The main advantage of microwave radiometers against these other instruments is a high temporal resolution with a reasonable good spatial resolution. Moreover, the measurement at a fixed location allows to observe local atmospheric dynamics over a long time period, which is crucial for climate research. TEMPERA (TEMPERature RAdiometer) is a newly developed ground-based microwave radiometer designed, built and operated at the University of Bern. The instrument and the retrieval of temperature profiles has been described in detail in [2]. TEMPERA is measuring a pressure broadened oxygen line at 53.1 GHz in order to determine stratospheric temperature profiles. The retrieved profiles of TEMPERA cover an altitude range of approximately 20 to 45 km with a vertical resolution in the order of 15 km. The lower limit is given by the instrumental baseline and the bandwidth of the measured spectrum. The upper limit is given by the fact that above 50 km the oxygen lines are splitted by the Zeeman effect in the terrestrial magnetic field. In this study we present a comparison of stratospheric temperature profiles retrieved from TEMPERA radiometer with the ones obtained from different techniques such as in-situ (radiosondes), active remote sensing (lidar) and passive remote sensing on board of Aura satellite (MLS) measurements. Moreover, a statistical analysis of the stratospheric temperature from TEMPERA measurements for three years of data have been performed.The results evidence the capability of TEMPERA radiometer to monitor the temperature in the stratosphere for a long-term. The detection of some singular sudden stratospheric warming (SSW) during the analyzed period shows the necessity of these continuous monitoring in order to measure and understand some important processes which could happen on a short time scale. References [1] D. W. Thompson, D. J. Seidel, W. J. Randel, C.-Z. Zou, A. H. Butler, C. Mears, A. Osso, C. Long, and R. Lin, "The mystery of recent stratospheric temperature trends," Nature, vol. 491, no. 7426, pp. 692-697, 2012. [2] O. Stähli, A. Murk, N. Kämpfer, C. Mätzler, and P. Eriksson, "Microwave radiometer to retrieve temperature profiles from the surface to the stratopause," Atmospheric Measurement Techniques Discussions, vol. 6, no. 2, pp. 2857-2905, 2013.