Development of a fire weather index using meteorological observations within the Northeast United States

Treesearch

Michael J. Erickson; Joseph J. Charney; Brian A. Colle

2016-01-01

A fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily...

On the predictability of land surface fluxes from meteorological variables

NASA Astrophysics Data System (ADS)

Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.

2018-01-01

Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

Interannual variability of ammonia concentrations over the United States: sources and implications

NASA Astrophysics Data System (ADS)

Schiferl, Luke D.; Heald, Colette L.; Van Damme, Martin; Clarisse, Lieven; Clerbaux, Cathy; Coheur, Pierre-François; Nowak, John B.; Neuman, J. Andrew; Herndon, Scott C.; Roscioli, Joseph R.; Eilerman, Scott J.

2016-09-01

The variability of atmospheric ammonia (NH3), emitted largely from agricultural sources, is an important factor when considering how inorganic fine particulate matter (PM2.5) concentrations and nitrogen cycling are changing over the United States. This study combines new observations of ammonia concentration from the surface, aboard aircraft, and retrieved by satellite to both evaluate the simulation of ammonia in a chemical transport model (GEOS-Chem) and identify which processes control the variability of these concentrations over a 5-year period (2008-2012). We find that the model generally underrepresents the ammonia concentration near large source regions (by 26 % at surface sites) and fails to reproduce the extent of interannual variability observed at the surface during the summer (JJA). Variability in the base simulation surface ammonia concentration is dominated by meteorology (64 %) as compared to reductions in SO2 and NOx emissions imposed by regulation (32 %) over this period. Introduction of year-to-year varying ammonia emissions based on animal population, fertilizer application, and meteorologically driven volatilization does not substantially improve the model comparison with observed ammonia concentrations, and these ammonia emissions changes have little effect on the simulated ammonia concentration variability compared to those caused by the variability of meteorology and acid-precursor emissions. There is also little effect on the PM2.5 concentration due to ammonia emissions variability in the summer when gas-phase changes are favored, but variability in wintertime emissions, as well as in early spring and late fall, will have a larger impact on PM2.5 formation. This work highlights the need for continued improvement in both satellite-based and in situ ammonia measurements to better constrain the magnitude and impacts of spatial and temporal variability in ammonia concentrations.

Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data.

PubMed

Adde, Antoine; Roux, Emmanuel; Mangeas, Morgan; Dessay, Nadine; Nacher, Mathieu; Dusfour, Isabelle; Girod, Romain; Briolant, Sébastien

2016-01-01

Local variation in the density of Anopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. Vector distribution is driven by environmental factors. Based on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of Anopheles darlingi in the municipality of Saint-Georges de l'Oyapock (French Guiana). Longitudinal sampling sessions of An. darlingi densities were conducted between September 2012 and October 2014. Landscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to An. darlingi ecology. Based on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of An. darlingi in Saint-Georges de l'Oyapock. The final cross-validated model integrated two landscape variables-dense forest surface and built surface-together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. Extrapolation of the model allowed the generation of predictive weekly maps of An. darlingi densities at a resolution of 10-m. Our results supported the use of satellite imagery and meteorological data to predict malaria vector densities. Such fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner.

Meteorological Automatic Weather Station (MAWS) Instrument Handbook

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

Holdridge, Donna J; Kyrouac, Jenni A

The Meteorological Automatic Weather Station (MAWS) is a surface meteorological station, manufactured by Vaisala, Inc., dedicated to the balloon-borne sounding system (BBSS), providing surface measurements of the thermodynamic state of the atmosphere and the wind speed and direction for each radiosonde profile. These data are automatically provided to the BBSS during the launch procedure and included in the radiosonde profile as the surface measurements of record for the sounding. The MAWS core set of measurements is: Barometric Pressure (hPa), Temperature (°C), Relative Humidity (%), Arithmetic-Averaged Wind Speed (m/s), and Vector-Averaged Wind Direction (deg). The sensors that collect the core variablesmore » are mounted at the standard heights defined for each variable.« less

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

NASA Astrophysics Data System (ADS)

Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

2016-11-01

The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

An Investigation of Turbulent Heat Exchange in the Subtropics

DTIC Science & Technology

2014-09-30

meteorological sensors aboard the research vessel the R/V Revelle during the DYNAMO field program. In situ meteorology and high-rate flux sensors operated...continuously while in the sampling period for DYNAMO Leg 3. This included all sensors operating during Leg 2 with the addition of a closed-path LI...stress; wave data; surface and near surface sea temperatures, salinity and currents; and other key variables specifically requested by DYNAMO /LASP PIs

Observed and predicted sensitivities of extreme surface ozone to meteorological drivers in three US cities

NASA Astrophysics Data System (ADS)

Fix, Miranda J.; Cooley, Daniel; Hodzic, Alma; Gilleland, Eric; Russell, Brook T.; Porter, William C.; Pfister, Gabriele G.

2018-03-01

We conduct a case study of observed and simulated maximum daily 8-h average (MDA8) ozone (O3) in three US cities for summers during 1996-2005. The purpose of this study is to evaluate the ability of a high resolution atmospheric chemistry model to reproduce observed relationships between meteorology and high or extreme O3. We employ regional coupled chemistry-transport model simulations to make three types of comparisons between simulated and observational data, comparing (1) tails of the O3 response variable, (2) distributions of meteorological predictor variables, and (3) sensitivities of high and extreme O3 to meteorological predictors. This last comparison is made using two methods: quantile regression, for the 0.95 quantile of O3, and tail dependence optimization, which is used to investigate even higher O3 extremes. Across all three locations, we find substantial differences between simulations and observational data in both meteorology and meteorological sensitivities of high and extreme O3.

Variation of surface ozone in Campo Grande, Brazil: meteorological effect analysis and prediction.

PubMed

Pires, J C M; Souza, A; Pavão, H G; Martins, F G

2014-09-01

The effect of meteorological variables on surface ozone (O3) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O3 concentration, relative humidity and solar radiation. The threshold model that considers two O3 regimes was the one that correctly describes the effect of important meteorological variables in O3 behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O3 regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O3 concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O3 behaviour, being useful to define policy strategies for human health protection regarding air pollution.

Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

NASA Astrophysics Data System (ADS)

Gundogdu, Ismail Bulent

2017-01-01

Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.

The Aggregate Description of Semi-Arid Vegetation with Precipitation-Generated Soil Moisture Heterogeneity

NASA Technical Reports Server (NTRS)

White, Cary B.; Houser, Paul R.; Arain, Altaf M.; Yang, Zong-Liang; Syed, Kamran; Shuttleworth, W. James

1997-01-01

Meteorological measurements in the Walnut Gulch catchment in Arizona were used to synthesize a distributed, hourly-average time series of data across a 26.9 by 12.5 km area with a grid resolution of 480 m for a continuous 18-month period which included two seasons of monsoonal rainfall. Coupled surface-atmosphere model runs established the acceptability (for modelling purposes) of assuming uniformity in all meteorological variables other than rainfall. Rainfall was interpolated onto the grid from an array of 82 recording rain gauges. These meteorological data were used as forcing variables for an equivalent array of stand-alone Biosphere-Atmosphere Transfer Scheme (BATS) models to describe the evolution of soil moisture and surface energy fluxes in response to the prevalent, heterogeneous pattern of convective precipitation. The calculated area-average behaviour was compared with that given by a single aggregate BATS simulation forced with area-average meteorological data. Heterogeneous rainfall gives rise to significant but partly compensating differences in the transpiration and the intercepted rainfall components of total evaporation during rain storms. However, the calculated area-average surface energy fluxes given by the two simulations in rain-free conditions with strong heterogeneity in soil moisture were always close to identical, a result which is independent of whether default or site-specific vegetation and soil parameters were used. Because the spatial variability in soil moisture throughout the catchment has the same order of magnitude as the amount of rain failing in a typical convective storm (commonly 10% of the vegetation's root zone saturation) in a semi-arid environment, non-linearitv in the relationship between transpiration and the soil moisture available to the vegetation has limited influence on area-average surface fluxes.

Representative Values of Icing-Related Variables Aloft in Freezing Rain and Freezing Drizzle

DOT National Transportation Integrated Search

1996-03-01

Radiosonde and surface observations in freezing rain (ZR) and freezing drizzle (ZL), and a limited number of aircraft measurements in ZR, have been examined for information on the magnitude and altitude dependence of meteorological variables associat...

Coupling of snow and permafrost processes using the Basic Modeling Interface (BMI)

NASA Astrophysics Data System (ADS)

Wang, K.; Overeem, I.; Jafarov, E. E.; Piper, M.; Stewart, S.; Clow, G. D.; Schaefer, K. M.

2017-12-01

We developed a permafrost modeling tool based by implementing the Kudryavtsev empirical permafrost active layer depth model (the so-called "Ku" component). The model is specifically set up to have a basic model interface (BMI), which enhances the potential coupling to other earth surface processes model components. This model is accessible through the Web Modeling Tool in Community Surface Dynamics Modeling System (CSDMS). The Kudryavtsev model has been applied for entire Alaska to model permafrost distribution at high spatial resolution and model predictions have been verified by Circumpolar Active Layer Monitoring (CALM) in-situ observations. The Ku component uses monthly meteorological forcing, including air temperature, snow depth, and snow density, and predicts active layer thickness (ALT) and temperature on the top of permafrost (TTOP), which are important factors in snow-hydrological processes. BMI provides an easy approach to couple the models with each other. Here, we provide a case of coupling the Ku component to snow process components, including the Snow-Degree-Day (SDD) method and Snow-Energy-Balance (SEB) method, which are existing components in the hydrological model TOPOFLOW. The work flow is (1) get variables from meteorology component, set the values to snow process component, and advance the snow process component, (2) get variables from meteorology and snow component, provide these to the Ku component and advance, (3) get variables from snow process component, set the values to meteorology component, and advance the meteorology component. The next phase is to couple the permafrost component with fully BMI-compliant TOPOFLOW hydrological model, which could provide a useful tool to investigate the permafrost hydrological effect.

Estimating historical groundwater levels based on relations with hydrologic and meteorological variables in the U.S. glacial aquifer system

NASA Astrophysics Data System (ADS)

Dudley, R. W.; Hodgkins, G. A.; Nielsen, M. G.; Qi, S. L.

2018-07-01

A number of previous studies have examined relations between groundwater levels and hydrologic and meteorological variables over parts of the glacial aquifer system, but systematic analyses across the entire U.S. glacial aquifer system are lacking. We tested correlations between monthly groundwater levels measured at 1043 wells in the U.S. glacial aquifer system considered to be minimally influenced by human disturbance and selected hydrologic and meteorological variables with the goal of extending historical groundwater records where there were strong correlations. Groundwater levels in the East region correlated most strongly with short-term (1 and 3 month) averages of hydrologic and meteorological variables, while those in the Central and West Central regions yielded stronger correlations with hydrologic and meteorological variables averaged over longer time intervals (6-12 months). Variables strongly correlated with high and low annual groundwater levels were identified as candidate records for use in statistical linear models as a means to fill in and extend historical high and low groundwater levels respectively. Overall, 37.4% of study wells meeting data criteria had successful models for high and (or) low groundwater levels; these wells shared characteristics of relatively higher local precipitation, higher local land-surface slope, lower amounts of clay within the surficial sediments, and higher base-flow index. Streamflow and base flow served as explanatory variables in about two thirds of both high- and low-groundwater-level models in all three regions, and generally yielded more and better models compared to precipitation and Palmer Drought Severity Index. The use of variables such as streamflow with substantially longer and more complete records than those of groundwater wells provide a means for placing contemporary groundwater levels in a longer historical context and can support site-specific analyses such as groundwater modeling.

Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017

NASA Astrophysics Data System (ADS)

Revuelto, Jesús; Azorin-Molina, Cesar; Alonso-González, Esteban; Sanmiguel-Vallelado, Alba; Navarro-Serrano, Francisco; Rico, Ibai; López-Moreno, Juan Ignacio

2017-12-01

This work describes the snow and meteorological data set available for the Izas Experimental Catchment in the Central Spanish Pyrenees, from the 2011 to 2017 snow seasons. The experimental site is located on the southern side of the Pyrenees between 2000 and 2300 m above sea level, covering an area of 55 ha. The site is a good example of a subalpine environment in which the evolution of snow accumulation and melt are of major importance in many mountain processes. The climatic data set consists of (i) continuous meteorological variables acquired from an automatic weather station (AWS), (ii) detailed information on snow depth distribution collected with a terrestrial laser scanner (TLS, lidar technology) for certain dates across the snow season (between three and six TLS surveys per snow season) and (iii) time-lapse images showing the evolution of the snow-covered area (SCA). The meteorological variables acquired at the AWS are precipitation, air temperature, incoming and reflected solar radiation, infrared surface temperature, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface), and soil temperature; all were taken at 10 min intervals. Snow depth distribution was measured during 23 field campaigns using a TLS, and daily information on the SCA was also retrieved from time-lapse photography. The data set (https://doi.org/10.5281/zenodo.848277) is valuable since it provides high-spatial-resolution information on the snow depth and snow cover, which is particularly useful when combined with meteorological variables to simulate snow energy and mass balance. This information has already been analyzed in various scientific studies on snow pack dynamics and its interaction with the local climatology or topographical characteristics. However, the database generated has great potential for understanding other environmental processes from a hydrometeorological or ecological perspective in which snow dynamics play a determinant role.

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.

Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps)

NASA Astrophysics Data System (ADS)

Réveillet, Marion; Six, Delphine; Vincent, Christian; Rabatel, Antoine; Dumont, Marie; Lafaysse, Matthieu; Morin, Samuel; Vionnet, Vincent; Litt, Maxime

2018-04-01

This study focuses on simulations of the seasonal and annual surface mass balance (SMB) of Saint-Sorlin Glacier (French Alps) for the period 1996-2015 using the detailed SURFEX/ISBA-Crocus snowpack model. The model is forced by SAFRAN meteorological reanalysis data, adjusted with automatic weather station (AWS) measurements to ensure that simulations of all the energy balance components, in particular turbulent fluxes, are accurately represented with respect to the measured energy balance. Results indicate good model performance for the simulation of summer SMB when using meteorological forcing adjusted with in situ measurements. Model performance however strongly decreases without in situ meteorological measurements. The sensitivity of the model to meteorological forcing indicates a strong sensitivity to wind speed, higher than the sensitivity to ice albedo. Compared to an empirical approach, the model exhibited better performance for simulations of snow and firn melting in the accumulation area and similar performance in the ablation area when forced with meteorological data adjusted with nearby AWS measurements. When such measurements were not available close to the glacier, the empirical model performed better. Our results suggest that simulations of the evolution of future mass balance using an energy balance model require very accurate meteorological data. Given the uncertainties in the temporal evolution of the relevant meteorological variables and glacier surface properties in the future, empirical approaches based on temperature and precipitation could be more appropriate for simulations of glaciers in the future.

Interannual Variability of Ammonia Concentrations over the United States: Sources and Implications for Inorganic Particulate Matter

NASA Astrophysics Data System (ADS)

Schiferl, L. D.; Heald, C. L.; Van Damme, M.; Pierre-Francois, C.; Clerbaux, C.

2015-12-01

Modern agricultural practices have greatly increased the emission of ammonia (NH3) to the atmosphere. Recent controls to reduce the emissions of sulfur and nitrogen oxides (SOX and NOX) have increased the importance of understanding the role ammonia plays in the formation of surface fine inorganic particulate matter (PM2.5) in the United States. In this study, we identify the interannual variability in ammonia concentration, explore the sources of this variability and determine their contribution to the variability in surface PM2.5 concentration. Over the summers of 2008-2012, measurements from the Ammonia Monitoring Network (AMoN) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument show considerable variability in both surface and column ammonia concentrations (+/- 29% and 28% of the mean), respectively. This observed variability is larger than that simulated by the GEOS-Chem chemical transport model, where meteorology dominates the variability in ammonia and PM2.5 concentrations compared to the changes caused by SOX and NOX reductions. Our initial simulation does not include year-to-year changes in ammonia agricultural emissions. We use county-wide information on fertilizer sales and livestock populations, as well as meteorological variations to account for the interannual variability in agricultural activity and ammonia volatilization. These sources of ammonia emission variability are important for replicating observed variations in ammonia and PM2.5, highlighting how accurate ammonia emissions characterization is central to PM air quality prediction.

Interannual, seasonal and diurnal Mars surface environmental cycles observed from Viking to Curiosity

NASA Astrophysics Data System (ADS)

Martinez, German; Vicente-Retortillo, Álvaro; Kemppinen, Osku; Fischer, Erik; Fairen, Alberto G.; Guzewich, Scott David; Haberle, Robert; Lemmon, Mark T.; Newman, Claire E.; Renno, Nilton O.; Richardson, Mark I.; Smith, Michael D.; De la Torre, Manuel; Vasavada, Ashwin R.

2016-10-01

We analyze in-situ environmental data from the Viking landers to the Curiosity rover to estimate atmospheric pressure, near-surface air and ground temperature, relative humidity, wind speed and dust opacity with the highest confidence possible. We study the interannual, seasonal and diurnal variability of these quantities at the various landing sites over a span of more than twenty Martian years to characterize the climate on Mars and its variability. Additionally, we characterize the radiative environment at the various landing sites by estimating the daily UV irradiation (also called insolation and defined as the total amount of solar UV energy received on flat surface during one sol) and by analyzing its interannual and seasonal variability.In this study we use measurements conducted by the Viking Meteorology Instrument System (VMIS) and Viking lander camera onboard the Viking landers (VL); the Atmospheric Structure Instrument/Meteorology (ASIMET) package and the Imager for Mars Pathfinder (IMP) onboard the Mars Pathfinder (MPF) lander; the Miniature Thermal Emission Spectrometer (Mini-TES) and Pancam instruments onboard the Mars Exploration Rovers (MER); the Meteorological Station (MET), Thermal Electrical Conductivity Probe (TECP) and Phoenix Surface Stereo Imager (SSI) onboard the Phoenix (PHX) lander; and the Rover Environmental Monitoring Station (REMS) and Mastcam instrument onboard the Mars Science Laboratory (MSL) rover.A thorough analysis of in-situ environmental data from past and present missions is important to aid in the selection of the Mars 2020 landing site. We plan to extend our analysis of Mars surface environmental cycles by using upcoming data from the Temperature and Wind sensors (TWINS) instrument onboard the InSight mission and the Mars Environmental Dynamics Analyzer (MEDA) instrument onboard the Mars 2020 mission.

Modelling the variation of land surface temperature as determinant of risk of heat-related health events

PubMed Central

2011-01-01

Background The evaluation of exposure to ambient temperatures in epidemiological studies has generally been based on records from meteorological stations which may not adequately represent local temperature variability. Here we propose a spatially explicit model to estimate local exposure to temperatures of large populations under various meteorological conditions based on satellite and meteorological data. Methods A general linear model was used to estimate surface temperatures using 15 LANDSAT 5 and LANDSAT 7 images for Quebec Province, Canada between 1987 and 2002 and spanning the months of June to August. The images encompassed both rural and urban landscapes and predictors included: meteorological records of temperature and wind speed, distance to major water bodies, Normalized Differential Vegetation Index (NDVI), land cover (built and bare land, water, or vegetation), latitude, longitude, and week of the year. Results The model explained 77% of the variance in surface temperature, accounting for both temporal and spatial variations. The standard error of estimates was 1.42°C. Land cover and NDVI were strong predictors of surface temperature. Conclusions This study suggests that a statistical approach to estimating surface temperature incorporating both spatially explicit satellite data and time-varying meteorological data may be relevant to assessing exposure to heat during the warm season in the Quebec. By allowing the estimation of space- and time-specific surface temperatures, this model may also be used to assess the possible impacts of land use changes under various meteorological conditions. It can be applied to assess heat exposure within a large population and at relatively fine-grained scale. It may be used to evaluate the acute health effect of heat exposure over long time frames. The method proposed here could be replicated in other areas around the globe for which satellite data and meteorological data is available. PMID:21251286

The Calculation of the Heat Required for Wing Thermal Ice Prevention in Specified Icing Conditions

NASA Technical Reports Server (NTRS)

Bergrun, Norman R.; Jukoff, David; Schlaff, Bernard A.; Neel, Carr B., Jr.

1947-01-01

Flight tests were made in natural icing conditions with two 8-ft-chord heated airfoils of different sections. Measurements of meteorological variables conducive to ice formation were made simultaneously with the procurement of airfoil thermal data. The extent of knowledge on the meteorology of icing, the impingement of water drops on airfoil surfaces, and the processes of heat transfer and evaporation from a wetted airfoil surface have been increased to a point where the design of heated wings on a fundamental, wet-air basis now can be undertaken with reasonable certainty.

The impact of snow and glaciers on meteorological variables in the Khumbu Valley, Nepalese Himalaya.

NASA Astrophysics Data System (ADS)

Potter, E.; Orr, A.; Willis, I.

2017-12-01

Previous observational studies have suggested that snow and glaciers have a big impact on local meteorological variables in the Himalayas, in particular affecting near surface temperature and the localised wind system. Understanding the impact of changing surface conditions on these systems and is crucial in improving future predictions of glacier melt and precipitation in the Himalayas. However, the mechanisms that control the local meteorology remain poorly understood due to the lack of in-situ data and detailed modelling studies. To investigate these mechanisms, we run the Weather Research and Forecasting (WRF) model at kilometre scale resolution for one month during the monsoon over the Khumbu Valley, Nepalese Himalaya. The model is run with and without snow and glacier coverage at the surface. The impact of adding debris cover into the model is also investigated. In the control run with snow and ice, thermally-driven near-surface winds are found to travel up valley during the day except over the glacier slopes. When the snow and ice is removed from the model, the up valley winds extend over the entire slope. Removal of the snow and ice also results in changes to cloud cover and hydrometeors. A momentum budget approach is used to fully understand the mechanisms that maintain the localised wind system, e.g. to determine the contributions from local forcing or synoptic forcing.

The relationship between Arabian Sea upwelling and Indian monsoon revisited

NASA Astrophysics Data System (ADS)

Yi, X.; Hünicke, B.; Tim, N.; Zorita, E.

2015-11-01

Studies based on upwelling indices (sediment records, sea-surface temperature and wind) suggest that upwelling along the western coast of Arabian Sea is strongly affected by the Indian summer monsoon (ISM). In order to examine this relationship directly, we employ the vertical water mass transport produced by the eddy-resolving global ocean simulation STORM driven by meteorological reanalysis over the last 61 years. With its very high spatial resolution (10 km), STORM allows us to identify characteristics of the upwelling system. We analyze the co-variability between upwelling and meteorological and oceanic variables from 1950 to 2010. The analyses reveal high interannual correlations between coastal upwelling and along-shore wind-stress (r=0.73) as well as with sea-surface temperature (r0.83). However, the correlation between the upwelling and the ISM is small and other factors might contribute to the upwelling variability. In addition, no long-term trend is detected in our modeled upwelling time series.

A 60-year reconstructed high-resolution local meteorological data set in Central Sahel (1950-2009): evaluation, analysis and application to land surface modelling

NASA Astrophysics Data System (ADS)

Leauthaud, Crystele; Cappelaere, Bernard; Demarty, Jérôme; Guichard, Françoise; Velluet, Cécile; Kergoat, Laurent; Vischel, Théo; Grippa, Manuela; Mouhaimouni, Mohammed; Bouzou Moussa, Ibrahim; Mainassara, Ibrahim; Sultan, Benjamin

2017-04-01

The Sahel has experienced strong climate variability in the past decades. Understanding its implications for natural and cultivated ecosystems is pivotal in a context of high population growth and mainly agriculture-based livelihoods. However, efforts to model processes at the land-atmosphere interface are hindered, particularly when the multi-decadal timescale is targeted, as climatic data are scarce, largely incomplete and often unreliable. This study presents the generation of a long-term, high-temporal resolution, multivariate local climatic data set for Niamey, Central Sahel. The continuous series spans the period 1950-2009 at a 30-min timescale and includes ground station-based meteorological variables (precipitation, air temperature, relative and specific humidity, air pressure, wind speed, downwelling long- and short-wave radiation) as well as process-modelled surface fluxes (upwelling long- and short-wave radiation,latent, sensible and soil heat fluxes and surface temperature). A combination of complementary techniques (linear/spline regressions, a multivariate analogue method, artificial neural networks and recursive gap filling) was used to reconstruct missing meteorological data. The complete surface energy budget was then obtained for two dominant land cover types, fallow bush and millet, by applying the meteorological forcing data set to a finely field-calibrated land surface model. Uncertainty in reconstructed data was expressed by means of a stochastic ensemble of plausible historical time series. Climatological statistics were computed at sub-daily to decadal timescales and compared with local, regional and global data sets such as CRU and ERA-Interim. The reconstructed precipitation statistics, ˜1°C increase in mean annual temperature from 1950 to 2009, and mean diurnal and annual cycles for all variables were in good agreement with previous studies. The new data set, denoted NAD (Niamey Airport-derived set) and publicly available, can be used to investigate the water and energy cycles in Central Sahel, while the methodology can be applied to reconstruct series at other stations. The study has been published in Int. J. Climatol. (2016), DOI: 10.1002/joc.4874

Spatial clustering and meteorological drivers of summer ozone in Europe

NASA Astrophysics Data System (ADS)

Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

2017-10-01

We have applied the k-means clustering technique on a maximum daily 8-h running average near-surface ozone (MDA8 O3) gridded dataset over Europe at 1° × 1° resolution for summer 1998-2012. This has resulted in a spatial division of nine regions where ozone presents coherent spatiotemporal patterns. The role of meteorology in the variability of ozone at different time scales has been investigated by using daily meteorological fields from the NCEP-NCAR meteorological reanalysis. In the five regions of central-southern Europe ozone extremes (exceedances of the summer 95th percentile) occur mostly under anticyclonic circulation or weak sea level pressure gradients which trigger elevated temperatures and the recirculation of air masses. In the four northern regions extremes are associated with high-latitude anticyclones that divert the typical westerly flow at those latitudes and cause the advection of aged air masses from the south. The impact of meteorology on the day-to-day variability of ozone has been assessed by means of two different types of multiple linear models. These include as predictors meteorological fields averaged within the regions (;region-based; approach) or synoptic indices indicating the degree of resemblance between the daily meteorological fields over a large domain (25°-70° N, 35° W - 35° E) and their corresponding composites for extreme ozone days (;index-based; approach). With the first approach, a reduced set of variables, always including daily maximum temperature within the region, explains 47-66% of the variability (adjusted R2) in central-southern Europe, while more complex models are needed to explain 27-49% of the variability in the northern regions. The index-based approach yields better results for the regions of northern Europe, with adjusted R2 = 40-57%. Finally, both methodologies have also been applied to reproduce the interannual variability of ozone, with the best models explaining 66-88% of the variance in central-southern Europe and 45-66% in the north. Thus, the regionalisation carried out in this work has allowed establishing clear distinctions between the meteorological drivers of ozone in northern Europe and in the rest of the continent. These drivers are consistent across the different time scales examined (extremes, day-to-day and interannual), which gives confidence in the robustness of the results.

[Application of regression tree in analyzing the effects of climate factors on NDVI in loess hilly area of Shaanxi Province].

PubMed

Liu, Yang; Lü, Yi-he; Zheng, Hai-feng; Chen, Li-ding

2010-05-01

Based on the 10-day SPOT VEGETATION NDVI data and the daily meteorological data from 1998 to 2007 in Yan' an City, the main meteorological variables affecting the annual and interannual variations of NDVI were determined by using regression tree. It was found that the effects of test meteorological variables on the variability of NDVI differed with seasons and time lags. Temperature and precipitation were the most important meteorological variables affecting the annual variation of NDVI, and the average highest temperature was the most important meteorological variable affecting the inter-annual variation of NDVI. Regression tree was very powerful in determining the key meteorological variables affecting NDVI variation, but could not build quantitative relations between NDVI and meteorological variables, which limited its further and wider application.

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Probabilistic Forecasting of Surface Ozone with a Novel Statistical Approach

NASA Technical Reports Server (NTRS)

Balashov, Nikolay V.; Thompson, Anne M.; Young, George S.

2017-01-01

The recent change in the Environmental Protection Agency's surface ozone regulation, lowering the surface ozone daily maximum 8-h average (MDA8) exceedance threshold from 75 to 70 ppbv, poses significant challenges to U.S. air quality (AQ) forecasters responsible for ozone MDA8 forecasts. The forecasters, supplied by only a few AQ model products, end up relying heavily on self-developed tools. To help U.S. AQ forecasters, this study explores a surface ozone MDA8 forecasting tool that is based solely on statistical methods and standard meteorological variables from the numerical weather prediction (NWP) models. The model combines the self-organizing map (SOM), which is a clustering technique, with a step wise weighted quadratic regression using meteorological variables as predictors for ozone MDA8. The SOM method identifies different weather regimes, to distinguish between various modes of ozone variability, and groups them according to similarity. In this way, when a regression is developed for a specific regime, data from the other regimes are also used, with weights that are based on their similarity to this specific regime. This approach, regression in SOM (REGiS), yields a distinct model for each regime taking into account both the training cases for that regime and other similar training cases. To produce probabilistic MDA8 ozone forecasts, REGiS weighs and combines all of the developed regression models on the basis of the weather patterns predicted by an NWP model. REGiS is evaluated over the San Joaquin Valley in California and the northeastern plains of Colorado. The results suggest that the model performs best when trained and adjusted separately for an individual AQ station and its corresponding meteorological site.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Attribution of Trends and Variability in Surface Ozone over the United States

NASA Technical Reports Server (NTRS)

Strode, Sarah; Cooper, Owen; Damo, Megan; Logan, Jennifer; Rodriquez, Jose; Strahan, Susan; Witte, Jacquie

2013-01-01

Concentrations of tropospheric ozone, a greenhouse gas and air pollutant, are impacted by changes in precursor emissions as well meteorology and influx from the stratosphere. Observations show a decreasing trend in summertime surface ozone at rural stations in the eastern United States, while some western stations show increasing trends, particularly in springtime. We use the Global Modeling Initiative (GMI) global chemical transport model to investigate the roles of precursor emission changes, meteorological variability, and stratosphere-troposphere exchange (STE) in explaining observed trends in surface ozone from rural sites in the United States from 1991-2010. The model's interannual variability shows significant correlations with observations from many of the surface sites. We also compare the simulated ozone to ozonesonde data for several locations with sufficiently long records. We compare a simulation with time-dependent precursor emissions, including emission reductions over the United States and Europe and increases over Asia, to a simulation with fixed emissions to quantify the impact of changing emissions on the surface trends. The simulation with varying emissions reproduces much of the east-west difference in summertime ozone over the U.S., although it generally underestimates the negative trend in the East. In contrast, the fixed-emission simulation shows increasing ozone at both eastern and western sites. We will discuss possible causes of this behavior, including long-range transport and STE.

A one-year study of the diurnal cycle of meteorology, clouds and radiation in the West African Sahel region

DOE PAGES

Collow, Allison B.; Ghate, Virendra P.; Miller, Mark A.; ...

2015-09-09

Here, the diurnal cycles of meteorological and radiation variables are analysed during the wet and dry seasons over the Sahel region of West Africa during 2006 using surface data collected by the Atmospheric Radiation Measurement (ARM) programme's Mobile Facility, satellite radiation measurements from the Geostationary Earth Radiation Budget (GERB) instrument aboard Meteosat 8, and reanalysis products from the National Centers for Environmental Prediction (NCEP). The meteorological analysis builds upon past studies of the diurnal cycle in the region by incorporating diurnal cycles of lower tropospheric wind profiles, thermodynamic profiles, integrated water vapour and liquid water measurements, and cloud radar measurementsmore » of frequency and location. These meteorological measurements are complemented by 3 h measurements of the diurnal cycles of the top-of-atmosphere (TOA) and surface short-wave (SW) and long-wave (LW) radiative fluxes and cloud radiative effects (CREs), and the atmospheric radiative flux divergence (RFD) and atmospheric CREs. Cirrus cloudiness during the dry season is shown to peak in coverage in the afternoon, while convective clouds during the wet season are shown to peak near dawn and have an afternoon minimum related to the rise of the lifting condensation level into the Saharan Air Layer. The LW and SW RFDs and CREs exhibit diurnal cycles during both seasons, but there is a relatively small difference in the LW cycles during the two seasons (10 – 30 W m –2 depending on the variable and time of day). Small differences in the TOA CREs during the two seasons are overwhelmed by large differences in the surface SW CREs, which exceed 100 W m –2. A significant surface SW CRE during the wet season combined with a negligible TOA SW CRE produces a diurnal cycle in the atmospheric CRE that is modulated primarily by the SW surface CRE, peaks at midday at ~150 W m –2, and varies widely from day to day.« less

A One-Year Study of the Diurnal Cycle of Meteorology, Clouds, and Radiation in the West African Sahel Region

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

Marquardt-Collow, Allison; Ghate, Virendra P.; Miller, Mark A.

The diurnal cycles of meteorological and radiation variables are analyzed during the wet and dry seasons over the Sahel region of West Africa during 2006 using surface data collected by the Atmospheric Radiation Measurement (ARM) program’s Mobile Facility, satellite radiation measurements from the Geostationary Earth Radiation Budget (GERB) instrument aboard Meteosat 8, and reanalysis products from the National Center for Environmental Prediction (NCEP). The meteorological analysis builds upon past studies of the diurnal cycle in the region by incorporating diurnal cycles of lower tropospheric wind profiles, thermodynamic profiles, integrated water vapor and liquid water measurements, and cloud radar measurements ofmore » frequency and location. These meteorological measurements are complemented by 3-hour measurements of the diurnal cycles of the TOA and surface shortwave (SW) and longwave (LW) radiative fluxes and cloud radiative effects (CREs), and the atmospheric radiative flux divergence (RFD) and atmospheric CREs. Cirrus cloudiness during the dry season is shown to peak in coverage in the afternoon, while convective clouds during the wet season are shown to peak near dawn and have an afternoon minimum related to the rise of the Lifting Condensation Level into the Saharan Air Layer. The LW and SW RFDs and CREs exhibit diurnal cycles during both seasons, but there is a relatively small difference in the LW cycles during the two seasons (10-30 Wm^(-2) depending on the variable and time of day). Small differences in the TOA CREs during the two seasons are overwhelmed by large differences in the surface SW CREs, which exceed 100 Wm^(-2). A significant surface SW CRE during the wet season combined with a negligible TOA SW CRE produces a diurnal cycle in the atmospheric CRE that is modulated primarily by the SW surface CRE, peaks at midday at ~150 Wm^(-2), and varies widely from day to day.« less

A one-year study of the diurnal cycle of meteorology, clouds and radiation in the West African Sahel region

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

Collow, Allison B.; Ghate, Virendra P.; Miller, Mark A.

Here, the diurnal cycles of meteorological and radiation variables are analysed during the wet and dry seasons over the Sahel region of West Africa during 2006 using surface data collected by the Atmospheric Radiation Measurement (ARM) programme's Mobile Facility, satellite radiation measurements from the Geostationary Earth Radiation Budget (GERB) instrument aboard Meteosat 8, and reanalysis products from the National Centers for Environmental Prediction (NCEP). The meteorological analysis builds upon past studies of the diurnal cycle in the region by incorporating diurnal cycles of lower tropospheric wind profiles, thermodynamic profiles, integrated water vapour and liquid water measurements, and cloud radar measurementsmore » of frequency and location. These meteorological measurements are complemented by 3 h measurements of the diurnal cycles of the top-of-atmosphere (TOA) and surface short-wave (SW) and long-wave (LW) radiative fluxes and cloud radiative effects (CREs), and the atmospheric radiative flux divergence (RFD) and atmospheric CREs. Cirrus cloudiness during the dry season is shown to peak in coverage in the afternoon, while convective clouds during the wet season are shown to peak near dawn and have an afternoon minimum related to the rise of the lifting condensation level into the Saharan Air Layer. The LW and SW RFDs and CREs exhibit diurnal cycles during both seasons, but there is a relatively small difference in the LW cycles during the two seasons (10 – 30 W m –2 depending on the variable and time of day). Small differences in the TOA CREs during the two seasons are overwhelmed by large differences in the surface SW CREs, which exceed 100 W m –2. A significant surface SW CRE during the wet season combined with a negligible TOA SW CRE produces a diurnal cycle in the atmospheric CRE that is modulated primarily by the SW surface CRE, peaks at midday at ~150 W m –2, and varies widely from day to day.« less

The Southern Hemisphere lower stratosphere during August and September 1987 - Analyses based on the United Kingdom Meteorological Office Global Model

NASA Technical Reports Server (NTRS)

Mckenna, D. S.; Jones, R. L.; Buckland, A. T.; Austin, J.; Tuck, A. F.; Winkler, R. H.; Chan, K. R.

1989-01-01

This paper presents a series of meteorological analyses used to aid the interpretation of the in situ Airborne Antarctic Ozone Experiment (AAOE) observations obtained aboard the ER-2 and DC-8 aircraft and examines the basis and accuracy of the analytical procedure. Maps and sections of meteorological variables derived from the UK Meteorological Office Global Model are presented for ER-2 and DC-8 flight days. It is found that analyzed temperatures and winds are generally in good agreement with AAOE observations at all levels; minor discrepancies were evident only at DC-8 altitudes. Maps of potential vorticity presented on the 428-K potential temperature surface show that the vortex is essentially circumpolar, although there are periods when major distortions are apparent.

Selection of meteorological conditions to apply in an Ecotron facility

NASA Astrophysics Data System (ADS)

Leemans, Vincent; De Cruz, Lesley; Dumont, Benjamin; Hamdi, Rafiq; Delaplace, Pierre; Heinesh, Bernard; Garré, Sarah; Verheggen, François; Theodorakopoulos, Nicolas; Longdoz, Bernard

2017-04-01

This presentation aims to propose a generic method to produce meteorological input data that is useful for climate research infrastructures such as an Ecotron, where researchers will face the need to generate representative actual or future climatic conditions. Depending on the experimental objectives and the research purposes, typical conditions or more extreme values such as dry or wet climatic scenarios might be requested. Four variables were considered here, the near-surface air temperature, the near-surface relative humidity, the cloud cover and precipitation. The meteorological datasets, among which a specific meteorological year can be picked up, are produced by the ALARO-0 model from the RMIB (Royal Meteorological Institute of Belgium). Two future climate scenarios (RCP 4.5 and 8.5) and two time periods (2041-2070 and 2071-2100) were used as well as a historical run of the model (1981-2010) which is used as a reference. When the data from a historical run were compared to the observed historical data, biases were noticed. A linear correction was proposed for all the variables except for precipitation, for which a non-linear correction (using a power function) was chosen to maintain a zero-precipitation occurrences. These transformations were able to remove most of the differences between the observed and historical run of the model for the means and for the standard deviations. For the relative humidity, because of non-linearities, only one half of the average bias was corrected and a different path might have to be chosen. For the selection of a meteorological year, a position and a dispersion parameter have been proposed to characterise each meteorological year for each variable. For precipitation, a third parameter quantifying the importance of dry and wet periods has been defined. In order to select a specific climate, for each of these nine parameters the experimenter should provide a percentile and a weight to prioritize the importance of each variable in the process of a global climate selection. The proposed algorithm computed the weighted distance for each year between the parameters and the point representing the position of the percentile in the nine-dimensional space. The five closest values were then selected and represented in different graphs. The proposed method is able to provide a decision aid in the selection of the meteorological conditions to be generated within an Ecotron. However, with a limited number of years available in each case (thirty years for each RCP and each time period), there is no perfect match and the ultimate trade-off will be the responsibility of the researcher. For typical years, close to the median, the relative frequency is higher and the trade-off is more easy than for more extreme years where the relative frequency is low.

Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data

PubMed Central

Adde, Antoine; Roux, Emmanuel; Mangeas, Morgan; Dessay, Nadine; Nacher, Mathieu; Dusfour, Isabelle; Girod, Romain; Briolant, Sébastien

2016-01-01

Local variation in the density of Anopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. Vector distribution is driven by environmental factors. Based on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of Anopheles darlingi in the municipality of Saint-Georges de l’Oyapock (French Guiana). Longitudinal sampling sessions of An. darlingi densities were conducted between September 2012 and October 2014. Landscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to An. darlingi ecology. Based on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of An. darlingi in Saint-Georges de l’Oyapock. The final cross-validated model integrated two landscape variables—dense forest surface and built surface—together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. Extrapolation of the model allowed the generation of predictive weekly maps of An. darlingi densities at a resolution of 10-m. Our results supported the use of satellite imagery and meteorological data to predict malaria vector densities. Such fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner. PMID:27749938

Distinct wind convergence patterns in the Mexico City basin due to the interaction of the gap winds with the synoptic flow

NASA Astrophysics Data System (ADS)

de Foy, B.; Clappier, A.; Molina, L. T.; Molina, M. J.

2006-04-01

Mexico City lies in a high altitude basin where air quality and pollutant fate is strongly influenced by local winds. The combination of high terrain with weak synoptic forcing leads to weak and variable winds with complex circulation patterns. A gap wind entering the basin in the afternoon leads to very different wind convergence lines over the city depending on the meteorological conditions. Surface and upper-air meteorological observations are analysed during the MCMA-2003 field campaign to establish the meteorological conditions and obtain an index of the strength and timing of the gap wind. A mesoscale meteorological model (MM5) is used in combination with high-resolution satellite data for the land surface parameters and soil moisture maps derived from diurnal ground temperature range. A simple method to map the lines of wind convergence both in the basin and on the regional scale is used to show the different convergence patterns according to episode types. The gap wind is found to occur on most days of the campaign and is the result of a temperature gradient across the southern basin rim which is very similar from day to day. Momentum mixing from winds aloft into the surface layer is much more variable and can determine both the strength of the flow and the pattern of the convergence zones. Northerly flows aloft lead to a weak jet with an east-west convergence line that progresses northwards in the late afternoon and early evening. Westerlies aloft lead to both stronger gap flows due to channelling and winds over the southern and western basin rim. This results in a north-south convergence line through the middle of the basin starting in the early afternoon. Improved understanding of basin meteorology will lead to better air quality forecasts for the city and better understanding of the chemical regimes in the urban atmosphere.

A new approach used to explore associations of current Ambrosia pollen levels with current and past meteorological elements

NASA Astrophysics Data System (ADS)

Matyasovszky, István; Makra, László; Csépe, Zoltán; Deák, Áron József; Pál-Molnár, Elemér; Fülöp, Andrea; Tusnády, Gábor

2015-09-01

The paper examines the sensitivity of daily airborne Ambrosia (ragweed) pollen levels of a current pollen season not only on daily values of meteorological variables during this season but also on the past meteorological conditions. The results obtained from a 19-year data set including daily ragweed pollen counts and ten daily meteorological variables are evaluated with special focus on the interactions between the phyto-physiological processes and the meteorological elements. Instead of a Pearson correlation measuring the strength of the linear relationship between two random variables, a generalised correlation that measures every kind of relationship between random vectors was used. These latter correlations between arrays of daily values of the ten meteorological elements and the array of daily ragweed pollen concentrations during the current pollen season were calculated. For the current pollen season, the six most important variables are two temperature variables (mean and minimum temperatures), two humidity variables (dew point depression and rainfall) and two variables characterising the mixing of the air (wind speed and the height of the planetary boundary layer). The six most important meteorological variables before the current pollen season contain four temperature variables (mean, maximum, minimum temperatures and soil temperature) and two variables that characterise large-scale weather patterns (sea level pressure and the height of the planetary boundary layer). Key periods of the past meteorological variables before the current pollen season have been identified. The importance of this kind of analysis is that a knowledge of the past meteorological conditions may contribute to a better prediction of the upcoming pollen season.

A new approach used to explore associations of current Ambrosia pollen levels with current and past meteorological elements.

PubMed

Matyasovszky, István; Makra, László; Csépe, Zoltán; Deák, Áron József; Pál-Molnár, Elemér; Fülöp, Andrea; Tusnády, Gábor

2015-09-01

The paper examines the sensitivity of daily airborne Ambrosia (ragweed) pollen levels of a current pollen season not only on daily values of meteorological variables during this season but also on the past meteorological conditions. The results obtained from a 19-year data set including daily ragweed pollen counts and ten daily meteorological variables are evaluated with special focus on the interactions between the phyto-physiological processes and the meteorological elements. Instead of a Pearson correlation measuring the strength of the linear relationship between two random variables, a generalised correlation that measures every kind of relationship between random vectors was used. These latter correlations between arrays of daily values of the ten meteorological elements and the array of daily ragweed pollen concentrations during the current pollen season were calculated. For the current pollen season, the six most important variables are two temperature variables (mean and minimum temperatures), two humidity variables (dew point depression and rainfall) and two variables characterising the mixing of the air (wind speed and the height of the planetary boundary layer). The six most important meteorological variables before the current pollen season contain four temperature variables (mean, maximum, minimum temperatures and soil temperature) and two variables that characterise large-scale weather patterns (sea level pressure and the height of the planetary boundary layer). Key periods of the past meteorological variables before the current pollen season have been identified. The importance of this kind of analysis is that a knowledge of the past meteorological conditions may contribute to a better prediction of the upcoming pollen season.

Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate

PubMed Central

Hay, S. I.; Lennon, J. J.

2012-01-01

Summary This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration’s (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) on-board the European Meteorological Satellite programme’s (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study and control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy. PMID:10203175

Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate.

PubMed

Hay, S I; Lennon, J J

1999-01-01

This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) on-board the European Meteorological Satellite programme's (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study and control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy.

Decadal variations in atmospheric water vapor time series estimated using GNSS, ERA-Interim, and synoptic data

NASA Astrophysics Data System (ADS)

Alshawaf, Fadwa; Dick, Galina; Heise, Stefan; Balidakis, Kyriakos; Schmidt, Torsten; Wickert, Jens

2017-04-01

Ground-based GNSS (Global Navigation Satellite Systems) have efficiently been used since the 1990s as a meteorological observing system. Recently scientists used GNSS time series of precipitable water vapor (PWV) for climate research although they may not be sufficiently long. In this work, we compare the trend estimated from GNSS time series with that estimated from European Center for Medium-RangeWeather Forecasts Reanalysis (ERA-Interim) data and meteorological measurements.We aim at evaluating climate evolution in Central Europe by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: 1) estimated from ground-based GNSS observations using the method of precise point positioning, 2) inferred from ERA-Interim data, and 3) determined based on daily surface measurements of temperature and relative humidity. The other variables are available from surface meteorological stations or received from ERA-Interim. The PWV trend component estimated from GNSS data strongly correlates (>70%) with that estimated from the other data sets. The linear trend is estimated by straight line fitting over 30 years of seasonally-adjusted PWV time series obtained using the meteorological measurements. The results show a positive trend in the PWV time series with an increase of 0.2-0.7 mm/decade with a mean standard deviations of 0.016 mm/decade. In this paper, we present the results at three GNSS stations. The temporal increment of the PWV correlates with the temporal increase in the temperature levels.

Diurnal centroid of ecosystem energy and carbon fluxes at FLUXNET sites

Treesearch

Kell B. Wilson; Dennis Baldocchi; Eva Falge; Marc Aubinet; Paul Berbigier; Christian Bernhofer; Han Dolman; Chris Field; Allen Goldstein; Andre Granier; Dave Hollinger; Gabriel Katul; B.E. Law; Tilden Meyers; John Moncrieff; Russ Monson; John Tenhunen; Riccardo Valentini; Shashi Verma; Steve Wofsy

2003-01-01

Data from a network of eddy covariance stations in Europe and North America (FLUXNET) were analyzed to examine the diurnal patterns of surface energy and carbon fluxes during the summer period across a range of ecosystems and climates. Diurnal trends were quantified by assessing the time of day surface fluxes and meteorological variable reached peak values, using the...

Understanding the spatial complexity of surface hoar from slope to range scale

NASA Astrophysics Data System (ADS)

Hendrikx, J.

2015-12-01

Surface hoar, once buried, is a common weak layer type in avalanche accidents in continental and intermountain snowpacks around the World. Despite this, there is still limited understanding of the spatial variability in both the formation of, and eventual burial of, surface hoar at spatial scales which are of critical importance to avalanche forecasters. While it is relatively well understood that aspect plays an important role in the spatial location of the formation, and burial of these grain forms, due to the unequal distribution of incoming radiation, this factor alone does not explain the complex and often confusing spatial pattern of these grains forms throughout the landscape at different spatial scales. In this paper we present additional data from a unique data set including over two hundred days of manual observations of surface hoar at sixteen locations on Pioneer Mountain at the Yellowstone Club in southwestern Montana. Using this wealth of observational data located on different aspects, elevations and exposures, coupled with detailed meteorological observations, and detailed slope scale observation, we examine the spatial variability of surface hoar at this scale, and examine the factors that control its spatial distribution. Our results further supports our preliminary work, which shows that small-scale slope conditions, meteorological differences, and local scale lapse rates, can greatly influence the spatial variability of surface hoar, over and above that which aspect alone can explain. These results highlight our incomplete understanding of the processes at both the slope and range scale, and are likely to have implications for both regional and local scale avalanche forecasting in environments where surface hoar cause ongoing instabilities.

The Modern Near-Surface Martian Climate: A Review of In-Situ Meteorological Data from Viking to Curiosity

NASA Technical Reports Server (NTRS)

Martinez, G. M.; Newman, C. N.; De Vicente-Retortillo, A.; Fischer, E.; Renno, N. O.; Richardson, M. I.; Fairén, A. G.; Genzer, M.; Guzewich, S. D.; Haberle, R. M.;

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.

Spatial clustering and meteorological drivers of summer ozone in Europe

NASA Astrophysics Data System (ADS)

Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

2017-04-01

We present a regionalization of summer near-surface ozone (O3) in Europe. For this purpose we apply a K-means algorithm on a gridded MDA8 O3 (maximum daily average 8-h ozone) dataset covering a European domain [15° W - 30° E, 35°-70° N] at 1° x 1° horizontal resolution for the 1998-2012 period. This dataset was compiled by merging observations from the European Monitoring and Evaluation Programme (EMEP) and the European Environment Agency's air quality database (AirBase). The K-means method allows identifying sets of different regions where the O3 concentrations present coherent spatiotemporal patterns and are thus expected to be driven by similar meteorological factors. After some testing, 9 regions were selected: the British Isles, North-Central Europe, Northern Scandinavia, the Baltic countries, the Iberian Peninsula, Western Europe, South-Central Europe, Eastern Europe and the Balkans. For each region we examine the synoptic situations associated with elevated ozone extremes (days exceeding the 95th percentile of the summer MDA8 O3 distribution). Our analyses reveal that there are basically two different kinds of regions in Europe: (a) those in the centre and south of the continent where ozone extremes are associated with elevated temperature within the same region and (b) those in northern Europe where ozone extremes are driven by southerly advection of air masses from warmer, more polluted areas. Even when the observed patterns were initially identified only for days registering high O3 extremes, all summer days can be projected on such patterns to identify the main modes of meteorological variability of O3. We have found that such modes are partly responsible for the day-to-day variability in the O3 concentrations and can explain a relatively large fraction (from 44 to 88 %, depending on the region) of the interannual variability of summer mean MDA8 O3 during the period of analysis. On the other hand, some major teleconnection patterns have been tested but do not seem to exert a large impact on the variability of surface O3 over most regions. The identification of these independent regions where surface ozone presents a coherent behaviour and responds similarly to specific meteorological modes of variability has multiple applications. For instance, the performance of chemical transport models (CTMs) and chemistry-climate models (CCMs) can be separately assessed over such regions to identify areas where they present large biases that need to be corrected. Our results can also be used to test the models' sensitivity to the day-to-day changing meteorology and to climate change over specific regions.

Influence of weather and climate on subjective symptom intensity in atopic eczema

NASA Astrophysics Data System (ADS)

Vocks, E.; Busch, R.; Fröhlich, C.; Borelli, S.; Mayer, H.; Ring, J.

The frequent clinical observation that the course of atopic eczema, a skin disease involving a disturbed cutaneous barrier function, is influenced by climate and weather motivated us to analyse these relationships biometrically. In the Swiss high-mountain area of Davos the intensity of itching experienced by patients with atopic eczema was evaluated and compared to 15 single meteorological variables recorded daily during an entire 7-year observation period. By means of univariate analyses and multiple regressions, itch intensity was found to be correlated with some meteorological variables. A clear-cut inverse correlation exists with air temperature (coefficient of correlation: -0.235, P<0.001), but the effects of water vapour pressure, air pressure and hours of sunshine are less pronounced. The results show that itching in atopic eczema is significantly dependent on meteorological conditions. The data suggest that, in patients with atopic eczema, a certain range of thermo-hygric atmospheric conditions with a balance of heat and water loss on the skin surface is essential for the skin to feel comfortable.

Contribution of Modis Satellite Image to Estimate the Daily Air Temperature in the Casablanca City, Morocco

NASA Astrophysics Data System (ADS)

Bahi, Hicham; Rhinane, Hassan; Bensalmia, Ahmed

2016-10-01

Air temperature is considered to be an essential variable for the study and analysis of meteorological regimes and chronics. However, the implementation of a daily monitoring of this variable is very difficult to achieve. It requires sufficient of measurements stations density, meteorological parks and favourable logistics. The present work aims to establish relationship between day and night land surface temperatures from MODIS data and the daily measurements of air temperature acquired between [2011-20112] and provided by the Department of National Meteorology [DMN] of Casablanca, Morocco. The results of the statistical analysis show significant interdependence during night observations with correlation coefficient of R2=0.921 and Root Mean Square Error RMSE=1.503 for Tmin while the physical magnitude estimated from daytime MODIS observation shows a relatively coarse error with R2=0.775 and RMSE=2.037 for Tmax. A method based on Gaussian process regression was applied to compute the spatial distribution of air temperature from MODIS throughout the city of Casablanca.

Change in diurnal variations of meteorological variables induced by anthropogenic aerosols over the North China Plain in summer 2008

NASA Astrophysics Data System (ADS)

Gao, Yi; Zhang, Meigen; Liu, Xiaohong; Wang, Lili

2016-04-01

This study investigates the impacts of all anthropogenic aerosols and anthropogenic black carbon (BC) on the diurnal variations of meteorological variables in the atmospheric boundary layer over the North China Plain (NCP) during June to August 2008, using a coupled meteorology and chemistry model (WRF-Chem). The results of the ensemble numerical experiments show that surface air temperature decreases by about 0.6 to 1.2 K with the maximum decrease over the Beijing urban area and the southern part of Hebei province, and the surface relative humidity (RH) increases by 2-4 % owing to all anthropogenic aerosols. On the contrary, anthropogenic BC induces a small change of temperature and RH at surface. Averaged for Beijing, Tianjin, and Hebei province (BTH region) and High Particle Concentration (HPC) periods when PM2.5 surface concentration is more than 60 μg m-3 and daily AOD is more than 0.9, all anthropogenic aerosols decrease air temperature under 850 hPa and increase it between 500 and 850 hPa, while anthropogenic BC increases it for whole atmosphere. The maximum changes occur at 08:00-20:00 (local time). Aerosol-induced surface energy and diabatic heating change leads to a cooling at the surface and in the lower atmosphere and a warming in the middle troposphere at 08:00-17:00, with reversed effects at 20:00-05:00. BC cools the atmosphere at the surface and warms the atmosphere above for the whole day. As a result, the equivalent potential temperature profile change shows that the lower atmosphere is more stable at 08:00 and 14:00. All anthropogenic aerosols decrease the surface wind speed by 20-60 %, while anthropogenic BC decreases the wind speed by 10-40 % over the NCP with the maximum decrease at 08:00. The aerosol-induced stabilization of the lower atmosphere favors the accumulation of air pollutants and thus contributes to deterioration of visibility and fog-haze events.

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

NASA Astrophysics Data System (ADS)

Sommer, Philipp S.; Kaplan, Jed O.

2017-10-01

While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.

Air Quality and Meteorological Boundary Conditions during the MCMA-2003 Field Campaign

NASA Astrophysics Data System (ADS)

Sosa, G.; Arriaga, J.; Vega, E.; Magaña, V.; Caetano, E.; de Foy, B.; Molina, L. T.; Molina, M. J.; Ramos, R.; Retama, A.; Zaragoza, J.; Martínez, A. P.; Márquez, C.; Cárdenas, B.; Lamb, B.; Velasco, E.; Allwine, E.; Pressley, S.; Westberg, H.; Reyes, R.

2004-12-01

A comprehensive field campaign to characterize photochemical smog in the Mexico City Metropolitan Area (MCMA) was conducted during April 2003. An important number of equipment was deployed all around the urban core and its surroundings to measure gas and particles composition from the various sources and receptor sites. In addition to air quality measurements, meteorology variables were also taken by regular weather meteorological stations, tethered balloons, radiosondes, sodars and lidars. One important issue with regard to the field campaign was the characterization of the boundary conditions in order to feed meteorological and air quality models. Four boundary sites were selected to measure continuously criteria pollutants, VOC and meteorological variables at surface level. Vertical meteorological profiles were measured at three other sites : radiosondes in Tacubaya site were launched every six hours daily; tethered balloons were launched at CENICA and FES-Cuautitlan sites according to the weather conditions, and one sodar was deployed at UNAM site in the south of the city. Additionally to these measurements, two fixed meteorological monitoring networks deployed along the city were available to complement these measurements. In general, we observed that transport of pollutants from the city to the boundary sites changes every day, according to the coupling between synoptic and local winds. This effect were less important at elevated sites such as Cerro de la Catedral and ININ, where synoptic wind were more dominant during the field campaign. Also, local sources nearby boundary sites hide the influence of pollution coming from the city some days, particularly at the La Reforma site.

Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis

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

Vuichard, N.; Papale, D.

In this study, exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robustmore » method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.« less

Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis

DOE PAGES

Vuichard, N.; Papale, D.

2015-07-13

In this study, exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robustmore » method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.« less

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: a case study.

PubMed

Escarela, Gabriel

2012-06-01

The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.

APPLICATION OF BIAS AND ADJUSTMENT TECHNIQUES TO THE ETA-CMAQ AIR QUALITY FORECAST

EPA Science Inventory

The current air quality forecast system, based on linking NOAA's Eta meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, consistently overpredicts surface ozone concentrations, but simulates its day-to-day variability quite well. The ability of bias cor...

Sensitivity of the interannual variability of mineral aerosol simulations to meteorological forcing dataset

DOE PAGES

Smith, Molly B.; Mahowald, Natalie M.; Albani, Samuel; ...

2017-03-07

Interannual variability in desert dust is widely observed and simulated, yet the sensitivity of these desert dust simulations to a particular meteorological dataset, as well as a particular model construction, is not well known. Here we use version 4 of the Community Atmospheric Model (CAM4) with the Community Earth System Model (CESM) to simulate dust forced by three different reanalysis meteorological datasets for the period 1990–2005. We then contrast the results of these simulations with dust simulated using online winds dynamically generated from sea surface temperatures, as well as with simulations conducted using other modeling frameworks but the same meteorological forcings, in order tomore » determine the sensitivity of climate model output to the specific reanalysis dataset used. For the seven cases considered in our study, the different model configurations are able to simulate the annual mean of the global dust cycle, seasonality and interannual variability approximately equally well (or poorly) at the limited observational sites available. Altogether, aerosol dust-source strength has remained fairly constant during the time period from 1990 to 2005, although there is strong seasonal and some interannual variability simulated in the models and seen in the observations over this time period. Model interannual variability comparisons to observations, as well as comparisons between models, suggest that interannual variability in dust is still difficult to simulate accurately, with averaged correlation coefficients of 0.1 to 0.6. Because of the large variability, at least 1 year of observations at most sites are needed to correctly observe the mean, but in some regions, particularly the remote oceans of the Southern Hemisphere, where interannual variability may be larger than in the Northern Hemisphere, 2–3 years of data are likely to be needed.« less

Meteorological Contribution to Variability in Particulate Matter Concentrations

NASA Astrophysics Data System (ADS)

Woods, H. L.; Spak, S. N.; Holloway, T.

2006-12-01

Local concentrations of fine particulate matter (PM) are driven by a number of processes, including emissions of aerosols and gaseous precursors, atmospheric chemistry, and meteorology at local, regional, and global scales. We apply statistical downscaling methods, typically used for regional climate analysis, to estimate the contribution of regional scale meteorology to PM mass concentration variability at a range of sites in the Upper Midwestern U.S. Multiple years of daily PM10 and PM2.5 data, reported by the U.S. Environmental Protection Agency (EPA), are correlated with large-scale meteorology over the region from the National Centers for Environmental Prediction (NCEP) reanalysis data. We use two statistical downscaling methods (multiple linear regression, MLR, and analog) to identify which processes have the greatest impact on aerosol concentration variability. Empirical Orthogonal Functions of the NCEP meteorological data are correlated with PM timeseries at measurement sites. We examine which meteorological variables exert the greatest influence on PM variability, and which sites exhibit the greatest response to regional meteorology. To evaluate model performance, measurement data are withheld for limited periods, and compared with model results. Preliminary results suggest that regional meteorological processes account over 50% of aerosol concentration variability at study sites.

CentNet—A deployable 100-station network for surface exchange research

NASA Astrophysics Data System (ADS)

Oncley, S.; Horst, T. W.; Semmer, S.; Militzer, J.; Maclean, G.; Knudson, K.

2014-12-01

Climate, air quality, atmospheric composition, surface hydrology, and ecological processes are directly affected by the Earth's surface. Complexity of this surface exists at multiple spatial scales, which complicates the understanding of these processes. NCAR/EOL currently provides a facility to the research community to make direct eddy-covariance flux observations to quantify surface-atmosphere interactions. However, just as model resolution has continued to increase, there is a need to increase the spatial density of flux measurements to capture the wide variety of scales that contribute to exchange processes close to the surface. NCAR/EOL now has developed the CentNet facility, that is envisioned to have on the order of 100 surface flux stations deployable for periods of months to years. Each station would measure standard meteorological variables, all components of the surface energy balance (including turbulence fluxes and radiation), atmospheric composition, and other quantities to characterize the surface. Thus, CentNet can support observational research in the biogeosciences, hydrology, urban meteorology, basic meteorology, and turbulence. CentNet has been designed to be adaptable to a wide variety of research problems while keeping operations manageable. Tower infrastructure has been designed to be lightweight, easily deployed, and with a minimal set-up footprint. CentNet uses sensor networks to increase spatial sampling at each station. The data system saves every sample on site to retain flexibility in data analysis. We welcome guidance on development and funding priorities as we build CentNet.

Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 1: Model description and evaluation of meteorological predictions

NASA Astrophysics Data System (ADS)

Zhang, Y.; Sartelet, K.; Wu, S.-Y.; Seigneur, C.

2013-02-01

Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e. the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID) are conducted over western Europe. Part 1 describes the background information for the model comparison and simulation design, as well as the application of WRF for January and July 2001 over triple-nested domains in western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°. Six simulated meteorological variables (i.e. temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients of major meteorological variables. While the domainwide performance of T2, Q2, RH2, and WD10 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in WS10 and Precip even at 0.025°. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and subgrid-scale meteorological phenomena, due to inaccuracies in model initialization parameterization (e.g. lack of soil temperature and moisture nudging), limitations in the physical parameterizations of the planetary boundary layer (e.g. cloud microphysics, cumulus parameterizations, and ice nucleation treatments) as well as limitations in surface heat and moisture budget parameterizations (e.g. snow-related processes, subgrid-scale surface roughness elements, and urban canopy/heat island treatments and CO2 domes). While the use of finer grid resolutions of 0.125° and 0.025° shows some improvement for WS10, Precip, and some mesoscale events (e.g. strong forced convection and heavy precipitation), it does not significantly improve the overall statistical performance for all meteorological variables except for Precip. These results indicate a need to further improve the model representations of the above parameterizations at all scales.

San Antonio Mountain Experiment (SAMEX).

NASA Astrophysics Data System (ADS)

McCutchan, Morris H.; Fox, Douglas G.; Furman, R. William

1982-10-01

The San Antonio Mountain Experiment (SAMEX) involves a 3325 m. conically shaped, isolated mountain in north-central New Mexico where hourly observations of temperature, relative humidity, wind speed, wind direction, and precipitation are being taken at nine locations over a three- to five-year period that began in 1980. The experiment is designed to isolate the effect of topography on these meteorological variables by using a geometric configuration sufficiently simple to lead to generalized results. One remote automatic weather station (RAWS) is located at the peak (3322 m); four are located at midslope (3033 m) on southwest, southeast, northeast, and northwest aspects; and four are at the base (2743 m) on southwest, southeast, northeast, and northwest aspects. The surface observations are supplemented by rawinsonde, pibal, tethersonde, and constant-level balloon observations at selected times during each year. The unique set of meteorological data collected in the experiment will be used to 1) determine the effect of elevation and aspect on the meteorological variables; 2) compare the temperature, humidity, and wind components on the mountain with observations and/or predictions of these variables in the free air nearby; and 3) validate temperature, humidity, and wind models in complex terrain.

Chemistry Simulations Using MERRA-2 Reanalysis with the GMI CTM and Replay in Support of the Atmospheric Composition Community

NASA Technical Reports Server (NTRS)

Oman, Luke D.; Strahan, Susan E.

2016-01-01

Simulations using reanalyzed meteorological conditions have been long used to understand causes of atmospheric composition change over the recent past. Using the new Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorology, chemistry simulations are being conducted to create products covering 1980-2016 for the atmospheric composition community. These simulations use the Global Modeling Initiative (GMI) chemical mechanism in two different models: the GMI Chemical Transport Model (CTM) and the GEOS-5 model developed Replay mode. Replay mode means an integration of the GEOS-5 general circulation model that is incrementally adjusted each time step toward the MERRA-2 analysis. The GMI CTM is a 1 x 1.25 simulation and the MERRA-2 GMI Replay simulation uses the native MERRA-2 approximately horizontal resolution on the cubed sphere. The Replay simulations is driven by the online use of key MERRA-2 meteorological variables (i.e. U, V, T, and surface pressure) with all other variables calculated in response to those variables. A specialized set of transport diagnostics is included in both runs to better understand trace gas transport and changes over the recent past.

An advanced stochastic weather generator for simulating 2-D high-resolution climate variables

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo

2017-07-01

A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications.

Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe - Part 1: Model description, evaluation of meteorological predictions, and aerosol-meteorology interactions

NASA Astrophysics Data System (ADS)

Zhang, Y.; Sartelet, K.; Wu, S.-Y.; Seigneur, C.

2013-07-01

Comprehensive model evaluation and comparison of two 3-D air quality modeling systems (i.e., the Weather Research and Forecast model (WRF)/Polyphemus and WRF with chemistry and the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (WRF/Chem-MADRID)) are conducted over Western Europe. Part 1 describes the background information for the model comparison and simulation design, the application of WRF for January and July 2001 over triple-nested domains in Western Europe at three horizontal grid resolutions: 0.5°, 0.125°, and 0.025°, and the effect of aerosol/meteorology interactions on meteorological predictions. Nine simulated meteorological variables (i.e., downward shortwave and longwave radiation fluxes (SWDOWN and LWDOWN), outgoing longwave radiation flux (OLR), temperature at 2 m (T2), specific humidity at 2 m (Q2), relative humidity at 2 m (RH2), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and precipitation (Precip)) are evaluated using available observations in terms of spatial distribution, domainwide daily and site-specific hourly variations, and domainwide performance statistics. The vertical profiles of temperature, dew points, and wind speed/direction are also evaluated using sounding data. WRF demonstrates its capability in capturing diurnal/seasonal variations and spatial gradients and vertical profiles of major meteorological variables. While the domainwide performance of LWDOWN, OLR, T2, Q2, and RH2 at all three grid resolutions is satisfactory overall, large positive or negative biases occur in SWDOWN, WS10, and Precip even at 0.125° or 0.025° in both months and in WD10 in January. In addition, discrepancies between simulations and observations exist in T2, Q2, WS10, and Precip at mountain/high altitude sites and large urban center sites in both months, in particular, during snow events or thunderstorms. These results indicate the model's difficulty in capturing meteorological variables in complex terrain and subgrid-scale meteorological phenomena, due to inaccuracies in model initialization parameterization (e.g., lack of soil temperature and moisture nudging), limitations in the physical parameterizations (e.g., shortwave radiation, cloud microphysics, cumulus parameterizations, and ice nucleation treatments) as well as limitations in surface heat and moisture budget parameterizations (e.g., snow-related processes, subgrid-scale surface roughness elements, and urban canopy/heat island treatments and CO2 domes). While the use of finer grid resolutions of 0.125° and 0.025° shows some improvements for WS10, WD10, Precip, and some mesoscale events (e.g., strong forced convection and heavy precipitation), it does not significantly improve the overall statistical performance for all meteorological variables except for Precip. The WRF/Chem simulations with and without aerosols show that aerosols lead to reduced net shortwave radiation fluxes, 2 m temperature, 10 m wind speed, planetary boundary layer (PBL) height, and precipitation and increase aerosol optical depth, cloud condensation nuclei, cloud optical depth, and cloud droplet number concentrations over most of the domain. These results indicate a need to further improve the model representations of the above parameterizations as well as aerosol-meteorology interactions at all scales.

Methodologies for evaluating performance and assessing uncertainty of atmospheric dispersion models

NASA Astrophysics Data System (ADS)

Chang, Joseph C.

This thesis describes methodologies to evaluate the performance and to assess the uncertainty of atmospheric dispersion models, tools that predict the fate of gases and aerosols upon their release into the atmosphere. Because of the large economic and public-health impacts often associated with the use of the dispersion model results, these models should be properly evaluated, and their uncertainty should be properly accounted for and understood. The CALPUFF, HPAC, and VLSTRACK dispersion modeling systems were applied to the Dipole Pride (DP26) field data (˜20 km in scale), in order to demonstrate the evaluation and uncertainty assessment methodologies. Dispersion model performance was found to be strongly dependent on the wind models used to generate gridded wind fields from observed station data. This is because, despite the fact that the test site was a flat area, the observed surface wind fields still showed considerable spatial variability, partly because of the surrounding mountains. It was found that the two components were comparable for the DP26 field data, with variability more important than uncertainty closer to the source, and less important farther away from the source. Therefore, reducing data errors for input meteorology may not necessarily increase model accuracy due to random turbulence. DP26 was a research-grade field experiment, where the source, meteorological, and concentration data were all well-measured. Another typical application of dispersion modeling is a forensic study where the data are usually quite scarce. An example would be the modeling of the alleged releases of chemical warfare agents during the 1991 Persian Gulf War, where the source data had to rely on intelligence reports, and where Iraq had stopped reporting weather data to the World Meteorological Organization since the 1981 Iran-Iraq-war. Therefore the meteorological fields inside Iraq must be estimated by models such as prognostic mesoscale meteorological models, based on observational data from areas outside of Iraq, and using the global fields simulated by the global meteorological models as the initial and boundary conditions for the mesoscale models. It was found that while comparing model predictions to observations in areas outside of Iraq, the predicted surface wind directions had errors between 30 to 90 deg, but the inter-model differences (or uncertainties) in the predicted surface wind directions inside Iraq, where there were no onsite data, were fairly constant at about 70 deg. (Abstract shortened by UMI.)

The Modern Near-Surface Martian Climate: A Review of In-situ Meteorological Data from Viking to Curiosity

NASA Astrophysics Data System (ADS)

Martínez, G. M.; Newman, C. N.; De Vicente-Retortillo, A.; Fischer, E.; Renno, N. O.; Richardson, M. I.; Fairén, A. G.; Genzer, M.; Guzewich, S. D.; Haberle, R. M.; Harri, A.-M.; Kemppinen, O.; Lemmon, M. T.; Smith, M. D.; de la Torre-Juárez, M.; Vasavada, A. R.

2017-10-01

We analyze the complete set of in-situ meteorological data obtained from the Viking landers in the 1970s to today's Curiosity rover to review our understanding of the modern near-surface climate of Mars, with focus on the dust, CO2 and H2O cycles and their impact on the radiative and thermodynamic conditions near the surface. In particular, we provide values of the highest confidence possible for atmospheric opacity, atmospheric pressure, near-surface air temperature, ground temperature, near-surface wind speed and direction, and near-surface air relative humidity and water vapor content. Then, we study the diurnal, seasonal and interannual variability of these quantities over a span of more than twenty Martian years. Finally, we propose measurements to improve our understanding of the Martian dust and H2O cycles, and discuss the potential for liquid water formation under Mars' present day conditions and its implications for future Mars missions. Understanding the modern Martian climate is important to determine if Mars could have the conditions to support life and to prepare for future human exploration.

The impacts of urban surface characteristics on radiation balance and meteorological variables in the boundary layer around Beijing in summertime

NASA Astrophysics Data System (ADS)

Liu, Ruiting; Han, Zhiwei; Wu, Jian; Hu, Yonghong; Li, Jiawei

2017-11-01

In this study, some key geometric and thermal parameters derived from recent field and satellite observations in Beijing were collected and incorporated into WRF-UCM (Weather Research and Forecasting) model instead of previous default ones. A series of sensitivity model simulations were conducted to investigate the influences of these parameters on radiation balance, meteorological variables, turbulence kinetic energy (TKE), as well as planetary boundary layer height (PBLH) in regions around Beijing in summer 2014. Model validation demonstrated that the updated parameters represented urban surface characteristics more realistically and the simulations of meteorological variables were evidently improved to be closer to observations than the default parameters. The increase in building height tended to increase and slightly decrease surface air temperature at 2 m (T2) at night and around noon, respectively, and to reduce wind speed at 10 m (WS10) through a day. The increase in road width led to significant decreases in T2 and WS10 through the whole day, with the maximum changes in early morning and in evening, respectively. Both lower surface albedo and inclusion of anthropogenic heat (AH) resulted in increases in T2 and WS10 over the day, with stronger influence from AH. The vertical extension of the impact of urban surface parameters was mainly confined within 300 m at night and reached as high as 1600 m during daytime. The increase in building height tended to increase TKE and PBLH and the TKE increase was larger at night than during daytime due to enhancements of both mechanical and buoyant productions. The increase in road width generally reduced TKE and PBLH except for a few hours in the afternoon. The lower surface albedo and the presence of AH consistently resulted in increases of TKE and PBLH through both day and night. The increase in building height induced a slight divergence by day and a notable convergence at night, whereas the increase in road width led to a remarkable divergence through the entire day. Both AH and lower surface albedo induced a wind convergence over the day, which tended to strengthen nighttime mountain downslope wind and daytime southerly wind to the south of Beijing, but to weaken daytime upslope wind in mountain areas.

ICOADS: A Foundational Database with a new Release

NASA Astrophysics Data System (ADS)

Angel, W.; Freeman, E.; Woodruff, S. D.; Worley, S. J.; Brohan, P.; Dumenil-Gates, L.; Kent, E. C.; Smith, S. R.

2016-02-01

The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) offers surface marine data spanning the past three centuries and is the world's largest collection of marine surface in situ observations with approximately 300 million unique records from 1662 to the present in a common International Maritime Meteorological Archive (IMMA) format. Simple gridded monthly summary products (including netCDF) for 2° latitude x 2° longitude boxes back to 1800 and 1° x 1° boxes since 1960 are computed for each month. ICOADS observations made available in the IMMA format are taken primarily from ships (merchant, ocean research, fishing, navy, etc.) and moored and drifting buoys. Each report contains individual observations of meteorological and oceanographic variables, such as sea surface and air temperatures, winds, pressure, humidity, wet bulb, dew point, ocean waves and cloudiness. A monthly summary for an area box includes ten statistics (e.g. mean, median, standard deviation, etc.) for 22 observed and computed variables (e.g. sea surface and air temperature, wind, pressure, humidity, cloudiness, etc.). ICOADS is the most complete and heterogeneous collection of surface marine data in existence. A major new historical update, Release 3.0 (R3.0), now in production (with availability anticipated in mid-2016) will contain a variety of important updates. These updates will include unique IDs (UIDs), new IMMA attachments, ICOADS Value-Added Database (IVAD), and numerous new or improved historical and contemporary data sources. UIDs are assigned to each individual marine report, which will greatly facilitate interaction between users and data developers, and affords record traceability. A new Near-Surface Oceanographic (Nocn) attachment has been developed to include oceanographic profile elements, such as sea surface salinity, sea surface temperatures, and their associated measurement depths. Additionally, IVAD allows a feedback mechanism of data adjustments which can be stored within each IMMA report. R3.0 includes near-surface ocean profile measurements from sources such as the World Ocean Database (WOD), Shipboard Automated Meteorological and Oceanographic System (SAMOS), as well as many others. An in-depth look at the improvements and the data inputs planned for R3.0 will be further discussed.

The cross wavelet analysis of dengue fever variability influenced by meteorological conditions

NASA Astrophysics Data System (ADS)

Lin, Yuan-Chien; Yu, Hwa-Lung; Lee, Chieh-Han

2015-04-01

The multiyear variation of meteorological conditions induced by climate change causes the changing diffusion pattern of infectious disease and serious epidemic situation. Among them, dengue fever is one of the most serious vector-borne diseases distributed in tropical and sub-tropical regions. Dengue virus is transmitted by several species of mosquito and causing lots amount of human deaths every year around the world. The objective of this study is to investigate the impact of meteorological variables to the temporal variation of dengue fever epidemic in southern Taiwan. Several extreme and average indices of meteorological variables, i.e. temperature and humidity, were used for this analysis, including averaged, maximum and minimum temperature, and average rainfall, maximum 1-hr rainfall, and maximum 24-hr rainfall. This study plans to identify and quantify the nonlinear relationship of meteorological variables and dengue fever epidemic, finding the non-stationary time-frequency relationship and phase lag effects of those time series from 1998-2011 by using cross wavelet method. Results show that meteorological variables all have a significant time-frequency correlation region to dengue fever epidemic in frequency about one year (52 weeks). The associated phases can range from 0 to 90 degrees (0-13 weeks lag from meteorological factors to dengue incidences). Keywords: dengue fever, cross wavelet analysis, meteorological factor

Decadal application of WRF/Chem for regional air quality and climate modeling over the U.S. under the representative concentration pathways scenarios. Part 1: Model evaluation and impact of downscaling

NASA Astrophysics Data System (ADS)

Yahya, Khairunnisa; Wang, Kai; Campbell, Patrick; Chen, Ying; Glotfelty, Timothy; He, Jian; Pirhalla, Michael; Zhang, Yang

2017-03-01

An advanced online-coupled meteorology-chemistry model, i.e., the Weather Research and Forecasting Model with Chemistry (WRF/Chem), is applied for current (2001-2010) and future (2046-2055) decades under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios to examine changes in future climate, air quality, and their interactions. In this Part I paper, a comprehensive model evaluation is carried out for current decade to assess the performance of WRF/Chem and WRF under both scenarios and the benefits of downscaling the North Carolina State University's (NCSU) version of the Community Earth System Model (CESM_NCSU) using WRF/Chem. The evaluation of WRF/Chem shows an overall good performance for most meteorological and chemical variables on a decadal scale. Temperature at 2-m is overpredicted by WRF (by ∼0.2-0.3 °C) but underpredicted by WRF/Chem (by ∼0.3-0.4 °C), due to higher radiation from WRF. Both WRF and WRF/Chem show large overpredictions for precipitation, indicating limitations in their microphysics or convective parameterizations. WRF/Chem with prognostic chemical concentrations, however, performs much better than WRF with prescribed chemical concentrations for radiation variables, illustrating the benefit of predicting gases and aerosols and representing their feedbacks into meteorology in WRF/Chem. WRF/Chem performs much better than CESM_NCSU for most surface meteorological variables and O3 hourly mixing ratios. In addition, WRF/Chem better captures observed temporal and spatial variations than CESM_NCSU. CESM_NCSU performance for radiation variables is comparable to or better than WRF/Chem performance because of the model tuning in CESM_NCSU that is routinely made in global models.

Enhancing seasonal climate prediction capacity for the Pacific countries

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Hendon, H.; Charles, A.; Cottrill, A.; Lim, E.-P.; Langford, S.; de Wit, R.; Shelton, K.

2012-04-01

Seasonal and inter-annual climate variability is a major factor in determining the vulnerability of many Pacific Island Countries to climate change and there is need to improve weekly to seasonal range climate prediction capabilities beyond what is currently available from statistical models. In the seasonal climate prediction project under the Australian Government's Pacific Adaptation Strategy Assistance Program (PASAP), we describe a comprehensive project to strengthen the climate prediction capacities in National Meteorological Services in 14 Pacific Island Countries and East Timor. The intent is particularly to reduce the vulnerability of current services to a changing climate, and improve the overall level of information available assist with managing climate variability. Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record. In contrast, dynamical physics-based models implicitly include the effects of a changing climate whatever its character or cause and can predict outcomes not seen previously. The transition from a statistical to a dynamical prediction system provides more valuable and applicable climate information to a wide range of climate sensitive sectors throughout the countries of the Pacific region. In this project, we have developed seasonal climate outlooks which are based upon the current dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia) seasonal forecast system. At present, meteorological services of the Pacific Island Countries largely employ statistical models for seasonal outlooks. Outcomes of the PASAP project enhanced capabilities of the Pacific Island Countries in seasonal prediction providing National Meteorological Services with an additional tool to analyse meteorological variables such as sea surface temperatures, air temperature, pressure and rainfall using POAMA outputs and prepare more accurate seasonal climate outlooks.

Medium-range fire weather forecasts

Treesearch

J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

1991-01-01

The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

Evaluation of cumulus cloud – radiation interaction effects on air quality –relevant meteorological variables from WRF, from a regional climate perspective

EPA Science Inventory

Aware only of the resolved, grid-scale clouds, the Weather Research & Forecasting model (WRF) does not consider the interactions between subgrid-scale convective clouds and radiation. One consequence of this omission may be WRF’s overestimation of surface precipitation during sum...

A statistical examination of Nimbus 7 SMMR data and remote sensing of sea surface temperature, liquid water content in the atmosphere and surfaces wind speed

NASA Technical Reports Server (NTRS)

Prabhakara, C.; Wang, I.; Chang, A. T. C.; Gloersen, P.

1982-01-01

Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperature measurements over the global oceans have been examined with the help of statistical and empirical techniques. Such analyses show that zonal averages of brightness temperature measured by SMMR, over the oceans, on a large scale are primarily influenced by the water vapor in the atmosphere. Liquid water in the clouds and rain, which has a much smaller spatial and temporal scale, contributes substantially to the variability of the SMMR measurements within the latitudinal zones. The surface wind not only increases the surface emissivity but through its interactions with the atmosphere produces correlations, in the SMMR brightness temperature data, that have significant meteorological implications. It is found that a simple meteorological model can explain the general characteristics of the SMMR data. With the help of this model methods to infer over the global oceans, the surface temperature, liquid water content in the atmosphere, and surface wind speed are developed. Monthly mean estimates of the sea surface temperature and surface winds are compared with the ship measurements. Estimates of liquid water content in the atmosphere are consistent with earlier satellite measurements.

Instantaneous and daily values of the surface energy balance over agricultural fields using remote sensing and a reference field in an arid environment

USGS Publications Warehouse

Kustas, William P.; Moran, M.S.; Jackson, R. D.; Gay, L.W.; Duell, L.F.W.; Kunkel, K.E.; Matthias, A.D.

1990-01-01

Remotely sensed surface temperature and reflectance in the visible and near infrared wavebands along with ancilliary meteorological data provide the capability of computing three of the four surface energy balance components (i.e., net radiation, soil heat flux, and sensible heat flux) at different spatial and temporal scales. As a result, under nonadvective conditions, this enables the estimation of the remaining term (i.e., the latent heat flux). One of the practical applications with this approach is to produce evapotranspiration (ET) maps for agricultural regions which consist of an array of fields containing different crops at varying stages of growth and soil moisture conditions. Such a situation exists in the semiarid southwest at the University of Arizona Maricopa Agricultural Center, south of Phoenix. For one day (14 June 1987), surface temperature and reflectance measurements from an aircraft 150 m above ground level (agl) were acquired over fields from zero to nearly full cover at four times between 1000 MST and 1130 MST. The diurnal pattern of the surface energy balance was measured over four fields, which included alfalfa at 60% cover, furrowed cotton at 20% and 30% cover, and partially plowed what stubble. Instantaneous and daily values of ET were estimated for a representative area around each flux site with an energy balance model that relies on a reference ET. This reference value was determined with remotely sensed data and several meteorological inputs. The reference ET was adjusted to account for the different surface conditions in the other fields using only remotely sensed variables. A comparison with the flux measurements suggests the model has difficulties with partial canopy conditions, especially related to the estimation of the sensible heat flux. The resulting errors for instantaneous ET were on the order of 100 W m-2 and for daily values of order 2 mm day-1. These findings suggest future research should involve development of methods to account for the variability of meteorological parameters brought about by changes in surface conditions and improvements in the modeling of sensible heat transfer across the surface-atmosphere interface for partial canopy conditions using remote sensing information. ?? 1990.

Decadal evaluation of regional climate, air quality, and their interactions using WRF/Chem Version 3.6.1

NASA Astrophysics Data System (ADS)

Yahya, K.; Wang, K.; Campbell, P.; Glotfelty, T.; He, J.; Zhang, Y.

2015-08-01

The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 with the Carbon Bond 2005 (CB05) gas-phase mechanism is evaluated for its first decadal application during 2001-2010 using the Representative Concentration Pathway (RCP 8.5) emissions to assess its capability and appropriateness for long-term climatological simulations. The initial and boundary conditions are downscaled from the modified Community Earth System Model/Community Atmosphere Model (CESM/CAM5) v1.2.2. The meteorological initial and boundary conditions are bias-corrected using the National Center for Environmental Protection's Final (FNL) Operational Global Analysis data. Climatological evaluations are carried out for meteorological, chemical, and aerosol-cloud-radiation variables against data from surface networks and satellite retrievals. The model performs very well for the 2 m temperature (T2) for the 10 year period with only a small cold bias of -0.3 °C. Biases in other meteorological variables including relative humidity at 2 m, wind speed at 10 m, and precipitation tend to be site- and season-specific; however, with the exception of T2, consistent annual biases exist for most of the years from 2001 to 2010. Ozone mixing ratios are slightly overpredicted at both urban and rural locations but underpredicted at rural locations. PM2.5 concentrations are slightly overpredicted at rural sites, but slightly underpredicted at urban/suburban sites. In general, the model performs relatively well for chemical and meteorological variables, and not as well for aerosol-cloud-radiation variables. Cloud-aerosol variables including aerosol optical depth, cloud water path, cloud optical thickness, and cloud droplet number concentration are generally underpredicted on average across the continental US. Overpredictions of several cloud variables over eastern US result in underpredictions of radiation variables and overpredictions of shortwave and longwave cloud forcing which are important climate variables. While the current performance is deemed to be acceptable, improvements to the bias-correction method for CESM downscaling and the model parameterizations of cloud dynamics and thermodynamics, as well as aerosol-cloud interactions can potentially improve model performance for long-term climate simulations.

Analysis of observed surface ozone in the dry season over Eastern Thailand during 1997-2012

NASA Astrophysics Data System (ADS)

Assareh, Nosha; Prabamroong, Thayukorn; Manomaiphiboon, Kasemsan; Theramongkol, Phunsak; Leungsakul, Sirakarn; Mitrjit, Nawarat; Rachiwong, Jintarat

2016-09-01

This study analyzed observed surface ozone (O3) in the dry season over a long-term period of 1997-2012 for the eastern region of Thailand and incorporated several technical tools or methods in investigating different aspects of O3. The focus was the urbanized and industrialized coastal areas recently recognized as most O3-polluted areas. It was found that O3 is intensified most in the dry-season months when meteorological conditions are favorable to O3 development. The diurnal variations of O3 and its precursors show the general patterns of urban background. From observational O3 isopleth diagrams and morning ratios of non-methane volatile organic compounds (NMVOC) and nitrogen oxides (NOx), the chemical regime of O3 formation was identified as VOC-sensitive, and the degree of VOC sensitivity tends to increase over the years, suggesting emission control on VOC to be suitable for O3 management. Both total oxidant analysis and back-trajectory modeling (together with K-means clustering) indicate the potential role of regional transport or influence in enhancing surface O3 level over the study areas. A meteorological adjustment with generalized linear modeling was performed to statistically exclude meteorological effects on the variability of O3. Local air-mass recirculation factor was included in the modeling to support the coastal application. The derived trends in O3 based on the meteorological adjustment were found to be significantly positive using a Mann-Kendall test with block bootstrapping.

Meteorological factors controlling year-to-year variations in the spring onset of snow melt over the Arctic sea ice

NASA Astrophysics Data System (ADS)

Maksimovich, E.

2010-09-01

The spring onset of snow melt on the Arctic sea ice shows large inter-annual variability. Surface melt triggers positive feedback mechanisms between the albedo, snow properties and thickness, as well as sea ice thickness. Hence, it is important to quantify the factors contributing to inter-annual variability of the melt onset (MO) in various parts of the Arctic Ocean. Meteorological factors controlling surface heat budget and surface melting/freezing are the shortwave and longwave radiative fluxes and the turbulent fluxes of sensible and latent heat. These fluxes depend on the weather conditions, including the radiative impact of clouds, heat advection and wind speed. We make use of SSM/I-based MO time series (Markus, Miller and Stroeve) and the ECMWF ERA Interim reanalysis on the meteorological conditions and surface fluxes, both data sets spanning the period 1989-2008 and covering recent years with a rapid sea ice decline. The advantage is that SSM/I-based MO time series are independent of the ERA-Interim data. Our objective is to investigate if there exists a physically consistent and statistically significant relationship between MO timing and corresponding meteorological conditions. Results based on the regression analysis between the MO timing and seasonal anomalies of surface longwave radiative fluxes reveal strong relationships. Synoptic scale (3-14 days) anomalies in downward longwave radiation are essential in the Western Arctic. Regarding the longer history (20-60 days) the distinct contribution from the downward longwave radiative fluxes is captured within the whole study region. Positive anomalies in the downward longwave radiation dominate over the simultaneous negative anomalies in the downward shortwave radiation. The anomalies in downward radiative fluxes are consistent with the total column water vapor, sea level pressure and 10-m wind direction. Sensible and latent heat fluxes affect surface melt timing in the Beaufort Sea and in the Atlantic sector of the Arctic Basin. Stronger winds strengthen the relationship between the turbulent fluxes and the MO timing. The turbulent surface fluxes in spring are relatively weak, of the order of 1-10W/m2, compared to the downward shortwave and longwave radiative fluxes, which are of the order of 100-150W/m2. As soon as data uncertainties are comparable to the anomaly in turbulent fluxes, statistical relationships found between MO timing and preceding anomaly in turbulent fluxes do not necessarily prove their reasonal-causal relationship. This joint study of SSM/I-based MO record and the ERA-Interim meteorological fields region-wide with a focus on the seasonal transition demonstrates their consistency in time and space. Such result could be regarded as an important indicator that both data sets have the appropriate performance of the surface state in the Arctic Ocean. Nevertheless, an important additional effort is needed for to resolve better the cloud radiative and boundary layer turbulent processes over the sea ice.

The value of using seasonality and meteorological variables to model intra-urban PM2.5 variation

NASA Astrophysics Data System (ADS)

Olvera Alvarez, Hector A.; Myers, Orrin B.; Weigel, Margaret; Armijos, Rodrigo X.

2018-06-01

A yearlong air monitoring campaign was conducted to assess the impact of local temperature, relative humidity, and wind speed on the temporal and spatial variability of PM2.5 in El Paso, Texas. Monitoring was conducted at four sites purposely selected to capture the local traffic variability. Effects of meteorological events on seasonal PM2.5 variability were identified. For instance, in winter low-wind and low-temperature conditions were associated with high PM2.5 events that contributed to elevated seasonal PM2.5 levels. Similarly, in spring, high PM2.5 events were associated with high-wind and low-relative humidity conditions. Correlation coefficients between meteorological variables and PM2.5 fluctuated drastically across seasons. Specifically, it was observed that for most sites correlations between PM2.5 and meteorological variables either changed from positive to negative or dissolved depending on the season. Overall, the results suggest that mixed effects analysis with season and site as fixed factors and meteorological variables as covariates could increase the explanatory value of LUR models for PM2.5.

Atmospheric aerosols parameters behavior and its association with meteorological activities variables over western Indian tropical semi-urban site i.e., Udaipur

NASA Astrophysics Data System (ADS)

Vyas, B. M.; Saxenna, Abhishek; Panwar, Chhagan

2016-05-01

The present study has been focused to the identify the role of meteorological processes on changing the monthly variation of AOD at 550nm, Angstrom Exponent Coefficient (AEC, 440/670nm) and Cloud Effective Radius (CER, μm) measured during January, 2005 to December 2013 over western Indian location i.e., Udaipur (24.6° N, 73.7° E, 560 m amsl). The monthly variation of AOD 550nm, AEC and during entire study period have shown the strong combined influence of different local surface meteorological parameters in varying amplitude with different nature. The higher values of wind speed, ambient surface temperature, planetary boundary layer, and favorable wind direction coming from desert and oceanic region (W and SW) may be recognize as some of possible factor to exhibit the higher aerosols loading of bigger aerosol size particles in pre-monsoon. These meteorological factors seem also to be plausible responsible factors for drastically reducing the cloud effective radius in pre-monsoon season. In contrary to this, in winter, lower atmospheric aerosols burden and more abundance of fine size particles along with increasing the CER sizes also seem to be influenced and governed by the adverse nature of meteorological conditions such lowering the PBL, T, WS as well as with air pollutants transportation by wind from the N and NE region, of high aerosols loading of fine size particles as anthropogenic aerosols located far away to the observing site.

Global Meteorological Drought: A Synthesis of Current Understanding with a Focus on SST Drivers of Precipitation Deficits

NASA Technical Reports Server (NTRS)

Schubert, S.; Stewart, R.; Wang, H.; Barlow, M.; Berbery, H.; Cai, W.; Hoerling, M.; Kanikicharla, K.; Koster, R.; Lyon, B.;

Analysis of source regions and meteorological factors for the variability of spring PM10 concentrations in Seoul, Korea

NASA Astrophysics Data System (ADS)

Lee, Jangho; Kim, Kwang-Yul

2018-02-01

CSEOF analysis is applied for the springtime (March, April, May) daily PM10 concentrations measured at 23 Ministry of Environment stations in Seoul, Korea for the period of 2003-2012. Six meteorological variables at 12 pressure levels are also acquired from the ERA Interim reanalysis datasets. CSEOF analysis is conducted for each meteorological variable over East Asia. Regression analysis is conducted in CSEOF space between the PM10 concentrations and individual meteorological variables to identify associated atmospheric conditions for each CSEOF mode. By adding the regressed loading vectors with the mean meteorological fields, the daily atmospheric conditions are obtained for the first five CSEOF modes. Then, HYSPLIT model is run with the atmospheric conditions for each CSEOF mode in order to back trace the air parcels and dust reaching Seoul. The K-means clustering algorithm is applied to identify major source regions for each CSEOF mode of the PM10 concentrations in Seoul. Three main source regions identified based on the mean fields are: (1) northern Taklamakan Desert (NTD), (2) Gobi Desert and (GD), and (3) East China industrial area (ECI). The main source regions for the mean meteorological fields are consistent with those of previous study; 41% of the source locations are located in GD followed by ECI (37%) and NTD (21%). Back trajectory calculations based on CSEOF analysis of meteorological variables identify distinct source characteristics associated with each CSEOF mode and greatly facilitate the interpretation of the PM10 variability in Seoul in terms of transportation route and meteorological conditions including the source area.

A Reduced Form Model for Ozone Based on Two Decades of ...

EPA Pesticide Factsheets

A Reduced Form Model (RFM) is a mathematical relationship between the inputs and outputs of an air quality model, permitting estimation of additional modeling without costly new regional-scale simulations. A 21-year Community Multiscale Air Quality (CMAQ) simulation for the continental United States provided the basis for the RFM developed in this study. Predictors included the principal component scores (PCS) of emissions and meteorological variables, while the predictand was the monthly mean of daily maximum 8-hour CMAQ ozone for the ozone season at each model grid. The PCS form an orthogonal basis for RFM inputs. A few PCS incorporate most of the variability of emissions and meteorology, thereby reducing the dimensionality of the source-receptor problem. Stochastic kriging was used to estimate the model. The RFM was used to separate the effects of emissions and meteorology on ozone concentrations. by running the RFM with emissions constant (ozone dependent on meteorology), or constant meteorology (ozone dependent on emissions). Years with ozone-conducive meteorology were identified, and meteorological variables best explaining meteorology-dependent ozone were identified. Meteorology accounted for 19% to 55% of ozone variability in the eastern US, and 39% to 92% in the western US. Temporal trends estimated for original CMAQ ozone data and emission-dependent ozone were mostly negative, but the confidence intervals for emission-dependent ozone are much

Assessment of Seasonal Water Balance Components over India Using Macroscale Hydrological Model

NASA Astrophysics Data System (ADS)

Joshi, S.; Raju, P. V.; Hakeem, K. A.; Rao, V. V.; Yadav, A.; Issac, A. M.; Diwakar, P. G.; Dadhwal, V. K.

2016-12-01

Hydrological models provide water balance components which are useful for water resources assessment and for capturing the seasonal changes and impact of anthropogenic interventions and climate change. The study under description is a national level modeling framework for country India using wide range of geo-spatial and hydro-meteorological data sets for estimating daily Water Balance Components (WBCs) at 0.15º grid resolution using Variable Infiltration Capacity model. The model parameters were optimized through calibration of model computed stream flow with field observed yielding Nash-Sutcliffe efficiency between 0.5 to 0.7. The state variables, evapotranspiration (ET) and soil moisture were also validated, obtaining R2 values of 0.57 and 0.69, respectively. Using long-term meteorological data sets, model computation were carried to capture hydrological extremities. During 2013, 2014 and 2015 monsoon seasons, WBCs were estimated and were published in web portal with 2-day time lag. In occurrence of disaster events, weather forecast was ingested, high surface runoff zones were identified for forewarning and disaster preparedness. Cumulative monsoon season rainfall of 2013, 2014 and 2015 were 105, 89 and 91% of long period average (LPA) respectively (Source: India Meteorological Department). Analysis of WBCs indicated that corresponding seasonal surface runoff was 116, 81 and 86% LPA and evapotranspiration was 109, 104 and 90% LPA. Using the grid-wise data, the spatial variation in WBCs among river basins/administrative regions was derived to capture the changes in surface runoff, ET between the years and in comparison with LPA. The model framework is operational and is providing periodic account of national level water balance fluxes which are useful for quantifying spatial and temporal variation in basin/sub-basin scale water resources, periodical water budgeting to form vital inputs for studies on water resources and climate change.

Spatiotemporal Variability of Surface Meteorological Variables During Fog and No-Fog Events in the Heber Valley, UT; Selected Case Studies From MATERHORN-Fog

NASA Astrophysics Data System (ADS)

van den Bossche, Michael; De Wekker, Stephan F. J.

2016-09-01

We investigated the spatiotemporal variability of surface meteorological variables in the nocturnal boundary layer using six automatic weather stations deployed in the Heber Valley, UT, during the MATERHORN-Fog experiment. The stations were installed on the valley floor within a 1.5 km × 0.8 km area and collected 1-Hz wind and pressure data and 0.2-Hz temperature and humidity data. We describe the weather stations and analyze the spatiotemporal variability of the measured variables during three nights with radiative cooling. Two nights were characterized by the presence of dense ice fog, one night with a persistent (`heavy') fog, and one with a short-lived (`moderate') fog, while the third night had no fog. Frost-point depressions were larger preceding the night without fog and showed a continued decrease during the no-fog night. On both fog nights, the frost-point depression reached values close to zero early in the night, but ~5 h earlier on the heavy-fog night than on the moderate-fog night. Spatial variability of temperature and humidity was smallest during the heavy-fog night and increased temporarily during short periods when wind speeds increased and the fog lifted. During all three nights, wind speeds did not exceed 2 m/s. The temporal variability of the wind speed and direction was larger during the fog nights than during the no-fog nights, but was particularly large during the heavy-fog night. The large variability corresponded with short-lived (5-10 min) pressure variations with amplitudes on the order of 0.5 hPa, indicating gravity wave activity. These pressure fluctuations occurred at all stations and were correlated in particular with variability in wind direction. Although not able to provide a complete picture of the nocturnal boundary layer, our low-cost weather stations were able to continuously collect data that were comparable to those of nearby research-grade instruments. From these data, we distinguished between fog and no-fog events, successfully quantified spatiotemporal variations in surface properties during these events, and detected gravity waves.

Meteorological and associated data collected over agricultural fields in Pinal County, Arizona, 1989 and 1990

USGS Publications Warehouse

Owen-Joyce, Sandra J.; Brown, Paul W.

1995-01-01

Data were collected at temporary meteorological stations installed in agricultural fields in Pinal County, Arizona, to evaluate the spatial and temporal variability of point data and to examine how station location affects ground-based meteorological data and the resulting values of evapotranspiration calculated using remotely sensed multispectral data from satellites. Time-specific data were collected to correspond with satellite overpasses from April to October 1989, and June 27-28, 1990. Meteorological data consisting of air temperature, relative humidity, wind speed, solar radiation, and net radiation were collected at each station during all periods of the project. Supplementary measurements of soil temperature, soil heat flux density, and surface or canopy temperature were obtained at some locations during certain periods of the project. Additional data include information on data-collection periods, station positions, instrumentation, sensor heights, and field dimensions. Other data, which correspond to the extensive field measurements made in con- junction with satellite overpasses in 1989 and 1990, include crop type, canopy cover, canopy height, irrigation, cultivation, and orientation of rows. Field boundaries and crop types were mapped in a 2- to 3-square-kilometer area surrounding each meteorological station. Field data are presented in tabular and graphic form. Meteorological and supplementary data are available, upon request, in digital form.

Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States

NASA Astrophysics Data System (ADS)

Zhang, Yang; Hong, Chaopeng; Yahya, Khairunnisa; Li, Qi; Zhang, Qiang; He, Kebin

2016-08-01

An online-coupled meteorology-chemistry model, WRF/Chem-MADRID, has been deployed for real time air quality forecast (RT-AQF) in southeastern U.S. since 2009. A comprehensive evaluation of multi-year RT-AQF shows overall good performance for temperature and relative humidity at 2-m (T2, RH2), downward surface shortwave radiation (SWDOWN) and longwave radiation (LWDOWN), and cloud fraction (CF), ozone (O3) and fine particles (PM2.5) at surface, tropospheric ozone residuals (TOR) in O3 seasons (May-September), and column NO2 in winters (December-February). Moderate-to-large biases exist in wind speed at 10-m (WS10), precipitation (Precip), cloud optical depth (COT), ammonium (NH4+), sulfate (SO42-), and nitrate (NO3-) from the IMPROVE and SEARCH networks, organic carbon (OC) at IMPROVE, and elemental carbon (EC) and OC at SEARCH, aerosol optical depth (AOD) and column carbon monoxide (CO), sulfur dioxide (SO2), and formaldehyde (HCHO) in both O3 and winter seasons, column nitrogen dioxide (NO2) in O3 seasons, and TOR in winters. These biases indicate uncertainties in the boundary layer and cloud process treatments (e.g., surface roughness, microphysics cumulus parameterization), emissions (e.g., O3 and PM precursors, biogenic, mobile, and wildfire emissions), upper boundary conditions for all major gases and PM2.5 species, and chemistry and aerosol treatments (e.g., winter photochemistry, aerosol thermodynamics). The model shows overall good skills in reproducing the observed multi-year trends and inter-seasonal variability in meteorological and radiative variables such as T2, WS10, Precip, SWDOWN, and LWDOWN, and relatively well in reproducing the observed trends in surface O3 and PM2.5, but relatively poor in reproducing the observed column abundances of CO, NO2, SO2, HCHO, TOR, and AOD. The sensitivity simulations using satellite-constrained boundary conditions for O3 and CO show substantial improvement for both spatial distribution and domain-mean performance statistics. The model's forecasting skills for air quality can be further enhanced through improving model inputs (e.g., anthropogenic emissions for urban areas and upper boundary conditions of chemical species), meteorological forecasts (e.g., WS10, Precip) and meteorologically-dependent emissions (e.g., biogenic and wildfire emissions), and model physics and chemical treatments (e.g., gas-phase chemistry in winter conditions, cloud processes and their interactions with radiation and aerosol).

Impacts of weather versus climate and driver uncertainty on multi-centennial ecosystem model simulations

NASA Astrophysics Data System (ADS)

Rollinson, C.; Simkins, J.; Fer, I.; Desai, A. R.; Dietze, M.

2017-12-01

Simulations of ecosystem dynamics and comparisons with empirical data require accurate, continuous, and often sub-daily meteorology records that are spatially aligned to the scale of the empirical data. A wealth of meteorology data for the past, present, and future is available through site-specific observations, modern reanalysis products, and gridded GCM simulations. However, these products are mismatched in spatial and temporal resolution, often with both different means and seasonal patterns. We have designed and implemented a two-step meteorological downscaling and ensemble generation method that combines multiple meteorology data products through debiasing and temporal downscaling protocols. Our methodology is designed to preserve the covariance among seven meteorological variables for use as drivers in ecosystem model simulations: temperature, precipitation, short- and longwave radiation, surface pressure, humidity, and wind. Furthermore, our method propagates uncertainty through the downscaling process and results in ensembles of meteorology that can be compared to paleoclimate reconstructions and used to analyze the effects of both high- and low-frequency climate anomalies on ecosystem dynamics. Using a multiple linear regression approach, we have combined hourly, 0.125-degree gridded data from the NLDAS (1980-present) with CRUNCEP (1901-2010) and CMIP5 historical (1850-2005), past millennium (850-1849), and future (1950-2100) GCM simulations. This has resulted in an ensemble of continuous, hourly-resolved meteorology from from the paleo era into the future with variability in weather events as well as low-frequency climatic changes. We investigate the influence of extreme sub-daily weather phenomena versus long-term climatic changes in an ensemble of ecosystem models that range in atmospheric and biological complexity. Through data assimilation with paleoclimate reconstructions of past climate, we can improve data-model comparisons using observations of vegetation change from the past 1200 years. Accounting for driver uncertainty in model evaluation can help determine the relative influence of structural versus parameterization errors in ecosystem modelings.

Forward-Looking Infrared Cameras for Micrometeorological Applications within Vineyards

PubMed Central

Katurji, Marwan; Zawar-Reza, Peyman

2016-01-01

We apply the principles of atmospheric surface layer dynamics within a vineyard canopy to demonstrate the use of forward-looking infrared cameras measuring surface brightness temperature (spectrum bandwidth of 7.5 to 14 μm) at a relatively high temporal rate of 10 s. The temporal surface brightness signal over a few hours of the stable nighttime boundary layer, intermittently interrupted by periods of turbulent heat flux surges, was shown to be related to the observed meteorological measurements by an in situ eddy-covariance system, and reflected the above-canopy wind variability. The infrared raster images were collected and the resultant self-organized spatial cluster provided the meteorological context when compared to in situ data. The spatial brightness temperature pattern was explained in terms of the presence or absence of nighttime cloud cover and down-welling of long-wave radiation and the canopy turbulent heat flux. Time sequential thermography as demonstrated in this research provides positive evidence behind the application of thermal infrared cameras in the domain of micrometeorology, and to enhance our spatial understanding of turbulent eddy interactions with the surface. PMID:27649208

Evaluating WRF Simulations of Urban Boundary Layer Processes during DISCOVER-AQ

NASA Astrophysics Data System (ADS)

Hegarty, J. D.; Henderson, J.; Lewis, J. R.; McGrath-Spangler, E. L.; Scarino, A. J.; Ferrare, R. A.; DeCola, P.; Welton, E. J.

2015-12-01

The accurate representation of processes in the planetary boundary layer (PBL) in meteorological models is of prime importance to air quality and greenhouse gas simulations as it governs the depth to which surface emissions are vertically mixed and influences the efficiency by which they are transported downwind. In this work we evaluate high resolution (~1 km) WRF simulations of PBL processes in the Washington DC - Baltimore and Houston urban areas during the respective DISCOVER-AQ 2011 and 2013 field campaigns using MPLNET micro-pulse lidar (MPL), mini-MPL, airborne high spectral resolution lidar (HSRL), Doppler wind profiler and CALIPSO satellite measurements along with complimentary surface and aircraft measurements. We will discuss how well WRF simulates the spatiotemporal variability of the PBL height in the urban areas and the development of fine-scale meteorological features such as bay and sea breezes that influence the air quality of the urban areas studied.

Investigation of Primary Factors Affecting the Variation of Modeled Oak Pollen Concentrations: A Case Study for Southeast Texas in 2010

NASA Astrophysics Data System (ADS)

Jeon, Wonbae; Choi, Yunsoo; Roy, Anirban; Pan, Shuai; Price, Daniel; Hwang, Mi-Kyoung; Kim, Kyu Rang; Oh, Inbo

2018-02-01

Oak pollen concentrations over the Houston-Galveston-Brazoria (HGB) area in southeastern Texas were modeled and evaluated against in-situ data. We modified the Community Multi-scale Air Quality (CMAQ) model to include oak pollen emission, dispersion, and deposition. The Oak Pollen Emission Model (OPEM) calculated gridded oak pollen emissions, which are based on a parameterized equation considering a plant-specific factor ( C e ), surface characteristics, and meteorology. The simulation period was chosen to be February 21 to April 30 in the spring of 2010, when the observed monthly mean oak pollen concentrations were the highest in six years (2009-2014). The results indicated C e and meteorology played an important role in the calculation of oak pollen emissions. While C e was critical in determining the magnitude of oak pollen emissions, meteorology determined their variability. In particular, the contribution of the meteorology to the variation in oak pollen emissions increased with the oak pollen emission rate. The evaluation results using in-situ surface data revealed that the model underestimated pollen concentrations and was unable to accurately reproduce the peak pollen episodes. The model error was likely due to uncertainty in climatology-based C e used for the estimation of oak pollen emissions and inaccuracy in the wind fields from the Weather Research and Forecast (WRF) model.

The influence of meteorological variables on CO2 and CH4 trends recorded at a semi-natural station.

PubMed

Pérez, Isidro A; Sánchez, M Luisa; García, M Ángeles; Pardo, Nuria; Fernández-Duque, Beatriz

2018-03-01

CO 2 and CH 4 evolution is usually linked with sources, sinks and their changes. However, this study highlights the role of meteorological variables. It aims to quantify their contribution to the trend of these greenhouse gases and to determine which contribute most. Six years of measurements at a semi-natural site in northern Spain were considered. Three sections are established: the first focuses on monthly deciles, the second explores the relationship between pairs of meteorological variables, and the third investigates the relationship between meteorological variables and changes in CO 2 and CH 4 . In the first section, monthly outliers were more marked for CO 2 than for CH 4 . The evolution of monthly deciles was fitted to three simple expressions, linear, quadratic and exponential. The linear and exponential are similar, whereas the quadratic evolution is the most flexible since it provided a variable rate of concentration change and a better fit. With this last evolution, a decrease in the change rate was observed for low CO 2 deciles, whereas an increasing change rate prevailed for the rest and was more accentuated for CH 4 . In the second section, meteorological variables were provided by a trajectory model. Backward trajectories from 1-day prior to reaching the measurement site were used to calculate distance and direction averages as well as the recirculation factor. Terciles of these variables were determined in order to establish three intervals with low, medium and high values. These intervals were used to classify the variables following their interval widths and skewnesses. The best correlation between pairs of meteorological variables was observed for the average distance, in particular with horizontal wind speed. Sinusoidal relationships with the average direction were obtained for average distance and for vertical wind speed. Finally, in the third section, the quadratic evolution was considered in each interval of all the meteorological variables. As regards the main result, the greatest increases were obtained for high potential temperature for both gases followed by low and medium boundary layer height for CO 2 and CH 4 , respectively. Combining both meteorological variables provided increases of 22 ± 9 and 0.070 ± 0.019 ppm for CO 2 and CH 4 , respectively, although the number of observations affected is small, around 7%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sensitivity analysis of WRF model PBL schemes in simulating boundary-layer variables in southern Italy: An experimental campaign

NASA Astrophysics Data System (ADS)

Avolio, E.; Federico, S.; Miglietta, M. M.; Lo Feudo, T.; Calidonna, C. R.; Sempreviva, A. M.

2017-08-01

The sensitivity of boundary layer variables to five (two non-local and three local) planetary boundary-layer (PBL) parameterization schemes, available in the Weather Research and Forecasting (WRF) mesoscale meteorological model, is evaluated in an experimental site in Calabria region (southern Italy), in an area characterized by a complex orography near the sea. Results of 1 km × 1 km grid spacing simulations are compared with the data collected during a measurement campaign in summer 2009, considering hourly model outputs. Measurements from several instruments are taken into account for the performance evaluation: near surface variables (2 m temperature and relative humidity, downward shortwave radiation, 10 m wind speed and direction) from a surface station and a meteorological mast; vertical wind profiles from Lidar and Sodar; also, the aerosol backscattering from a ceilometer to estimate the PBL height. Results covering the whole measurement campaign show a cold and moist bias near the surface, mostly during daytime, for all schemes, as well as an overestimation of the downward shortwave radiation and wind speed. Wind speed and direction are also verified at vertical levels above the surface, where the model uncertainties are, usually, smaller than at the surface. A general anticlockwise rotation of the simulated flow with height is found at all levels. The mixing height is overestimated by all schemes and a possible role of the simulated sensible heat fluxes for this mismatching is investigated. On a single-case basis, significantly better results are obtained when the atmospheric conditions near the measurement site are dominated by synoptic forcing rather than by local circulations. From this study, it follows that the two first order non-local schemes, ACM2 and YSU, are the schemes with the best performance in representing parameters near the surface and in the boundary layer during the analyzed campaign.

A Methodology for the Parametric Reconstruction of Non-Steady and Noisy Meteorological Time Series

NASA Astrophysics Data System (ADS)

Rovira, F.; Palau, J. L.; Millán, M.

2009-09-01

Climatic and meteorological time series often show some persistence (in time) in the variability of certain features. One could regard annual, seasonal and diurnal time variability as trivial persistence in the variability of some meteorological magnitudes (as, e.g., global radiation, air temperature above surface, etc.). In these cases, the traditional Fourier transform into frequency space will show the principal harmonics as the components with the largest amplitude. Nevertheless, meteorological measurements often show other non-steady (in time) variability. Some fluctuations in measurements (at different time scales) are driven by processes that prevail on some days (or months) of the year but disappear on others. By decomposing a time series into time-frequency space through the continuous wavelet transformation, one is able to determine both the dominant modes of variability and how those modes vary in time. This study is based on a numerical methodology to analyse non-steady principal harmonics in noisy meteorological time series. This methodology combines both the continuous wavelet transform and the development of a parametric model that includes the time evolution of the principal and the most statistically significant harmonics of the original time series. The parameterisation scheme proposed in this study consists of reproducing the original time series by means of a statistically significant finite sum of sinusoidal signals (waves), each defined by using the three usual parameters: amplitude, frequency and phase. To ensure the statistical significance of the parametric reconstruction of the original signal, we propose a standard statistical t-student analysis of the confidence level of the amplitude in the parametric spectrum for the different wave components. Once we have assured the level of significance of the different waves composing the parametric model, we can obtain the statistically significant principal harmonics (in time) of the original time series by using the Fourier transform of the modelled signal. Acknowledgements The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (València, Spain). This study has been partially funded by the European Commission (FP VI, Integrated Project CIRCE - No. 036961) and by the Ministerio de Ciencia e Innovación, research projects "TRANSREG” (CGL2007-65359/CLI) and "GRACCIE” (CSD2007-00067, Program CONSOLIDER-INGENIO 2010).

Designing a better weather display

NASA Astrophysics Data System (ADS)

Ware, Colin; Plumlee, Matthew

2012-01-01

The variables most commonly displayed on weather maps are atmospheric pressure, wind speed and direction, and surface temperature. But they are usually shown separately, not together on a single map. As a design exercise, we set the goal of finding out if it is possible to show all three variables (two 2D scalar fields and a 2D vector field) simultaneously such that values can be accurately read using keys for all variables, a reasonable level of detail is shown, and important meteorological features stand out clearly. Our solution involves employing three perceptual "channels", a color channel, a texture channel, and a motion channel in order to perceptually separate the variables and make them independently readable. We conducted an experiment to evaluate our new design both against a conventional solution, and against a glyph-based solution. The evaluation tested the abilities of novice subjects both to read values using a key, and to see meteorological patterns in the data. Our new scheme was superior especially in the representation of wind patterns using the motion channel, and it also performed well enough in the representation of pressure using the texture channel to suggest it as a viable design alternative.

Tropical Ocean Global Atmosphere (TOGA) Meteorological and Oceanographic Data Sets for 1985 and 1986

NASA Technical Reports Server (NTRS)

Halpern, D.; Ashby, H.; Finch, C.; Smith, E.; Robles, J.

1990-01-01

The Tropical Ocean Global Atmosphere (TOGA) Program is a component of the World Meteorological Organization (WMO)/International Council of Scientific Unions (ICSU) World Climate Research Program (WCRP). One of the objectives of TOGA, which began in 1985, is to determine the limits of predictability of monthly mean sea surface temperature variations in tropical regions. The TOGA program created a raison d'etre for an explosive growth of the tropical ocean observing system and a substantial improvement in numerical simulations from atmospheric and oceanic general circulation models. Institutions located throughout the world are involved in the TOGA-distributed active data archive system. The diverse TOGA data sets for 1985 and 1986, including results from general circulation models, are included on a CD-ROM. Variables on the CD-ROM are barometric pressure, surface air temperature, dewpoint temperature Cartesian components of surface wind, surface sensible and latent heat fluxes,Cartesian components of surface wind stress and of an index of surface wind stress, sea level, sea surface temperature, and depth profiles of temperature and current in the upper ocean. Some data sets are global in extent, some are regional and cover portions of an ocean basin. Data on the CD-ROM can be extracted with an Apple Macintosh or an IBM PC.

Southern hemisphere low level wind circulation statistics from the Seasat scatterometer

NASA Technical Reports Server (NTRS)

Levy, Gad

1994-01-01

Analyses of remotely sensed low-level wind vector data over the Southern Ocean are performed. Five-day averages and monthly means are created and the month-to-month variability during the winter (July-September) of 1978 is investigated. The remotely sensed winds are compared to the Australian Bureau of Meteorology (ABM) and the National Meteorological Center (NMC) surface analyses. In southern latitudes the remotely sensed winds are stronger than what the weather services' analyses suggest, indicating under-estimation by ABM and NMC in these regions. The evolution of the low-level jet and the major stormtracks during the season are studied and different flow regimes are identified. The large-scale variability of the meridional flow is studied with the aid of empirical orthogonal function (EOF) analysis. The dominance of quasi-stationary wave numbers 3,4, and 5 in the winter flows is evident in both the EOF analysis and the mean flow. The signature of an exceptionally strong blocking situation is evident in July and the special conditions leading to it are discussed. A very large intraseasonal variability with different flow regimes at different months is documented.

A comprehensive sensitivity analysis of the WRF model for air quality applications over the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Borge, Rafael; Alexandrov, Vassil; José del Vas, Juan; Lumbreras, Julio; Rodríguez, Encarnacion

Meteorological inputs play a vital role on regional air quality modelling. An extensive sensitivity analysis of the Weather Research and Forecasting (WRF) model was performed, in the framework of the Integrated Assessment Modelling System for the Iberian Peninsula (SIMCA) project. Up to 23 alternative model configurations, including Planetary Boundary Layer schemes, Microphysics, Land-surface models, Radiation schemes, Sea Surface Temperature and Four-Dimensional Data Assimilation were tested in a 3 km spatial resolution domain. Model results for the most significant meteorological variables, were assessed through a series of common statistics. The physics options identified to produce better results (Yonsei University Planetary Boundary Layer, WRF Single-Moment 6-class microphysics, Noah Land-surface model, Eta Geophysical Fluid Dynamics Laboratory longwave radiation and MM5 shortwave radiation schemes) along with other relevant user settings (time-varying Sea Surface Temperature and combined grid-observational nudging) where included in a "best case" configuration. This setup was tested and found to produce more accurate estimation of temperature, wind and humidity fields at surface level than any other configuration for the two episodes simulated. Planetary Boundary Layer height predictions showed a reasonable agreement with estimations derived from routine atmospheric soundings. Although some seasonal and geographical differences were observed, the model showed an acceptable behaviour overall. Despite being useful to define the most appropriate setup of the WRF model for air quality modelling over the Iberian Peninsula, this study provides a general overview of WRF sensitivity and can constitute a reference for future mesoscale meteorological modelling exercises.

Surface Energy Budget Components Over an Arid Scrubland Site in Idaho

NASA Astrophysics Data System (ADS)

Zurawski, A. M.; Russell, E. S.; Liu, H.; Gao, Z.

2015-12-01

Sagebrush ecosystems comprise a large area of the North American West, and serve as habitat to threatened species such as the sagebrush sparrow. Due to natural and anthropogenic disturbances, these ecosystems are experiencing widespread degradation, causing changes to the ecosystem-atmosphere interactions. Quantifying the surface energy budget components is crucial to understanding the impacts of ecosystem degradation on climate. Eddy covariance data were collected from May through August of 2014 from sensors installed at a height of 16 m over sagebrush-dominated ecosystems near Idaho Falls, Idaho. Our objective is to study how meteorological variables affect the partitioning of surface-based net radiation into latent, sensible, and soil heat fluxes. In this arid region, decrease in soil moisture led to a decrease in latent heat flux, and an increase in sensible heat flux. Air temperature increase had no noticeable effect on latent heat flux, and led to increase in sensible heat flux. Consequently, potential climate warming and drought in this region will likely lead to increased sensible heat flux during the day time. An increase in sensible heat flux will cause an increase in atmospheric heat. This indicates that this ecosystem exhibits a positive feedback to climate warming. Night time data needs to be analyzed to better understand the effect of meteorological variables on heat fluxes during the summer season in this ecosystem.

Hydrological variability in the Fraser River Basin during the 20th century: A sensitivity study with the VIC model

NASA Astrophysics Data System (ADS)

Kang, D.; Gao, H.; Dery, S. J.

2012-12-01

The Variable Infiltration Capacity (VIC) model, a macroscale surface hydrology model, was applied to the Fraser River Basin (FRB) of British Columbia, Canada. Previous modeling studies have demonstrated that the FRB is a snow-dominated system but with climate change may evolve to a pluvial regime. The ultimate goal of this model application is to evaluate the changing contribution of snowmelt to streamflow in the FRB both spatially and temporally. To this end, the National Centers for Environmental Prediction (NCEP) reanalysis data combined with meteorological observations over 1953 to 2006 are used to drive the model at a resolution of 0.25°. Model simulations are first validated with daily discharge observations from the Water Survey of Canada (WSC). In addition, the snow water equivalent (SWE) results from VIC are compared with snow pillow observations from the B.C. Ministry of Environment. Then peak SWE values simulated each winter are compared with the annual runoff data to quantify the changing contribution of snowmelt to the hydrology of the FRB. With perturbed model forcings such as precipitation and air temperature, how streamflow and surface energy-mass balance are changed is evaluated. Finally, interactions between the land surface and ambient atmosphere are evaluated by analyzing VIC results such as evaporation, soil moisture, snowmelt and sensible-latent heat flux with corresponding meteorological forcings, i.e. precipitation and air temperature.

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

TopoSCALE v.1.0: downscaling gridded climate data in complex terrain

NASA Astrophysics Data System (ADS)

Fiddes, J.; Gruber, S.

2014-02-01

Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to downscale coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to downscale short-wave radiation. Long-wave radiation is downscaled by deriving a cloud-component of all-sky emissivity at grid level and using downscaled temperature and relative humidity fields to describe variability with elevation. Precipitation is downscaled with a simple non-linear lapse and optionally disaggregated using a climatology approach. We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statistical methods are not possible, due to lack of observations (i.e. remote areas or future periods).

Forecast of Antarctic Sea Ice and Meteorological Fields

NASA Astrophysics Data System (ADS)

Barreira, S.; Orquera, F.

2017-12-01

Since 2001, we have been forecasting the climatic fields of the Antarctic sea ice (SI) and surface air temperature, surface pressure and precipitation anomalies for the Southern Hemisphere at the Meteorological Department of the Argentine Naval Hydrographic Service with different techniques that have evolved with the years. Forecast is based on the results of Principal Components Analysis applied to SI series (S-Mode) that gives patterns of temporal series with validity areas (these series are important to determine which areas in Antarctica will have positive or negative SI anomalies based on what happen in the atmosphere) and, on the other hand, to SI fields (T-Mode) that give us the form of the SI fields anomalies based on a classification of 16 patterns. Each T-Mode pattern has unique atmospheric fields associated to them. Therefore, it is possible to forecast whichever atmosphere variable we decide for the Southern Hemisphere. When the forecast is obtained, each pattern has a probability of occurrence and sometimes it is necessary to compose more than one of them to obtain the final result. S-Mode and T-Mode are monthly updated with new data, for that reason the forecasts improved with the increase of cases since 2001. We used the Monthly Polar Gridded Sea Ice Concentrations database derived from satellite information generated by NASA Team algorithm provided monthly by the National Snow and Ice Data Center of USA that begins in November 1978. Recently, we have been experimenting with multilayer Perceptron (neuronal network) with supervised learning and a back-propagation algorithm to improve the forecast. The Perceptron is the most common Artificial Neural Network topology dedicated to image pattern recognition. It was implemented through the use of temperature and pressure anomalies field images that were associated with a the different sea ice anomaly patterns. The variables analyzed included only composites of surface air temperature and pressure anomalies to simplify the density of input data and avoid a non-converging solution. Sea ice and atmospheric variables forecast can be checked every month at our web page http://www.hidro.gob.ar/smara/sb/sb.asp and at World Meteorological web page (Global Cryosphere Watch) http://globalcryospherewatch.org/state_of_cryo/seaice/.

New gridded database of clear-sky solar radiation derived from ground-based observations over Europe

NASA Astrophysics Data System (ADS)

Bartok, Blanka; Wild, Martin; Sanchez-Lorenzo, Arturo; Hakuba, Maria Z.

2017-04-01

Since aerosols modify the entire energy balance of the climate system through different processes, assessments regarding aerosol multiannual variability are highly required by the climate modelling community. Because of the scarcity of long-term direct aerosol measurements, the retrieval of aerosol data/information from other type of observations or satellite measurements are very relevant. One approach frequently used in the literature is analyze of the clear-sky solar radiation which offer a better overview of changes in aerosol content. In the study first two empirical methods are elaborated in order to separate clear-sky situations from observed values of surface solar radiation available at the World Radiation Data Center (WRDC), St. Petersburg. The daily data has been checked for temporal homogeneity by applying the MASH method (Szentimrey, 2003). In the first approach, clear sky situations are detected based on clearness index, namely the ratio of the surface solar radiation to the extraterrestrial solar irradiation. In the second approach the observed values of surface solar radiation are compared to the climatology of clear-sky surface solar radiation calculated by the MAGIC radiation code (Muller et al. 2009). In both approaches the clear-sky radiation values highly depend on the applied thresholds. In order to eliminate this methodological error a verification of clear-sky detection is envisaged through a comparison with the values obtained by a high time resolution clear-sky detection and interpolation algorithm (Long and Ackermann, 2000) making use of the high quality data from the Baseline Surface Radiation Network (BSRN). As the consequences clear-sky data series are obtained for 118 European meteorological stations. Next a first attempt has been done in order to interpolate the point-wise clear-sky radiation data by applying the MISH (Meteorological Interpolation based on Surface Homogenized Data Basis) method for the spatial interpolation of surface meteorological elements developed at the Hungarian Meteorological Service (Szentimrey 2007). In this way new gridded database of clear-sky solar radiation is created suitable for further investigations regarding the role of aerosols in the energy budget, and also for validations of climate model outputs. References 1. Long CN, Ackerman TP. 2000. Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects, J. Geophys. Res., 105(D12), 15609-15626, doi:10.1029/2000JD900077. 2. Mueller R, Matsoukas C, Gratzki A, Behr H, Hollmann R. 2009. The CM-SAF operational scheme for the satellite based retrieval of solar surface irradiance - a LUT based eigenvector hybrid approach, Remote Sensing of Environment, 113 (5), 1012-1024, doi:10.1016/j.rse.2009. 01.012 3. Szentimrey T. 2014. Multiple Analysis of Series for Homogenization (MASHv3.03), Hungarian Meteorological Service, https://www.met.hu/en/omsz/rendezvenyek/homogenization_and_interpolation/software/ 4. Szentimrey T. Bihari Z. 2014: Meteorological Interpolation based on Surface Homogenized Data Basis (MISHv1.03) https://www.met.hu/en/omsz/rendezvenyek/homogenization_and_interpolation/software/

Advances in satellite remote sensing of environmental variables for epidemiological applications.

PubMed

Goetz, S J; Prince, S D; Small, J

2000-01-01

Earth-observing satellites have provided an unprecedented view of the land surface but have been exploited relatively little for the measurement of environmental variables of particular relevance to epidemiology. Recent advances in techniques to recover continuous fields of air temperature, humidity, and vapour pressure deficit from remotely sensed observations have significant potential for disease vector monitoring and related epidemiological applications. We report on the development of techniques to map environmental variables with relevance to the prediction of the relative abundance of disease vectors and intermediate hosts. Improvements to current methods of obtaining information on vegetation properties, canopy and surface temperature and soil moisture over large areas are also discussed. Algorithms used to measure these variables incorporate visible, near-infrared and thermal infrared radiation observations derived from time series of satellite-based sensors, focused here primarily but not exclusively on the Advanced Very High Resolution Radiometer (AVHRR) instruments. The variables compare favourably with surface measurements over a broad array of conditions at several study sites, and maps of retrieved variables captured patterns of spatial variability comparable to, and locally more accurate than, spatially interpolated meteorological observations. Application of multi-temporal maps of these variables are discussed in relation to current epidemiological research on the distribution and abundance of some common disease vectors.

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.

Controlling Factors of the Surface Energy and Water Balances in cities located in cold climate regions

NASA Astrophysics Data System (ADS)

Järvi, L.; Grimmond, S. B.; Christen, A.; McFadden, J. P.; Strachan, I. B.

2016-12-01

Urban effects on climate are often pronounced in winter due to large anthropogenic heat releases and differences in snow cover between urban and surrounding rural areas. In this study, we simulate energy and water balances in cities characterized by cold winter climates with snow. Eleven urban sites from Helsinki (Finland), Basel (Switzerland), Montreal (Canada) and Minneapolis (USA) are analysed. The sites were selected based on the availability of either measured turbulent fluxes (from eddy covariance) or surface runoff to be used for model evaluation. The sites vary with respect to land cover fractions, irrigation habits and population densities. For example, the plan area fraction of impervious surface varies from 5% in Minneapolis to 84% in Basel. To simulate urban energy and water balances, we use the Surface Urban Energy and Water balance Scheme (SUEWS) model, which has been designed to minimize the number of required input variables and model parameters. For each site, the model is run in an offline mode using measured hourly meteorological data with a time step of 5-min. As the modelled time periods range from one (Basel) to 7.5 years (Helsinki), a wide range of meteorological conditions occur. Our results show how both evaporation and surface runoff are highly dependent on the fraction of impervious surface cover (r > |0.8|) during snow-free periods. However, high year-to-year variability in simulated evaporation and runoff indicates that climatological factors are also important. In winter, the amount and duration of snow cover become import controlling factor in determining the two components of water balance. The shorter the snow cover period is, the larger the cumulative runoff tends to be. Thus, our results suggest that warmer winters with less snow will increase the stress on drainage systems and modify the urban ecosystem via changes in evaporation and Bowen ratio. Also, our results indicate that simply using the fraction of impervious or pervious surfaces when estimating the surface runoff at different sites is not sufficient, but rather inter-annual variability in climatology also needs to be considered.

Spatial interpolation of GPS PWV and meteorological variables over the west coast of Peninsular Malaysia during 2013 Klang Valley Flash Flood

NASA Astrophysics Data System (ADS)

Suparta, Wayan; Rahman, Rosnani

2016-02-01

Global Positioning System (GPS) receivers are widely installed throughout the Peninsular Malaysia, but the implementation for monitoring weather hazard system such as flash flood is still not optimal. To increase the benefit for meteorological applications, the GPS system should be installed in collocation with meteorological sensors so the precipitable water vapor (PWV) can be measured. The distribution of PWV is a key element to the Earth's climate for quantitative precipitation improvement as well as flash flood forecasts. The accuracy of this parameter depends on a large extent on the number of GPS receiver installations and meteorological sensors in the targeted area. Due to cost constraints, a spatial interpolation method is proposed to address these issues. In this paper, we investigated spatial distribution of GPS PWV and meteorological variables (surface temperature, relative humidity, and rainfall) by using thin plate spline (tps) and ordinary kriging (Krig) interpolation techniques over the Klang Valley in Peninsular Malaysia (longitude: 99.5°-102.5°E and latitude: 2.0°-6.5°N). Three flash flood cases in September, October, and December 2013 were studied. The analysis was performed using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) to determine the accuracy and reliability of the interpolation techniques. Results at different phases (pre, onset, and post) that were evaluated showed that tps interpolation technique is more accurate, reliable, and highly correlated in estimating GPS PWV and relative humidity, whereas Krig is more reliable for predicting temperature and rainfall during pre-flash flood events. During the onset of flash flood events, both methods showed good interpolation in estimating all meteorological parameters with high accuracy and reliability. The finding suggests that the proposed method of spatial interpolation techniques are capable of handling limited data sources with high accuracy, which in turn can be used to predict future floods.

Global trends in visibility: Implications for dust sources

USGS Publications Warehouse

Mahowald, N.M.; Ballantine, J.A.; Feddema, J.; Ramankutty, N.

2007-01-01

There is a large uncertainty in the relative roles of human land use, climate change and carbon dioxide fertilization in changing desert dust source strength over the past 100 years, and the overall sign of human impacts on dust is not known. We used visibility data from meteorological stations in dusty regions to assess the anthropogenic impact on long term trends in desert dust emissions. Visibility data are available at thousands of stations globally from 1900 to the present, but we focused on 359 stations with more than 30 years of data in regions where mineral aerosols play a dominant role in visibility observations. We evaluated the 1974 to 2003 time period because most of these stations have reliable records only during this time. We first evaluated the visibility data against AERONET aerosol optical depth data, and found that only in dusty regions are the two moderately correlated. Correlation coefficients between visibility derived variables and AERONET optical depths indicate a moderate correlation (???0.47), consistent with capturing about 20% of the variability in optical depths. Two visibility derived variables appear to compare the best with AERONET observations: the fraction of observations with visibility less than 5 km (VIS5) and the surface extinction (EXT). Regional trends show that in many dusty places, VIS5 and EXT are statistically significantly correlated with the palmer drought severity index (based on precipitation and temperature) or surface wind speeds, consistent with dust temporal variability being largely driven by meteorology. This is especially true for North African and Chinese dust sources, but less true in the Middle East, Australia or South America, where there are not consistent patterns in the correlations. Climate indices such as El Nino or the North Atlantic Oscillation are not correlated with visibility derived variables in this analysis. There are few stations where visibility measures are correlated with cultivation or grazing estimates on a temporal basis, although this may be a function of the very coarse temporal resolution of the land use datasets. On the other hand, spatial analysis of the visibility data suggests that natural topographic lows are not correlated with visibility, but land use is correlated at a moderate level. This analysis is consistent with land use being important in some regions, but meteorology driving interannual variability during 1974-2003.

A practical approach for deriving all-weather soil moisture content using combined satellite and meteorological data

NASA Astrophysics Data System (ADS)

Leng, Pei; Li, Zhao-Liang; Duan, Si-Bo; Gao, Mao-Fang; Huo, Hong-Yuan

2017-09-01

Soil moisture has long been recognized as one of the essential variables in the water cycle and energy budget between Earth's surface and atmosphere. The present study develops a practical approach for deriving all-weather soil moisture using combined satellite images and gridded meteorological products. In this approach, soil moisture over the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky pixels are estimated from the Vegetation Index/Temperature (VIT) trapezoid scheme in which theoretical dry and wet edges were determined pixel to pixel by China Meteorological Administration Land Data Assimilation System (CLDAS) meteorological products, including air temperature, solar radiation, wind speed and specific humidity. For cloudy pixels, soil moisture values are derived by the calculation of surface and aerodynamic resistances from wind speed. The approach is capable of filling the soil moisture gaps over remaining cloudy pixels by traditional optical/thermal infrared methods, allowing for a spatially complete soil moisture map over large areas. Evaluation over agricultural fields indicates that the proposed approach can produce an overall generally reasonable distribution of all-weather soil moisture. An acceptable accuracy between the estimated all-weather soil moisture and in-situ measurements at different depths could be found with an Root Mean Square Error (RMSE) varying from 0.067 m3/m3 to 0.079 m3/m3 and a slight bias ranging from 0.004 m3/m3 to -0.011 m3/m3. The proposed approach reveals significant potential to derive all-weather soil moisture using currently available satellite images and meteorological products at a regional or global scale in future developments.

Investigation of the stochastic nature of solar radiation for renewable resources management

NASA Astrophysics Data System (ADS)

Koudouris, Giannis; Dimitriadis, Panayiotis; Iliopoulou, Theano; Mamasis, Nikos; Koutsoyiannis, Demetris

2017-04-01

A detailed investigation of the variability of solar radiation can be proven useful towards more efficient and sustainable design of renewable resources systems. This variability is mainly caused from the regular seasonal and diurnal variation, as well as its stochastic nature of the atmospheric processes, i.e. sunshine duration. In this context, we analyze numerous observations in Greece (Hellenic National Meteorological Service; http://www.hnms.gr/) and around the globe (NASA SSE - Surface meteorology and Solar Energy; http://www.soda-pro.com/web-services/radiation/nasa-sse) and we investigate the long-term behaviour and double periodicity of the solar radiation process. Also, we apply a parsimonious double-cyclostationary stochastic model to a theoretical scenario of solar energy production for an island in the Aegean Sea. Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

BOREAS Derived Surface Meteorological Data

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Newcomer, Jeffrey A. (Editor); Twine, Tracy; Rinker, Donald; Knapp, David

2000-01-01

In 1995, the BOREAS science teams identified the need for a continuous surface meteorological and radiation data set to support flux and surface process modeling efforts. This data set contains actual, substituted, and interpolated 15-minute meteorological and radiation data compiled from several surface measurements sites over the BOREAS SSA and NSA. Temporally, the data cover 01-Jan-1994 to 31-Dec-1996. The data are stored in tabular ASCII files, and are classified as AFM-Staff data.

Ozone time scale decomposition and trend assessment from surface observations

NASA Astrophysics Data System (ADS)

Boleti, Eirini; Hueglin, Christoph; Takahama, Satoshi

2017-04-01

Emissions of ozone precursors have been regulated in Europe since around 1990 with control measures primarily targeting to industries and traffic. In order to understand how these measures have affected air quality, it is now important to investigate concentrations of tropospheric ozone in different types of environments, based on their NOx burden, and in different geographic regions. In this study, we analyze high quality data sets for Switzerland (NABEL network) and whole Europe (AirBase) for the last 25 years to calculate long-term trends of ozone concentrations. A sophisticated time scale decomposition method, called the Ensemble Empirical Mode Decomposition (EEMD) (Huang,1998;Wu,2009), is used for decomposition of the different time scales of the variation of ozone, namely the long-term trend, seasonal and short-term variability. This allows subtraction of the seasonal pattern of ozone from the observations and estimation of long-term changes of ozone concentrations with lower uncertainty ranges compared to typical methodologies used. We observe that, despite the implementation of regulations, for most of the measurement sites ozone daily mean values have been increasing until around mid-2000s. Afterwards, we observe a decline or a leveling off in the concentrations; certainly a late effect of limitations in ozone precursor emissions. On the other hand, the peak ozone concentrations have been decreasing for almost all regions. The evolution in the trend exhibits some differences between the different types of measurement. In addition, ozone is known to be strongly affected by meteorology. In the applied approach, some of the meteorological effects are already captured by the seasonal signal and already removed in the de-seasonalized ozone time series. For adjustment of the influence of meteorology on the higher frequency ozone variation, a statistical approach based on Generalized Additive Models (GAM) (Hastie,1990;Wood,2006), which corrects for meteorological effects, has been developed in order to a) investigate if trends are masked by meteorological variability and b) to understand which part of the observed trends is meteorology driven. By correlating short-term variation of ozone, as obtained from the EEMD, with the corresponding short-term variation of relevant meteorological parameters, we subtract the variation of ozone concentrations that is related to the meteorological effects explained by the GAM. We find that higher frequency meteorological correction reduces further the uncertainty in trend estimation by a small factor. In addition, the seasonal variability of ozone as obtained from the EEMD has been studied in more detail for possible changes in its behavior. A shortening of the seasonal cycle was observed, i.e. reduction of maximum and in-crease of minimum concentration per year, while the occurrence of maximum is shifted to earlier times during a year. In summary, we present a sophisticated and consistent approach for detecting and categorizing trends and meteorological influences on ozone concentrations in long-term measurements across Europe.

Trans-Pacific transport and evolution of aerosols: evaluation of quasi-global WRF-Chem simulation with multiple observations

NASA Astrophysics Data System (ADS)

Hu, Zhiyuan; Zhao, Chun; Huang, Jianping; Leung, L. Ruby; Qian, Yun; Yu, Hongbin; Huang, Lei; Kalashnikova, Olga V.

2016-05-01

A fully coupled meteorology-chemistry model (WRF-Chem, the Weather Research and Forecasting model coupled with chemistry) has been configured to conduct quasi-global simulation for 5 years (2010-2014) and evaluated with multiple observation data sets for the first time. The evaluation focuses on the simulation over the trans-Pacific transport region using various reanalysis and observational data sets for meteorological fields and aerosol properties. The simulation generally captures the overall spatial and seasonal variability of satellite retrieved aerosol optical depth (AOD) and absorbing AOD (AAOD) over the Pacific that is determined by the outflow of pollutants and dust and the emissions of marine aerosols. The assessment of simulated extinction Ångström exponent (EAE) indicates that the model generally reproduces the variability of aerosol size distributions as seen by satellites. In addition, the vertical profile of aerosol extinction and its seasonality over the Pacific are also well simulated. The difference between the simulation and satellite retrievals can be mainly attributed to model biases in estimating marine aerosol emissions as well as the satellite sampling and retrieval uncertainties. Compared with the surface measurements over the western USA, the model reasonably simulates the observed magnitude and seasonality of dust, sulfate, and nitrate surface concentrations, but significantly underestimates the peak surface concentrations of carbonaceous aerosol likely due to model biases in the spatial and temporal variability of biomass burning emissions and secondary organic aerosol (SOA) production. A sensitivity simulation shows that the trans-Pacific transported dust, sulfate, and nitrate can make significant contribution to surface concentrations over the rural areas of the western USA, while the peaks of carbonaceous aerosol surface concentrations are dominated by the North American emissions. Both the retrievals and simulation show small interannual variability of aerosol characteristics for 2010-2014 averaged over three Pacific sub-regions. The evaluation in this study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA, allowing one to further understand the impact of transported pollutants on the regional air quality and climate with high-resolution nested regional modeling.

Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes

DOE PAGES

Paschalis, Athanasios; Fatichi, Simone; Katul, Gabriel G.; ...

2015-08-07

While the importance of ecosystem functioning is undisputed in the context of climate change and Earth system modeling, the role of short-scale temporal variability of hydrometeorological forcing (~1 h) on the related ecosystem processes remains to be fully understood. Additionally, various impacts of meteorological forcing variability on water and carbon fluxes across a range of scales are explored here using numerical simulations. Synthetic meteorological drivers that highlight dynamic features of the short temporal scale in series of precipitation, temperature, and radiation are constructed. These drivers force a mechanistic ecohydrological model that propagates information content into the dynamics of water andmore » carbon fluxes for an ensemble of representative ecosystems. The focus of the analysis is on a cross-scale effect of the short-scale forcing variability on the modeled evapotranspiration and ecosystem carbon assimilation. Interannual variability of water and carbon fluxes is emphasized in the analysis. The main study inferences are summarized as follows: (a) short-scale variability of meteorological input does affect water and carbon fluxes across a wide range of time scales, spanning from the hourly to the annual and longer scales; (b) different ecosystems respond to the various characteristics of the short-scale variability of the climate forcing in various ways, depending on dominant factors limiting system productivity; (c) whenever short-scale variability of meteorological forcing influences primarily fast processes such as photosynthesis, its impact on the slow-scale variability of water and carbon fluxes is small; and (d) whenever short-scale variability of the meteorological forcing impacts slow processes such as movement and storage of water in the soil, the effects of the variability can propagate to annual and longer time scales.« less

Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes

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

Paschalis, Athanasios; Fatichi, Simone; Katul, Gabriel G.

While the importance of ecosystem functioning is undisputed in the context of climate change and Earth system modeling, the role of short-scale temporal variability of hydrometeorological forcing (~1 h) on the related ecosystem processes remains to be fully understood. Additionally, various impacts of meteorological forcing variability on water and carbon fluxes across a range of scales are explored here using numerical simulations. Synthetic meteorological drivers that highlight dynamic features of the short temporal scale in series of precipitation, temperature, and radiation are constructed. These drivers force a mechanistic ecohydrological model that propagates information content into the dynamics of water andmore » carbon fluxes for an ensemble of representative ecosystems. The focus of the analysis is on a cross-scale effect of the short-scale forcing variability on the modeled evapotranspiration and ecosystem carbon assimilation. Interannual variability of water and carbon fluxes is emphasized in the analysis. The main study inferences are summarized as follows: (a) short-scale variability of meteorological input does affect water and carbon fluxes across a wide range of time scales, spanning from the hourly to the annual and longer scales; (b) different ecosystems respond to the various characteristics of the short-scale variability of the climate forcing in various ways, depending on dominant factors limiting system productivity; (c) whenever short-scale variability of meteorological forcing influences primarily fast processes such as photosynthesis, its impact on the slow-scale variability of water and carbon fluxes is small; and (d) whenever short-scale variability of the meteorological forcing impacts slow processes such as movement and storage of water in the soil, the effects of the variability can propagate to annual and longer time scales.« less

BOREAS AFM-07 SRC Surface Meteorological Data

NASA Technical Reports Server (NTRS)

Osborne, Heather; Hall, Forrest G. (Editor); Newcomer, Jeffrey A. (Editor); Young, Kim; Wittrock, Virginia; Shewchuck, Stan; Smith, David E. (Technical Monitor)

2000-01-01

The Saskatchewan Research Council (SRC) collected surface meteorological and radiation data from December 1993 until December 1996. The data set comprises Suite A (meteorological and energy balance measurements) and Suite B (diffuse solar and longwave measurements) components. Suite A measurements were taken at each of ten sites, and Suite B measurements were made at five of the Suite A sites. The data cover an approximate area of 500 km (North-South) by 1000 km (East-West) (a large portion of northern Manitoba and northern Saskatchewan). The measurement network was designed to provide researchers with a sufficient record of near-surface meteorological and radiation measurements. The data are provided in tabular ASCII files, and were collected by Aircraft Flux and Meteorology (AFM)-7. The surface meteorological and radiation data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Simulating land-atmosphere feedbacks and response to widespread forest disturbance: The role of lower boundary configuration and dynamic water table in meteorological modeling

NASA Astrophysics Data System (ADS)

Forrester, M.; Maxwell, R. M.; Bearup, L. A.; Gochis, D.

2017-12-01

Numerical meteorological models are frequently used to diagnose land-atmosphere interactions and predict large-scale response to extreme or hazardous events, including widespread land disturbance or perturbations to near-surface moisture. However, few atmospheric modeling platforms consider the impact that dynamic groundwater storage, specifically 3D subsurface flow, has on land-atmosphere interactions. In this study, we use the Weather Research and Forecasting (WRF) mesoscale meteorological model to identify ecohydrologic and land-atmosphere feedbacks to disturbance by the mountain pine beetle (MPB) over the Colorado Headwaters region. Disturbance simulations are applied to WRF with various lower boundary configurations: Including default Noah land surface model soil moisture representation; a version of WRF coupled to ParFlow (PF), an integrated groundwater-surface water model that resolves variably saturated flow in the subsurface; and WRF coupled to PF in a static water table version, simulating only vertical and no lateral subsurface flow. Our results agree with previous literature showing MPB-induced reductions in canopy transpiration in all lower boundary scenarios, as well as energy repartitioning, higher water tables, and higher planetary boundary layer over infested regions. Simulations show that expanding from local to watershed scale results in significant damping of MPB signal as unforested and unimpacted regions are added; and, while deforestation appears to have secondary feedbacks to planetary boundary layer and convection, these slight perturbations to cumulative summer precipitation are insignificant in the context of ensemble methodologies. Notably, the results suggest that groundwater representation in atmospheric modeling affects the response intensity of a land disturbance event. In the WRF-PF case, energy and atmospheric processes are more sensitive to disturbance in regions with higher water tables. Also, when dynamic subsurface hydrology is removed, WRF simulates a greater response to MPB at the land-atmosphere interface, including greater changes to daytime skin temperature, Bowen ratio and near-surface humidity. These findings highlight lower boundary representations in computational meteorology and numerical land-atmosphere modeling.

Relationship between Surface Urban Heat Island intensity and sensible heat flux retrieved from meteorological parameters observed by road weather stations in urban area

NASA Astrophysics Data System (ADS)

Gawuć, Lech

2017-04-01

Urban Heat Island (UHI) is a direct consequence of altered energy balance in urban areas (Oke 1982). There has been a significant effort put into an understanding of air temperature variability in urban areas and underlying mechanisms (Arnfield 2003, Grimmond 2006, Stewart 2011, Barlow 2014). However, studies that are concerned on surface temperature are less frequent. Therefore, Voogt & Oke (2003) proposed term "Surface Urban Heat Island (SUHI)", which is analogical to UHI and it is defined as a difference in land surface temperature (LST) between urban and rural areas. SUHI is a phenomenon that is not only concerned with high spatial variability, but also with high temporal variability (Weng and Fu 2014). In spite of the fact that satellite remote sensing techniques give a full spatial pattern over a vast area, such measurements are strictly limited to cloudless conditions during a satellite overpass (Sobrino et al., 2012). This significantly reduces the availability and applicability of satellite LST observations, especially over areas and seasons with high cloudiness occurrence. Also, the surface temperature is influenced by synoptic conditions (e.g., wind and humidity) (Gawuc & Struzewska 2016). Hence, utilising single observations is not sufficient to obtain a full image of spatiotemporal variability of urban LST and SUHI intensity (Gawuc & Struzewska 2016). One of the possible solutions would be a utilisation of time-series of LST data, which could be useful to monitor the UHI growth of individual cities and thus, to reveal the impact of urbanisation on local climate (Tran et al., 2006). The relationship between UHI and synoptic conditions have been summarised by Arnfield (2003). However, similar analyses conducted for urban LST and SUHI are lacking. We will present analyses of the relationship between time series of remotely-sensed LST and SUHI intensity and in-situ meteorological observations collected by road weather stations network, namely: road surface kinetic temperature, wind speed, air temperature, soil temperature at a depth of 30 cm, road surface condition, relative humidity. Also, as there are wind speed and temperature observations at different heights available, we will calculate sensible heat flux in order to relate it to the intensity of SUHI.

Evaluating the performance of ENVI-met model in diurnal cycles for different meteorological conditions

NASA Astrophysics Data System (ADS)

Acero, Juan A.; Arrizabalaga, Jon

2018-01-01

Urban areas are known to modify meteorological variables producing important differences in small spatial scales (i.e. microscale). These affect human thermal comfort conditions and the dispersion of pollutants, especially those emitted inside the urban area, which finally influence quality of life and the use of public open spaces. In this study, the diurnal evolution of meteorological variables measured in four urban spaces is compared with the results provided by ENVI-met (v 4.0). Measurements were carried out during 3 days with different meteorological conditions in Bilbao in the north of the Iberian Peninsula. The evaluation of the model accuracy (i.e. the degree to which modelled values approach measured values) was carried out with several quantitative difference metrics. The results for air temperature and humidity show a good agreement of measured and modelled values independently of the regional meteorological conditions. However, in the case of mean radiant temperature and wind speed, relevant differences are encountered highlighting the limitation of the model to estimate these meteorological variables precisely during diurnal cycles, in the considered evaluation conditions (sites and weather).

The surface climatology of the Ross Ice Shelf Antarctica.

PubMed

Costanza, Carol A; Lazzara, Matthew A; Keller, Linda M; Cassano, John J

2016-12-01

The University of Wisconsin-Madison Antarctic Automatic Weather Station (AWS) project has been making meteorological surface observations on the Ross Ice Shelf (RIS) for approximately 30 years. This network offers the most continuous set of routine measurements of surface meteorological variables in this region. The Ross Island area is excluded from this study. The surface climate of the RIS is described using the AWS measurements. Temperature, pressure, and wind data are analysed on daily, monthly, seasonal, and annual time periods for 13 AWS across the RIS. The AWS are separated into three representative regions - central, coastal, and the area along the Transantarctic Mountains - in order to describe specific characteristics of sections of the RIS. The climatology describes general characteristics of the region and significant changes over time. The central AWS experiences the coldest mean temperature, and the lowest resultant wind speed. These AWSs also experience the coldest potential temperatures with a minimum of 209.3 K at Gill AWS. The AWS along the Transantarctic Mountains experiences the warmest mean temperature, the highest mean sea-level pressure, and the highest mean resultant wind speed. Finally, the coastal AWS experiences the lowest mean pressure. Climate indices (MEI, SAM, and SAO) are compared to temperature and pressure data of four of the AWS with the longest observation periods, and significant correlation is found for most AWS in sea-level pressure and temperature. This climatology study highlights characteristics that influence the climate of the RIS, and the challenges of maintaining a long-term Antarctic AWS network. Results from this effort are essential for the broader Antarctic meteorology community for future research.

Sensitivity of river discharge to the quality of external meteorological forcings

NASA Astrophysics Data System (ADS)

Materia, S.; Dirmeyer, P.; Guo, Z.; Alessandri, A.; Navarra, A.

2009-09-01

Large-scale river routing models are essential tools to close the hydrological cycle in fully coupled climate models. Moreover, the availability of a realistic routing scheme is a powerful instrument to assess the validity of land surface parameterization, which has been recognized to be a crucial component of the global climate. This study is dedicated to assess the sensitivity of river discharge to the variation of external meteorological forcing. The Land Surface Scheme created at the Center for Ocean, Land and Atmosphere Studies (COLA), the SSiB model, was constrained with different meteorological fields. The resulting surface and sub-surface runoffs were used as forcing data for the HD River Routing Scheme. As expected, river flow is mainly sensitive to precipitation variability, but changes in radiative forcing affect discharge as well, presumably due to the interaction with evaporation. Also, this analysis provided an estimate of the sensitivity of river discharge to precipitation variations. A few areas, like Central and Eastern Asia, Southern and Central Europe and the majority of the US, show a magnified response of river discharge to a given percentage change in precipitation. Hence, an amplified effect of droughts following the reduction in precipitation, as it is indicated by many climate scenarios, may occur in places such as the Mediterranean. Conversely, increasing summer precipitation foreseen in Southern and Eastern Asia may amplify floods in one the poorest and most populated regions in the world. These results can be used for the definition and assessment of new strategies for land use and water management in the near future.

Assessment of Tropical Cyclone Structure Variability

DTIC Science & Technology

2013-09-01

Oceanic and Atmospheric Administration, cited 2007: Background on the HRD surface wind analysis system . [Available from http://www.aoml.noaa.gov/ hrd... Atmospheric Administration (NOAA)-Atlantic Oceanographic and Meteorological Laboratory (AOML) Hurricane Wind Analysis System (H*Wind; Powell and Houston 1996...emissions from the ocean and atmosphere in the form of brightness temperatures (TB) for each of six frequencies from 4.55 to 7.22 GHz (Uhlhorn and Black 2003

On estimating total daily evapotranspiration from remote surface temperature measurements

NASA Technical Reports Server (NTRS)

Carlson, Toby N.; Buffum, Martha J.

1989-01-01

A method for calculating daily evapotranspiration from the daily surface energy budget using remotely sensed surface temperature and several meteorological variables is presented. Vaules of the coefficients are determined from simulations with a one-dimensional boundary layer model with vegetation cover. Model constants are obtained for vegetation and bare soil at two air temperature and wind speed levels over a range of surface roughness and wind speeds. A different means of estimating the daily evapotranspiration based on the time rate of increase of surface temperature during the morning is also considered. Both the equations using our model-derived constants and field measurements are evaluated, and a discussion of sources of error in the use of the formulation is given.

An Extended Objective Evaluation of the 29-km Eta Model for Weather Support to the United States Space Program

NASA Technical Reports Server (NTRS)

Nutter, Paul; Manobianco, John

1998-01-01

This report describes the Applied Meteorology Unit's objective verification of the National Centers for Environmental Prediction 29-km eta model during separate warm and cool season periods from May 1996 through January 1998. The verification of surface and upper-air point forecasts was performed at three selected stations important for 45th Weather Squadron, Spaceflight Meteorology Group, and National Weather Service, Melbourne operational weather concerns. The statistical evaluation identified model biases that may result from inadequate parameterization of physical processes. Since model biases are relatively small compared to the random error component, most of the total model error results from day-to-day variability in the forecasts and/or observations. To some extent, these nonsystematic errors reflect the variability in point observations that sample spatial and temporal scales of atmospheric phenomena that cannot be resolved by the model. On average, Meso-Eta point forecasts provide useful guidance for predicting the evolution of the larger scale environment. A more substantial challenge facing model users in real time is the discrimination of nonsystematic errors that tend to inflate the total forecast error. It is important that model users maintain awareness of ongoing model changes. Such changes are likely to modify the basic error characteristics, particularly near the surface.

Multi-year application of WRF-CAM5 over East Asia-Part II: Interannual variability, trend analysis, and aerosol indirect effects

NASA Astrophysics Data System (ADS)

Zhang, Yang; Wang, Kai; He, Jian

2017-09-01

Following a comprehensive evaluation of WRF-CAM5 in Part I, Part II describes analyses of interannual variability, multi-year variation trends, and the direct, indirect, and total effects of anthropogenic aerosols. The interannual variations of chemical column and surface concentrations, and ozone (O3)/particulate matter (PM) indicators are strongly correlated to anthropogenic emission changes. Despite model biases, the model captures well the observed interannual variations of temperature at 2-m, cloud fraction, shortwave cloud forcing, downwelling shortwave radiation, cloud droplet number concentration, column O3, and column formaldehyde (HCHO) for the whole domain. While the model reproduces the volatile organic compound (VOC)-limited regimes of O3 chemistry at sites in Hong Kong, Taiwan, Japan, South Korea, and from the Acid Deposition Monitoring Network in East Asia (EANET) and the degree of sulfate neutralization at the EANET sites, it has limited capability in capturing the interannual variations of the ratio of O3 and nitrogen dioxide (O3/NO2) and PM chemical regime indicators, due to uncertainties in the emissions of precursors for O3 and secondary PM, the model assumption for ammonium bisulfate (NH4HSO4) as well as lack of gas/particle partitioning of total ammonia and total nitrate. While the variation trends in multi-year periods in aerosol optical depth and column concentrations of carbon monoxide, sulfur dioxide, and NO2 are mainly caused by anthropogenic emissions, those of major meteorological and cloud variables partly reflect feedbacks of chemistry to meteorological variables. The impacts of anthropogenic aerosol indirect effects either dominate or play an important role in the aerosol total effects for most cloud and chemical predictions, whereas anthropogenic aerosol direct effects influence most meteorological and radiation variables. The direct, indirect, and total effects of anthropogenic aerosols exhibit a strong interannual variability in 2001, 2006, and 2011.

Integrated firn elevation change model for glaciers and ice caps

NASA Astrophysics Data System (ADS)

Saß, Björn; Sauter, Tobias; Braun, Matthias

2016-04-01

We present the development of a firn compaction model in order to improve the volume to mass conversion of geodetic glacier mass balance measurements. The model is applied on the Arctic ice cap Vestfonna. Vestfonna is located on the island Nordaustlandet in the north east of Svalbard. Vestfonna covers about 2400 km² and has a dome like shape with well-defined outlet glaciers. Elevation and volume changes measured by e.g. satellite techniques are becoming more and more popular. They are carried out over observation periods of variable length and often covering different meteorological and snow hydrological regimes. The elevation change measurements compose of various components including dynamic adjustments, firn compaction and mass loss by downwasting. Currently, geodetic glacier mass balances are frequently converted from elevation change measurements using a constant conversion factor of 850 kg m-³ or the density of ice (917 kg m-³) for entire glacier basins. However, the natural conditions are rarely that static. Other studies used constant densities for the ablation (900 kg m-³) and accumulation (600 kg m-³) areas, whereby density variations with varying meteorological and climate conditions are not considered. Hence, each approach bears additional uncertainties from the volume to mass conversion that are strongly affected by the type and timing of the repeat measurements. We link and adapt existing models of surface energy balance, accumulation and snow and firn processes in order to improve the volume to mass conversion by considering the firn compaction component. Energy exchange at the surface is computed by a surface energy balance approach and driven by meteorological variables like incoming short-wave radiation, air temperature, relative humidity, air pressure, wind speed, all-phase precipitation, and cloud cover fraction. Snow and firn processes are addressed by a coupled subsurface model, implemented with a non-equidistant layer discretisation. On our poster we present a general view on the model structure, the input data (model forcing) and finally, an exemplary test case with basic approaches of validation.

Impact of High Resolution Land-Use Data in Meteorology and Air Quality Modeling Systems

EPA Science Inventory

Accurate land use information is important in meteorology for land surface exchanges, in emission modeling for emission spatial allocation, and in air quality modeling for chemical surface fluxes. Currently, meteorology, emission, and air quality models often use outdated USGS Gl...

Near-Surface Meteorology During the Arctic Summer Cloud Ocean Study (ASCOS): Evaluation of Reanalyses and Global Climate Models.

NASA Technical Reports Server (NTRS)

De Boer, G.; Shupe, M.D.; Caldwell, P.M.; Bauer, Susanne E.; Persson, O.; Boyle, J.S.; Kelley, M.; Klein, S.A.; Tjernstrom, M.

2014-01-01

Atmospheric measurements from the Arctic Summer Cloud Ocean Study (ASCOS) are used to evaluate the performance of three atmospheric reanalyses (European Centre for Medium Range Weather Forecasting (ECMWF)- Interim reanalysis, National Center for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis, and NCEP-DOE (Department of Energy) reanalysis) and two global climate models (CAM5 (Community Atmosphere Model 5) and NASA GISS (Goddard Institute for Space Studies) ModelE2) in simulation of the high Arctic environment. Quantities analyzed include near surface meteorological variables such as temperature, pressure, humidity and winds, surface-based estimates of cloud and precipitation properties, the surface energy budget, and lower atmospheric temperature structure. In general, the models perform well in simulating large-scale dynamical quantities such as pressure and winds. Near-surface temperature and lower atmospheric stability, along with surface energy budget terms, are not as well represented due largely to errors in simulation of cloud occurrence, phase and altitude. Additionally, a development version of CAM5, which features improved handling of cloud macro physics, has demonstrated to improve simulation of cloud properties and liquid water amount. The ASCOS period additionally provides an excellent example of the benefits gained by evaluating individual budget terms, rather than simply evaluating the net end product, with large compensating errors between individual surface energy budget terms that result in the best net energy budget.

Study of Spatial Variability of Air Temperature by Means of Remote Sensing Data and Weather Stations in Urban Areas: A Case Study for Campinas - São Paulo, Brazil.

NASA Astrophysics Data System (ADS)

Bezerra, L. M.; Avila, A. M. H. D.; Pereira, V. R.; Gonçalves, R. R. D. V.; Coltri, P. P.

2016-12-01

The surface meteorological and satellite Landsat8 data time series the city of Campinas, southeastern Brazil, has shown the rising temperatures in recent decades. According to scientific studies, part of this increase may be related to the urban sprawl of the city that currently has degree urbanization 98.28% and 1,164,098 inhabitants. Thus, the thermal images can represent reliable information of the surface temperature and this varies according to the land use and land cover. Therefore, were used 17 images of TIRS sensor (Thermal Infrared Sensor), band 10 and spatial resolution of 100 meters aboard satellite Landsat8 between 2013 and 2015 and temperature data from three meteorological stations of the city in different locations. After, was used the Pearson correlation between the measured weather data under 1.5 meters above ground level and surface temperature data estimated by satellite with a real difference 1 hour to less between stations and the satellite. The results indicated the 49% correlation in University of Campinas / Cepagri station, 86% in the Agronomic Institute of Campinas station and 90% in the Viracopos International Airport. This fact can be explained by the different degrees of urbanization where the weather stations are located and the heterogeneous characteristics of the local surface, as its roughness, impermeability, vegetation cover and concentration of buildings. Although these factors contribute to that there is a distortion of the surface temperature values detected by the satellite, the satellite was Landsat8 efficient to represent the spatial variability of temperature. In future studies, new techniques to obtain more accurate data through remote sensing will be studied.

Identifying Meteorological Controls on Open and Closed Mesoscale Cellular Convection as Associated with Marine Cold Air Outbreaks

NASA Astrophysics Data System (ADS)

McCoy, Isabel; Wood, Robert; Fletcher, Jennifer

Marine low clouds are key influencers of the climate and contribute significantly to uncertainty in model climate sensitivity due to their small scale and complex processes. Many low clouds occur in large-scale cellular patterns, known as open and closed mesoscale cellular convection (MCC), which have significantly different radiative and microphysical properties. Investigating MCC development and meteorological controls will improve our understanding of their impacts on the climate. We conducted an examination of time-varying meteorological conditions associated with satellite-determined open and closed MCC. The spatial and temporal patterns of MCC clouds were compared with key meteorological control variables calculated from ERA-Interim Reanalysis to highlight dependencies and major differences. This illustrated the influence of environmental stability and surface forcing as well as the role of marine cold air outbreaks (MCAO, the movement of cold air from polar-regions across warmer waters) in MCC cloud formation. Such outbreaks are important to open MCC development and may also influence the transition from open to closed MCC. Our results may lead to improvements in the parameterization of cloudiness and advance the simulation of marine low clouds. National Science Foundation Graduate Research Fellowship Grant (DGE-1256082).

Meteorological variables to aid forecasting deep slab avalanches on persistent weak layers

USGS Publications Warehouse

Marienthal, Alex; Hendrikx, Jordy; Birkeland, Karl; Irvine, Kathryn M.

2015-01-01

Deep slab avalanches are particularly challenging to forecast. These avalanches are difficult to trigger, yet when they release they tend to propagate far and can result in large and destructive avalanches. We utilized a 44-year record of avalanche control and meteorological data from Bridger Bowl ski area in southwest Montana to test the usefulness of meteorological variables for predicting seasons and days with deep slab avalanches. We defined deep slab avalanches as those that failed on persistent weak layers deeper than 0.9 m, and that occurred after February 1st. Previous studies often used meteorological variables from days prior to avalanches, but we also considered meteorological variables over the early months of the season. We used classification trees and random forests for our analyses. Our results showed seasons with either dry or wet deep slabs on persistent weak layers typically had less precipitation from November through January than seasons without deep slabs on persistent weak layers. Days with deep slab avalanches on persistent weak layers often had warmer minimum 24-hour air temperatures, and more precipitation over the prior seven days, than days without deep slabs on persistent weak layers. Days with deep wet slab avalanches on persistent weak layers were typically preceded by three days of above freezing air temperatures. Seasonal and daily meteorological variables were found useful to aid forecasting dry and wet deep slab avalanches on persistent weak layers, and should be used in combination with continuous observation of the snowpack and avalanche activity.

High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea

NASA Astrophysics Data System (ADS)

Park, Moon-Soo; Park, Sung-Hwa; Chae, Jung-Hoon; Choi, Min-Hyeok; Song, Yunyoung; Kang, Minsoo; Roh, Joon-Woo

2017-04-01

To improve our knowledge of urban meteorology, including those processes applicable to high-resolution meteorological models in the Seoul Metropolitan Area (SMA), the Weather Information Service Engine (WISE) Urban Meteorological Observation System (UMS-Seoul) has been designed and installed. The UMS-Seoul incorporates 14 surface energy balance (EB) systems, 7 surface-based three-dimensional (3-D) meteorological observation systems and applied meteorological (AP) observation systems, and the existing surface-based meteorological observation network. The EB system consists of a radiation balance system, sonic anemometers, infrared CO2/H2O gas analyzers, and many sensors measuring the wind speed and direction, temperature and humidity, precipitation, and air pressure. The EB-produced radiation, meteorological, and turbulence data will be used to quantify the surface EB according to land use and to improve the boundary-layer and surface processes in meteorological models. The 3-D system, composed of a wind lidar, microwave radiometer, aerosol lidar, or ceilometer, produces the cloud height, vertical profiles of backscatter by aerosols, wind speed and direction, temperature, humidity, and liquid water content. It will be used for high-resolution reanalysis data based on observations and for the improvement of the boundary-layer, radiation, and microphysics processes in meteorological models. The AP system includes road weather information, mosquito activity, water quality, and agrometeorological observation instruments. The standardized metadata for networks and stations are documented and renewed periodically to provide a detailed observation environment. The UMS-Seoul data are designed to support real-time acquisition and display and automatically quality check within 10 min from observation. After the quality check, data can be distributed to relevant potential users such as researchers and policy makers. Finally, two case studies demonstrate that the observed data have a great potential to help to understand the boundary-layer structures more deeply, improve the performance of high-resolution meteorological models, and provide useful information customized based on the user demands in the SMA.

Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1

NASA Astrophysics Data System (ADS)

Yahya, Khairunnisa; Wang, Kai; Campbell, Patrick; Glotfelty, Timothy; He, Jian; Zhang, Yang

2016-02-01

The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 with the Carbon Bond 2005 (CB05) gas-phase mechanism is evaluated for its first decadal application during 2001-2010 using the Representative Concentration Pathway 8.5 (RCP 8.5) emissions to assess its capability and appropriateness for long-term climatological simulations. The initial and boundary conditions are downscaled from the modified Community Earth System Model/Community Atmosphere Model (CESM/CAM5) v1.2.2. The meteorological initial and boundary conditions are bias-corrected using the National Center for Environmental Protection's Final (FNL) Operational Global Analysis data. Climatological evaluations are carried out for meteorological, chemical, and aerosol-cloud-radiation variables against data from surface networks and satellite retrievals. The model performs very well for the 2 m temperature (T2) for the 10-year period, with only a small cold bias of -0.3 °C. Biases in other meteorological variables including relative humidity at 2 m, wind speed at 10 m, and precipitation tend to be site- and season-specific; however, with the exception of T2, consistent annual biases exist for most of the years from 2001 to 2010. Ozone mixing ratios are slightly overpredicted at both urban and rural locations with a normalized mean bias (NMB) of 9.7 % but underpredicted at rural locations with an NMB of -8.8 %. PM2.5 concentrations are moderately overpredicted with an NMB of 23.3 % at rural sites but slightly underpredicted with an NMB of -10.8 % at urban/suburban sites. In general, the model performs relatively well for chemical and meteorological variables, and not as well for aerosol-cloud-radiation variables. Cloud-aerosol variables including aerosol optical depth, cloud water path, cloud optical thickness, and cloud droplet number concentration are generally underpredicted on average across the continental US. Overpredictions of several cloud variables over the eastern US result in underpredictions of radiation variables (such as net shortwave radiation - GSW - with a mean bias - MB - of -5.7 W m-2) and overpredictions of shortwave and longwave cloud forcing (MBs of ˜ 7 to 8 W m-2), which are important climate variables. While the current performance is deemed to be acceptable, improvements to the bias-correction method for CESM downscaling and the model parameterizations of cloud dynamics and thermodynamics, as well as aerosol-cloud interactions, can potentially improve model performance for long-term climate simulations.

BOREAS AES Five-Day Averaged Surface Meteorological and Upper Air Data

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Strub, Richard; Newcomer, Jeffrey A.

2000-01-01

The Canadian Atmospheric Environment Service (AES) provided BOREAS with hourly and daily surface meteorological data from 23 of the AES meteorological stations located across Canada and upper air data from 1 station at The Pas, Manitoba. Due to copyright restrictions on the full resolution surface meteorological data, this data set contains 5-day average values for the surface parameters. The upper air data are provided in their full resolution form. The 5-day averaging was performed in order to create a data set that could be publicly distributed at no cost. Temporally, the surface meteorological data cover the period of January 1975 to December 1996 and the upper air data cover the period of January 1961 to November 1996. The data are provided in tabular ASCII files, and are classified as AFM-staff data. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

NY-uHMT: A dense hydro-meteorological network to characterize urban land-atmosphere interactions

NASA Astrophysics Data System (ADS)

Ramamurthy, P.; Lakhankar, T.; Khanbilvardi, R.; Devineni, N.

2016-12-01

Most people in the US live in large Metropolitan areas that have a dense urban core in the center, dominated by built surfaces and surrounded by residential/suburban areas that consist a mix of built, vegetated and permeable surfaces. This creates a gradient in the hydro-meteorological environment giving rise to complex land-atmosphere interactions. Current modeling platforms and observational techniques like tower measurements do not adequately account for the underlying heterogeneity. To address this critical gap in our understanding we have instituted a dense network of sensors in the New York Metropolitan area. This unique urban sensor network consists of instrumentation to monitor soil moisture at multiple depths along with air temperature, relative humidity and precipitation, with room to add additional sensors in the future. The network is autonomous and connected to a centralized server using cellular towers. Apart from describing the spatial variability in hydro-meteorological quantities the network will also aid in conducting high-resolution numerical simulations to study and forecast urban weather and climate. In one such simulation conducted to partition the influence of storage flux, wind pattern and circulation and soil moisture deficit on urban heat island intensity (UHI), we found that the daily variability in UHI in NYC was sensitive to available energy and wind pattern. The long-term trend in UHI was however related to soil moisture deficit. In fact a prolonged heat wave period witnessed during summer 2006 correlated well with an extended dry period and the daily UHI in NYC almost doubled. Moreover, the urban soils also suffered from high degree of dessication, owing to drier urban boundary layer.

Hydrological state of the Large Aral Sea in the fall season of 2013

NASA Astrophysics Data System (ADS)

Izhitskiy, Alexander; Zavialov, Peter

2014-05-01

We report here the results of the latest expedition of the Shirshov Institute to the Aral Sea. The survey encompassed 8 field days in October-November, 2013. Direct measurements of thermohaline characteristics and water currents were conducted in the western basin of the Large Aral Sea during the expedition. Vertical profiles of temperature and salinity were obtained using a CTD profiler at 9 stations, situated on two cross-sections of the western basin. Four mooring stations equipped with current meters, as well as pressure gauges, were deployed for 4-6 days on the slopes of the deepest portion of the western basin. A portable automatic meteorological station, continuously recording the variability of wind and principal meteorological parameters, was installed near the mooring sites. Analysis of the current measurements data along with the meteorological data records demonstrated the current velocity and level anomalies responded energetically to winds. Correlation analysis of the velocity series versus the wind stress allowed to quantify the response of the system to the wind forcing. Together with the similar results of more earlier surveys, recently collected data shows that the mean surface circulation of the western basin remains anti-cyclonic under the predominant winds. Character of the interannual variability of salinity values in the Aral Sea water manifested increase in the surface layer during last 5 years. On the other hand, salinity values in the bottom layer appear to be decreased due to ceasing of the influence of the interbasin water exchange since 2010. Water level of the Large Aral Sea is still falling. Assessment of the on-going changes holds promise to help predicting the subsequent state of the Aral Sea region.

Development of gridded solar radiation data over Belgium based on Meteosat and in-situ observations

NASA Astrophysics Data System (ADS)

Journée, Michel; Vanderveken, Gilles; Bertrand, Cédric

2013-04-01

Knowledge on solar resources is highly important for all forms of solar energy applications. With the recent development in solar-based technologies national meteorological services are faced with increasing demands for high-quality and reliable site-time specific solar resource information. Traditionally, solar radiation is observed by means of networks of meteorological stations. Costs for installation and maintenance of such networks are very high and national networks comprise only few stations. Consequently the availability of ground-based solar radiation measurements has proven to be spatially and temporally inadequate for many applications. To overcome such a limitation, a major effort has been undertaken at the Royal Meteorological Institute of Belgium (RMI) to provide the solar energy industry, the electricity sector, governments, and renewable energy organizations and institutions with the most suitable and accurate information on the solar radiation resources at the Earth's surface over the Belgian territory. Only space-based observations can deliver a global coverage of the solar irradiation impinging on horizontal surface at the ground level. Because only geostationary data allow to capture the diurnal cycle of the solar irradiance at the Earth's surface, a method that combines information from Meteosat Second Generation satellites and ground-measurement has been implemented at RMI to generate high resolution solar products over Belgium on an operational basis. Besides these new products, the annual and seasonal variability of solar energy resource was evaluated, solar radiation climate zones were defined and the recent trend in solar radiation was characterized.

Global trends in visibility: Implications for dust sources

USGS Publications Warehouse

Mahowald, N.M.; Ballantine, J.A.; Feddema, J.; Ramankutty, N.

2007-01-01

There is a large uncertainty in the relative roles of human land use, climate change and carbon dioxide fertilization in changing desert dust source strength over the past 100 years, and the overall sign of human impacts on dust is not known. We used visibility data from meteorological stations in dusty regions to assess the anthropogenic impact on long term trends in desert dust emissions. We did this by looking at time series of visibility derived variables and their correlations with precipitation, drought, winds, land use and grazing. Visibility data are available at thousands of stations globally from 1900 to the present, but we focused on 357 stations with more than 30 years of data in regions where mineral aerosols play a dominant role in visibility observations. We evaluated the 1974 to 2003 time period because most of these stations have reliable records only during this time. We first evaluated the visibility data against AERONET aerosol optical depth data, and found that only in dusty regions are the two moderately correlated. Correlation coefficients between visibility-derived variables and AERONET optical depths indicate a moderate correlation (0.47), consistent with capturing about 20% of the variability in optical depths. Two visibility-derived variables appear to compare the best with AERONET observations: the fraction of observations with visibility less than 5 km (VIS5) and the surface extinction (EXT). Regional trends show that in many dusty places, VIS5 and EXT are statistically significantly correlated with the Palmer drought severity index (based on precipitation and temperature) or surface wind speeds, consistent with dust temporal variability being largely driven by meteorology. This is especially true for North African and Chinese dust sources, but less true in the Middle East, Australia or South America, where there are not consistent patterns in the correlations. Climate indices such as El Nino or the North Atlantic Oscillation are not correlated with visibility-derived variables in this analysis. There are few stations where visibility measures are correlated with cultivation or grazing estimates on a temporal basis, although this may be a function of the very coarse temporal resolution of the land use datasets. On the other hand, spatial analysis of the visibility data suggests that natural topographic lows are not correlated with VIS5 or EXT, but land use is correlated at a moderate level. This analysis is consistent with land use being important in some regions, but meteorology driving interannual variability during 1974-2003.

How useful are meteorological drought indicators to assess agricultural drought impacts across Europe?

NASA Astrophysics Data System (ADS)

Bachmair, Sophie; Tanguy, Maliko; Hannaford, Jamie; Stahl, Kerstin

2016-04-01

Drought monitoring and early warning (M&EW) is an important component of agricultural and silvicultural risk management. Meteorological indicators such as the Standardized Precipitation Index (SPI) are widely used in operational M&EW systems and for drought hazard assessment. Meteorological drought yet does not necessarily equate to agricultural drought given differences in drought susceptibility, e.g. crop-specific vulnerability, soil water holding capacity, irrigation and other management practices. How useful are meteorological indicators such as SPI to assess agricultural drought? Would the inclusion of vegetation indicators into drought M&EW systems add value for the agricultural sector? To answer these questions, it is necessary to investigate the link between meteorological indicators and agricultural impacts of drought. Crop yield or loss data is one source of information for drought impacts, yet mostly available as aggregated data at the annual scale. Remotely sensed vegetation stress data offer another possibility to directly assess agricultural impacts with high spatial and temporal resolution and are already used by some M&EW systems. At the same time, reduced crop yield and satellite-based vegetation stress potentially suffer from multi-causality. The aim of this study is therefore to investigate the relation between meteorological drought indicators and agricultural drought impacts for Europe, and to intercompare different agricultural impact variables. As drought indicators we used SPI and the Standardized Precipitation Evaporation Index (SPEI) for different accumulation periods. The focus regarding drought impact variables was on remotely sensed vegetation stress derived from MODIS NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature) data, but the analysis was complemented with crop yield data and text-based information from the European Drought Impact report Inventory (EDII) for selected countries. A correlation analysis between meteorological drought indicators and remotely sensed vegetation stress at the EU NUTS3 region level revealed a high correlation between the two types of indicators for many regions; however some spatial variability was observed in (i) strength of correlation, (ii) performance of SPI versus SPEI, and (iii) best linked SPI/SPEI time scale. We additionally explored whether geographic properties like climate, soil texture, land use, and location explain the observed spatial patterns. Our study revealed that climatically dryer areas (water limited) showed high correlations between SPI/SPEI and vegetation stress, whereas the wettest parts of Europe (radiation limited regions) showed negative correlations especially for short accumulation periods, suggesting that for these regions, short droughts could actually be beneficial for vegetation growth. These findings suggest that relying solely on meteorological indicators for agricultural risk assessment in some regions might be inadequate. Overall, such information may help to tailor agricultural drought M&EW systems to specific regions.

Investigating Dry Deposition of Ozone to Vegetation

NASA Astrophysics Data System (ADS)

Silva, Sam J.; Heald, Colette L.

2018-01-01

Atmospheric ozone loss through dry deposition to vegetation is a critically important process for both air quality and ecosystem health. The majority of atmospheric chemistry models calculate dry deposition using a resistance-in-series parameterization by Wesely (1989), which is dependent on many environmental variables and lookup table values. The uncertainties contained within this parameterization have not been fully explored, ultimately challenging our ability to understand global scale biosphere-atmosphere interactions. In this work, we evaluate the GEOS-Chem model simulation of ozone dry deposition using a globally distributed suite of observations. We find that simulated daytime deposition velocities generally reproduce the magnitude of observations to within a factor of 1.4. When correctly accounting for differences in land class between the observations and model, these biases improve, most substantially over the grasses and shrubs land class. These biases do not impact the global ozone burden substantially; however, they do lead to local absolute changes of up to 4 ppbv and relative changes of 15% in summer surface concentrations. We use MERRA meteorology from 1979 to 2008 to assess that the interannual variability in simulated annual mean ozone dry deposition due to model input meteorology is small (generally less than 5% over vegetated surfaces). Sensitivity experiments indicate that the simulation is most sensitive to the stomatal and ground surface resistances, as well as leaf area index. To improve ozone dry deposition models, more measurements are necessary over rainforests and various crop types, alongside constraints on individual depositional pathways and other in-canopy ozone loss processes.

Response of South American Ecosystems to Precipitation Variability

NASA Astrophysics Data System (ADS)

Knox, R. G.; Kim, Y.; Longo, M.; Medvigy, D.; Wang, J.; Moorcroft, P. R.; Bras, R. L.

2009-12-01

The Ecosystem Demography Model 2 is a dynamic ecosystem model and land surface energy balance model. ED2 discretizes landscapes of particular terrain and meteorology into fractional areas of unique disturbance history. Each fraction, defined by a shared vertical soil column and canopy air space, contains a stratum of plant groups unique in functional type, size and number density. The result is a vertically distributed representation of energy transfer and plant dynamics (mortality, productivity, recruitment, disturbance, resource competition, etc) that successfully approximates the behaviour of individual-based vegetation models. In previous exercises simulating Amazonian land surface dynamics with ED 2, it was observed that when using grid averaged precipitation as an external forcing the resulting water balance typically over-estimated leaf interception and leaf evaporation while under estimating through-fall and transpiration. To investigate this result, two scenario were conducted in which land surface biophysics and ecosystem demography over the Northern portion of South America are simulated over ~200 years: (1) ED2 is forced with grid averaged values taken from the ERA40 reanalysis meteorological dataset; (2) ED2 is forced with ERA40 reanalysis, but with its precipitation re-sampled to reflect statistical qualities of point precipitation found at rain gauge stations in the region. The findings in this study suggest that the equilibrium moisture states and vegetation demography are co-dependent and show sensitivity to temporal variability in precipitation. These sensitivities will need to be accounted for in future projections of coupled climate-ecosystem changes in South America.

METEOROLOGICAL AND TRANSPORT MODELING

EPA Science Inventory

Advanced air quality simulation models, such as CMAQ, as well as other transport and dispersion models, require accurate and detailed meteorology fields. These meteorology fields include primary 3-dimensional dynamical and thermodynamical variables (e.g., winds, temperature, mo...

Large-scale experimental technology with remote sensing in land surface hydrology and meteorology

NASA Technical Reports Server (NTRS)

Brutsaert, Wilfried; Schmugge, Thomas J.; Sellers, Piers J.; Hall, Forrest G.

1988-01-01

Two field experiments to study atmospheric and land surface processes and their interactions are summarized. The Hydrologic-Atmospheric Pilot Experiment, which tested techniques for measuring evaporation, soil moisture storage, and runoff at scales of about 100 km, was conducted over a 100 X 100 km area in France from mid-1985 to early 1987. The first International Satellite Land Surface Climatology Program field experiment was conducted in 1987 to develop and use relationships between current satellite measurements and hydrologic, climatic, and biophysical variables at the earth's surface and to validate these relationships with ground truth. This experiment also validated surface parameterization methods for simulation models that describe surface processes from the scale of vegetation leaves up to scales appropriate to satellite remote sensing.

High-resolution surface analysis for extended-range downscaling with limited-area atmospheric models

NASA Astrophysics Data System (ADS)

Separovic, Leo; Husain, Syed Zahid; Yu, Wei; Fernig, David

2014-12-01

High-resolution limited-area model (LAM) simulations are frequently employed to downscale coarse-resolution objective analyses over a specified area of the globe using high-resolution computational grids. When LAMs are integrated over extended time frames, from months to years, they are prone to deviations in land surface variables that can be harmful to the quality of the simulated near-surface fields. Nudging of the prognostic surface fields toward a reference-gridded data set is therefore devised in order to prevent the atmospheric model from diverging from the expected values. This paper presents a method to generate high-resolution analyses of land-surface variables, such as surface canopy temperature, soil moisture, and snow conditions, to be used for the relaxation of lower boundary conditions in extended-range LAM simulations. The proposed method is based on performing offline simulations with an external surface model, forced with the near-surface meteorological fields derived from short-range forecast, operational analyses, and observed temperatures and humidity. Results show that the outputs of the surface model obtained in the present study have potential to improve the near-surface atmospheric fields in extended-range LAM integrations.

What is the sensitivity of the mineral dust cycle at the regional scale to surface wind speed? First insights from the DRUMS project.

NASA Astrophysics Data System (ADS)

Bouet, Christel; Siour, Guillaume; Poulet, David; Bergametti, Gilles; Laurent, Benoit; Brocheton, Fabien; Forêt, Gilles; Xu, Yiwen; Marticorena, Béatrice

2017-04-01

Modelling of the mineral dust cycle is still a challenging issue both at the global and regional scales: during the last decade, several exercises of model intercomparison highlighted the wide variability of the existing dust models to estimate dust emission fluxes and atmospheric load at both scales. For instance, within the framework of the international AEROCOM Project (http://aerocom.met.no/), 15 different global dust models provide a range of possible dust emission fluxes from 400 to 2200 Tg yr-1 for North Africa and from 26 to 526 Tg yr-1 for the Middle East, i.e. still a factor of 5 and 20 respectively (Huneeus et al., 2011). Whatever the scale, a critical aspect for any dust model is the sensitivity to the meteorological fields used to compute dust emission fluxes (external forcing or simulated by the coupled meteorological or climatic model). Indeed, the intensity of dust emission varies as a power 3 of the surface wind speed, and the number of dust emission events is the number of times the surface wind speed exceeds the wind erosion threshold. As a result, the simulations of dust emissions are extremely sensitive to the way the surface wind speeds are accounted for both in global and regional models. In this context, the aim of the DRUMS (DeseRt dUst Modeling: performance and Sensitivity evaluation) project was to investigate the sensitivity of a regional dust model (CHIMERE) to this parameter. This sensitivity study was conducted for 3 years from 2006 to 2008 over the North of Africa (45°N-0°N; 45°W-55°E), where dust emissions are the most intense. Emission fluxes can be simulated there with the most relevant data set of surface properties controlling dust emissions and accounting for the heterogeneity of land surfaces (surface roughness, soil size distribution and texture) of desert regions (Laurent et al., 2008). Meteorological products (forecasts and re-analysis) provided by the most recognized international meteorological centres (US NCEP and ECMWF), and thus the most widely used for the simulations of the mineral dust cycle, were tested. In addition, the benefit provided by the use of the WRF model to downscale the meteorological forcing was evaluated. The estimation of the performance of the CHIMERE model forced by the different meteorological fields was conducted using a unique validation data set compiled during the project by analysing and evaluating (i) the large number of experimental data resulting from the AMMA (African Monsoon Multidisciplinary Analysis) field campaigns, (ii) long-term aerosol monitoring over West Africa (Sahelian Dust Transect) and downwind the Sahara/Sahel region (AERONET), and (iii) recent satellite aerosol products (SeaWIFS AOD). This dataset allowed to validate the main characteristics of the dust cycle (emission, transport, and deposit).

Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model

NASA Astrophysics Data System (ADS)

Ghotbi, Saba; Sotoudeheian, Saeed; Arhami, Mohammad

2016-09-01

Satellite remote sensing products of AOD from MODIS along with appropriate meteorological parameters were used to develop statistical models and estimate ground-level PM10. Most of previous studies obtained meteorological data from synoptic weather stations, with rather sparse spatial distribution, and used it along with 10 km AOD product to develop statistical models, applicable for PM variations in regional scale (resolution of ≥10 km). In the current study, meteorological parameters were simulated with 3 km resolution using WRF model and used along with the rather new 3 km AOD product (launched in 2014). The resulting PM statistical models were assessed for a polluted and largely variable urban area, Tehran, Iran. Despite the critical particulate pollution problem, very few PM studies were conducted in this area. The issue of rather poor direct PM-AOD associations existed, due to different factors such as variations in particles optical properties, in addition to bright background issue for satellite data, as the studied area located in the semi-arid areas of Middle East. Statistical approach of linear mixed effect (LME) was used, and three types of statistical models including single variable LME model (using AOD as independent variable) and multiple variables LME model by using meteorological data from two sources, WRF model and synoptic stations, were examined. Meteorological simulations were performed using a multiscale approach and creating an appropriate physic for the studied region, and the results showed rather good agreements with recordings of the synoptic stations. The single variable LME model was able to explain about 61%-73% of daily PM10 variations, reflecting a rather acceptable performance. Statistical models performance improved through using multivariable LME and incorporating meteorological data as auxiliary variables, particularly by using fine resolution outputs from WRF (R2 = 0.73-0.81). In addition, rather fine resolution for PM estimates was mapped for the studied city, and resulting concentration maps were consistent with PM recordings at the existing stations.

Mining Environmental Data from a Coupled Modelling System to Examine the Impact of Agricultural Management Practices on Groundwater and Air Quality

NASA Astrophysics Data System (ADS)

Garcia, V.; Cooter, E. J.; Hayes, B.; Murphy, M. S.; Bash, J. O.

2014-12-01

Excess nitrogen (N) resulting from current agricultural management practices can leach into sources of drinking water as nitrate, increasing human health risks of 'blue baby syndrome', hypertension, and some cancers and birth defects. Nitrogen also enters the atmosphere from land surfaces forming air pollution increasing human health risks of pulmonary and cardio-vascular disease. Characterizing and attributing nitrogen from agricultural management practices is difficult due to the complex and inter-related chemical and biological reactions associated with the nitrogen cascade. Coupled physical process-based models, however, present new opportunities to investigate relationships among environmental variables on new scales; particularly because they link emission sources with meteorology and the pollutant concentration ultimately found in the environment. In this study, we applied a coupled meteorology (NOAA-WRF), agricultural (USDA-EPIC) and air quality modelling system (EPA-CMAQ) to examine the impact of nitrogen inputs from corn production on ecosystem and human health and wellbeing. The coupled system accounts for the nitrogen flux between the land surface and air, and the soil surface and groundwater, providing a unique opportunity to examine the effect of management practices such as type and timing of fertilization, tilling and irrigation on both groundwater and air quality across the conterminous US. In conducting the study, we first determined expected relationships based on literature searches and then identified model variables as direct or surrogate variables. We performed extensive and methodical multi-variate regression modelling and variable selection to examine associations between agricultural management practices and environmental condition. We then applied the regression model to predict and contrast pollution levels between two corn production scenarios (Figure 1). Finally, we applied published health functions (e.g., spina bifida and cardio-vascular mortality rates) and economic impact functions (e.g., loss of work/school days, decontamination of drinking water wells). The results of this analysis will be presented at the conference.

SAMOS Surface Fluxes

NASA Astrophysics Data System (ADS)

Smith, Shawn; Bourassa, Mark

2014-05-01

The development of a new surface flux dataset based on underway meteorological observations from research vessels will be presented. The research vessel data center at the Florida State University routinely acquires, quality controls, and distributes underway surface meteorological and oceanographic observations from over 30 oceanographic vessels. These activities are coordinated by the Shipboard Automated Meteorological and Oceanographic System (SAMOS) initiative in partnership with the Rolling Deck to Repository (R2R) project. Recently, the SAMOS data center has used these underway observations to produce bulk flux estimates for each vessel along individual cruise tracks. A description of this new flux product, along with the underlying data quality control procedures applied to SAMOS observations, will be provided. Research vessels provide underway observations at high-temporal frequency (1 min. sampling interval) that include navigational (position, course, heading, and speed), meteorological (air temperature, humidity, wind, surface pressure, radiation, rainfall), and oceanographic (surface sea temperature and salinity) samples. Vessels recruited to the SAMOS initiative collect a high concentration of data within the U.S. continental shelf and also frequently operate well outside routine shipping lanes, capturing observations in extreme ocean environments (Southern, Arctic, South Atlantic, and South Pacific oceans). These observations are atypical for their spatial and temporal sampling, making them very useful for many applications including validation of numerical models and satellite retrievals, as well as local assessments of natural variability. Individual SAMOS observations undergo routine automated quality control and select vessels receive detailed visual data quality inspection. The result is a quality-flagged data set that is ideal for calculating turbulent flux estimates. We will describe the bulk flux algorithms that have been applied to the observations and the choices of constants that are used. Analysis of the preliminary SAMOS flux products will be presented, including spatial and temporal coverage for each derived parameter. The unique quality and sampling locations of research vessel observations and their independence from many models and products makes them ideal for validation studies. The strengths and limitations of research observations for flux validation studies will be discussed. The authors welcome a discussion with the flux community regarding expansion of the SAMOS program to include additional international vessels, thus facilitating and expansion of this research vessel-based flux product.

Bayesian dynamic modeling of time series of dengue disease case counts.

PubMed

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

Tracking near-surface atmospheric conditions using an infrasound network.

PubMed

Marcillo, O; Johnson, J B

2010-07-01

Continuous volcanic infrasound signal was recorded on a three-microphone network at Kilauea in July 2008 and inverted for near-surface horizontal winds. Inter-station phase delays, determined by signal cross-correlation, vary by up to 4% and are attributable to variable atmospheric conditions. The results suggest two predominant weather regimes during the study period: (1) 6-9 m/s easterly trade winds and (2) lower-intensity 2-5 m/s mountain breezes from Mauna Loa. The results demonstrate the potential of using infrasound for tracking local averaged meteorological conditions, which has implications for modeling plume dispersal and quantifying gas flux.

A Generalized Method for Vertical Profiles of Mean Layer Values of Meteorological Variables

DTIC Science & Technology

2015-09-01

NUMBER OF PAGES 62 19a. NAME OF RESPONSIBLE PERSON James Cogan a. REPORT Unclassified b. ABSTRACT Unclassified c . THIS PAGE...Message Zones, Primary Data Structure, and Sample METB3 Weighting Array 35 Appendix C . Samples of Input and Output 41 List of Symbols...39 Table C -1 Example of RAOB for Albuquerque, New Mexico. The first 3 mandatory levels are below the actual terrain surface and

Estimation of Surface Air Temperature from MODIS 1km Resolution Land Surface Temperature Over Northern China

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina

2010-01-01

Surface air temperature is a critical variable to describe the energy and water cycle of the Earth-atmosphere system and is a key input element for hydrology and land surface models. It is a very important variable in agricultural applications and climate change studies. This is a preliminary study to examine statistical relationships between ground meteorological station measured surface daily maximum/minimum air temperature and satellite remotely sensed land surface temperature from MODIS over the dry and semiarid regions of northern China. Studies were conducted for both MODIS-Terra and MODIS-Aqua by using year 2009 data. Results indicate that the relationships between surface air temperature and remotely sensed land surface temperature are statistically significant. The relationships between the maximum air temperature and daytime land surface temperature depends significantly on land surface types and vegetation index, but the minimum air temperature and nighttime land surface temperature has little dependence on the surface conditions. Based on linear regression relationship between surface air temperature and MODIS land surface temperature, surface maximum and minimum air temperatures are estimated from 1km MODIS land surface temperature under clear sky conditions. The statistical errors (sigma) of the estimated daily maximum (minimum) air temperature is about 3.8 C(3.7 C).

Use of a scenario-neutral approach to identify the key hydro-meteorological attributes that impact runoff from a natural catchment

NASA Astrophysics Data System (ADS)

Guo, Danlu; Westra, Seth; Maier, Holger R.

2017-11-01

Scenario-neutral approaches are being used increasingly for assessing the potential impact of climate change on water resource systems, as these approaches allow the performance of these systems to be evaluated independently of climate change projections. However, practical implementations of these approaches are still scarce, with a key limitation being the difficulty of generating a range of plausible future time series of hydro-meteorological data. In this study we apply a recently developed inverse stochastic generation approach to support the scenario-neutral analysis, and thus identify the key hydro-meteorological variables to which the system is most sensitive. The stochastic generator simulates synthetic hydro-meteorological time series that represent plausible future changes in (1) the average, extremes and seasonal patterns of rainfall; and (2) the average values of temperature (Ta), relative humidity (RH) and wind speed (uz) as variables that drive PET. These hydro-meteorological time series are then fed through a conceptual rainfall-runoff model to simulate the potential changes in runoff as a function of changes in the hydro-meteorological variables, and runoff sensitivity is assessed with both correlation and Sobol' sensitivity analyses. The method was applied to a case study catchment in South Australia, and the results showed that the most important hydro-meteorological attributes for runoff were winter rainfall followed by the annual average rainfall, while the PET-related meteorological variables had comparatively little impact. The high importance of winter rainfall can be related to the winter-dominated nature of both the rainfall and runoff regimes in this catchment. The approach illustrated in this study can greatly enhance our understanding of the key hydro-meteorological attributes and processes that are likely to drive catchment runoff under a changing climate, thus enabling the design of tailored climate impact assessments to specific water resource systems.

Modeling land surface hydrology sensitivity in the Colorado River Basin to historical climate variability

NASA Astrophysics Data System (ADS)

Whitney, K. M.; Bohn, T. J.; Vivoni, E. R.

2017-12-01

Over the past century, the Colorado River Basin (CRB) has experienced substantial warming and interannual climate variations, including prolonged drought periods. These patterns are projected to accelerate in the 21st century, with major consequences for water resources in the southwestern U.S. and northwestern Mexico. To evaluate future projections appropriately, however, it is important to first quantify the regional hydrologic response to historical climate variability in the CRB. In the current effort, we force the Variable Infiltration Capacity (VIC) land surface hydrology model and a river routing model with historical meteorological data to estimate water balance components and naturalized streamflow response in the CRB at 1/16o spatial resolution and at an hourly time step over the period 1950-2013. We utilize data products from satellite remote sensing to specify spatiotemporal variations in vegetation parameters and include an irrigation scheme to account for evapotranspiration from croplands in the CRB. Furthermore, we apply recent modifications in VIC to more properly account for bare soil evaporation in arid and semiarid ecosystems. Analyses of the historical model simulations are focused on quantifying the spatiotemporal variability of the soil moisture, evapotranspiration, streamflow and snowmelt response and their linkages to extreme meteorological events. Here we characterize the annual and monthly distributions, trends, and statistical extremes and central tendencies of water balance terms averaged over the CRB and its sub-basins for the entire study period 1950-2013. By building a model-based hydrologic climatology and catalog of historical extreme events for the CRB, we aim to construct a basis for future activities that analyze the impact of statistically downscaled climate change projections on the hydrology of the CRB and its urban areas.

Saskatchewan Forest Fire Control Centre Surface Meteorological Data

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Newcomer, Jeffrey A. (Editor); Funk, Barry; Strub, Richard

2000-01-01

The Saskatchewan Forest Fire Control Centre (SFFCC) provided surface meteorological data to BOREAS from its archive. This data set contains hourly surface meteorological data from 18 of the Meteorological stations located across Saskatchewan. Included in these data are parameters of date, time, temperature, relative humidity, wind direction, wind speed, and precipitation. Temporally, the data cover the period of May through September of 1994 and 1995. The data are provided in comma-delimited ASCII files, and are classified as AFM-Staff data. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

North Polar Radiative Flux Variability from 2002 Through 2014

NASA Technical Reports Server (NTRS)

Rutan, David; Rose, Fred; Doelling, David; Kato, Seiji; Smith, Bill, Jr.

2017-01-01

NASA's Clouds and the Earth's Radiant Energy System (CERES) project produces the SYN1Deg data product. SYN1deg provides global, 1deg gridded, hourly estimates of Top of Atmosphere (TOA) (CERES observations and calculations) and atmospheric and surface radiative flux (calculations). Examples of 12 year North Polar averages of some variables are shown to the right. Given recent interest in polar science we focus here on TOA and Surface validation of calculated irradiant fluxes. TOA upward longwave irradiance calculations match the CERES observations well both spatially and temporally with correlations remaining strong through PC 6. Compare SYN1Deg Calculations & Meteorological Teleconnections. TOA reflected shortwave irradiance calculations match the CERES observations well both spatially and temporally with correlations remaining string through PC 7. Comparing SYN1Deg calculations to teleconnection patterns requires expanding the area to 30N for EOF analyses. Correlating the Principal Components of various variables to teleconnection time series indicates which variable is most highly correlated with which teleconnection signal. The tables indicate the Pacific North American Oscillation is most correlated to the OLR EOF 1, and the North American Oscillation is correlated most closely to surface LW flux down EOF 1.

Interpolation of scattered temperature data measurements onto a worldwide regular grid using radial basis functions with applications to global warming

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

Kansa, E.J.; Axelrod, M.C.; Kercher, J.R.

1994-05-01

Our current research into the response of natural ecosystems to a hypothesized climatic change requires that we have estimates of various meteorological variables on a regularly spaced grid of points on the surface of the earth. Unfortunately, the bulk of the world`s meteorological measurement stations is located at airports that tend to be concentrated on the coastlines of the world or near populated areas. We can also see that the spatial density of the station locations is extremely non-uniform with the greatest density in the USA, followed by Western Europe. Furthermore, the density of airports is rather sparse in desertmore » regions such as the Sahara, the Arabian, Gobi, and Australian deserts; likewise the density is quite sparse in cold regions such as Antarctica Northern Canada, and interior northern Russia. The Amazon Basin in Brazil has few airports. The frequency of airports is obviously related to the population centers and the degree of industrial development of the country. We address the following problem here. Given values of meteorological variables, such as maximum monthly temperature, measured at the more than 5,500 airport stations, interpolate these values onto a regular grid of terrestrial points spaced by one degree in both latitude and longitude. This is known as the scattered data problem.« less

Testing the use of standardised indices and GRACE satellite data to estimate the European 2015 groundwater drought in near-real time

NASA Astrophysics Data System (ADS)

Van Loon, Anne F.; Kumar, Rohini; Mishra, Vimal

2017-04-01

In 2015, central and eastern Europe were affected by a severe drought. This event has recently been studied from meteorological and streamflow perspective, but no analysis of the groundwater situation has been performed. One of the reasons is that real-time groundwater level observations often are not available. In this study, we evaluate two alternative approaches to quantify the 2015 groundwater drought over two regions in southern Germany and eastern Netherlands. The first approach is based on spatially explicit relationships between meteorological conditions and historic groundwater level observations. The second approach uses the Gravity Recovery Climate Experiment (GRACE) terrestrial water storage (TWS) and groundwater anomalies derived from GRACE-TWS and (near-)surface storage simulations by the Global Land Data Assimilation System (GLDAS) models. We combined the monthly groundwater observations from 2040 wells to establish the spatially varying optimal accumulation period between the Standardised Groundwater Index (SGI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at a 0.25° gridded scale. The resulting optimal accumulation periods range between 1 and more than 24 months, indicating strong spatial differences in groundwater response time to meteorological input over the region. Based on the estimated optimal accumulation periods and available meteorological time series, we reconstructed the groundwater anomalies up to 2015 and found that in Germany a uniform severe groundwater drought persisted for several months during this year, whereas the Netherlands appeared to have relatively high groundwater levels. The differences between this event and the 2003 European benchmark drought are striking. The 2003 groundwater drought was less uniformly pronounced, both in the Netherlands and Germany. This is because slowly responding wells (the ones with optimal accumulation periods of more than 12 months) still were above average from the wet year of 2002, which experienced severe flooding in central Europe. GRACE-TWS and GRACE-based groundwater anomalies did not capture the spatial variability of the 2003 and 2015 drought events satisfactorily. GRACE-TWS did show that both 2003 and 2015 were relatively dry, but the differences between Germany and the Netherlands in 2015 and the spatially variable groundwater drought pattern in 2003 were not captured. This could be associated with the coarse spatial scale of GRACE. The simulated groundwater anomalies based on GRACE-TWS deviated considerably from the GRACE-TWS signal and from observed groundwater anomalies. The uncertainty in the GRACE-based groundwater anomalies mainly results from uncertainties in the simulation of soil moisture by the different GLDAS models. The GRACE-based groundwater anomalies are therefore not suitable for use in real-time groundwater drought monitoring in our case study regions. The alternative approach based on the spatially variable relationship between meteorological conditions and groundwater levels is more suitable to quantify groundwater drought in near real-time. Compared to the meteorological drought and streamflow drought (described in previous studies), the groundwater drought of 2015 had a more pronounced spatial variability in its response to meteorological conditions, with some areas primarily influenced by short-term meteorological deficits and others influenced by meteorological deficits accumulated over the preceding 2 years or more. In drought management, this information is very useful and our approach to quantify groundwater drought can be used until real-time groundwater observations become readily available.

Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions.

PubMed

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition.

Modelling the Meteorological Forest Fire Niche in Heterogeneous Pyrologic Conditions

PubMed Central

De Angelis, Antonella; Ricotta, Carlo; Conedera, Marco; Pezzatti, Gianni Boris

2015-01-01

Fire regimes are strongly related to weather conditions that directly and indirectly influence fire ignition and propagation. Identifying the most important meteorological fire drivers is thus fundamental for daily fire risk forecasting. In this context, several fire weather indices have been developed focussing mainly on fire-related local weather conditions and fuel characteristics. The specificity of the conditions for which fire danger indices are developed makes its direct transfer and applicability problematic in different areas or with other fuel types. In this paper we used the low-to-intermediate fire-prone region of Canton Ticino as a case study to develop a new daily fire danger index by implementing a niche modelling approach (Maxent). In order to identify the most suitable weather conditions for fires, different combinations of input variables were tested (meteorological variables, existing fire danger indices or a combination of both). Our findings demonstrate that such combinations of input variables increase the predictive power of the resulting index and surprisingly even using meteorological variables only allows similar or better performances than using the complex Canadian Fire Weather Index (FWI). Furthermore, the niche modelling approach based on Maxent resulted in slightly improved model performance and in a reduced number of selected variables with respect to the classical logistic approach. Factors influencing final model robustness were the number of fire events considered and the specificity of the meteorological conditions leading to fire ignition. PMID:25679957

DEVELOPMENT OF A LAND-SURFACE MODEL PART I: APPLICATION IN A MESOSCALE METEOROLOGY MODEL

EPA Science Inventory

Parameterization of land-surface processes and consideration of surface inhomogeneities are very important to mesoscale meteorological modeling applications, especially those that provide information for air quality modeling. To provide crucial, reliable information on the diurn...

Random forest meteorological normalisation models for Swiss PM10 trend analysis

NASA Astrophysics Data System (ADS)

Grange, Stuart K.; Carslaw, David C.; Lewis, Alastair C.; Boleti, Eirini; Hueglin, Christoph

2018-05-01

Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between -0.09 and -1.16 µg m-3 yr-1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at -0.77 µg m-3 yr-1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

Association of monthly frequencies of diverse diseases in the calls to the public emergency service of the city of Buenos Aires during 1999-2004 with meteorological variables and seasons.

PubMed

Alexander, P

2013-01-01

This work aims to study associations between monthly averages of meteorological variables and monthly frequencies of diverse diseases in the calls to the public ambulance emergency service of the city of Buenos Aires during the years 1999-2004. Throughout this time period no changes were made in the classification codes of the illnesses. Heart disease, arrhythmia, heart failure, cardiopulmonary arrest, angina pectoris, psychiatric diseases, stroke, transient ischemic attack, syncope and the total number of calls were analyzed against 11 weather variables and the four seasons. All illnesses exhibited some seasonal behavior, except cardiorespiratory arrest and angina pectoris. The largest frequencies of illnesses that exhibited some association with the meteorological variables used to occur in winter, except the psychiatric cases. Heart failure, stroke, psychiatric diseases and the total number of calls showed significant correlations with the 11 meteorological variables considered, and the largest indices (absolute values above 0.6) were found for the former two pathologies. On the other side, cardiorespiratory arrest and angina pectoris revealed no significant correlations and nearly null indices. Variables associated with temperature were the meteorological proxies with the largest correlations against diseases. Pressure and humidity mostly exhibited positive correlations, which is the opposite of variables related to temperature. Contrary to all other diseases, psychiatric pathologies showed a clear predominance of positive correlations. Finally, the association degree of the medical dataset with recurrent patterns was further evaluated through Fourier analysis, to assess the presence of statistically significant behavior. In the Northern Hemisphere high morbidity and mortality rates in December are usually assigned to diverse factors in relation to the holidays, but such an effect is not observed in the present analysis. There seems to be no clearly preferred meteorological proxy among the different types of temperatures used. It is shown that the amount of occurrences depends mainly on season rather on its strength quantified by temperature.

Association of monthly frequencies of diverse diseases in the calls to the public emergency service of the city of Buenos Aires during 1999-2004 with meteorological variables and seasons

NASA Astrophysics Data System (ADS)

Alexander, P.

2013-01-01

This work aims to study associations between monthly averages of meteorological variables and monthly frequencies of diverse diseases in the calls to the public ambulance emergency service of the city of Buenos Aires during the years 1999-2004. Throughout this time period no changes were made in the classification codes of the illnesses. Heart disease, arrhythmia, heart failure, cardiopulmonary arrest, angina pectoris, psychiatric diseases, stroke, transient ischemic attack, syncope and the total number of calls were analyzed against 11 weather variables and the four seasons. All illnesses exhibited some seasonal behavior, except cardiorespiratory arrest and angina pectoris. The largest frequencies of illnesses that exhibited some association with the meteorological variables used to occur in winter, except the psychiatric cases. Heart failure, stroke, psychiatric diseases and the total number of calls showed significant correlations with the 11 meteorological variables considered, and the largest indices (absolute values above 0.6) were found for the former two pathologies. On the other side, cardiorespiratory arrest and angina pectoris revealed no significant correlations and nearly null indices. Variables associated with temperature were the meteorological proxies with the largest correlations against diseases. Pressure and humidity mostly exhibited positive correlations, which is the opposite of variables related to temperature. Contrary to all other diseases, psychiatric pathologies showed a clear predominance of positive correlations. Finally, the association degree of the medical dataset with recurrent patterns was further evaluated through Fourier analysis, to assess the presence of statistically significant behavior. In the Northern Hemisphere high morbidity and mortality rates in December are usually assigned to diverse factors in relation to the holidays, but such an effect is not observed in the present analysis. There seems to be no clearly preferred meteorological proxy among the different types of temperatures used. It is shown that the amount of occurrences depends mainly on season rather on its strength quantified by temperature.

The surface climatology of the Ross Ice Shelf Antarctica

PubMed Central

Lazzara, Matthew A.; Keller, Linda M.; Cassano, John J.

2016-01-01

ABSTRACT The University of Wisconsin‐Madison Antarctic Automatic Weather Station (AWS) project has been making meteorological surface observations on the Ross Ice Shelf (RIS) for approximately 30 years. This network offers the most continuous set of routine measurements of surface meteorological variables in this region. The Ross Island area is excluded from this study. The surface climate of the RIS is described using the AWS measurements. Temperature, pressure, and wind data are analysed on daily, monthly, seasonal, and annual time periods for 13 AWS across the RIS. The AWS are separated into three representative regions – central, coastal, and the area along the Transantarctic Mountains – in order to describe specific characteristics of sections of the RIS. The climatology describes general characteristics of the region and significant changes over time. The central AWS experiences the coldest mean temperature, and the lowest resultant wind speed. These AWSs also experience the coldest potential temperatures with a minimum of 209.3 K at Gill AWS. The AWS along the Transantarctic Mountains experiences the warmest mean temperature, the highest mean sea‐level pressure, and the highest mean resultant wind speed. Finally, the coastal AWS experiences the lowest mean pressure. Climate indices (MEI, SAM, and SAO) are compared to temperature and pressure data of four of the AWS with the longest observation periods, and significant correlation is found for most AWS in sea‐level pressure and temperature. This climatology study highlights characteristics that influence the climate of the RIS, and the challenges of maintaining a long‐term Antarctic AWS network. Results from this effort are essential for the broader Antarctic meteorology community for future research. PMID:28008213

Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

PubMed

Carvajal, Thaddeus M; Viacrusis, Katherine M; Hernandez, Lara Fides T; Ho, Howell T; Amalin, Divina M; Watanabe, Kozo

2018-04-17

Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting. Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 - December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated. Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model. The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.

Exploring the spatio-temporal relationship between two key aeroallergens and meteorological variables in the United Kingdom

NASA Astrophysics Data System (ADS)

Khwarahm, Nabaz; Dash, Jadunandan; Atkinson, Peter M.; Newnham, R. M.; Skjøth, C. A.; Adams-Groom, B.; Caulton, Eric; Head, K.

2014-05-01

Constructing accurate predictive models for grass and birch pollen in the air, the two most important aeroallergens, for areas with variable climate conditions such as the United Kingdom, require better understanding of the relationships between pollen count in the air and meteorological variables. Variations in daily birch and grass pollen counts and their relationship with daily meteorological variables were investigated for nine pollen monitoring sites for the period 2000-2010 in the United Kingdom. An active pollen count sampling method was employed at each of the monitoring stations to sample pollen from the atmosphere. The mechanism of this method is based on the volumetric spore traps of Hirst design (Hirst in Ann Appl Biol 39(2):257-265, 1952). The pollen season (start date, finish date) for grass and birch were determined using a first derivative method. Meteorological variables such as daily rainfall; maximum, minimum and average temperatures; cumulative sum of Sunshine duration; wind speed; and relative humidity were related to the grass and birch pollen counts for the pre-peak, post peak and the entire pollen season. The meteorological variables were correlated with the pollen count data for the following temporal supports: same-day, 1-day prior, 1-day mean prior, 3-day mean prior, 7-day mean prior. The direction of influence (positive/negative) of meteorological variables on pollen count varied for birch and grass, and also varied when the pollen season was treated as a whole season, or was segmented into the pre-peak and post-peak seasons. Maximum temperature, sunshine duration and rainfall were the most important variables influencing the count of grass pollen in the atmosphere. Both maximum temperature (pre-peak) and sunshine produced a strong positive correlation, and rain produced a strong negative correlation with grass pollen count in the air. Similarly, average temperature, wind speed and rainfall were the most important variables influencing the count of birch pollen in the air. Both wind speed and rain produced a negative correlation with birch pollen count in the air and average temperature produced a positive correlation.

Assessment of the natural sources of particulate matter on the opencast mines air quality.

PubMed

Huertas, J I; Huertas, M E; Cervantes, G; Díaz, J

2014-09-15

Particulate matter is the main air pollutant in open pit mining areas. Preferred models that simulate the dispersion of the particles have been used to assess the environmental impact of the mining activities. Results obtained through simulation have been compared with the particle concentration measured in several sites and a coefficient of determination R(2)<0.78 has been reported. This result indicates that in the open pit mining areas there may be additional sources of particulate matter that have not been considered in the modeling process. This work proposes that the unconsidered sources of emissions are of regional scope such as the re-suspension particulate matter due to the wind action over uncovered surfaces. Furthermore, this work proposes to estimate the impact of such emissions on air quality as a function of the present and past meteorological conditions. A statistical multiple regression model was implemented in one of the world's largest open pit coal mining regions which is located in northern Colombia. Data from 9 particle-concentration monitoring stations and 3 meteorological stations obtained from 2009 to 2012 were statistically compared. Results confirmed the existence of a high linear relation (R(2)>0.95) between meteorological variables and particulate matter concentration being humidity, humidity of the previous day and temperature, the meteorological variables that contributed most significantly in the variance of the particulate matter concentration measured in the mining area while the contribution of the AERMOD estimations to the short term TSP (Total Suspended Particles) measured concentrations was negligible (<5%). The multiple regression model was used to identify the meteorological condition that leads to pollution episodes. It was found that conditions drier than 54% lead to pollution episodes while humidities greater than 70% maintain safe air quality conditions in the mining region in northern Colombia. Copyright © 2014 Elsevier B.V. All rights reserved.

Spatio-temporal response of maize yield to edaphic and meteorological conditions in a saline farmland

USDA-ARS?s Scientific Manuscript database

Spatio-temporal variability of crop production strongly depends on soil heterogeneity, meteorological conditions, and their interaction. Canopy reflectance can be used to describe crop status and yield spatial variability. The objectives of this work were to understand the spatio-temporal variabilit...

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.

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.

Mixed Layer Temperature Budget for the Northward Propagating Summer Monsoon Intraseasonal Oscillation (MISO) in the Central Bay of Bengal

NASA Astrophysics Data System (ADS)

Girishkumar, M. S.; Joseph, J.; Thangaprakash, V. P.; Pottapinjara, V.; McPhaden, M. J.

2017-11-01

Composite analyses of mixed layer temperature (MLT) budget terms from near-surface meteorological and oceanic observations in the central Bay of Bengal are utilized to evaluate the modulation of air-sea interactions and MLT processes in response to the summer monsoon intraseasonal oscillation (MISO). For this purpose, we use moored buoy data at 15°N, 12°N, and 8°N along 90°E together with TropFlux meteorological parameters and the Ocean Surface Current Analyses Real-time (OSCAR) current product. Our analysis shows a strong cooling tendency in MLT with maximum amplitude in the central and northern BoB during the northward propagation of enhanced convective activity associated with the active phase of the MISO; conversely, warming occurs during the suppressed phase of the MISO. The surface mixed layer is generally heated during convectively inactive phases of the MISO primarily due to increased net surface heat flux into the ocean. During convectively active MISO phases, the surface mixed layer is cooled by the combined influence of net surface heat loss to the atmosphere and entrainment cooling at the base of mixed layer. The variability of net surface heat flux is primarily due to modulation of latent heat flux and shortwave radiation. Shortwave is mostly controlled by an enhancement or reduction of cloudiness during the active and inactive MISO phases and latent heat flux is mostly controlled by variations in air-sea humidity difference.

Daily weather variables and affective disorder admissions to psychiatric hospitals

NASA Astrophysics Data System (ADS)

McWilliams, Stephen; Kinsella, Anthony; O'Callaghan, Eadbhard

2014-12-01

Numerous studies have reported that admission rates in patients with affective disorders are subject to seasonal variation. Notwithstanding, there has been limited evaluation of the degree to which changeable daily meteorological patterns influence affective disorder admission rates. A handful of small studies have alluded to a potential link between psychiatric admission rates and meteorological variables such as environmental temperature (heat waves in particular), wind direction and sunshine. We used the Kruskal-Wallis test, ARIMA and time-series regression analyses to examine whether daily meteorological variables—namely wind speed and direction, barometric pressure, rainfall, hours of sunshine, sunlight radiation and temperature—influence admission rates for mania and depression across 12 regions in Ireland over a 31-year period. Although we found some very weak but interesting trends for barometric pressure in relation to mania admissions, daily meteorological patterns did not appear to affect hospital admissions overall for mania or depression. Our results do not support the small number of papers to date that suggest a link between daily meteorological variables and affective disorder admissions. Further study is needed.

Subtropical Gyre Variability Observed by Ocean Color Satellites

NASA Technical Reports Server (NTRS)

McClain, Charles R.; Signorini, Sergio R.; Christian, James R.

2002-01-01

The subtropical gyres of the world are extensive, coherent regions that occupy about 40% of the surface of the earth. Once thought to be homogeneous and static habitats, there is increasing evidence that mid-latitude gyres exhibit substantial physical and biological variability on a variety of time scales. While biological productivity within these oligotrophic regions may be relatively small, their immense size makes their total contribution significant. Global distributions of dynamic height derived from satellite altimeter data, and chlorophyll concentration derived from satellite ocean color data, show that the dynamic center of the gyres, the region of maximum dynamic height where the thermocline is deepest, does not coincide with the region of minimum chlorophyll concentration. The physical and biological processes by which this distribution of ocean properties is maintained, and the spatial and temporal scales of variability associated with these processes, are analyzed using global surface chlorophyll-a concentrations, sea surface height, sea surface temperature and surface winds from operational satellite and meteorological sources, and hydrographic data from climatologies and individual surveys. Seasonal and interannual variability in the areal extent of the subtropical gyres are examined using 8 months (November 1996 - June 1997) of OCTS and nearly 5 years (September 1997 - June 02) of SeaWiFS ocean color data and are interpreted in the context of climate variability and measured changes in other ocean properties (i.e., wind forcing, surface currents, Ekman pumping, and vertical mixing). The North Pacific and North Atlantic gyres are observed to be shrinking over this period, while the South Pacific, South Atlantic, and South Indian Ocean gyres appear to be expanding.

Enviro-HIRLAM in Studies of Urban and Aerosol Impacts on Metropolitan Areas: Science-Education Approach

NASA Astrophysics Data System (ADS)

Mahura, Alexander; Nuterman, Roman; Mazeikis, Adomas; Gonzalez-Aparicio, Iratxe; Ivanov, Sergey; Palamarchuk, Julia

2014-05-01

To attract more perspective young scientists (and especially, MSc and PhD students) for advanced research and development of complex and modern modelling systems, a specific approach is required. It should allow within a short period of time to evaluate personal background levels, skills, capabilities, etc. To learn more about new potential science-oriented developers of the models, it is often not enough to look into the personal resume. Thus, a special event such as Young Scientist Summer School (YSSS) can be organized, where young researchers could have an opportunity to attend not only relevant lectures, but also participate in practical exercises allowing to solidify lecture materials. Here, the practical exercises are presented as independent small-scale (having duration of up to a week) research projects or studies oriented on specific topics of YSSS. Developed approach was tested and realized during 2008 and 2011 YSSS events held and organized in Zelenogorsk, Russia (by NetFAM et al.; http://netfam.fmi.fi/YSSS08) and Odessa, Ukraine (by MUSCATEN et al.; http://atmos.physic.ut.ee/~muscaten/YSSS/1info.html), respectively. It has been refined for the new YSSS (Jul 2014) to be organized by the COST Action EuMetChem. The main focus of all these YSSSs was/is on the integrated modelling of meteorological and chemical transport processes and impact of chemical weather on numerical weather prediction and climate modelling. During previous YSSSs some of such projects - "URBAN: The Influence of Metropolitan Areas on Meteorology", "AEROSOL: The Impact of Aerosols Effects on Meteorology", and "COASTAL: The Coastal & Cities Effects on Meteorology" - were focused on evaluation of influence of metropolitan areas on formation of meteorological and chemical fields above urban areas (such as Paris, France; Copenhagen, Denmark, and Bilbao, Spain) and surroundings. The Environment - HIgh Resolution Limited Area Model (Enviro-HIRLAM) was used and modifications were made taking into account urban (anthropogenic heat flux, roughness, buildings and their characteristics), chemical species/ aerosol (feedback mechanisms) effects with further analysis of temporal and spatial variability of diurnal cycle for meteorological variables of key importance. Main items of listed above YSSS small-scale research projects include the following: • Introduction with background discussions (with brainstorming to outline research and technical tasks planned including main goal, specific objectives, etc.) in groups; • Analysis of meteorological situations (selecting specific cases/ dates using surface maps, diagrams of vertical sounding, and surface meteorological measurements); • Learning practical technical steps (in order to make necessary changes in the model and implementing urban and aerosol effects, compiling executables, making test runs); • Performing model runs/simulations at different options (dates, control vs. modified urban and aerosol runs, forecast lengths, spatial and temporal resolutions, etc.); • Visualization/ plotting of results obtained (in a form of graphs, tables, animations); • Evaluation of possible impact on urban areas (estimating differences between the control and modified runs through temporal and spatial variability of simulated meteorological (air temperature, wind speed, relative humidity, sensible and latent heat fluxes, etc.) and chemical pollutants (concentration and deposition) fields/ patterns; • Team's oral presentation of the project about results and findings and following guidelines (including aim and specific objectives, methodology and approaches, results and discussions with examples, conclusions, acknowledgements, references). Outline and detailed description of the developed approach, key items of the research projects and their schedules, preparatory steps including team of students' familiarization with general information on planned exercises and literature list (composed of required, recommended, and additional readings), requirements for successful completion and defense of the project, team independent work as well as under supervision are presented and discussed.

Sweat Rate Prediction Equations for Outdoor Exercise with Transient Solar Radiation

DTIC Science & Technology

2012-01-01

AD] 15 Interchangeable variables gSL W/m2 Global solar load Direct weather station data; pyranometer values 25 Direct measurement from weather station ...Fanger equations 2, 4, 13, Direct or weather station values Rdif W Diffuse irradiance Rref W Reflected irradiance AD m2 Body surface area (BSA) from DuBois...assuming the given weather station uses standard meteorological measuring instru- ments. In the heat flow form expressed by Matthew et al. (25

Developing Predictive Models for Algal Bloom Occurrence and Identifying Factors Controlling their Occurrence in the Charlotte County and Surroundings

NASA Astrophysics Data System (ADS)

Karki, S.; Sultan, M.; Elkadiri, R.; Chouinard, K.

2017-12-01

Numerous occurrences of harmful algal blooms (Karenia Brevis) were reported from Southwest Florida along the coast of Charlotte County, Florida. We are developing data-driven (remote sensing, field, and meteorological data) models to accomplish the following: (1) identify the factors controlling bloom development, (2) forecast bloom occurrences, and (3) make recommendations for monitoring variables that are found to be most indicative of algal bloom occurrences and for identifying optimum locations for monitoring stations. To accomplish these three tasks we completed/are working on the following steps. Firstly, we developed an automatic system for downloading and processing of ocean color data acquired through MODIS Terra and MODIS Aqua products using SeaDAS ocean color processing software. Examples of extracted variables include: chlorophyll a (OC3M), chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel- Maritorena (GSM), sea surface temperature (SST), Secchi disk depth, euphotic depth, turbidity index, wind direction and speed, colored dissolved organic material (CDOM). Secondly we are developing a GIS database and a web-based GIS to host the generated remote sensing-based products in addition to relevant meteorological and field data. Examples of the meteorological and field inputs include: precipitation amount and rates, concentrations of nitrogen, phosphorous, fecal coliform and Dissolved Oxygen (DO). Thirdly, we are constructing and validating a multivariate regression model and an artificial neural network model to simulate past algal bloom occurrences using the compiled archival remote sensing, meteorological, and field data. The validated model will then be used to predict the timing and location of algal bloom occurrences. The developed system, upon completion, could enhance the decision making process, improve the citizen's quality of life, and strengthen the local economy.

What determines transitions between energy- and moisture-limited evaporative regimes?

NASA Astrophysics Data System (ADS)

Haghighi, E.; Gianotti, D.; Akbar, R.; Salvucci, G.; Entekhabi, D.

2017-12-01

The relationship between evaporative fraction (EF) and soil moisture (SM) has traditionally been used in atmospheric and land-surface modeling communities to determine the strength of land-atmosphere coupling in the context of the dominant evaporative regime (energy- or moisture-limited). However, recent field observations reveal that EF-SM relationship is not unique and could vary substantially with surface and/or meteorological conditions. This implies that conventional EF-SM relationships (exclusive of surface and meteorological conditions) are embedded in more complex dependencies and that in fact it is a multi-dimensional function. To fill the fundamental knowledge gaps on the important role of varying surface and meteorological conditions not accounted for by the traditional evaporative regime conceptualization, we propose a generalized EF framework using a mechanistic pore-scale model for evaporation and energy partitioning over drying soil surfaces. Nonlinear interactions among the components of the surface energy balance are reflected in a critical SM that marks the onset of transition between energy- and moisture-limited evaporative regimes. The new generalized EF framework enables physically based estimates of the critical SM, and provides new insights into the origin of land surface EF partitioning linked to meteorological input data and the evolution of land surface temperature during surface drying that affect the relative efficiency of surface energy balance components. Our results offer new opportunities to advance predictive capabilities quantifying land-atmosphere coupling for a wide range of present and projected meteorological input data.

Surface terrain characteristics and monsoon season mass balance of debris-covered glaciers in the Khumbu Himal, Nepal, obtained from high resolution Pléiades imagery.

NASA Astrophysics Data System (ADS)

Klug, Christoph; Nicholson, Lindsey; Rieg, Lorenzo; Sailer, Rudolf; Wirbel, Anna

2016-04-01

Debris-covered glaciers in the eastern Himalaya have pronounced surface relief consisting of hummocks and hollows, ice cliffs, lakes and former lake beds. This relief and spatially variable surface properties are expected to influence the spatially distributed surface energy balance and related ice mass loss and atmospheric interactions, but only a few studies have so far explicitly examined the nature of the surface terrain and its textures . In this work we present a new high-resolution digital terrain model (DTM) of a portion of the Khumbu Himal in the eastern Nepalese Himalaya, derived from Pléiades satellite imagery sampled in spring 2015. We use this DTM to study the terrain characteristics of five sample glaciers and analyse the inter- and intra- glacier variability of terrain characteristics in the context of glacier flow velocities and surface changes presented in previous studies in the area. In parallel to this analysis we also present the seasonal geodetic mass balance between spring and fall 2015, and relate it to the terrain properties, surface velocity and limited knowledge of the local lapse rates in meteorological conditions during this monsoon season.

Comparison of tropical and subtropical glacier surface energy balance in Africa and South America

NASA Astrophysics Data System (ADS)

Nicholson, L.; Prinz, R.; Kinnard, C.; Mölg, T.; Winkler, M.; Kaser, G.

2010-05-01

Tropical glaciers exist only at high altitude, and meteorological and surface energy balance studies of these glaciers can tell us much about the conditions and changes occurring in the mid troposphere. Understanding the surface energy balance and resultant mass balance regime of tropical glaciers is prerequisite to predicting glacier evolution, and future meltwater contributions to local hydrological resources, in response to future climate scenarios. Tropical glacier mass balance variability is strongly linked to precipitation and, via this, to multi-annual climate oscillations such as ENSO and IOZM, so it is useful to understand what role these differing regional influences play in comparison to the similarities imposed by the overarching tropical climate conditions and seasonality. New surface energy balance and mass balance data is available from Lewis glacier (Kenya, 0°09' S; 37°18' E), and here we use an energy and mass balance model to determine the surface energy flux characteristics at this site through a wet and dry season. Results are compared with those from Kersten glacier (Tanzania, 3°04' S; 37°21' E) to understand how conditions at these two glaciers compare and thus what coherent and contrasting climatic information glaciological records from these two sites can be expected to deliver. Meteorological data available from glacier stations on Antizana (Ecuador, 0°25' S; 78°09' W), Artesonraju (Peru, 8°28' S; 77°38' W) Zongo (Bolivia, 16°39' S; 67°47' W) and Guanaco (Chile, 29°20' S; 70°00' W) glaciers in South America offer the opportunity to examine how the surface fluxes and seasonal variability of the energy balance compares to those of the African glaciers. We include the extra-tropical Chilean example for comparison with the similarly high altitude, cold ice of Kersten glacier.

Small-scale and mesoscale lake surface water temperature structure: Thermography and in situ measurements from Lake Geneva, Switzerland

NASA Astrophysics Data System (ADS)

Irani Rahaghi, Abolfazl; Lemmin, Ulrich; Bouffard, Damien; Riffler, Michael; Wunderle, Stefan; Barry, Andrew

2017-04-01

Lake surface water temperature (LSWT), which varies spatially and temporarily, reflects meteorological and climatological forcing more than any other physical lake parameter. There are different data sources for LSWT mapping, including remote sensing and in situ measurements. Depending on cloud cover, satellite data can depict large-scale thermal patterns, but not the meso- or small-scale processes. Meso-scale thermography allows complementing (and hence ground-truth) satellite imagery at the sub-pixel scale. A Balloon Launched Imaging and Monitoring Platform (BLIMP) was used to measure the LSWT at the meso-scale. The BLIMP consists of a small balloon tethered to a boat and is equipped with thermal and RGB cameras, as well as other instrumentation for geo-location and communication. A feature matching-based algorithm was implemented to create composite thermal images. Simultaneous ground-truthing of the BLIMP data were achieved using an autonomous craft measuring among other in situ surface/near surface temperatures, radiation and meteorological data. Latent and sensible surface heat fluxes were calculated using the bulk parameterization algorithm based on similarity theory. Results are presented for the day-time stratified low wind speed (up to 3 ms-1) conditions over Lake Geneva for two field campaigns, each of 6 h on 18 March and 19 July 2016. The meso-scale temperature field ( 1-m pixel resolution) had a range and standard deviation of 2.4°C and 0.3°C, respectively, over a 1-km2 area (typical satellite pixel size). Interestingly, at the sub-pixel scale, various temporal and spatial thermal structures are evident - an obvious example being streaks in the along-wind direction during March, which we hypothesize are caused by the steady 3 h wind condition. The results also show that the spatial variability of the estimated total heat flux is due to the corresponding variability of the longwave cooling from the water surface and the latent heat flux.

SSE Data and Information

Atmospheric Science Data Center

2013-01-31

Surface meteorology and Solar Energy (SSE) Data and Information   The Release 6.0 Surface meteorology and Solar ... Collaboration Benefits International Priorities of Energy Management" features SSE data and the RETScreen renewable energy tool. ( Read ...

Trans-Pacific transport and evolution of aerosols: Evaluation of quasi-global WRF-Chem simulation with multiple observations

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

Hu, Zhiyuan; Zhao, Chun; Huang, Jianping

A fully coupled meteorology-chemistry model (WRF-Chem, the Weather Research and Forecasting model coupled with chemistry) has been configured to conduct quasi-global simulation for 5 years (2010–2014) and evaluated with multiple observation data sets for the first time. The evaluation focuses on the simulation over the trans-Pacific transport region using various reanalysis and observational data sets for meteorological fields and aerosol properties. The simulation generally captures the overall spatial and seasonal variability of satellite retrieved aerosol optical depth (AOD) and absorbing AOD (AAOD) over the Pacific that is determined by the outflow of pollutants and dust and the emissions of marine aerosols.more » The assessment of simulated extinction Ångström exponent (EAE) indicates that the model generally reproduces the variability of aerosol size distributions as seen by satellites. In addition, the vertical profile of aerosol extinction and its seasonality over the Pacific are also well simulated. The difference between the simulation and satellite retrievals can be mainly attributed to model biases in estimating marine aerosol emissions as well as the satellite sampling and retrieval uncertainties. Compared with the surface measurements over the western USA, the model reasonably simulates the observed magnitude and seasonality of dust, sulfate, and nitrate surface concentrations, but significantly underestimates the peak surface concentrations of carbonaceous aerosol likely due to model biases in the spatial and temporal variability of biomass burning emissions and secondary organic aerosol (SOA) production. A sensitivity simulation shows that the trans-Pacific transported dust, sulfate, and nitrate can make significant contribution to surface concentrations over the rural areas of the western USA, while the peaks of carbonaceous aerosol surface concentrations are dominated by the North American emissions. Both the retrievals and simulation show small interannual variability of aerosol characteristics for 2010–2014 averaged over three Pacific sub-regions. Furthermore, the evaluation in this study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA, allowing one to further understand the impact of transported pollutants on the regional air quality and climate with high-resolution nested regional modeling.« less

Trans-Pacific transport and evolution of aerosols: Evaluation of quasi-global WRF-Chem simulation with multiple observations

DOE PAGES

Hu, Zhiyuan; Zhao, Chun; Huang, Jianping; ...

2016-05-10

A fully coupled meteorology-chemistry model (WRF-Chem, the Weather Research and Forecasting model coupled with chemistry) has been configured to conduct quasi-global simulation for 5 years (2010–2014) and evaluated with multiple observation data sets for the first time. The evaluation focuses on the simulation over the trans-Pacific transport region using various reanalysis and observational data sets for meteorological fields and aerosol properties. The simulation generally captures the overall spatial and seasonal variability of satellite retrieved aerosol optical depth (AOD) and absorbing AOD (AAOD) over the Pacific that is determined by the outflow of pollutants and dust and the emissions of marine aerosols.more » The assessment of simulated extinction Ångström exponent (EAE) indicates that the model generally reproduces the variability of aerosol size distributions as seen by satellites. In addition, the vertical profile of aerosol extinction and its seasonality over the Pacific are also well simulated. The difference between the simulation and satellite retrievals can be mainly attributed to model biases in estimating marine aerosol emissions as well as the satellite sampling and retrieval uncertainties. Compared with the surface measurements over the western USA, the model reasonably simulates the observed magnitude and seasonality of dust, sulfate, and nitrate surface concentrations, but significantly underestimates the peak surface concentrations of carbonaceous aerosol likely due to model biases in the spatial and temporal variability of biomass burning emissions and secondary organic aerosol (SOA) production. A sensitivity simulation shows that the trans-Pacific transported dust, sulfate, and nitrate can make significant contribution to surface concentrations over the rural areas of the western USA, while the peaks of carbonaceous aerosol surface concentrations are dominated by the North American emissions. Both the retrievals and simulation show small interannual variability of aerosol characteristics for 2010–2014 averaged over three Pacific sub-regions. Furthermore, the evaluation in this study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA, allowing one to further understand the impact of transported pollutants on the regional air quality and climate with high-resolution nested regional modeling.« less

Editorial for Journal of Hydrology: Regional Studies

USGS Publications Warehouse

Willems, Patrick; Batelaan, Okke; Hughes, Denis A.; Swarzenski, Peter W.

2014-01-01

Hydrological regimes and processes show strong regional differences. While some regions are affected by extreme drought and desertification, others are under threat of increased fluvial and/or pluvial floods. Changes to hydrological systems as a consequence of natural variations and human activities are region-specific. Many of these changes have significant interactions with and implications for human life and ecosystems. Amongst others, population growth, improvements in living standards and other demographic and socio-economic trends, related changes in water and energy demands, change in land use, water abstractions and returns to the hydrological system (UNEP, 2008), introduce temporal and spatial changes to the system and cause contamination of surface and ground waters. Hydro-meteorological boundary conditions are also undergoing spatial and temporal changes. Climate change has been shown to increase temporal and spatial variations of rainfall, increase temperature and cause changes to evapotranspiration and other hydro-meteorological variables (IPCC, 2013). However, these changes are also region specific. In addition to these climate trends, (multi)-decadal oscillatory changes in climatic conditions and large variations in meteorological conditions will continue to occur.

Influence of different land surfaces on atmospheric conditions measured by a wireless sensor network

NASA Astrophysics Data System (ADS)

Lengfeld, Katharina; Ament, Felix

2010-05-01

Atmospheric conditions close to the surface, like temperature, wind speed and humidity, vary on small scales because of surface heterogeneities. Therefore, the traditional measuring approach of using a single, highly accurate station is of limited representativeness for a larger domain, because it is not able to determine these small scale variabilities. However, both the variability and the domain averages are important information for the development and validation of atmospheric models and soil-vegetation-atmosphere-transfer (SVAT) schemes. Due to progress in microelectronics it is possible to construct networks of comparably cheap meteorological stations with moderate accuracy. Such a network provides data in high spatial and temporal resolution. The EPFL Lausanne developed such a network called SensorScope, consisting of low cost autonomous stations. Each station observes air and surface temperature, humidity, wind direction and speed, incoming solar radiation, precipitations, soil moisture and soil temperature and sends the data via radio communication to a base station. This base station forwards the collected data via GSM/GPRS to a central server. Within the FLUXPAT project in August 2009 we deployed 15 stations as a twin transect near Jülich, Germany. One aim of this first experiment was to test the quality of the low cost sensors by comparing them to more accurate reference measurements. It turned out, that although the network is not highly accurate, the measurements are consistent. Consequently an analysis of the pattern of atmospheric conditions is feasible. For example, we detect a variability of ± 0.5K in the mean temperature at a distance of only 2.3 km. The transect covers different types of vegetation and a small river. Therefore, we analyzed the influence of different land surfaces and the distance to the river on meteorological conditions. On the one hand, some results meet our expectations, e.g. the relative humidity decreases with increasing distance to the river. But on the other hand we found unexpected anomalies in the air temperature, which will be discussed in detail by selected case studies.

BOREAS AES MARSII Surface Meteorological Data

NASA Technical Reports Server (NTRS)

Atkinson, G. Barrie; Funk, Barry; Hall, Forrest G. (Editor); Knapp, David E. (Editor)

2000-01-01

Canadian AES personnel collected several data sets related to surface and atmospheric meteorological conditions over the BOREAS region. This data set contains 15-minute meteorological data from six MARSII meteorology stations in the BOREAS region in Canada. Parameters include site, time, temperature, dewpoint, visibility, wind speed, wind gust, wind direction, two cloud groups, precipitation, and station pressure. Temporally, the data cover the period of May to September 1994. Geo-graphically, the stations are spread across the provinces of Saskatchewan and Manitoba. The data are provided in tabular ASCII files, and are classified as AFM-Staff data.

Weather and eared grebe winter migration near the Great Salt Lake, Utah.

PubMed

Williams, Augusta A; Laird, Neil F

2018-03-01

This study provides insight from the use of weather radar observations to understand the characteristics of the eared grebe migration near the Great Salt Lake (GSL) and provides unique information on weather conditions connected to these migration events. Doppler weather radar measurements from the Salt Lake City, Utah WSR-88D radar site (KMTX), along with meteorological surface and rawinsonde data, were used to identify and examine 281 eared grebe migration events across 15 winters from 1997/1998 through 2011/2012. An average of about 19 migration events occurred each winter with considerable interannual variability, as well as large variance in the spatial area and number of birds departing the GSL during each event. The migration events typically occurred during clear sky conditions in the presence of surface high pressure and colder than average surface temperatures. Migration events began 55 min after sunset, on average across the winter seasons, and in one case we demonstrate that an extended, nonstop flight was initiated of the departing eared grebes to northern Mexico. Eared grebes leaving the GSL largely flew above the freezing level with a mean northerly tailwind at flight altitude of 3.1 m s -1 and a westerly, cross-flight wind of 5.0 m s -1 while having an average flight speed at cruising altitude of 16.9 m s -1 , or 61 km h -1 . In addition to determining the variability of meteorological conditions during migration events across the 15 winters, atmospheric conditions during the largest migration event observed are presented and discussed.

Weather and eared grebe winter migration near the Great Salt Lake, Utah

NASA Astrophysics Data System (ADS)

Williams, Augusta A.; Laird, Neil F.

2018-03-01

This study provides insight from the use of weather radar observations to understand the characteristics of the eared grebe migration near the Great Salt Lake (GSL) and provides unique information on weather conditions connected to these migration events. Doppler weather radar measurements from the Salt Lake City, Utah WSR-88D radar site (KMTX), along with meteorological surface and rawinsonde data, were used to identify and examine 281 eared grebe migration events across 15 winters from 1997/1998 through 2011/2012. An average of about 19 migration events occurred each winter with considerable interannual variability, as well as large variance in the spatial area and number of birds departing the GSL during each event. The migration events typically occurred during clear sky conditions in the presence of surface high pressure and colder than average surface temperatures. Migration events began 55 min after sunset, on average across the winter seasons, and in one case we demonstrate that an extended, nonstop flight was initiated of the departing eared grebes to northern Mexico. Eared grebes leaving the GSL largely flew above the freezing level with a mean northerly tailwind at flight altitude of 3.1 m s-1 and a westerly, cross-flight wind of 5.0 m s-1 while having an average flight speed at cruising altitude of 16.9 m s-1, or 61 km h-1. In addition to determining the variability of meteorological conditions during migration events across the 15 winters, atmospheric conditions during the largest migration event observed are presented and discussed.

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.

On the role of "internal variability" on soil erosion assessment

NASA Astrophysics Data System (ADS)

Kim, Jongho; Ivanov, Valeriy; Fatichi, Simone

2017-04-01

Empirical data demonstrate that soil loss is highly non-unique with respect to meteorological or even runoff forcing and its frequency distributions exhibit heavy tails. However, all current erosion assessments do not describe the large associated uncertainties of temporal erosion variability and make unjustified assumptions by relying on central tendencies. Thus, the predictive skill of prognostic models and reliability of national-scale assessments have been repeatedly questioned. In this study, we attempt to reveal that the high variability in soil losses can be attributed to two sources: (1) 'external variability' referring to the uncertainties originating at macro-scale, such as climate, topography, and land use, which has been extensively studied; (2) 'geomorphic internal variability' referring to the micro-scale variations of pedologic properties (e.g., surface erodibility in soils with multi-sized particles), hydrologic properties (e.g., soil structure and degree of saturation), and hydraulic properties (e.g., surface roughness and surface topography). Using data and a physical hydraulic, hydrologic, and erosion and sediment transport model, we show that the geomorphic internal variability summarized by spatio-temporal variability in surface erodibility properties is a considerable source of uncertainty in erosion estimates and represents an overlooked but vital element of geomorphic response. The conclusion is that predictive frameworks of soil erosion should embed stochastic components together with deterministic assessments, if they do not want to largely underestimate uncertainty. Acknowledgement: This study was supported by the Basic Science Research Program of the National Research Foundation of Korea funded by the Ministry of Education (2016R1D1A1B03931886).

Evaluation of ozone hindcasts: optimal data sets to use, and results from simulations with the GMI model for 1990-2010.

NASA Astrophysics Data System (ADS)

Logan, J. A.; Megretskaia, I.; Liu, J.; Rodriguez, J. M.; Strahan, S. E.; Damon, M.; Steenrod, S. D.

2012-12-01

Simulations of atmospheric composition in the recent past (hindcasts) are a valuable tool for determining the causes of interannual variability (IAV) and trends in tropospheric ozone, including factors such as anthropogenic emissions, biomass burning, stratospheric input, and variability in meteorology. We will review the ozone data sets (balloon, satellite, and surface) that are the most reliable for evaluating hindcasts, and demonstrate their application with the GMI model. The GMI model is driven by the GEOS-5/MERRA reanalysis and includes both stratospheric and tropospheric chemistry. Preliminary analysis of a simulation for 1990-2010 using constant fossil fuel emissions is promising. The model reproduces the recent interannual variability (IAV) in ozone in the lowermost stratosphere seen in MLS and sonde data, as well as the IAV seen in sonde data in the lower stratosphere since 1995, and captures much of the IAV and short-term trends in surface ozone at remote sites, showing the influence of variability in dynamics. There was considerable IAV in ozone in the lowermost stratosphere in the Aura period, but almost none at European alpine sites in winter/spring, when ozone at 150 hPa has been shown to be correlated with that at 700 hPa in earlier years. The model matches the IAV in alpine ozone in Europe in July-September, including the high values in heat-waves, showing the role of variability in meteorology. A focus on IAV in each season is essential. The model matches IAV in MLS in the upper troposphere, TES tropical ozone, and the tropospheric ozone column (OMI/MLS) the best in tSropical regions controlled by ENSO related changes in dynamics. This study, combined with sensitivity simulations with changes to emissions, and simulations with passive tracers (see Abstract by Rodriguez et al. Session A76), lays the foundations for assessment of the mechanisms that have influenced tropospheric ozone in the past two decades.

Meteorological Variables and Behavior of Learners with Autism: An Examination of Possible Relationships

ERIC Educational Resources Information Center

VanBuskirk, Sabrina E.; Simpson, Richard L.

2013-01-01

For this study, we collected classroom behavioral data for three children with autism relative to daily meteorological conditions. Meteorological data, including barometric pressure, humidity, outdoor temperature, and moon illumination, were obtained from the National Weather Service. Relationships between children's individual target behaviors…

The spatiotemporal inhomogeneity of pollutant concentrations and its dependence on regional weather conditions in a coastal city of China.

PubMed

Zhou, Baohua; Yu, Lejiang; Zhong, Shiyuan; Bian, Xindi

2018-04-02

Hourly data for sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and inhalable particulate matter (PM 10 ) over a 33-month period from a network of air quality monitoring stations across Qingdao, a major coastal city in eastern China, along with surface and upper-air meteorological data, are used to characterize the spatiotemporal variability of these pollutants in the region and the role of meteorological conditions play in pollution episodes. Large differences in the concentrations of all three pollutants are found between densely populated or industrial areas and suburban commercial or residential or coastal tourist areas, but the differences are relatively small between older and newer parts of the residential-commercial areas and between old and newly developed industrial areas. Wavelet analyses revealed a strong seasonal cycle for all three pollutants, introseasonal variability with a periodicity depending on pollutant and location, and diurnal and a semi-diurnal variability with season-dependent amplitude and phase. Low wind speed is found to be the leading factor for pollution buildup in the region. These results may prove useful for urban planning and development and implementation of effective air pollution control strategies for other coastal regions with economic development similar to Qingdao.

Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islandsbased on the Beijing urban meteorological network

NASA Astrophysics Data System (ADS)

Jiang, S.; Wang, K.; Wang, J.; Zhou, C.; Wang, X.; Lee, X.

2017-12-01

This study compared the diurnal and seasonal cycles of atmospheric and surface urban heat islands (UHIs) based on hourly air temperatures (Ta) collected at 65 out of 262 stations in Beijing and land surface temperature (Ts) derived from Moderate Resolution Imaging Spectroradiometer in the years 2013-2014. We found that the nighttime atmospheric and surface UHIs referenced to rural cropland stations exhibited significant seasonal cycles, with the highest in winter. However, the seasonal variations in the nighttime UHIs referenced to mountainous forest stations were negligible, because mountainous forests have a higher nighttime Ts in winter and a lower nighttime T a in summer than rural croplands. Daytime surface UHIs showed strong seasonal cycles, with the highest in summer. The daytime atmospheric UHIs exhibited a similar but less seasonal cycle under clear-sky conditions, which was not apparent under cloudy-sky conditions. Atmospheric UHIs in urban parks were higher in daytime. Nighttime atmospheric UHIs are influenced by energy stored in urban materials during daytime and released during nighttime. The stronger anthropogenic heat release in winter causes atmospheric UHIs to increase with time during winter nights, but decrease with time during summer nights. The percentage of impervious surfaces is responsible for 49%-54% of the nighttime atmospheric UHI variability and 31%-38% of the daytime surface UHI variability. However, the nighttime surface UHI was nearly uncorrelated with the percentage of impervious surfaces around the urban stations.

Fine-scale application of WRF-CAM5 during a dust storm episode over East Asia: Sensitivity to grid resolutions and aerosol activation parameterizations

NASA Astrophysics Data System (ADS)

Wang, Kai; Zhang, Yang; Zhang, Xin; Fan, Jiwen; Leung, L. Ruby; Zheng, Bo; Zhang, Qiang; He, Kebin

2018-03-01

An advanced online-coupled meteorology and chemistry model WRF-CAM5 has been applied to East Asia using triple-nested domains at different grid resolutions (i.e., 36-, 12-, and 4-km) to simulate a severe dust storm period in spring 2010. Analyses are performed to evaluate the model performance and investigate model sensitivity to different horizontal grid sizes and aerosol activation parameterizations and to examine aerosol-cloud interactions and their impacts on the air quality. A comprehensive model evaluation of the baseline simulations using the default Abdul-Razzak and Ghan (AG) aerosol activation scheme shows that the model can well predict major meteorological variables such as 2-m temperature (T2), water vapor mixing ratio (Q2), 10-m wind speed (WS10) and wind direction (WD10), and shortwave and longwave radiation across different resolutions with domain-average normalized mean biases typically within ±15%. The baseline simulations also show moderate biases for precipitation and moderate-to-large underpredictions for other major variables associated with aerosol-cloud interactions such as cloud droplet number concentration (CDNC), cloud optical thickness (COT), and cloud liquid water path (LWP) due to uncertainties or limitations in the aerosol-cloud treatments. The model performance is sensitive to grid resolutions, especially for surface meteorological variables such as T2, Q2, WS10, and WD10, with the performance generally improving at finer grid resolutions for those variables. Comparison of the sensitivity simulations with an alternative (i.e., the Fountoukis and Nenes (FN) series scheme) and the default (i.e., AG scheme) aerosol activation scheme shows that the former predicts larger values for cloud variables such as CDNC and COT across all grid resolutions and improves the overall domain-average model performance for many cloud/radiation variables and precipitation. Sensitivity simulations using the FN series scheme also have large impacts on radiations, T2, precipitation, and air quality (e.g., decreasing O3) through complex aerosol-radiation-cloud-chemistry feedbacks. The inclusion of adsorptive activation of dust particles in the FN series scheme has similar impacts on the meteorology and air quality but to lesser extent as compared to differences between the FN series and AG schemes. Compared to the overall differences between the FN series and AG schemes, impacts of adsorptive activation of dust particles can contribute significantly to the increase of total CDNC (∼45%) during dust storm events and indicate their importance in modulating regional climate over East Asia.

Atmosphere and climate studies of Mars using the Mars Observer pressure modulator infrared radiometer

NASA Technical Reports Server (NTRS)

Mccleese, D. J.; Haskins, R. D.; Schofield, J. T.; Zurek, R. W.; Leovy, C. B.; Paige, D. A.; Taylor, F. W.

1992-01-01

Studies of the climate and atmosphere of Mars are limited at present by a lack of meteorological data having systematic global coverage with good horizontal and vertical resolution. The Mars Observer spacecraft in a low, nearly circular, polar orbit will provide an excellent platform for acquiring the data needed to advance significantly our understanding of the Martian atmosphere and its remarkable variability. The Mars Observer pressure modulator infrared radiometer (PMIRR) is a nine-channel limb and nadir scanning atmospheric sounder which will observe the atmosphere of Mars globally from 0 to 80 km for a full Martian year. PMIRR employs narrow-band radiometric channels and two pressure modulation cells to measure atmospheric and surface emission in the thermal infrared. PMIRR infrared and visible measurements will be combined to determine the radiative balance of the polar regions, where a sizeable fraction of the global atmospheric mass annually condenses onto and sublimes from the surface. Derived meteorological fields, including diabatic heating and cooling and the vertical variation of horizontal winds, are computed from the globally mapped fields retrieved from PMIRR data.

Near-field Mercury Deposition During Summertime Precipitation Events: the Impact of Coal Fired Utilities

NASA Astrophysics Data System (ADS)

Christianson, E. M.; Keeler, G. J.; Landis, M. S.

2008-12-01

Mercury (Hg) is a bioaccumulative neurotoxin that has been shown to enter water bodies, and consequently the food chain, via atmospheric deposition to the earth's surface. Anthropogenic emissions of the pollutant play a significant role in contributing to the atmospheric pool of Hg, but the near filed impact from point source on surface deposition has been poorly defined to date. An intensive study during July-September 2006 established eight networked precipitation collection sites in northeastern Ohio, U.S.A., located at varying proximities to coal combustion facilities to evaluate the spatial scale of Hg wet deposition concentration enhancement about the sources. It was found that an average of 42% of the Hg wet deposited at sites in the immediate vicinity (<1 km) of coal fired utilities could be attributed to that adjacent source. Several meteorological variables were shown to account for the degree to which Hg concentration in precipitation was enhanced. A detailed meteorological analysis of the individual precipitation events as well as overall implications of local deposition gradients will be discussed.

Bayesian dynamic modeling of time series of dengue disease case counts

PubMed Central

López-Quílez, Antonio; Torres-Prieto, Alexander

2017-01-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. PMID:28671941

Meteorological adjustment of yearly mean values for air pollutant concentration comparison

NASA Technical Reports Server (NTRS)

Sidik, S. M.; Neustadter, H. E.

1976-01-01

Using multiple linear regression analysis, models which estimate mean concentrations of Total Suspended Particulate (TSP), sulfur dioxide, and nitrogen dioxide as a function of several meteorologic variables, two rough economic indicators, and a simple trend in time are studied. Meteorologic data were obtained and do not include inversion heights. The goodness of fit of the estimated models is partially reflected by the squared coefficient of multiple correlation which indicates that, at the various sampling stations, the models accounted for about 23 to 47 percent of the total variance of the observed TSP concentrations. If the resulting model equations are used in place of simple overall means of the observed concentrations, there is about a 20 percent improvement in either: (1) predicting mean concentrations for specified meteorological conditions; or (2) adjusting successive yearly averages to allow for comparisons devoid of meteorological effects. An application to source identification is presented using regression coefficients of wind velocity predictor variables.

SSE Data and Information

Atmospheric Science Data Center

2018-04-03

Surface meteorology and Solar Energy (SSE) Data and Information The Release 6.0 Surface meteorology and Solar Energy ( SSE ) data set contains parameters formulated for assessing and designing renewable energy systems. This latest release contains new parameters based on ...

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia.

PubMed

Loha, Eskindir; Lindtjørn, Bernt

2010-06-16

Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors.

Effects of meteorological factors on incidence of scarlet fever during different periods in different districts of China.

PubMed

Duan, Yu; Yang, Li-Juan; Zhang, Yan-Jie; Huang, Xiao-Lei; Pan, Gui-Xia; Wang, Jing

2017-03-01

To reveal the difference of meteorological effect on scarlet fever in Beijing and Hong Kong, China, during different periods among 2004-2014. The data of monthly incidence of scarlet fever and meteorological variables from 2004 to 2014 in Beijing and Hong Kong were collected from Chinese science data center of public health, meteorological data website and Hong Kong observatory website. The whole study period was separated into two periods by the outbreak year 2011 (Jan 2004-Dec 2010 and Jan 2011-Dec 2014). A generalized additive Poisson model was conducted to estimate the effect of meteorological variables on monthly incidence of scarlet fever during two periods in Beijing and Hong Kong, China. Incidence of scarlet fever in two districts were compared and found the average incidence during period of 2004-2010 were significantly different (Z=203.973, P<0.001) while average incidence became generally equal during 2011-2014 (Z=2.125, P>0.05). There was also significant difference in meteorological variables between Beijing and Hong Kong during whole study period, except air pressure (Z=0.165, P=0.869). After fitting GAM model, it could be found monthly mean temperature showed a negative effect (RR=0.962, 95%CI: 0.933, 0.992) on scarlet fever in Hong Kong during the period of 2004-2010. By comparison, for data in Beijing during the period of 2011-2014, the RRs of monthly mean temperature range growing 1°C and monthly sunshine duration growing 1h was equal to 1.196(1.022, 1.399) and 1.006(1.001, 1.012), respectively. The changes of meteorological effect on scarlet fever over time were not significant both in Beijing and Hong Kong. This study suggests that meteorological variables were important factors for incidence of scarlet fever during different period in Beijing and Hong Kong. It also support that some meteorological effects were opposite in different period although these differences might not completely statistically significant. Copyright © 2017 Elsevier B.V. All rights reserved.

Soil water content assessment: critical issues concerning the operational application of the triangle method.

PubMed

Maltese, Antonino; Capodici, Fulvio; Ciraolo, Giuseppe; La Loggia, Goffredo

2015-03-19

Knowledge of soil water content plays a key role in water management efforts to improve irrigation efficiency. Among the indirect estimation methods of soil water content via Earth Observation data is the triangle method, used to analyze optical and thermal features because these are primarily controlled by water content within the near-surface evaporation layer and root zone in bare and vegetated soils. Although the soil-vegetation-atmosphere transfer theory describes the ongoing processes, theoretical models reveal limits for operational use. When applying simplified empirical formulations, meteorological forcing could be replaced with alternative variables when the above-canopy temperature is unknown, to mitigate the effects of calibration inaccuracies or to account for the temporal admittance of the soil. However, if applied over a limited area, a characterization of both dry and wet edges could not be properly achieved; thus, a multi-temporal analysis can be exploited to include outer extremes in soil water content. A diachronic empirical approach introduces the need to assume a constancy of other meteorological forcing variables that control thermal features. Airborne images were acquired on a Sicilian vineyard during most of an entire irrigation period (fruit-set to ripening stages, vintage 2008), during which in situ soil water content was measured to set up the triangle method. Within this framework, we tested the triangle method by employing alternative thermal forcing. The results were inaccurate when air temperature at airborne acquisition was employed. Sonic and aerodynamic air temperatures confirmed and partially explained the limits of simultaneous meteorological forcing, and the use of proxy variables improved model accuracy. The analysis indicates that high spatial resolution does not necessarily imply higher accuracies.

Soil Water Content Assessment: Critical Issues Concerning the Operational Application of the Triangle Method

PubMed Central

Maltese, Antonino; Capodici, Fulvio; Ciraolo, Giuseppe; La Loggia, Goffredo

2015-01-01

Knowledge of soil water content plays a key role in water management efforts to improve irrigation efficiency. Among the indirect estimation methods of soil water content via Earth Observation data is the triangle method, used to analyze optical and thermal features because these are primarily controlled by water content within the near-surface evaporation layer and root zone in bare and vegetated soils. Although the soil-vegetation-atmosphere transfer theory describes the ongoing processes, theoretical models reveal limits for operational use. When applying simplified empirical formulations, meteorological forcing could be replaced with alternative variables when the above-canopy temperature is unknown, to mitigate the effects of calibration inaccuracies or to account for the temporal admittance of the soil. However, if applied over a limited area, a characterization of both dry and wet edges could not be properly achieved; thus, a multi-temporal analysis can be exploited to include outer extremes in soil water content. A diachronic empirical approach introduces the need to assume a constancy of other meteorological forcing variables that control thermal features. Airborne images were acquired on a Sicilian vineyard during most of an entire irrigation period (fruit-set to ripening stages, vintage 2008), during which in situ soil water content was measured to set up the triangle method. Within this framework, we tested the triangle method by employing alternative thermal forcing. The results were inaccurate when air temperature at airborne acquisition was employed. Sonic and aerodynamic air temperatures confirmed and partially explained the limits of simultaneous meteorological forcing, and the use of proxy variables improved model accuracy. The analysis indicates that high spatial resolution does not necessarily imply higher accuracies. PMID:25808771

75 FR 52692 - Approval and Promulgation of Implementation Plans; New Mexico; Interstate Transport of Pollution...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-27

... an indication of potential variability in future projections due to differences in actual meteorology... maintaining attainment of the NAAQS at these locations if there are adverse variations in meteorology or... the Central Regional Air Planning Association (CENRAP) modeling of 2002 emissions and meteorology.\\22...

Meteorological fluctuations define long-term crop yield patterns in conventional and organic production systems

USDA-ARS?s Scientific Manuscript database

Periodic variability in meteorological patterns presents significant challenges to crop production consistency and yield stability. Meteorological influences on corn and soybean grain yields were analyzed over an 18-year period at a long-term experiment in Beltsville, Maryland, U.S.A., comparing c...

Meteorology, Emissions, and Grid Resolution: Effects on Discrete and Probabilistic Model Performance

EPA Science Inventory

In this study, we analyze the impacts of perturbations in meteorology and emissions and variations in grid resolution on air quality forecast simulations. The meteorological perturbations con-sidered in this study introduce a typical variability of ~1°C, 250 - 500 m, 1 m/s, and 1...

Comparison of MTI Satellite-Derived Surface Water Temperatures and In-Situ Measurements

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

Kurzeja, R.

2001-07-26

Temperatures of the water surface of a cold, mid-latitude lake and the tropical Pacific Ocean were determined from MTI images and from in situ concurrent measurements. In situ measurements were obtained at the time of the MTI image with a floating, anchored platform, which measured the surface and bulk water temperatures and relevant meteorological variables, and also from a boat moving across the target area. Atmospheric profiles were obtained from concurrent radiosonde soundings. Radiances at the satellite were calculated with the Modtran radiative transfer model. The MTI infrared radiances were within 1 percent of the calculated values at the Pacificmore » Ocean site but were 1-2 percent different over the mid-latitude lake.« less

The effect of soil moisture anomalies on maize yield in Germany

NASA Astrophysics Data System (ADS)

Peichl, Michael; Thober, Stephan; Meyer, Volker; Samaniego, Luis

2018-03-01

Crop models routinely use meteorological variations to estimate crop yield. Soil moisture, however, is the primary source of water for plant growth. The aim of this study is to investigate the intraseasonal predictability of soil moisture to estimate silage maize yield in Germany. We also evaluate how approaches considering soil moisture perform compare to those using only meteorological variables. Silage maize is one of the most widely cultivated crops in Germany because it is used as a main biomass supplier for energy production in the course of the German Energiewende (energy transition). Reduced form fixed effect panel models are employed to investigate the relationships in this study. These models are estimated for each month of the growing season to gain insights into the time-varying effects of soil moisture and meteorological variables. Temperature, precipitation, and potential evapotranspiration are used as meteorological variables. Soil moisture is transformed into anomalies which provide a measure for the interannual variation within each month. The main result of this study is that soil moisture anomalies have predictive skills which vary in magnitude and direction depending on the month. For instance, dry soil moisture anomalies in August and September reduce silage maize yield more than 10 %, other factors being equal. In contrast, dry anomalies in May increase crop yield up to 7 % because absolute soil water content is higher in May compared to August due to its seasonality. With respect to the meteorological terms, models using both temperature and precipitation have higher predictability than models using only one meteorological variable. Also, models employing only temperature exhibit elevated effects.

Numerical simulation of the impact of reforestation on winter meteorology and environment in a semi-arid urban valley, Northwestern China.

PubMed

Yu, Ye; He, Jianjun; Zhao, Suping; Liu, Na; Chen, Jinbei; Mao, Hongjun; Wu, Lin

2016-11-01

Since 1999 Chinese government has made great effort to reforest the south and north mountains surrounding urban Lanzhou - a city located in a river valley, Northwestern China. Until 2009 obvious land use change occurred, with 69.2% of the reforested area been changed from grasslands, croplands, barren or sparsely vegetated land to closed shrublands and 20.6% from closed shrublands, grasslands, and croplands to forests. Reforestation changes land-surface properties, with possible impact on the evolution of atmospheric variables. To understand to what extent the local meteorology and environment could be affected by reforestation in winter, and through what processes, two sets of simulations were conducted using the Weather Research and Forecasting model (WRF) and the FLEXible PARTicle (FLEXPART) dispersion model for a control case with high-resolution remotely sensed land cover data for 2009 and a scenario assuming no reforestation since 1999. Results suggested that the changes in albedo, surface exchange coefficient and surface soil heat conductivity related to reforestation led to the changes in surface net radiation and surface energy partitioning, which in turn affected the meteorological fields and enhanced the mountain-valley wind circulation. Replacement of shrublands and grassland with forest in the south mountain through reforestation play a dominant role in the enhancement of mountain-valley wind circulation. Reforestation increased the amount of air exchanged between the valley and the outside during the day, with the largest hourly increase of 10% on calm weather days and a monthly mean hourly increase of 2% for the study period (Dec. 2009). Reforestation affected the spatial distribution of pollutants and slightly improved the urban air quality, especially in the eastern valley. Results from this study provide useful information for future urban air quality management and reforestation plan, and some experience for cities with similar situations in the world. Copyright © 2016 Elsevier B.V. All rights reserved.

Meteorological satellite accomplishments

NASA Technical Reports Server (NTRS)

Allison, L. J.; Arking, A.; Bandeen, W. R.; Shenk, W. E.; Wexler, R.

1974-01-01

The various types of meteorological satellites are enumerated. Vertical sounding, parameter extraction technique, and both macroscale and mesoscale meteorological phenomena are discussed. The heat budget of the earth-atmosphere system is considered, along with ocean surface and hydrology.

Fog Characteristics at Otis AFB, Massachusetts.

DTIC Science & Technology

1980-10-01

AFGL-owned EG&G Forward Scatter Meters at heights of S, 30, 45 and 60 m above the surface. The scope of Calspan’s contract did not permit more than...characteristics, and supporting meteorological variables. For fog microphysics, a Calspan drop sampler, a hi-vol LWC sampler, AFGL’s Forward Scatter Meters and a...Eq. (i), was measured as "scattering" coefficient in fog at Otis with EG&G Forward Scatter Meters . The measured extinction can be related to visual

Performance and evaluation of a coupled prognostic model TAPM over a mountainous complex terrain industrial area

NASA Astrophysics Data System (ADS)

Matthaios, Vasileios N.; Triantafyllou, Athanasios G.; Albanis, Triantafyllos A.; Sakkas, Vasileios; Garas, Stelios

2018-05-01

Atmospheric modeling is considered an important tool with several applications such as prediction of air pollution levels, air quality management, and environmental impact assessment studies. Therefore, evaluation studies must be continuously made, in order to improve the accuracy and the approaches of the air quality models. In the present work, an attempt is made to examine the air pollution model (TAPM) efficiency in simulating the surface meteorology, as well as the SO2 concentrations in a mountainous complex terrain industrial area. Three configurations under different circumstances, firstly with default datasets, secondly with data assimilation, and thirdly with updated land use, ran in order to investigate the surface meteorology for a 3-year period (2009-2011) and one configuration applied to predict SO2 concentration levels for the year of 2011.The modeled hourly averaged meteorological and SO2 concentration values were statistically compared with those from five monitoring stations across the domain to evaluate the model's performance. Statistical measures showed that the surface temperature and relative humidity are predicted well in all three simulations, with index of agreement (IOA) higher than 0.94 and 0.70 correspondingly, in all monitoring sites, while an overprediction of extreme low temperature values is noted, with mountain altitudes to have an important role. However, the results also showed that the model's performance is related to the configuration regarding the wind. TAPM default dataset predicted better the wind variables in the center of the simulation than in the boundaries, while improvement in the boundary horizontal winds implied the performance of TAPM with updated land use. TAPM assimilation predicted the wind variables fairly good in the whole domain with IOA higher than 0.83 for the wind speed and higher than 0.85 for the horizontal wind components. Finally, the SO2 concentrations were assessed by the model with IOA varied from 0.37 to 0.57, mostly dependent on the grid/monitoring station of the simulated domain. The present study can be used, with relevant adaptations, as a user guideline for future conducting simulations in mountainous complex terrain.

Meteorological data for four sites at surface-disruption features in Yucca Flat, Nevada Test Site, Nye County, Nevada, 1985-86

USGS Publications Warehouse

Carman, Rita L.

1994-01-01

Surface-disruption features, or craters, resulting from underground nuclear testing at the Nevada Test Site may increase the potential for ground-water recharge in an area that would normally produce little, if any, recharge. This report presents selected meteorological data resulting from a study of two surface-disruption features during May 1985 through June 1986. The data were collected at four adjacent sites in Yucca Flat, about 56 kilometers north of Mercury, Nevada. Three sites (one in each of two craters and one at an undisturbed site at the original land surface) were instrumented to collect meteorological data for calculating bare-soil evaporation. These data include (1) long-wave radiation, (2) short-wave radiation, (3) net radiation, (4) air temperae, and (5) soil surface temperature. Meteorological data also were collected at a weather station at an undisturbed site near the study craters. Data collected at this site include (1) air temperature, (2) relative humidity, (3) wind velocity, and (4) wind direction.

Validation of Land-Surface Mosaic Heterogeneity in the GEOS DAS

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Molod, Andrea; Houser, Paul R.; Schubert, Siegfried

1999-01-01

The Mosaic Land-surface Model (LSM) has been included into the current GEOS Data Assimilation System (DAS). The LSM uses a more advanced representation of physical processes than previous versions of the GEOS DAS, including the representation of sub-grid heterogeneity of the land-surface through the Mosaic approach. As a first approximation, Mosaic assumes that all similar surface types within a grid-cell can be lumped together as a single'tile'. Within one GCM grid-cell, there might be 1 - 5 different tiles or surface types. All tiles are subjected to the grid-scale forcing (radiation, air temperature and specific humidity, and precipitation), and the sub-grid variability is a function of the tile characteristics. In this paper, we validate the LSM sub-grid scale variability (tiles) using a variety of surface observing stations from the Southern Great Plains (SGP) site of the Atmospheric Radiation Measurement (ARM) Program. One of the primary goals of SGP ARM is to study the variability of atmospheric radiation within a G,CM grid-cell. Enough surface data has been collected by ARM to extend this goal to sub-grid variability of the land-surface energy and water budgets. The time period of this study is the Summer of 1998 (June I - September 1). The ARM site data consists of surface meteorology, energy flux (eddy correlation and bowen ratio), soil water observations spread over an area similar to the size of a G-CM grid-cell. Various ARM stations are described as wheat and alfalfa crops, pasture and range land. The LSM tiles considered at the grid-space (2 x 2.5) nearest the ARM site include, grassland, deciduous forests, bare soil and dwarf trees. Surface energy and water balances for each tile type are compared with observations. Furthermore, we will discuss the land-surface sub-grid variability of both the ARM observations and the DAS.

Meteorological factors and timing of the initiating event of human parturition

NASA Astrophysics Data System (ADS)

Hirsch, Emmet; Lim, Courtney; Dobrez, Deborah; Adams, Marci G.; Noble, William

2011-03-01

The aim of this study was to determine whether meteorological factors are associated with the timing of either onset of labor with intact membranes or rupture of membranes prior to labor—together referred to as `the initiating event' of parturition. All patients delivering at Evanston Hospital after spontaneous labor or rupture of membranes at ≥20 weeks of gestation over a 6-month period were studied. Logistic regression models of the initiating event of parturition using clinical variables (maternal age, gestational age, parity, multiple gestation and intrauterine infection) with and without the addition of meteorological variables (barometric pressure, temperature and humidity) were compared. A total of 1,088 patients met the inclusion criteria. Gestational age, multiple gestation and chorioamnionitis were associated with timing of initiation of parturition ( P < 0.01). The addition of meteorological to clinical variables generated a statistically significant improvement in prediction of the initiating event; however, the magnitude of this improvement was small (less than 2% difference in receiver-operating characteristic score). These observations held regardless of parity, fetal number and gestational age. Meteorological factors are associated with the timing of parturition, but the magnitude of this association is small.

BOREAS AES READAC Surface Meteorological Data

NASA Technical Reports Server (NTRS)

Atkinson, G. Barrie; Funk, Barry; Hall, Forrest G. (Editor); Knapp, David E. (Editor)

2000-01-01

Canadian AES personnel collected and processed data related to surface atmospheric meteorological conditions over the BOREAS region. This data set contains 15-minute meteorological data from one READAC meteorology station in Hudson Bay, Saskatchewan. Parameters include day, time, type of report, sky condition, visibility, mean sea level pressure, temperature, dewpoint, wind, altimeter, opacity, minimum and maximum visibility, station pressure, minimum and maximum air temperature, a wind group, precipitation, and precipitation in the last hour. The data were collected non-continuously from 24-May-1994 to 20-Sep-1994. The data are provided in tabular ASCII files, and are classified as AFM-Staff data.

Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes

NASA Astrophysics Data System (ADS)

Zipper, Samuel C.; Qiu, Jiangxiao; Kucharik, Christopher J.

2016-09-01

Maximizing agricultural production on existing cropland is one pillar of meeting future global food security needs. To close crop yield gaps, it is critical to understand how climate extremes such as drought impact yield. Here, we use gridded, daily meteorological data and county-level annual yield data to quantify meteorological drought sensitivity of US maize and soybean production from 1958 to 2007. Meteorological drought negatively affects crop yield over most US crop-producing areas, and yield is most sensitive to short-term (1-3 month) droughts during critical development periods from July to August. While meteorological drought is associated with 13% of overall yield variability, substantial spatial variability in drought effects and sensitivity exists, with central and southeastern US becoming increasingly sensitive to drought over time. Our study illustrates fine-scale spatiotemporal patterns of drought effects, highlighting where variability in crop production is most strongly associated with drought, and suggests that management strategies that buffer against short-term water stress may be most effective at sustaining long-term crop productivity.

Effect of meteorological variables on Plasmodium vivax and Plasmodium falciparum malaria in outbreak prone districts of Rajasthan, India.

PubMed

Lingala, Mercy A L

Malaria is a public health problem caused by Plasmodium parasite and transmitted by anopheline mosquitoes. Arid and semi-arid regions of western India are prone to malaria outbreaks. Malaria outbreak prone districts viz. Bikaner, Barmer and Jodhpur were selected to study the effect of meteorological variables on Plasmodium vivax and Plasmodium falciparum malaria outbreaks for the period of 2009-2012. The data of monthly malaria cases and meteorological variables was analysed using SPSS 20v. Spearman correlation analysis was conducted to examine the strength of the relationship between meteorological variables, P. vivax and P. falciparum malaria cases. Pearson's correlation analysis was carried out among the meteorological variables to observe the independent effect of each independent variable on the outcome. Results indicate that malaria outbreaks have occurred in Bikaner and Barmer due to continuous rains for more than two months. Rainfall has shown to be an important predictor of malaria outbreaks in Rajasthan. P. vivax is more significantly correlated with rainfall, minimum temperature (P<0.01) and less significantly with relative humidity (P<0.05); whereas P. falciparum is significantly correlated with rainfall, relative humidity (P<0.01) and less significantly with temperature (P<0.05). The determination of the lag period for P. vivax is relative humidity and for P. falciparum is temperature. The lag period between malaria cases and rainfall is shorter for P. vivax than P. falciparum. In conclusion, the knowledge generated is not only useful to take prompt malaria control interventions but also helpful to develop better forecasting model in outbreak prone regions. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

Meteorological Influence on the 2009 Influenza A (H1N1) Pandemic in Mainland China.

NASA Astrophysics Data System (ADS)

Zhao, X.; Cai, J.; Feng, D.; Bai, Y.; Xu, B.

2015-12-01

Since May 2009, a novel influenza A (H1N1) pandemic has spread rapidly in mainland China from Mexico. Although there has been substantial analysis of this influenza, reliable work estimating its spatial dynamics and determinants remain scarce. The survival and transmission of this pandemic virus not only depends on its biological properties, but also a correlation with external environmental factors. In this study, we collected daily influenza A (H1N1) cases and corresponding annual meteorological factors in mainland China from May 2009 to April 2010. By analyzing these data at county-level, a similarity index, which considered the spatio-temporal characteristics of the disease, was proposed to evaluate the role and lag time of meteorological factors in the influenza transmission. The results indicated that the influenza spanned a large geographical area, following an overall trend from east to west across the country. The spatio-temporal transmission of the disease was affected by a series of meteorological variables, especially absolute humidity with a 3-week lag. These findings confirmed that the absolute humidity and other meteorological variables contributed to the local occurrence and dispersal of influenza A (H1N1). The impact of meteorological variables and their lag effects could be involved in the improvement of effective strategies to control and prevent disease outbreaks.

Investigation Hydrometeorological Regime of the White Sea Based on Satellite Altimetry Data

NASA Astrophysics Data System (ADS)

Lebedev, Sergey A.

2016-08-01

The White Sea are the seas of the Arctic Ocean. Today complicated hydrodynamic, tidal, ice, and meteorological regimes of these seas may be investigated on the basis of remote sensing data, specifically of satellite altimetry data. Results of calibration and validation of satellite altimetry measurements (sea surface height and sea surface wind speed) and comparison with regional tidal model show that this type of data may be successfully used in scientific research and in monitoring of the environment. Complex analysis of the tidal regime of the White Sea and comparison between global and regional tidal models show advantages of regional tidal model for use in tidal correction of satellite altimetry data. Examples of using the sea level data in studying long-term variability of the Barents and White Seas are presented. Interannual variability of sea ice edge position is estimated on the basis of altimetry data.

A Method for a Multi-Platform Approach to Generate Gridded Surface Evaporation

NASA Astrophysics Data System (ADS)

Badger, A.; Livneh, B.; Small, E. E.; Abolafia-Rosenzweig, R.

2017-12-01

Evapotranspiration is an integral component of the surface water balance. While there are many estimates of evapotranspiration, there are fewer estimates that partition evapotranspiration into evaporation and transpiration components. This study aims to generate a CONUS-scale, observationally-based soil evaporation dataset by using the time difference of surface soil moisture by Soil Moisture Active Passive (SMAP) satellite with adjustments for transpiration and a bottom flux out of the surface layer. In concert with SMAP, the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, North American Land Data Assimilation Systems (NLDAS) and the Hydrus-1D model are used to fully analyze the surface water balance. A biome specific estimate of the total terrestrial ET is calculated through a variation of the Penman-Monteith equation with NLDAS forcing and NLDAS Noah Model output for meteorological variables. A root density restriction and SMAP-based soil moisture restriction are applied to obtain terrestrial transpiration estimates. By forcing Hydrus-1D with NLDAS meteorology and our terrestrial transpiration estimates, an estimate of the flux between the soil surface and root zone layers (qbot) will dictate the proportion of water that is available for soil evaporation. After constraining transpiration and the bottom flux from the surface layer, we estimate soil evaporation as the residual of the surface water balance. Application of this method at Fluxnet sites shows soil evaporation estimates of approximately 0­3 mm/day and less than ET estimates. Expanding this methodology to produce a gridded product for CONUS, and eventually a global-scale product, will enable a better understanding of water balance processes and contribute a dataset to validate land-surface model's surface flux processes.

Contrastive Analysis of Meteorological Element Effect Simulated by parameterization schemes Land Surface Process of Noah and CLM4 over the Yellow River Source Region

NASA Astrophysics Data System (ADS)

Zhang, Y.; Wen, X.

2017-12-01

The Yellow River source region is situated in the northeast Tibetan Plateau, which is considered as a global climate change hot-spot and one of the most sensitive areas in terms of response to global warming in view of its fragile ecosystem. This region plays an irreplaceable role for downstream water supply of The Yellow River because of its unique topography and variable climate. The water energy cycle processes of the Yellow River source Region from July to September in 2015 were simulated by using the WRF mesoscale numerical model. The two groups respectively used Noah and CLM4 parameterization schemes of land surface process. Based on the observation data of GLDAS data set, ground automatic weather station and Zoige plateau wetland ecosystem research station, the simulated values of near surface meteorological elements and surface energy parameters of two different schemes were compared. The results showed that the daily variations about meteorological factors in Zoige station in September were simulated quite well by the model. The correlation coefficient between the simulated temperature and humidity of the CLM scheme were 0.88 and 0.83, the RMSE were 1.94 ° and 9.97%, and the deviation Bias were 0.04 ° and 3.30%, which was closer to the observation data than the Noah scheme. The correlation coefficients of net radiation, surface heat flux, upward short wave and upward longwave radiation were respectively 0.86, 0.81, 0.84 and 0.88, which corresponded better than the observation data. The sensible heat flux and latent heat flux distribution of the Noah scheme corresponded quite well to GLDAS. the distribution and magnitude of 2m relative humidity and soil moisture were closer to surface observation data because the CLM scheme described the photosynthesis and evapotranspiration of land surface vegetation more rationally. The simulating abilities of precipitation and downward longwave radiation need to be improved. This study provides a theoretical basis for the numerical simulation of water energy cycle in the source region over the Yellow River basin.

Study on the cloud detection of GOCI by using the simulated surface reflectance from BRDF-model for the land application and meteorological utilization

NASA Astrophysics Data System (ADS)

Kim, Hye-Won; Yeom, Jong-Min; Woo, Sun-Hee; Chae, Tae-Byeong

2016-04-01

COMS (Communication, Ocean, and Meteorological Satellite) was launched at French Guiana Kourou space center on 27 June 2010. Geostationary Ocean Color Imager (GOCI), which is the first ocean color geostationary satellite in the world for observing the ocean phenomena, is able to obtain the scientific data per an hour from 00UTC to 07UTC. Moreover, the spectral channels of GOCI would enable not only monitoring for the ocean, but for extracting the information of the land surface over the Korean Peninsula, Japan, and Eastern China. Since it is extremely important to utilize GOCI data accurately for the land application, cloud pixels over the surface have to be removed. Unfortunately, infra-red (IR) channels that can easily detect the water vapor with the cloud top temperature, are not included in the GOCI sensor. In this paper, the advanced cloud masking algorithm will be proposed with visible and near-IR (NIR) bands that are within GOCI bands. The main obstacle of cloud masking with GOCI is how to handle the high variable surface reflectance, which is mainly depending on the solar zenith angle. In this study, we use semi-empirical BRDF model to simulate the surface reflectance by using 16 day composite cloudy free image. When estimating the simulated surface reflectance, same geometry for GOCI observation was applied. The simulated surface reflectance is used to discriminate cloud areas especially for the thin cloud and shows more reasonable result than original threshold methods.

Leveraging Oceanic and Surface Intensive Field Campaign Data Sets for Validation and Improvement of Recent Hyperspectral IR Satellite Data Products

NASA Astrophysics Data System (ADS)

Joseph, E.; Nalli, N. R.; Oyola, M. I.; Morris, V. R.; Sakai, R.

2014-12-01

An overview is given of research to validate or improve the retrieval of environmental data records (EDRs) from recently deployed hyperspectral IR satellite sensors such as Suomi NPP Cross-track Infrared Microwave Sounder Suite (CrIMSS). The effort centers around several surface field intensive campaigns that are designed or leveraged for EDR validation. These data include ship-based observations of upper air ozone, pressure, temperature and relative humidity soundings; aerosol and cloud properties; and sea surface temperature. Similar intensive data from two land-based sites are also utilized as well. One site, the Howard University Beltsville site, is at a single point location but has a comprehensive array of observations for an extended period of time. The other land site, presently being deployed by the University at Albany, is under development with limited upper air soundings but will have regionally distributed surface based microwave profiling of temperature and relative humidity on the scale of 10 - 50 km and other standard meteorological observations. Combined these observations provide data that are unique in their wide range including, a variety of meteorological conditions and atmospheric compositions over the ocean and urban-suburban environments. With the distributed surface sites the variability of atmospheric conditions are captured concurrently across a regional spatial scale. Some specific examples are given of comparisons of moisture and temperature correlative EDRs from the satellite sensors and surface based observations. An additional example is given of the use of this data to correct sea surface temperature (SST) retrieval biases from the hyperspectral IR satellite observations due to aerosol contamination.

Temporal upscaling of instantaneous evapotranspiration on clear-sky days using the constant reference evaporative fraction method with fixed or variable surface resistances at two cropland sites

NASA Astrophysics Data System (ADS)

Tang, Ronglin; Li, Zhao-Liang; Sun, Xiaomin; Bi, Yuyun

2017-01-01

Surface evapotranspiration (ET) is an important component of water and energy in land and atmospheric systems. This paper investigated whether using variable surface resistances in the reference ET estimates from the full-form Penman-Monteith (PM) equation could improve the upscaled daily ET estimates in the constant reference evaporative fraction (EFr, the ratio of actual to reference grass/alfalfa ET) method on clear-sky days using ground-based measurements. Half-hourly near-surface meteorological variables and eddy covariance (EC) system-measured latent heat flux data on clear-sky days were collected at two sites with different climatic conditions, namely, the subhumid Yucheng station in northern China and the arid Yingke site in northwestern China and were used as the model input and ground-truth, respectively. The results showed that using the Food and Agriculture Organization (FAO)-PM equation, the American Society of Civil Engineers-PM equation, and the full-form PM equation to estimate the reference ET in the constant EFr method produced progressively smaller upscaled daily ET at a given time from midmorning to midafternoon. Using all three PM equations produced the best results at noon at both sites regardless of whether the energy imbalance of the EC measurements was closed. When the EC measurements were not corrected for energy imbalance, using variable surface resistance in the full-form PM equation could improve the ET upscaling in the midafternoon, but worse results may occur in the midmorning to noon. Site-to-site and time-to-time variations were found in the performances of a given PM equation (with fixed or variable surface resistances) before and after the energy imbalance was closed.

Applications of Meteorological Tower Data at Kennedy Space Center

NASA Technical Reports Server (NTRS)

Altino, Karen M.; Barbre, Robert E., Jr.

2009-01-01

Members of the National Aeronautics and Space Administration (NASA) design and operation communities rely on meteorological information collected at Kennedy Space Center (KSC), located near Cape Canaveral, Florida, to correctly apply the ambient environment to various tasks. The Natural Environments Branch/EV44, located at Marshall Space Flight Center (MSFC) in Huntsville, Alabama, is responsible for providing its NASA customers with meteorological data using various climatological data sources including balloons, surface stations, aircraft, hindcast models, and meteorological towers. Of the many resources available within the KSC region, meteorological towers are preferred for near-surface applications because they record data at regular, frequent intervals over an extensive period of record at a single location. This paper discusses the uses of data measured at several different meteorological towers for a common period of record and how the data can be applied to various engineering decisions for the new Constellation Program Ares and Orion space vehicles.

Measurement of inter- and intra-annual variability of landscape fire activity at a continental scale: The Australian case

Treesearch

Grant J. Williamson; Lynda D. Prior; Matt Jolly; Mark A. Cochrane; Brett P. Murphy; David M. J. S. Bowman

2016-01-01

Climate dynamics at diurnal, seasonal and inter-annual scales shape global fire activity, although difficulties of assembling reliable fire and meteorological data with sufficient spatio-temporal resolution have frustrated quantification of this variability. Using Australia as a case study, we combine data from 4760 meteorological stations with 12 years of satellite-...

Modeling the Effects of Meteorological Conditions on the Neutron Flux

DTIC Science & Technology

2017-05-22

a statistical model that predicts environmental neutron background as a function of five meteorological variables: inverse barometric pressure...variable of the model was inverse barometric pressure with a contribution an order of magnitude larger than any other variable’s contribution. The...is based on the sensitivity of each sensor. . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Neutron counts from the LNS and inverse pressure

SSE Data and Information Page

Atmospheric Science Data Center

2018-04-04

Surface meteorology and Solar Energy (SSE) Data and Information A new POWER home page ... The Release 6.0 Surface meteorology and Solar Energy (SSE) data set contains parameters formulated for assessing and designing renewable energy systems. This latest release contains new parameters based on ...

Surface and Tower Meteorological Instrumentation at NSA Handbook - January 2006

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

MT Ritsche

2006-01-30

The Surface and Tower Meteorological Instrumentation at Atqasuk (METTWR2H) uses mainly conventional in situ sensors to measure wind speed, wind direction, air temperature, dew point and humidity mounted on a 10-m tower. It also obtains barometric pressure, visibility, and precipitation data from sensors at or near the base of the tower. In addition, a Chilled Mirror Hygrometer is located at 1 m for comparison purposes. Temperature and relative humidity probes are mounted at 2 m and 5 m on the tower. For more information, see the Surface and Tower Meteorological Instrumentation at Atqasuk Handbook.

Investigatigating inter-/intra-annual variability of surface hydrology at northern high latitude from spaceborne measurements

NASA Astrophysics Data System (ADS)

Kang, K.; Duguay, C. R.

2014-12-01

Lakes encompass a large part of the surface cover in the northern boreal and tundra areas of northern Canada and are therefore a significant component of the terrestrial hydrological system. To understand the hydrologic cycle over subarctic and arctic landscapes, estimating surface parameters such as surface net radiation, soil moisture, and surface albedo is important. Although ground-based field measurements provide a good temporal resolution, these data provide a limited spatial representation and are often restricted to the summer period (from June to August), and few surface-based stations are located in high-latitude regions. In this respect, spaceborne remote sensing provides the means to monitor surface hydrology and to estimate components of the surface energy balance with reasonable spatial and temporal resolutions required for hydrological investigations, as well as for providing more spatially representative lake-relevant information than available from in situ measurements. The primary objective of this study is to quantify the sources of temporal and spatial variability in surface albedo over subarctic wetland from satellite derived albedo measurements in the Hudson Bay Lowlands near Churchill, Manitoba. The spatial variability in albedo within each land-cover type is investigated through optical satellite imagery from Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper Plus, and Landsat-8 Operational Land Imager obtained in different seasons from spring into fall (April and October) over a 30-year period (1984-2013). These data allowed for an examination of the spatial variability of surface albedo under relatively dry and wet summer conditions (i.e. 1984, 1998 versus 1991, 2005). A detailed analysis of Landsat-derived surface albedo (ranging from 0.09 to 0.15) conducted in the Churchill region for August is inversely related to surface water fraction calculated from Landsat images. Preliminary analysis of surface albedo observed between July and August are 0.10 to 0.15, and vary due to differences in meteorological parameters such as rainfall, surface moisture and surface air temperature. Overall, spaceborne optical data are an invaluable source for investigating changes and variability in surface albedo in relation to surface hydrology over subarctic regions.

Extending the Confrontation of Weather and Climate Models from Soil Moisture to Surface Flux Data

NASA Astrophysics Data System (ADS)

Dirmeyer, P.; Chen, L.; Wu, J.

2016-12-01

The atmosphere and land components of weather and climate models are typically developed separately and coupled as a last step before new model versions are released. Separate testing of land surface models (LSMs) and atmospheric models is often quite extensive in the development phase, but validation of coupled land-atmosphere behavior is often minimal if performed at all. This is partly because of this piecemeal model development approach and partly because the necessary in situ data to confront coupled land-atmosphere models (LAMs) has been meager until quite recently. Over the past 10-20 years there has been a growing number of networks of measurements of land surface states, surface fluxes, radiation and near-surface meteorology, although they have been largely uncoordinated and frequently incomplete across the range of variables necessary to validate LAMs. We extend recent work "confronting" a variety of LSMs and LAMs with in situ observations of soil moisture from cross-standardized networks to comparisons with measurements of surface latent and sensible heat fluxes at FLUXNET sites in a variety of climate regimes around the world. The motivation is to determine how well LSMs represent observed statistics of variability and co-variability, how much models differ from one another, and how those statistics change when the LSMs are coupled to atmospheric models. Furthermore, comparisons are made to several LAMs in both open-loop (free running) and reanalysis configurations. This shows to what extent data assimilation can constrain the processes involved in flux variability, and helps illuminate model development pathways to improve coupled land-atmosphere interactions in weather and climate models.

Characterizing energy budget variability at a Sahelian site: a test of NWP model behaviour

NASA Astrophysics Data System (ADS)

Mackie, Anna; Palmer, Paul I.; Brindley, Helen

2017-12-01

We use observations of surface and top-of-the-atmosphere (TOA) broadband radiation fluxes determined from the Atmospheric Radiation Measurement programme mobile facility, the Geostationary Earth Radiation Budget (GERB) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments and a range of meteorological variables at a site in the Sahel to test the ability of the ECMWF Integrated Forecasting System cycle 43r1 to describe energy budget variability. The model has daily average biases of -12 and 18 W m-2 for outgoing longwave and reflected shortwave TOA radiation fluxes, respectively. At the surface, the daily average bias is 12(13) W m-2 for the longwave downwelling (upwelling) radiation flux and -21(-13) W m-2 for the shortwave downwelling (upwelling) radiation flux. Using multivariate linear models of observation-model differences, we attribute radiation flux discrepancies to physical processes, and link surface and TOA fluxes. We find that model biases in surface radiation fluxes are mainly due to a low bias in ice water path (IWP), poor description of surface albedo and model-observation differences in surface temperature. We also attribute observed discrepancies in the radiation fluxes, particularly during the dry season, to the misrepresentation of aerosol fields in the model from use of a climatology instead of a dynamic approach. At the TOA, the low IWP impacts the amount of reflected shortwave radiation while biases in outgoing longwave radiation are additionally coupled to discrepancies in the surface upwelling longwave flux and atmospheric humidity.

A Study on the Relationships among Surface Variables to Adjust the Height of Surface Temperature for Data Assimilation.

NASA Astrophysics Data System (ADS)

Kang, J. H.; Song, H. J.; Han, H. J.; Ha, J. H.

2016-12-01

The observation processing system, KPOP (KIAPS - Korea Institute of Atmospheric Prediction Systems - Package for Observation Processing) have developed to provide optimal observations to the data assimilation system for the KIAPS Integrated Model (KIM). Currently, the KPOP has capable of processing almost all of observations for the KMA (Korea Meteorological Administration) operational global data assimilation system. The height adjustment of SURFACE observations are essential for the quality control due to the difference in height between observation station and model topography. For the SURFACE observation, it is usual to adjust the height using lapse rate or hypsometric equation, which decides values mainly depending on the difference of height. We have a question of whether the height can be properly adjusted following to the linear or exponential relationship solely with regard to the difference of height, with disregard the atmospheric conditions. In this study, firstly we analyse the change of surface variables such as temperature (T2m), pressure (Psfc), humidity (RH2m and Q2m), and wind components (U and V) according to the height difference. Additionally, we look further into the relationships among surface variables . The difference of pressure shows a strong linear relationship with difference of height. But the difference of temperature according to the height shows a significant correlation with difference of relative humidity than with the height difference. A development of reliable model for the height-adjustment of surface temperature is being undertaken based on the preliminary results.

Validation Results for LEWICE 2.0

NASA Technical Reports Server (NTRS)

Wright, William B.; Rutkowski, Adam

1999-01-01

A research project is underway at NASA Lewis to produce a computer code which can accurately predict ice growth under any meteorological conditions for any aircraft surface. This report will present results from version 2.0 of this code, which is called LEWICE. This version differs from previous releases due to its robustness and its ability to reproduce results accurately for different spacing and time step criteria across computing platform. It also differs in the extensive amount of effort undertaken to compare the results in a quantified manner against the database of ice shapes which have been generated in the NASA Lewis Icing Research Tunnel (IRT). The results of the shape comparisons are analyzed to determine the range of meteorological conditions under which LEWICE 2.0 is within the experimental repeatability. This comparison shows that the average variation of LEWICE 2.0 from the experimental data is 7.2% while the overall variability of the experimental data is 2.5%.

Field gamma-ray spectrometer GS256: measurements stability

NASA Astrophysics Data System (ADS)

Mojzeš, Andrej

2009-01-01

The stability of in situ readings of the portable gamma-ray spectrometer GS256 during the field season of 2006 was studied. The instrument is an impulse detector of gamma rays based on NaI(Tl) 3" × 3" scintillation unit and 256-channel spectral analyzer which allows simultaneous assessment of up to 8 radioisotopes in rocks. It is commonly used in surface geophysical survey for the measurement of natural 40K, 238U and 232Th but also artificial 137Cs quantities. The statistical evaluation is given of both repeated measurements - in the laboratory and at several field control points in different survey areas. The variability of values shows both the instrument stability and also the relative influence of some meteorological factors, mainly rainfalls. The analysis shows an acceptable level of instrument measurements stability, the necessity to avoid measurement under unfavourable meteorological conditions and to keep detailed field book information about time, position and work conditions.

Evaluation of surface layer flux parameterizations using in-situ observations

NASA Astrophysics Data System (ADS)

Katz, Jeremy; Zhu, Ping

2017-09-01

Appropriate calculation of surface turbulent fluxes between the atmosphere and the underlying ocean/land surface is one of the major challenges in geosciences. In practice, the surface turbulent fluxes are estimated from the mean surface meteorological variables based on the bulk transfer model combined with the Monnin-Obukhov Similarity (MOS) theory. Few studies have been done to examine the extent to which such a flux parameterization can be applied to different weather and surface conditions. A novel validation method is developed in this study to evaluate the surface flux parameterization using in-situ observations collected at a station off the coast of Gulf of Mexico. The main findings are: (a) the theoretical prediction that uses MOS theory does not match well with those directly computed from the observations. (b) The largest spread in exchange coefficients is shown in strong stable conditions with calm winds. (c) Large turbulent eddies, which depend strongly on the mean flow pattern and surface conditions, tend to break the constant flux assumption in the surface layer.

Spatial Variability of Surface Irradiance Measurements at the Manus ARM Site

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

Riihimaki, Laura D.; Long, Charles N.

2014-05-16

The location of the Atmospheric Radiation Measurement (ARM) site on Manus island in Papua New Guinea was chosen because it is very close the coast, in a geographically at, near-sea level area of the island, minimizing the impact of local island effects on the meteorology of the measurements [Ackerman et al., 1999]. In this study, we confirm that the Manus site is in deed less impacted by the island meteorology than slightly inland by comparing over a year of broadband surface irradiance and ceilometer measurements and derived quantities at the standard Manus site and a second location 7 km awaymore » as part of the AMIE-Manus campaign. The two sites show statistically similar distributions of irradiance and other derived quantities for all wind directions except easterly winds, when the inland site is down wind from the standard Manus site. Under easterly wind conditions, which occur 17% of the time, there is a higher occurrence of cloudiness at the down wind site likely do to land heating and orographic effects. This increased cloudiness is caused by shallow, broken clouds often with bases around 700 m in altitude. While the central Manus site consistently measures a frequency of occurrence of low clouds (cloud base height less than 1200 m) about 25+4% regardless of wind direction, the AMIE site has higher frequencies of low clouds (38%) when winds are from the east. This increase in low, locally produced clouds causes an additional -20 W/m2 shortwave surface cloud radiative effect at the AMIE site in easterly conditions than in other meteorological conditions that exhibit better agreement between the two sites.« less

The meta-Gaussian Bayesian Processor of forecasts and associated preliminary experiments

NASA Astrophysics Data System (ADS)

Chen, Fajing; Jiao, Meiyan; Chen, Jing

2013-04-01

Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.

Modelling of Black and Organic Carbon Variability in the Northern Hemisphere

NASA Astrophysics Data System (ADS)

Kurganskiy, Alexander; Nuterman, Roman; Mahura, Alexander; Kaas, Eigil; Baklanov, Alexander; Hansen Sass, Bent

2016-04-01

Black and organic carbon as short-lived climate forcers have influence on air quality and climate in Northern Europe and Arctic. Atmospheric dispersion, deposition and transport of these climate forcers from remote sources is especially difficult to model in Arctic regions due to complexity of meteorological and chemical processes and uncertainties of emissions. In our study, the online integrated meteorology-chemistry/aerosols model Enviro-HIRLAM (Environment - High Resolution Limited Area Model) was employed for evaluating spatio-temporal variability of black and organic carbon aerosols in atmospheric composition in the Northern Hemisphere regions. The model setup included horizontal resolution of 0.72 deg, time step of 450 sec, 6 h meteorological surface data assimilation, 1 month spin-up; and model was run for the full year of 2010. Emissions included anthropogenic (ECLIPSE), shipping (AU_RCP&FMI), wildfires (IS4FIRES), and interactive sea salt, dust and DMS. Meteorological (from IFS at 0.75 deg) and chemical (from MACC Reanalysis at 1.125 deg) boundary conditions were obtained from ECMWF. Annual and month-to-month variability of mean concentration, accumulated dry/wet and total deposition fluxes is analyzed for the model domain and selected European and Arctic observation sites. Modelled and observed BC daily mean concentrations during January and July showed fair-good correlation (0.31-0.64) for stations in Germany, UK and Italy; however, for Arctic stations (Tiksi, Russia and Zeppelin, Norway) the correlations were negative in January, but higher correlations and positive (0.2-0.7) in July. For OC, it varied 0.45-0.67 in January and 0.19-0.57 in July. On seasonal scale, during both summer and winter seasons the BC and OC correlations are positive and higher for European stations compared with Arctic. On annual scale, both BC and OC correlations are positive and vary between 0.4-0.6 for European stations, and these are smoothed to negligible values for Arctic stations. Results of simulations showed that in general the model tends to underestimate both black and organic carbon concentrations for the Arctic and European stations.

Ensemble data assimilation of total column ozone using a coupled meteorology-chemistry model and its impact on the structure of Typhoon Nabi (2005)

NASA Astrophysics Data System (ADS)

Lim, S.; Park, S. K.; Zupanski, M.

2015-09-01

Ozone (O3) plays an important role in chemical reactions and is usually incorporated in chemical data assimilation (DA). In tropical cyclones (TCs), O3 usually shows a lower concentration inside the eyewall and an elevated concentration around the eye, impacting meteorological as well as chemical variables. To identify the impact of O3 observations on TC structure, including meteorological and chemical information, we developed a coupled meteorology-chemistry DA system by employing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and an ensemble-based DA algorithm - the maximum likelihood ensemble filter (MLEF). For a TC case that occurred over East Asia, Typhoon Nabi (2005), our results indicate that the ensemble forecast is reasonable, accompanied with larger background state uncertainty over the TC, and also over eastern China. Similarly, the assimilation of O3 observations impacts meteorological and chemical variables near the TC and over eastern China. The strongest impact on air quality in the lower troposphere was over China, likely due to the pollution advection. In the vicinity of the TC, however, the strongest impact on chemical variables adjustment was at higher levels. The impact on meteorological variables was similar in both over China and near the TC. The analysis results are verified using several measures that include the cost function, root mean square (RMS) error with respect to observations, and degrees of freedom for signal (DFS). All measures indicate a positive impact of DA on the analysis - the cost function and RMS error have decreased by 16.9 and 8.87 %, respectively. In particular, the DFS indicates a strong positive impact of observations in the TC area, with a weaker maximum over northeastern China.

Determination of the optimal training principle and input variables in artificial neural network model for the biweekly chlorophyll-a prediction: a case study of the Yuqiao Reservoir, China.

PubMed

Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang

2015-01-01

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.

Relationships between Hg Air-surface exchange, Soil Moisture and Precipitation at a Background Vegetated Site in South-Eastern Australia.

NASA Astrophysics Data System (ADS)

Macsween, K.; Edwards, G. C.

2017-12-01

Despite many decades of research, the controlling mechanisms of mercury (Hg) air-surface exhange are still poorly understood. Particularly in Australian ecosystems where there are few anthropogenic inputs. A clear understanding of these mechanisms is vital for accurate representation in the global Hg models, particularly regarding re-emission. Water is known to have a considerable influence on Hg exchange within a terrestrial ecosystem. Precipitation has been found to cause spikes is Hg emissions during the initial stages of rain event. While, Soil moisture content is known to enhance fluxes between 15 and 30% Volumetric soil water (VSW), above which fluxes become suppressed. Few field experiments exist to verify these dominantly laboratory or controlled experiments. Here we present work looking at Hg fluxes over an 8-month period at a vegetated background site. The aim of this study is to identify how changes to precipitation intensity and duration, coupled with variable soil moisture content may influence Hg flux across seasons. As well as the influence of other meteorological variables. Experimentation was undertaken using aerodynamic gradient micrometeorological flux method, avoiding disruption to the surface, soil moisture probes and rain gauge measurements to monitor alterations to substrate conditions. Meteorological and air chemistry variables were also measured concurrently throughout the duration of the study. During the study period, South-Eastern Australia experienced several intense east coast low storm systems during the Autumn and Spring months and an unusually dry winter. VSW rarely reached above 30% even following the intense rainfall experienced during the east coast lows. The generally dry conditions throughout winter resulted in an initial spike in Hg emissions when rainfall occurred. Fluxes decreased shortly after the rain began but remained slightly elevated. Given the reduced net radiation and cooler temperatures experienced during the winter months soils took several days to dry out, resulting in slightly enhanced fluxes for the days preceding rainfall. It is thought that seasonality of rainfall has a significant impact of Hg air-surface exchange trends, both through increased recovery times once rain has past and through the increased occurrence of major storm events.

Climatological Downscaling and Evaluation of AGRMET Precipitation Analyses Over the Continental U.S.

NASA Astrophysics Data System (ADS)

Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Tian, Y.; Zeng, J.

2007-05-01

The spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land- atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. Collaborations between the Air Force Weather Agency (AFWA), NASA, and Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and downscaling of precipitation fields. Climatological information included in the LIS-based downscaling procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and near-real-time simulations in regions of interest. This work focuses on value added to the AGRMET precipitation product by the inclusion of high-quality climatological information on a monthly time scale. The AGRMET method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES and DMSP), infrared-based estimates from geostationary platforms (GOES, METEOSAT, etc.), related cloud analysis products, and surface gauge observations in a complex and hierarchical blending process. Results from processing of the legacy AGRMET precipitation products over the U.S. using LIS-based methods for downscaling, both with and without climatological factors, are evaluated against high-resolution monthly analyses using the PRISM knowledge- based method (Daly et al. 2002). It is demonstrated that the incorporation of climatological information in a downscaling procedure can significantly enhance the accuracy, and potential utility, of AFWA precipitation products for military and civilian customer applications.

Surface Water and Energy Budgets for Sub-Saharan Africa in GFDL Coupled Climate Model

NASA Astrophysics Data System (ADS)

Tian, D.; Wood, E. F.; Vecchi, G. A.; Jia, L.; Pan, M.

2015-12-01

This study compare surface water and energy budget variables from the Geophysical Fluid Dynamics Laboratory (GFDL) FLOR models with the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Princeton University Global Meteorological Forcing Dataset (PGF), and PGF-driven Variable Infiltration Capacity (VIC) model outputs, as well as available observations over the sub-Saharan Africa. The comparison was made for four configurations of the FLOR models that included FLOR phase 1 (FLOR-p1) and phase 2 (FLOR-p2) and two phases of flux adjusted versions (FLOR-FA-p1 and FLOR-FA-p2). Compared to p1, simulated atmospheric states in p2 were nudged to the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. The seasonal cycle and annual mean of major surface water (precipitation, evapotranspiration, runoff, and change of storage) and energy variables (sensible heat, ground heat, latent heat, net solar radiation, net longwave radiation, and skin temperature) over a 34-yr period during 1981-2014 were compared in different regions in sub-Saharan Africa (West Africa, East Africa, and Southern Africa). In addition to evaluating the means in three sub-regions, empirical orthogonal functions (EOFs) analyses were conducted to compare both spatial and temporal characteristics of water and energy budget variables from four versions of GFDL FLOR, NCEP CFSR, PGF, and VIC outputs. This presentation will show how well each coupled climate model represented land surface physics and reproduced spatiotemporal characteristics of surface water and energy budget variables. We discuss what caused differences in surface water and energy budgets in land surface components of coupled climate model, climate reanalysis, and reanalysis driven land surface model. The comparisons will reveal whether flux adjustment and nudging would improve depiction of the surface water and energy budgets in coupled climate models.

Surface Meteorology, Barrow, Alaska, Area A, B, C and D, Ongoing from 2012

DOE Data Explorer

Bob Busey; Larry Hinzman; William Cable; Vladimir Romanovsky

2014-12-04

Meteorological data are being collected at several points within four intensive study areas in Barrow. These data assist in the calculation of the energy balance at the land surface and are also useful as inputs into modeling activities.

Regional Differences in Stratospheric Intrusions over the USA Investigated using the NASA MERRA-2 Reanalysis

NASA Astrophysics Data System (ADS)

Knowland, K. E.; Ott, L.; Hodges, K.; Wargan, K.; Duncan, B. N.

2016-12-01

Stratospheric intrusions (SI) - the introduction of ozone-rich stratospheric air into the troposphere - have been linked with surface ozone air quality exceedences, especially at the high elevations in the western USA in springtime. However, the impact of SIs in the remaining seasons and over the rest of the USA is less clear. This study investigates the atmospheric dynamics that generate SIs over the western USA and the different mechanisms through which SIs may influence atmospheric chemistry and surface air quality over the eastern USA. An analysis of the spatiotemporal variability of SIs over the continental US is performed using NASA's Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) reanalysis dataset and other Goddard Earth Observing System Model, Version 5 (GEOS-5) model products. Both upper-level and lower-level dynamical features are examined on seasonal timescales using the tracking algorithm of Hodges (1995, 1999). We show how upper-level relative vorticity maxima - representing troughs and cut-off lows - can be tracked and related to the lower-level storm tracks. The influence of both sets of tracks on the assimilated MERRA-2 ozone and meteorological parameters throughout the troposphere and lower stratosphere is quantified. By focusing on the major modes of variability that influence the weather patterns in the USA, namely the Pacific North American (PNA) pattern, Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO), predicative patterns in the meteorological fields that are associated with SIs are identified for their regional effects.

The MeteoMet2 project—highlights and results

NASA Astrophysics Data System (ADS)

Merlone, A.; Sanna, F.; Beges, G.; Bell, S.; Beltramino, G.; Bojkovski, J.; Brunet, M.; del Campo, D.; Castrillo, A.; Chiodo, N.; Colli, M.; Coppa, G.; Cuccaro, R.; Dobre, M.; Drnovsek, J.; Ebert, V.; Fernicola, V.; Garcia-Benadí, A.; Garcia-Izquierdo, C.; Gardiner, T.; Georgin, E.; Gonzalez, A.; Groselj, D.; Heinonen, M.; Hernandez, S.; Högström, R.; Hudoklin, D.; Kalemci, M.; Kowal, A.; Lanza, L.; Miao, P.; Musacchio, C.; Nielsen, J.; Nogueras-Cervera, M.; Oguz Aytekin, S.; Pavlasek, P.; de Podesta, M.; Rasmussen, M. K.; del-Río-Fernández, J.; Rosso, L.; Sairanen, H.; Salminen, J.; Sestan, D.; Šindelářová, L.; Smorgon, D.; Sparasci, F.; Strnad, R.; Underwood, R.; Uytun, A.; Voldan, M.

2018-02-01

Launched in 2011 within the European Metrology Research Programme (EMRP) of EURAMET, the joint research project ‘MeteoMet’—Metrology for Meteorology—is the largest EMRP consortium; national metrology institutes, universities, meteorological and climate agencies, research institutes, collaborators and manufacturers are working together, developing new metrological techniques, as well as improving existing ones, for use in meteorological observations and climate records. The project focuses on humidity in the upper and surface atmosphere, air temperature, surface and deep-sea temperatures, soil moisture, salinity, permafrost temperature, precipitation, and the snow albedo effect on air temperature. All tasks are performed using a rigorous metrological approach and include the design and study of new sensors, new calibration facilities, the investigation of sensor characteristics, improved techniques for measurements of essential climate variables with uncertainty evaluation, traceability, laboratory proficiency and the inclusion of field influencing parameters, long-lasting measurements, and campaigns in remote and extreme areas. The vision for MeteoMet is to take a step further towards establishing full data comparability, coherency, consistency, and long-term continuity, through a comprehensive evaluation of the measurement uncertainties for the quantities involved in the global climate observing systems and the derived observations. The improvement in quality of essential climate variables records, through the inclusion of measurement uncertainty budgets, will also highlight possible strategies for the reduction of the uncertainty. This contribution presents selected highlights of the MeteoMet project and reviews the main ongoing activities, tasks and deliverables, with a view to its possible future evolution and extended impact.

A Global Drought and Flood Catalogue for the past 100 years

NASA Astrophysics Data System (ADS)

Sheffield, J.; He, X.; Peng, L.; Pan, M.; Fisher, C. K.; Wood, E. F.

2017-12-01

Extreme hydrological events cause the most impacts of natural hazards globally, impacting on a wide range of sectors including, most prominently, agriculture, food security and water availability and quality, but also on energy production, forestry, health, transportation and fisheries. Understanding how floods and droughts intersect, and have changed in the past provides the basis for understanding current risk and how it may change in the future. To do this requires an understanding of the mechanisms associated with events and therefore their predictability, attribution of long-term changes in risk, and quantification of projections of changes in the future. Of key importance are long-term records of relevant variables so that risk can be quantified more accurately, given the growing acknowledgement that risk is not stationary under long-term climate variability and climate change. To address this, we develop a catalogue of drought and flood events based on land surface and hydrodynamic modeling, forced by a hybrid meteorological dataset that draws from the continuity and coverage of reanalysis, and satellite datasets, merged with global gauge databases. The meteorological dataset is corrected for temporal inhomogeneities, spurious trends and variable inter-dependencies to ensure long-term consistency, as well as realistic representation of short-term variability and extremes. The VIC land surface model is run for the past 100 years at 0.25-degree resolution for global land areas. The VIC runoff is then used to drive the CaMa-Flood hydrodynamic model to obtain information on flood inundation risk. The model outputs are compared to satellite based estimates of flood and drought conditions and the observational flood record. The data are analyzed in terms of the spatio-temporal characteristics of large-scale flood and drought events with a particular focus on characterizing the long-term variability in risk. Significant changes in risk occur on multi-decadal time scales and are mostly associated with variability in the North Atlantic and Pacific. The catalogue can be used for analysis of extreme events, risk assessment, and as a benchmark for model evaluation.

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Validation of China-wide interpolated daily climate variables from 1960 to 2011

NASA Astrophysics Data System (ADS)

Yuan, Wenping; Xu, Bing; Chen, Zhuoqi; Xia, Jiangzhou; Xu, Wenfang; Chen, Yang; Wu, Xiaoxu; Fu, Yang

2015-02-01

Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961-2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients ( R 2) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2, and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66-95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based on the performance of these variables in estimating daily variations, interannual variability, and extreme events. Although longitude, latitude, and elevation data are included in the model, additional information, such as topography and cloud cover, should be integrated into the interpolation algorithm to improve performance in estimating wind speed, atmospheric pressure, and precipitation.

Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

NASA Astrophysics Data System (ADS)

Lange, Stefan

2018-05-01

Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.

Atmospheric conditions measured by a wireless sensor network on the local scale

NASA Astrophysics Data System (ADS)

Lengfeld, K.; Ament, F.

2010-09-01

Atmospheric conditions close to the surface, like temperature, wind speed and humidity, vary on small scales because of surface heterogeneities. Therefore, the traditional measuring approach of using a single, highly accurate station is of limited representativeness for a larger domain, because it is not able to determine these small scale variabilities. However, both the variability and the domain averages are important information for the development and validation of atmospheric models and soil-vegetation-atmosphere-transfer (SVAT) schemes. Due to progress in microelectronics it is possible to construct networks of comparably cheap meteorological stations with moderate accuracy. Such a network provides data in high spatial and temporal resolution. The EPFL Lausanne developed such a network called SensorScope, consisting of low cost autonomous stations. Each station observes air and surface temperature, humidity, wind direction and speed, incoming solar radiation, precipitation, soil moisture and soil temperature and sends the data via radio communication to a base station. This base station forwards the collected data via GSM/GPRS to a central server. The first measuring campaign took place within the FLUXPAT project in August 2009. We deployed 15 stations as a twin transect near Jülich, Germany. To test the quality of the low cost sensors we compared two of them to more accurate reference systems. It turned out, that although the network sensors are not highly accurate, the measurements are consistent. Consequently an analysis of the pattern of atmospheric conditions is feasible. The transect is 2.3 km long and covers different types of vegetation and a small river. Therefore, we analyse the influence of different land surfaces and the distance to the river on meteorological conditions. For example, we found a difference in air temperature of 0.8°C between the station closest to and the station farthest from the river. The decreasing relative humidity with increasing distance to the river meets our expectations. But there are also some unexpected anomalies in the air temperature, which will be discussed in detail by selected case studies. By analysing the correlation of the fluctuation of the meteorological conditions, we want to detect clusters depending on different land surfaces and distance to the river. Since April 2010 a second deployment is set up at the Airport Hamburg. It consists of 14 stations placed along the two runways in northward and in eastward direction. The aim of this project is to analyse whether the atmospheric conditions in such an uniform environment are really homogeneous. To do so we will apply the same analyses for these measurements we used for FLUXPAT.

Near Real Time Surface Solar Radiation and Meteorological Parameters From the CERES FLASHFlux Project: Examples of Usage for Energy-Related Applications

NASA Astrophysics Data System (ADS)

Hoell, J. M.; Stockhouse, P.; Chandler, W.; Zhang, T.; Kratz, D. P.; Gupta, S. K.; Wilber, A. C.; Sawaengphokhai, P.; Edwards, A. C.; Westberg, D.; Zell, E.; Leng, G.

2010-12-01

The NASA Langley Research Center Fast Longwave And SHortwave Radiative Fluxes (FLASHFlux) project is producing global near real-time surface and top of Atmosphere (TOA) radiative fluxes and analyzing these quantities and their variability on regional and global scales. This is being accomplished by using a portion of the existing Clouds and the Earth's Radiant Energy System (CERES) processing system that fuses CERES with MODIS (Moderate Resolution Imaging Spectrometer) to produce orbital flux products. The orbital products from both Terra and Aqua are subsequently merged to derive global gridded radiative flux products. The FLASHFlux processing system also uses meteorological surface and profile file information from NASA Global Modeling and Data Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) operational analysis version 5.2. The production of these together considering the latency times results in the global gridded surface radiative fluxes within 6-7 days of the original satellite observations. Data from the FLASHFlux have been merged and made available through a user-friendly web-based data portal (http://power.larc.nasa.gov/). Solar data from this portal are being continuously updated to provide time series of daily solar radiation to current time minus 7-days. While the current solar data represents an average over a 1-degree cell, comparison with ground observations exhibits a high degree of correlation on a daily time scale. These data are promoted to the web along with surface meteorological data from the GMAO GEOS 5.2 to provide a complete suite of parameters useful for many applications. This paper highlights the use of these data sets in the Ventyx Corporation database Velocity Suite that is being provided to utilities for power load forecasting. Examples of the usage and impact of this data on subsequent load forecasts are presented. The data sets are also being evaluated in collaboration with the Natural Resource Canada RETScreen International Energy Monitoring, Targeting and Verification tool (MTV). This tool allows the monitoring of building energy usage in correlation with variability in the environmental conditions and provides the flexibility of studying the economic and environmental feasibility of various energy efficient and renewable energy enhancements to the building. The FLASHFlux production system or similar is planned to continue as part as CERES for the upcoming NPP (NPOES Preparatory Project) and may be considered as part of the CERES data production stream on the joint NOAA/NASA JPSS missions. Lastly, we identify currently known usage needs requiring enhancement of the current data products that would be appropriate for these future satellite systems.

Urban airborne matter in central and southern Chile: Effects of meteorological conditions on fine and coarse particulate matter

NASA Astrophysics Data System (ADS)

Yáñez, Marco A.; Baettig, Ricardo; Cornejo, Jorge; Zamudio, Francisco; Guajardo, Jorge; Fica, Rodrigo

2017-07-01

Air pollution is one of the major global environmental problems affecting human health and life quality. Many cities of Chile are heavily polluted with PM2.5 and PM10, mainly in the cold season, and there is little understanding of how the variation in particle matter differs between cities and how this is affected by the meteorological conditions. The objective of this study was to assess the effect of meteorological variables on respirable particulate matter (PM) of the main cities in the central-south valley of Chile during the cold season (May to August) between 2014 and 2016. We used hourly PM2.5 and PMcoarse (PM10- PM2.5) information along with wind speed, temperature and relative humidity, and other variables derived from meteorological parameters. Generalized additive models (GAMs) were fitted for each of the eight cities selected, covering a latitudinal range of 929 km, from Santiago to Osorno. Great variation in PM was found between cities during the cold months, and that variation exhibited a marked latitudinal pattern. Overall, the more northerly cities tended to be less polluted in PM2.5 and more polluted in PMcoarse than the more southerly cities, and vice versa. The results show that other derived variables from meteorology were better related with PM than the use of traditional daily means. The main variables selected with regard to PM2.5 content were mean wind speed and minimum temperature (negative relationship). Otherwise, the main variables selected with regard to PMcoarse content were mean wind speed (negative), and the daily range in temperature (positive). Variables derived from relative humidity contributed differently to the models, having a higher effect on PMcoarse than PM2.5, and exhibiting both negative and positive effects. For the different cities the deviance explained by the GAMs ranged from 37.6 to 79.1% for PM2.5 and from 18.5 to 63.7% for PMcoarse. The percentage of deviance explained by the models for PM2.5 exhibited a latitudinal pattern, which was not observed in PMcoarse. This highlights the greater predictability of PM2.5 according to meteorological parameters in the cities to the south. Southern cities located spatially close to one another had similar patterns in both the selected variables for the models and the trends. The meteorological factor influencing the cities had a major impact on PM concentrations. The findings of this study may aid understanding of PM variation across the country, in the way of improving forecasting models.

Static stability and thermal wind in an atmosphere of variable composition Applications to Mars

NASA Technical Reports Server (NTRS)

Hess, S. L.

1979-01-01

Radiometric measurements of the temperature of the south polar cap of Mars in winter have yielded values significantly below the expected 148 K. One proposed explanation for this result is a substantial reduction in the CO2 content of the atmosphere and a lowering of the mean molecule weight near the surface. The meteorological consequences of this explanation are explored by deriving a criterion for vertical static stability and a thermal wind law for an atmosphere of variable composition. The atmosphere proves to be statically unstable unless the anomaly in the CO2 mixing ratio extends to heights of tens of kilometers. The effect of varying molecular weight exceeds the effect of temperature gradient, producing shears with height of reversed sign. The shears are baroclinically unstable, and this instability would eradicate the latitudinal gradient of molecular weight. This inconsistency can be resolved by invoking a reasonable elevation of the central polar cap and by imposing an adequate zonal wind. It is concluded that if the explanation requiring a change in atmospheric composition is correct, it must be accompanied by other special circumstances to make it meteorologically consistent.

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

PubMed Central

2010-01-01

Background Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors. PMID:20553590

Choice of Control Variables in Variational Data Assimilation and Its Analysis and Forecast Impact

NASA Astrophysics Data System (ADS)

Xie, Yuanfu; Sun, Jenny; Fang, Wei-ting

2014-05-01

Choice of control variables directly impacts the analysis qualify of a variational data assimilation and its forecasts. A theory on selecting control variables for wind and moisture field is introduced for 3DVAR or 4DVAR. For a good control variable selection, Parseval's theory is applied to 3-4DVAR and the behavior of different control variables is illustrated in physical and Fourier space in terms of minimization condition, meteorological dynamic scales and practical implementation. The computational and meteorological benefits will be discussed. Numerical experiments have been performed using WRF-DA for wind control variables and CRTM for moisture control variables. It is evident of the WRF forecast improvement and faster convergence of CRTM satellite data assimilation.

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

NASA Astrophysics Data System (ADS)

Pryor, Sara C.; Sullivan, Ryan C.; Schoof, Justin T.

2017-12-01

The static energy content of the atmosphere is increasing on a global scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming holes (i.e., locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θe are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity (Q) at 850 and 500 hPa geopotential heights (Z), and spatiotemporally averaged soil moisture (SM). SM is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes, confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in minimum and maximum θe, where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θe and the occurrence of extreme maximum θe. Over the entire domain, the ANN with three hidden layers exhibits high accuracy in predicting maximum θe > 347 K. The median hit rate for maximum θe > 347 K is > 0.60, while the median false alarm rate is ≈ 0.08.

Particulate matter air quality assessment over southeast United States using satellite and ground measurements

NASA Astrophysics Data System (ADS)

Gupta, Pawan

Fine particles (PM2.5, particles with aerodynamic diameter less than 2.5 mum) can penetrate deep inside the human lungs and recent scientific studies have shown thousands of deaths occur each year around the world, prematurely, due to a high concentration of particulate matter. Therefore, monitoring and forecasting of surface level fine particulate matter air quality is very important. Typically air quality measurements are made from ground stations. In recent years, linear regression relationships between satellite derived aerosol optical thickness (AOT) and surface measured PM2.5 mass concentration are formed and used to estimate PM2.5 in the areas where surface measurements are not available. This type of simple linear relationships varies with regions and seasons, and does not provide accurate enough estimation of surface level pollution and many studies have shown that AOT alone is not sufficient for PM2.5 mass concentration estimations. Furthermore, AOT represents aerosol loading in the entire column of the atmosphere whereas PM2.5 is measured at the surface; hence, the knowledge of vertical distribution of aerosols coupled with meteorology becomes critical in PM2.5 estimations. In this dissertation I used three years (2004-2006) of coincident hourly PM2.5, MODerate resolution Imaging Spectroradiometer (MODIS) derived AOT, and Rapid Update Cycle (RUC) analyzed meteorological fields to assess PM2.5 air quality in the Southeast United States. I explored the use of two-variate (TVM), multi-variate (MVM) and artificial neural network (ANN) methods for estimating PM2.5 over 85 stations in the region. First, satellite data were analyzed for sampling biases, quality, and impact of clouds. Results show that MODIS-Terra AOT data was available only about 50% of the days in any given month due to cloud over and unfavorable surface conditions, but this produced a sampling bias of less than 2 mugm-3. Results indicate that there is up to three fold improvements in the correlation coefficients (R) while using MVM (that includes meteorology) over different regions and seasons when compared to the TVM and further improvements were noticed when ANN method is applied. The improvement in absolute percentage error of estimation ranges from 5% to 50% over different seasons and regions when compared with TVM models. Overall ANN models performed better than TVM and MVM models. Based on these results, we recommend using meteorological variables along with satellite observations for improving particulate matter air quality assessment from satellite observations in the region.

Modeling and roles of meteorological factors in outbreaks of highly pathogenic avian influenza H5N1.

PubMed

Biswas, Paritosh K; Islam, Md Zohorul; Debnath, Nitish C; Yamage, Mat

2014-01-01

The highly pathogenic avian influenza A virus subtype H5N1 (HPAI H5N1) is a deadly zoonotic pathogen. Its persistence in poultry in several countries is a potential threat: a mutant or genetically reassorted progenitor might cause a human pandemic. Its world-wide eradication from poultry is important to protect public health. The global trend of outbreaks of influenza attributable to HPAI H5N1 shows a clear seasonality. Meteorological factors might be associated with such trend but have not been studied. For the first time, we analyze the role of meteorological factors in the occurrences of HPAI outbreaks in Bangladesh. We employed autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to assess the roles of different meteorological factors in outbreaks of HPAI. Outbreaks were modeled best when multiplicative seasonality was incorporated. Incorporation of any meteorological variable(s) as inputs did not improve the performance of any multivariable models, but relative humidity (RH) was a significant covariate in several ARIMA and SARIMA models with different autoregressive and moving average orders. The variable cloud cover was also a significant covariate in two SARIMA models, but air temperature along with RH might be a predictor when moving average (MA) order at lag 1 month is considered.

Impact of Subsurface Temperature Variability on Meteorological Variability: An AGCM Study

NASA Astrophysics Data System (ADS)

Mahanama, S. P.; Koster, R. D.; Liu, P.

2006-05-01

Anomalous atmospheric conditions can lead to surface temperature anomalies, which in turn can lead to temperature anomalies deep in the soil. The deep soil temperature (and the associated ground heat content) has significant memory -- the dissipation of a temperature anomaly may take weeks to months -- and thus deep soil temperature may contribute to the low frequency variability of energy and water variables elsewhere in the system. The memory may even provide some skill to subseasonal and seasonal forecasts. This study uses two long-term AGCM experiments to isolate the contribution of deep soil temperature variability to variability elsewhere in the climate system. The first experiment consists of a standard ensemble of AMIP-type simulations, simulations in which the deep soil temperature variable is allowed to interact with the rest of the system. In the second experiment, the coupling of the deep soil temperature to the rest of the climate system is disabled -- at each grid cell, the local climatological seasonal cycle of deep soil temperature (as determined from the first experiment) is prescribed. By comparing the variability of various atmospheric quantities as generated in the two experiments, we isolate the contribution of interactive deep soil temperature to that variability. The results show that interactive deep soil temperature contributes significantly to surface temperature variability. Interactive deep soil temperature, however, reduces the variability of the hydrological cycle (evaporation and precipitation), largely because it allows for a negative feedback between evaporation and temperature.

Influence of Climate on PM2.5 Concentrations over Europe : a Meteorological Analysis using a 9-year Model Simulation

NASA Astrophysics Data System (ADS)

Lecoeur, À.; Seigneur, C.; Terray, L.; Pagé, C.

2012-04-01

In the early 1970s, it has been demonstrated that a large number of deaths and health problems are associated with particulate pollution. As a consequence, several governments have set health-based air quality standards to protect public health. Particulate matter with an aerodynamical diameter of 2.5 μg.m-3 or less (PM2.5) is particularly concerned by these measures. As PM2.5 concentrations are strongly dependent on meteorological conditions, it is important to investigate the relationships between PM2.5 and meteorological parameters. This will help to understand the processes at play and anticipate the effects of climate change on PM2.5 air quality. Most of the previous work agree that temperature, wind speed, humidity, rain rate and mixing height are the meteorological variables that impact PM2.5 concentrations the most. A large number of those studies used Global Circulation Models (GCM) and Chemical Transport Models (CTM) and focus on the USA. They typically predict a diminution of PM2.5 concentrations in the future, with some geographical and/or temporal discrepancies, when only the climate evolution is considered. When considering changes in emissions along with climate, no consensus has yet been found. Furthermore, the correlations between PM2.5 concentrations and meteorological variables are often low, which prevents a straightforward analysis of their relationships. In this work, we consider that PM2.5 concentrations depend on both large-scale atmospheric circulation and local meteorological variables. We thus investigate the influence of present climate on PM2.5 concentrations over Europe by representing it using a weather regimes/types approach. We start by exploring the relationships between classical weather regimes, meteorological variables and PM2.5 concentrations over five stations in Europe, using the EMEP air quality database. The pressure at sea level is used in the classification as it effectively describes the atmospheric circulation. We experimentally verify some intuitive results: weather regimes associated with weak (resp. high) precipitation, wind and low (resp. high) temperatures correspond to higher (resp. lower) PM2.5 concentrations. We also observe that rain rate is the variable that impacts PM2.5 concentrations the most. Next, we search for better relationships by adding this second variable to the classification: we therefore build new weather regimes, called weather types. Because of the low number of the EMEP observations, we compute PM2.5 concentrations with the Polyphemus/Polair3D CTM for years between 2000 and 2008 in order to obtain a spatially and temporally complete dataset of PM2.5 concentrations and chemical components, which can be used to relate PM2.5 concentrations to meteorological regimes and specific variables. By classifying both a large-scale variable and a local variable that influence the PM2.5 concentrations and using gridded data of the modeled concentrations of PM2.5, we obtain a more robust analysis. The results of this work will provide the basis to predict the effects of climate change (via the evolution of weather regimes/types frequencies) on PM2.5 chemical composition and concentrations.

Impact of atmospheric boundary layer depth variability and wind reversal on the diurnal variability of aerosol concentration at a valley site.

PubMed

Pal, S; Lee, T R; Phelps, S; De Wekker, S F J

2014-10-15

The development of the atmospheric boundary layer (ABL) plays a key role in affecting the variability of atmospheric constituents such as aerosols, greenhouse gases, water vapor, and ozone. In general, the concentration of any tracers within the ABL varies due to the changes in the mixing volume (i.e. ABL depth). In this study, we investigate the impact on the near-surface aerosol concentration in a valley site of 1) the boundary layer dilution due to vertical mixing and 2) changes in the wind patterns. We use a data set obtained during a 10-day field campaign in which a number of remote sensing and in-situ instruments were deployed, including a ground-based aerosol lidar system for monitoring of the ABL top height (zi), a particle counter to determine the number concentration of aerosol particles at eight different size ranges, and tower-based standard meteorological instruments. Results show a clearly visible decreasing trend of the mean daytime zi from 2900 m AGL (above ground level) to 2200 m AGL during a three-day period which resulted in increased near-surface pollutant concentrations. An inverse relationship exists between the zi and the fine fraction (0.3-0.7 μm) accumulation mode particles (AMP) on some days due to the dilution effect in a well-mixed ABL. These days are characterized by the absence of daytime upvalley winds and the presence of northwesterly synoptic-driven winds. In contrast, on the days with an onset of an upvalley wind circulation after the morning transition, the wind-driven local transport mechanism outweighs the ABL-dilution effect in determining the variability of AMP concentration. The interplay between the ABL depth evolution and the onset of the upvalley wind during the morning transition period significantly governs the air quality in a valley and could be an important component in the studies of mountain meteorology and air quality. Copyright © 2014 Elsevier B.V. All rights reserved.

The "Dirty Weather" diaries of Reverend Richard Davis: insights about early Colonial-era meteorology and climate variability for Northern New Zealand, 1839-1851

NASA Astrophysics Data System (ADS)

Lorrey, A. M.; Chappell, P. R.

2015-08-01

Reverend Richard Davis (1790-1863) was a Colonial-era missionary stationed in the Far North of New Zealand who was a key figure in the early efforts of the Church Mission Society. He kept meticulous meteorological records for the early settlements of Waimate North and Kaikohe, and his observations are preserved in a two-volume set in the rare manuscripts archive at the Auckland City Library. The Davis diary volumes are significant because they constitute some of the earliest land-based meteorological measurements that were continually chronicled for New Zealand. The diary measurements cover nine years within the 1839-1851 timespan that are broken into two parts: 1839-1844 and 1848-1851. Davis' meteorological recordings include daily 9 AM and noon temperatures and mid-day pressure measurements. Qualitative comments in the diary note prevailing wind flow, wind strength, cloud cover, climate variability impacts, bio-indicators suggestive of drought, and notes on extreme weather events. "Dirty weather" comments scattered throughout the diary describe disturbed conditions with strong winds and driving rainfall. The Davis diary entries coincide with the end of the Little Ice Age (LIA) and they indicate southerly and westerly circulation influences and cooler winter temperatures were more frequent than today. A comparison of climate field reconstructions derived from the Davis diary data and tree ring-based winter temperature reconstructions are supported by tropical coral palaeotemperature evidence. Davis' pressure measurements were corroborated using ship log data from vessels associated with iconic Antarctic exploration voyages that were anchored in the Bay of Islands, and suggest the pressure series he recorded are robust and can be used as `station data'. The Reverend Davis meteorological data are expected to make a significant contribution to the Atmospheric Circulation Reconstructions across the Earth (ACRE) project, which feeds the major data requirements for the longest historical reanalysis - the 20th Century Reanalysis Project (20CR). Thus these new data will help extend surface pressure-based re-analysis reconstructions of past weather covering New Zealand within the data-sparse Southern Hemisphere.

The "dirty weather" diaries of Reverend Richard Davis: insights about early colonial-era meteorology and climate variability for northern New Zealand, 1839-1851

NASA Astrophysics Data System (ADS)

Lorrey, Andrew M.; Chappell, Petra R.

2016-03-01

Reverend Richard Davis (1790-1863) was a colonial-era missionary stationed in the Far North of New Zealand who was a key figure in the early efforts of the Church Mission Society. He kept meticulous meteorological records for the early settlements of Waimate North and Kaikohe, and his observations are preserved in a two-volume set in the Sir George Grey Special Collections in the Auckland Central Library. The Davis diary volumes are significant because they constitute some of the earliest land-based meteorological measurements that were continually chronicled for New Zealand. The diary measurements cover nine years within the 1839-1851 time span that are broken into two parts: 1839-1844 and 1848-1851. Davis' meteorological recordings include daily 9 a.m. and noon temperatures and midday pressure measurements. Qualitative comments in the diary note prevailing wind flow, wind strength, cloud cover, climate variability impacts, bio-indicators suggestive of drought, and notes on extreme weather events. "Dirty weather" comments scattered throughout the diary describe disturbed conditions with strong winds and driving rainfall. The Davis diary entries coincide with the end of the Little Ice Age (LIA) and they indicate southerly and westerly circulation influences and cooler winter temperatures were more frequent than today. A comparison of climate field reconstructions derived from the Davis diary data and tree-ring-based winter temperature reconstructions are supported by tropical coral palaeotemperature evidence. Davis' pressure measurements were corroborated using ship log data from vessels associated with iconic Antarctic exploration voyages that were anchored in the Bay of Islands, and suggest the pressure series he recorded are robust and can be used as "station data". The Reverend Davis meteorological data are expected to make a significant contribution to the Atmospheric Circulation Reconstructions across the Earth (ACRE) project, which feeds the major data requirements for the longest historical reanalysis - the 20th Century Reanalysis Project (20CR). Thus these new data will help extend surface pressure-based reanalysis reconstructions of past weather covering New Zealand within the data-sparse Southern Hemisphere.

Soil Moisture-Atmosphere Feedbacks on Atmospheric Tracers: The Effects of Soil Moisture on Precipitation and Near-Surface Chemistry

NASA Astrophysics Data System (ADS)

Tawfik, Ahmed B.

The atmospheric component is described by rapid fluctuations in typical state variables, such as temperature and water vapor, on timescales of hours to days and the land component evolves on daily to yearly timescales. This dissertation examines the connection between soil moisture and atmospheric tracers under varying degrees of soil moisture-atmosphere coupling. Land-atmosphere coupling is defined over the United States using a regional climate model. A newly examined soil moisture-precipitation feedback is identified for winter months extending the previous summer feedback to colder temperature climates. This feedback is driven by the freezing and thawing of soil moisture, leading to coupled land-atmosphere conditions near the freezing line. Soil moisture can also affect the composition of the troposphere through modifying biogenic emissions of isoprene (C5H8). A novel first-order Taylor series decomposition indicates that isoprene emissions are jointly driven by temperature and soil moisture in models. These compounds are important precursors for ozone formation, an air pollutant and a short-lived forcing agent for climate. A mechanistic description of commonly observed relationships between ground-level ozone and meteorology is presented using the concept of soil moisture-temperature coupling regimes. The extent of surface drying was found to be a better predictor of ozone concentrations than temperature or humidity for the Eastern U.S. This relationship is evaluated in a coupled regional chemistry-climate model under several land-atmosphere coupling and isoprene emissions cases. The coupled chemistry-climate model can reproduce the observed soil moisture-temperature coupling pattern, yet modeled ozone is insensitive to changes in meteorology due to the balance between isoprene and the primary atmospheric oxidant, the hydroxyl radical (OH). Overall, this work highlights the importance of soil moisture-atmosphere coupling for previously neglected cold climate regimes, controlling isoprene emissions variability, and providing a processed-based description of observed ozone-meteorology relationships. From the perspective of ozone air quality, the lack of sensitivity of ozone to meteorology suggests a systematic deficiency in chemistry models in high isoprene emission regions. This shortcoming must be addressed to better estimate tropospheric ozone radiative forcing and to understanding how ozone air quality may respond to future warming.

Application of an online-coupled regional climate model, WRF-CAM5, over East Asia for examination of ice nucleation schemes: Part I. Comprehensive model evaluation and trend analysis for 2006 and 2011

DOE PAGES

Chen, Ying; Zhang, Yang; Fan, Jiwen; ...

2015-08-18

Online-coupled climate and chemistry models are necessary to realistically represent the interactions between climate variables and chemical species and accurately simulate aerosol direct and indirect effects on cloud, precipitation, and radiation. In this Part I of a two-part paper, simulations from the Weather Research and Forecasting model coupled with the physics package of Community Atmosphere Model (WRF-CAM5) are conducted with the default heterogeneous ice nucleation parameterization over East Asia for two full years: 2006 and 2011. A comprehensive model evaluation is performed using satellite and surface observations. The model shows an overall acceptable performance for major meteorological variables at themore » surface and in the boundary layer, as well as column variables (e.g., precipitation, cloud fraction, precipitating water vapor, downward longwave and shortwave radiation). Moderate to large biases exist for cloud condensation nuclei over oceanic areas, cloud variables (e.g., cloud droplet number concentration, cloud liquid and ice water paths, cloud optical depth, longwave and shortwave cloud forcing). These biases indicate a need to improve the model treatments for cloud processes, especially cloud droplets and ice nucleation, as well as to reduce uncertainty in the satellite retrievals. The model simulates well the column abundances of chemical species except for column SO 2 but relatively poor for surface concentrations of several species such as CO, NO 2, SO 2, PM 2.5, and PM 10. Several reasons could contribute to the underestimation of major chemical species in East Asia including underestimations of anthropogenic emissions and natural dust emissions, uncertainties in the spatial and vertical distributions of the anthropogenic emissions, as well as biases in meteorological, radiative, and cloud predictions. Despite moderate to large biases in the chemical predictions, the model performance is generally consistent with or even better than that reported for East Asia with only a few exceptions. The model generally reproduces the observed seasonal variations and the difference between 2006 and 2011 for most variables or chemical species. Overall, these results demonstrate promising skills of WRF-CAM5 for long-term simulations at a regional scale and suggest several areas of potential improvements.« less

Application of an Online-Coupled Regional Climate Model, WRF-CAM5, over East Asia for Examination of Ice Nucleation Schemes: Part I. Comprehensive Model Evaluation and Trend Analysis for 2006 and 2011

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

Chen, Ying; Zhang, Yang; Fan, Jiwen

Online-coupled climate and chemistry models are necessary to realistically represent the interactions between climate variables and chemical species and accurately simulate aerosol direct and indirect effects on cloud, precipitation, and radiation. In this Part I of a two-part paper, simulations from the Weather Research and Forecasting model coupled with the physics package of Community Atmosphere Model (WRF-CAM5) are conducted with the default heterogeneous ice nucleation parameterization over East Asia for two full years: 2006 and 2011. A comprehensive model evaluation is performed using satellite and surface observations. The model shows an overall acceptable performance for major meteorological variables at themore » surface and in the boundary layer, as well as column variables (e.g., precipitation, cloud fraction, precipitating water vapor, downward longwave and shortwave radiation). Moderate to large biases exist for cloud condensation nuclei over oceanic areas, cloud variables (e.g., cloud droplet number concentration, cloud liquid and ice water paths, cloud optical depth, longwave and shortwave cloud forcing). These biases indicate a need to improve the model treatments for cloud processes, especially cloud droplets and ice nucleation, as well as to reduce uncertainty in the satellite retrievals. The model simulates well the column abundances of chemical species except for column SO 2 but relatively poor for surface concentrations of several species such as CO, NO 2, SO 2, PM2.5, and PM10. Several reasons could contribute to the underestimation of major chemical species in East Asia including underestimations of anthropogenic emissions and natural dust emissions, uncertainties in the spatial and vertical distributions of the anthropogenic emissions, as well as biases in meteorological, radiative, and cloud predictions. Despite moderate to large biases in the chemical predictions, the model performance is generally consistent with or even better than that reported for East Asia with only a few exceptions. The model generally reproduces the observed seasonal variations and the difference between 2006 and 2011 for most variables or chemical species. Overall, these results demonstrate promising skills of WRF-CAM5 for long-term simulations at a regional scale and suggest several areas of potential improvements.« less

Application of an online-coupled regional climate model, WRF-CAM5, over East Asia for examination of ice nucleation schemes: Part I. Comprehensive model evaluation and trend analysis for 2006 and 2011

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

Chen, Ying; Zhang, Yang; Fan, Jiwen

Online-coupled climate and chemistry models are necessary to realistically represent the interactions between climate variables and chemical species and accurately simulate aerosol direct and indirect effects on cloud, precipitation, and radiation. In this Part I of a two-part paper, simulations from the Weather Research and Forecasting model coupled with the physics package of Community Atmosphere Model (WRF-CAM5) are conducted with the default heterogeneous ice nucleation parameterization over East Asia for two full years: 2006 and 2011. A comprehensive model evaluation is performed using satellite and surface observations. The model shows an overall acceptable performance for major meteorological variables at themore » surface and in the boundary layer, as well as column variables (e.g., precipitation, cloud fraction, precipitating water vapor, downward longwave and shortwave radiation). Moderate to large biases exist for cloud condensation nuclei over oceanic areas, cloud variables (e.g., cloud droplet number concentration, cloud liquid and ice water paths, cloud optical depth, longwave and shortwave cloud forcing). These biases indicate a need to improve the model treatments for cloud processes, especially cloud droplets and ice nucleation, as well as to reduce uncertainty in the satellite retrievals. The model simulates well the column abundances of chemical species except for column SO 2 but relatively poor for surface concentrations of several species such as CO, NO 2, SO 2, PM 2.5, and PM 10. Several reasons could contribute to the underestimation of major chemical species in East Asia including underestimations of anthropogenic emissions and natural dust emissions, uncertainties in the spatial and vertical distributions of the anthropogenic emissions, as well as biases in meteorological, radiative, and cloud predictions. Despite moderate to large biases in the chemical predictions, the model performance is generally consistent with or even better than that reported for East Asia with only a few exceptions. The model generally reproduces the observed seasonal variations and the difference between 2006 and 2011 for most variables or chemical species. Overall, these results demonstrate promising skills of WRF-CAM5 for long-term simulations at a regional scale and suggest several areas of potential improvements.« less

Linking North American Summer Ozone Pollution Episodes to Subseasonal Atmospheric Variability

NASA Astrophysics Data System (ADS)

White, E. C.; Watt-Meyer, O.; Kushner, P. J.; Jones, D. B. A.

2017-12-01

Ozone concentrations in the planetary boundary layer (PBL) are positively correlated with surface air temperature due to shared influences including incident solar radiation and PBL stagnancy, as well as the temperature-sensitive emission of ozone precursor compounds. While previous studies have linked heat waves in North America to modes of subseasonal atmospheric variability, such analyses have not been applied to summertime ozone pollution episodes. This study investigates a possible link between subseasonal atmospheric variability in reanalysis data and summertime ozone pollution episodes identified in almost thirty years of in-situ measurements from the Air Quality System (AQS) network in the United States. AQS stations are grouped into regions likely to experience simultaneous extreme ozone concentrations using statistical clustering methods. Composite meteorological patterns are calculated for ozone episodes in each of these regions. The same analysis is applied to heat waves identified in AQS temperature records for comparison. Local meteorological features during typical ozone episodes include extreme temperatures and reduced cloud cover related to anomalous synoptic-scale anticyclonic circulation aloft. These anticyclonic anomalies are typically embedded in wave trains extending from the North Pacific to North Atlantic. Spectral analysis of these wave trains reveals that low-frequency standing waves play a prominent role. These long-lived circulation patterns may provide a means to increase air quality prediction lead-times and to estimate the frequency of ozone pollution episodes under climate change.

heterogeneous mixture distributions for multi-source extreme rainfall

NASA Astrophysics Data System (ADS)

Ouarda, T.; Shin, J.; Lee, T. S.

2013-12-01

Mixture distributions have been used to model hydro-meteorological variables showing mixture distributional characteristics, e.g. bimodality. Homogeneous mixture (HOM) distributions (e.g. Normal-Normal and Gumbel-Gumbel) have been traditionally applied to hydro-meteorological variables. However, there is no reason to restrict the mixture distribution as the combination of one identical type. It might be beneficial to characterize the statistical behavior of hydro-meteorological variables from the application of heterogeneous mixture (HTM) distributions such as Normal-Gamma. In the present work, we focus on assessing the suitability of HTM distributions for the frequency analysis of hydro-meteorological variables. In the present work, in order to estimate the parameters of HTM distributions, the meta-heuristic algorithm (Genetic Algorithm) is employed to maximize the likelihood function. In the present study, a number of distributions are compared, including the Gamma-Extreme value type-one (EV1) HTM distribution, the EV1-EV1 HOM distribution, and EV1 distribution. The proposed distribution models are applied to the annual maximum precipitation data in South Korea. The Akaike Information Criterion (AIC), the root mean squared errors (RMSE) and the log-likelihood are used as measures of goodness-of-fit of the tested distributions. Results indicate that the HTM distribution (Gamma-EV1) presents the best fitness. The HTM distribution shows significant improvement in the estimation of quantiles corresponding to the 20-year return period. It is shown that extreme rainfall in the coastal region of South Korea presents strong heterogeneous mixture distributional characteristics. Results indicate that HTM distributions are a good alternative for the frequency analysis of hydro-meteorological variables when disparate statistical characteristics are presented.

Implications of climate variability for monitoring the effectiveness of global mercury policy

NASA Astrophysics Data System (ADS)

Giang, A.; Monier, E.; Couzo, E. A.; Pike-thackray, C.; Selin, N. E.

2016-12-01

We investigate how climate variability affects ability to detect policy-related anthropogenic changes in mercury emissions in wet deposition monitoring data using earth system and atmospheric chemistry modeling. The Minamata Convention, a multilateral environmental agreement that aims to protect human health and the environment from anthropogenic emissions and releases of mercury, includes provisions for monitoring treaty effectiveness. Because meteorology can affect mercury chemistry and transport, internal variability is an important contributor to uncertainty in how effective policy may be in reducing the amount of mercury entering ecosystems through wet deposition. We simulate mercury chemistry using the GEOS-Chem global transport model to assess the influence of meteorology in the context of other uncertainties in mercury cycling and policy. In these simulations, we find that interannual variability in meteorology may be a dominant contributor to the spatial pattern and magnitude of historical regional wet deposition trends. To further assess the influence of climate variability in the GEOS-Chem mercury simulation, we use a 5-member ensemble of meteorological fields from the MIT Integrated Global System Model under present and future climate. Each member involves randomly initialized 20 year simulations centered around 2000 and 2050 (under a no-policy and a climate stabilization scenario). Building on previous efforts to understand climate-air quality interactions for ground-level O3 and particulate matter, we estimate from the ensemble the range of trends in mercury wet deposition given natural variability, and, to extend our previous results on regions that are sensitive to near-source vs. remote anthropogenic signals, we identify geographic regions where mercury wet deposition is most sensitive to this variability. We discuss how an improved understanding of natural variability can inform the Conference of Parties on monitoring strategy and policy ambition.

Weather Variability Associated with Aedes (Stegomyia) aegypti (Dengue Vector) Oviposition Dynamics in Northwestern Argentina

PubMed Central

Estallo, Elizabet L.; Ludueña-Almeida, Francisco F.; Introini, María V.; Zaidenberg, Mario; Almirón, Walter R.

2015-01-01

This study aims to develop a forecasting model by assessing the weather variability associated with seasonal fluctuation of Aedes aegypti oviposition dynamic at a city level in Orán, in northwestern Argentina. Oviposition dynamics were assessed by weekly monitoring of 90 ovitraps in the urban area during 2005-2007. Correlations were performed between the number of eggs collected weekly and weather variables (rainfall, photoperiod, vapor pressure of water, temperature, and relative humidity) with and without time lags (1 to 6 weeks). A stepwise multiple linear regression analysis was performed with the set of meteorological variables from the first year of study with the variables in the time lags that best correlated with the oviposition. Model validation was conducted using the data from the second year of study (October 2006- 2007). Minimum temperature and rainfall were the most important variables. No eggs were found at temperatures below 10°C. The most significant time lags were 3 weeks for minimum temperature and rains, 3 weeks for water vapor pressure, and 6 weeks for maximum temperature. Aedes aegypti could be expected in Orán three weeks after rains with adequate min temperatures. The best-fit forecasting model for the combined meteorological variables explained 70 % of the variance (adj. R2). The correlation between Ae. aegypti oviposition observed and estimated by the forecasting model resulted in rs = 0.80 (P < 0.05). The forecasting model developed would allow prediction of increases and decreases in the Ae. aegypti oviposition activity based on meteorological data for Orán city and, according to the meteorological variables, vector activity can be predicted three or four weeks in advance. PMID:25993415

Hydrometeorology as an Inversion Problem: Can River Discharge Observations Improve the Atmosphere by Ensemble Data Assimilation?

NASA Astrophysics Data System (ADS)

Sawada, Yohei; Nakaegawa, Tosiyuki; Miyoshi, Takemasa

2018-01-01

We examine the potential of assimilating river discharge observations into the atmosphere by strongly coupled river-atmosphere ensemble data assimilation. The Japan Meteorological Agency's Non-Hydrostatic atmospheric Model (JMA-NHM) is first coupled with a simple rainfall-runoff model. Next, the local ensemble transform Kalman filter is used for this coupled model to assimilate the observations of the rainfall-runoff model variables into the JMA-NHM model variables. This system makes it possible to do hydrometeorology backward, i.e., to inversely estimate atmospheric conditions from the information of river flows or a flood on land surfaces. We perform a proof-of-concept Observing System Simulation Experiment, which reveals that the assimilation of river discharge observations into the atmospheric model variables can improve the skill of the short-term severe rainfall forecast.

Dune mobility in the St. Anthony Dune Field, Idaho, USA: Effects of meteorological variables and lag time

NASA Astrophysics Data System (ADS)

Hoover, R. H.; Gaylord, D. R.; Cooper, C. M.

2018-05-01

The St. Anthony Dune Field (SADF) is a 300 km2 expanse of active to stabilized transverse, barchan, barchanoid, and parabolic sand dunes located in a semi-arid climate in southeastern Idaho. The northeastern portion of the SADF, 16 km2, was investigated to examine meteorological influences on dune mobility. Understanding meteorological predictors of sand-dune migration for the SADF informs landscape evolution and impacts assessment of eolian activity on sensitive agricultural lands in the western United States, with implications for semi-arid environments globally. Archival aerial photos from 1954 to 2011 were used to calculate dune migration rates which were subsequently compared to regional meteorological data, including temperature, precipitation and wind speed. Observational analyses based on aerial photo imagery and meteorological data indicate that dune migration is influenced by weather for up to 5-10 years and therefore decadal weather patterns should be taken into account when using dune migration rates as proxies from climate fluctuation. Statistical examination of meteorological variables in this study indicates that 24% of the variation of sand dune migration rates is attributed to temperature, precipitation and wind speed, which is increased to 45% when incorporating lag time.

BOREAS AES Campbell Scientific Surface Meteorological Data

NASA Technical Reports Server (NTRS)

Atkinson, G. Barrie; Funk, Barrie; Knapp. David E. (Editor); Hall, Forrest G. (Editor)

2000-01-01

Canadian AES personnel collected data related to surface and atmospheric meteorological conditions over the BOREAS region. This data set contains 15-minute meteorological data from 14 automated meteorology stations located across the BOREAS region. Included in this data are parameters of date, time, mean sea level pressure, station pressure, temperature, dew point, wind speed, resultant wind speed, resultant wind direction, peak wind, precipitation, maximum temperature in the last hour, minimum temperature in the last hour, pressure tendency, liquid precipitation in the last hour, relative humidity, precipitation from a weighing gauge, and snow depth. Temporally, the data cover the period of August 1993 to December 1996. The data are provided in tabular ASCII files, and are classified as AFM-Staff data.

Severe haze in Hangzhou in winter 2013/14 and associated meteorological anomalies

NASA Astrophysics Data System (ADS)

Chen, Yini; Zhu, Zhiwei; Luo, Ling; Zhang, Jiwei

2018-03-01

Aerosol pollution over eastern China has worsened considerably in recent years, resulting in heavy haze weather with low visibility and poor air quality. The present study investigates the characteristics of haze weather in Hangzhou city, and aims to unravel the meteorological anomalies associated with the heavy haze that occurred over Hangzhou in winter 2013/14. On the interannual timescale, because of the neutral condition of tropical sea surface temperature anomalies during winter 2013/14, no significant circulation and convection anomalies were induced over East Asia, leading to a stable atmospheric condition favorable for haze weather in Hangzhou. Besides, the shift of the polar vortex, caused by changes in surface temperature and ice cover at high latitudes, induced a barotropic anomalous circulation dipole pattern. The southerly anomaly associated with this anomalous dipole pattern hindered the transportation of cold/clear air mass from Siberia to central-eastern China, leading to abnormal haze during winter 2013/14 in Hangzhou. On the intraseasonal timescale, an eastward-propagating mid-latitude Rossby wave train altered the meridional wind anomaly over East Asia, causing the intraseasonal variability of haze weather during 2013/14 in Hangzhou.

Ocean Surface Vector Wind: Research Challenges and Operational Opportunities

NASA Technical Reports Server (NTRS)

Halpern, David

2012-01-01

The atmosphere and ocean are joined together over seventy percent of Earth, with ocean surface vector wind (OSVW) stress one of the linkages. Satellite OSVW measurements provide estimates of wind divergence at the bottom of the atmosphere and wind stress curl at the top of the ocean; both variables are critical for weather and climate applications. As is common with satellite measurements, a multitude of OSVW data products exist for each currently operating satellite instrument. In 2012 the Joint Technical Commission on Oceanography and Marine Meteorology (JCOMM) launched an initiative to coordinate production of OSVW data products to maximize the impact and benefit of existing and future OSVW measurements in atmospheric and oceanic applications. This paper describes meteorological and oceanographic requirements for OSVW data products; provides an inventory of unique data products to illustrate that the challenge is not the production of individual data products, but the generation of harmonized datasets for analysis and synthesis of the ensemble of data products; and outlines a vision for JCOMM, in partnership with other international groups, to assemble an international network to share ideas, data, tools, strategies, and deliverables to improve utilization of satellite OSVW data products for research and operational applications.

Distributions of eight meteorological variables at Cape Kennedy, Florida and Vandenberg Air Force Base, California

NASA Technical Reports Server (NTRS)

Graves, M. E.; King, R. L.; Brown, S. C.

1973-01-01

Extreme values, median values, and nine percentile values are tabulated for eight meteorological variables at Cape Kennedy, Florida and at Vandenberg Air Force Base, California. The variables are temperature, relative humidity, station pressure, water vapor pressure, water vapor mixing ratio, density, and enthalpy. For each month eight hours are tabulated, namely, 0100, 0400, 0700, 1000, 1300, 1600, 1900, and 2200 local time. These statistics are intended for general use for the space shuttle design trade-off analysis and are not to be used for specific design values.

Meteorological Modes of Variability for Fine Particulate Matter (PM2.5) Air Quality in the United States: Implications for PM2.5 Sensitivity to Climate Change

EPA Science Inventory

We applied a multiple linear regression model to understand the relationships of PM_2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM_2.5 to climate change. We used 2004-2008 PM_2.5 observations fro...

Internal variability of fine-scale components of meteorological fields in extended-range limited-area model simulations with atmospheric and surface nudging

NASA Astrophysics Data System (ADS)

Separovic, Leo; Husain, Syed Zahid; Yu, Wei

2015-09-01

Internal variability (IV) in dynamical downscaling with limited-area models (LAMs) represents a source of error inherent to the downscaled fields, which originates from the sensitive dependence of the models to arbitrarily small modifications. If IV is large it may impose the need for probabilistic verification of the downscaled information. Atmospheric spectral nudging (ASN) can reduce IV in LAMs as it constrains the large-scale components of LAM fields in the interior of the computational domain and thus prevents any considerable penetration of sensitively dependent deviations into the range of large scales. Using initial condition ensembles, the present study quantifies the impact of ASN on IV in LAM simulations in the range of fine scales that are not controlled by spectral nudging. Four simulation configurations that all include strong ASN but differ in the nudging settings are considered. In the fifth configuration, grid nudging of land surface variables toward high-resolution surface analyses is applied. The results show that the IV at scales larger than 300 km can be suppressed by selecting an appropriate ASN setup. At scales between 300 and 30 km, however, in all configurations, the hourly near-surface temperature, humidity, and winds are only partly reproducible. Nudging the land surface variables is found to have the potential to significantly reduce IV, particularly for fine-scale temperature and humidity. On the other hand, hourly precipitation accumulations at these scales are generally irreproducible in all configurations, and probabilistic approach to downscaling is therefore recommended.

Measurement of Ambient Ammonia and Surface-level Meteorological Forcing Variables near an Agricultural Emission Source

NASA Astrophysics Data System (ADS)

Myles, L.; Heuer, M. W.

2012-12-01

Atmospheric ammonia (NH3) is a reduced form of reactive nitrogen that is primarily emitted from agricultural activities. NH3 volatilizes from animal waste and fertilized land directly into the atmosphere where it can either react with other gases to form fine particulate matter or deposit on surfaces through air-surface exchange processes. Field measurements in different ecosystems and under various conditions are necessary to improve the understanding of the complex relationships between ambient NH3 and meteorological parameters, such as temperature and relative humidity, which influence volatilization rates and ultimately, ambient concentrations near emission sources. However, the measurement of ambient NH3 is challenging. NH3 is hydroscopic and reactive, and measurement techniques are subject to errors caused by sampling artifacts and other interferences. Recent advancements have led to improved techniques that allow real-time measurement of ambient NH3. A cavity ring-down spectrometer was deployed at a cattle research facility in Knoxville, TN during spring 2012 to measure ambient NH3, and meteorological instrumentation was collocated to measure 3-D winds, temperature, relative humidity, precipitation and other parameters (z = 2 m). The study site was rolling pasture typical of the eastern Tennessee Valley and included two large barns and approximately 30-40 cattle. Daytime ambient NH3 averaged 15-20 ppb most days with lows of approximately 7 ppb at night. Higher concentrations (greater than 50 ppb) seemed to correlate with higher temperatures (greater than 27 C), although the data are not consistent. Several instances of 100 ppb concentrations were measured when temperatures were high and winds were from the direction of the barns. Overall, the study shows that ambient NH3 levels near agricultural emission sources may vary greatly with time and a variety of factors, including meteorological conditions. The data support the need for real-time measurements of NH3 to determine how environmental conditions can affect ambient concentrations and therefore, the amount of NH3 available in the atmosphere to form particulate matter or participate in deposition processes.

Generation of Multivariate Surface Weather Series with Use of the Stochastic Weather Generator Linked to Regional Climate Model

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Farda, A.; Huth, R.

2012-12-01

The regional-scale simulations of weather-sensitive processes (e.g. hydrology, agriculture and forestry) for the present and/or future climate often require high resolution meteorological inputs in terms of the time series of selected surface weather characteristics (typically temperature, precipitation, solar radiation, humidity, wind) for a set of stations or on a regular grid. As even the latest Global and Regional Climate Models (GCMs and RCMs) do not provide realistic representation of statistical structure of the surface weather, the model outputs must be postprocessed (downscaled) to achieve the desired statistical structure of the weather data before being used as an input to the follow-up simulation models. One of the downscaling approaches, which is employed also here, is based on a weather generator (WG), which is calibrated using the observed weather series and then modified (in case of simulations for the future climate) according to the GCM- or RCM-based climate change scenarios. The present contribution uses the parametric daily weather generator M&Rfi to follow two aims: (1) Validation of the new simulations of the present climate (1961-1990) made by the ALADIN-Climate/CZ (v.2) Regional Climate Model at 25 km resolution. The WG parameters will be derived from the RCM-simulated surface weather series and compared to those derived from observational data in the Czech meteorological stations. The set of WG parameters will include selected statistics of the surface temperature and precipitation (characteristics of the mean, variability, interdiurnal variability and extremes). (2) Testing a potential of RCM output for calibration of the WG for the ungauged locations. The methodology being examined will consist in using the WG, whose parameters are interpolated from the surrounding stations and then corrected based on a RCM-simulated spatial variability. The quality of the weather series produced by the WG calibrated in this way will be assessed in terms of selected climatic characteristics focusing on extreme precipitation and temperature characteristics (including characteristics of dry/wet/hot/cold spells). Acknowledgements: The present experiment is made within the frame of projects ALARO (project P209/11/2405 sponsored by the Czech Science Foundation), WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports) and VALUE (COST ES 1102 action).

Covariance Between Arctic Sea Ice and Clouds Within Atmospheric State Regimes at the Satellite Footprint Level

NASA Technical Reports Server (NTRS)

Taylor, Patrick C.; Kato, Seiji; Xu, Kuan-Man; Cai, Ming

2015-01-01

Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and mid-tropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.

Covariance between Arctic sea ice and clouds within atmospheric state regimes at the satellite footprint level

PubMed Central

Kato, Seiji; Xu, Kuan‐Man; Cai, Ming

2015-01-01

Abstract Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice‐cloud relationship in the Arctic using a satellite footprint‐level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A‐Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and midtropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest‐magnitude cloud‐sea ice covariance occurs between 500 m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near‐surface static stability is found at larger sea ice concentrations. PMID:27818851

Covariance between Arctic sea ice and clouds within atmospheric state regimes at the satellite footprint level.

PubMed

Taylor, Patrick C; Kato, Seiji; Xu, Kuan-Man; Cai, Ming

2015-12-27

Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and midtropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500 m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.

Utilization of Satellite Data in Land Surface Hydrology: Sensitivity and Assimilation

NASA Technical Reports Server (NTRS)

Lakshmi, Venkataraman; Susskind, Joel

1999-01-01

This paper investigates the sensitivity of potential evapotranspiration to input meteorological variables, viz- surface air temperature and surface vapor pressure. The sensitivity studies have been carried out for a wide range of land surface variables such as wind speed, leaf area index and surface temperatures. Errors in the surface air temperature and surface vapor pressure result in errors of different signs in the computed potential evapotranspiration. This result has implications for use of estimated values from satellite data or analysis of surface air temperature and surface vapor pressure in large scale hydrological modeling. The comparison of cumulative potential evapotranspiration estimates using ground observations and satellite observations over Manhattan, Kansas for a period of several months shows very little difference between the two. The cumulative differences between the ground based and satellite based estimates of potential evapotranspiration amounted to less that 20mm over a 18 month period and a percentage difference of 15%. The use of satellite estimates of surface skin temperature in hydrological modeling to update the soil moisture using a physical adjustment concept is studied in detail including the extent of changes in soil moisture resulting from the assimilation of surface skin temperature. The soil moisture of the surface layer is adjusted by 0.9mm over a 10 day period as a result of a 3K difference between the predicted and the observed surface temperature. This is a considerable amount given the fact that the top layer can hold only 5mm of water.

The influence of changing UVB radiation in near-surface ozone time series

NASA Astrophysics Data System (ADS)

BröNnimann, Stefan; Voigt, Stefan; Wanner, Heinz

2000-04-01

UVB radiation plays an important role in tropospheric photochemistry since it determines the rate of ozone photolysis J(O1D) and subsequent formation of OH radicals. Consequently, changes of UVB radiation, for example due to changes of the stratospheric ozone amount, could alter the concentration of reactive tropospheric gases including ozone. An observation-based attempt is made to quantify the effect of changing UVB radiation on surface ozone peaks on a day-to-day scale using a time series of measurements at a Swiss mountain site. Seven years data of ozone, NO, NOx, and meteorological measurements from Chaumont (1140 m above sea level (asl)), total ozone and UVB measurements from Arosa (1847 m asl), and surface albedo from satellite observations are investigated. The study is restricted to fair weather days with moderately high NOx concentrations. Multiple regression analysis is performed using chemical, meteorological, and UV dependent variables to predict afternoon ozone peaks. From autumn to spring, positive deviations of ozone peaks are clearly connected with positive UVB deviations. The relation is statistically significant only in part of the seasonal data subsets; however, it is consistent with model studies. The estimated net effect on ozone peaks is normally within a range of 4 ppb, a range of about 6 ppb is predicted for large UVB changes. Applying the coefficients for the large interannual variability of the stratospheric ozone layer observed in spring in the last 10 years results in a range of variation of at most 1 to 1.5 ppb for monthly mean ozone peaks. For trends of J(O1D) from 1970 to 1990, a trend bias of surface ozone peaks on polluted fair weather days of less than 0.12 ppb/yr is calculated. Although the numbers are rather small, they may play a role in certain circumstances.

Meltwater flux and runoff modeling in the abalation area of jakobshavn Isbrae, West Greenland

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

Mernild, Sebastian Haugard; Chylek, Petr; Liston, Glen

2009-01-01

The temporal variability in surface snow and glacier melt flux and runoff were investigated for the ablation area of lakobshavn Isbrae, West Greenland. High-resolution meteorological observations both on and outside the Greenland Ice Sheet (GrIS) were used as model input. Realistic descriptions of snow accumulation, snow and glacier-ice melt, and runoff are essential to understand trends in ice sheet surface properties and processes. SnowModel, a physically based, spatially distributed meteorological and snow-evolution modeling system was used to simulate the temporal variability of lakobshavn Isbrre accumulation and ablation processes for 2000/01-2006/07. Winter snow-depth observations and MODIS satellite-derived summer melt observations weremore » used for model validation of accumulation and ablation. Simulations agreed well with observed values. Simulated annual surface melt varied from as low as 3.83 x 10{sup 9} m{sup 3} (2001/02) to as high as 8.64 x 10{sup 9} m{sup 3} (2004/05). Modeled surface melt occurred at elevations reaching 1,870 m a.s.l. for 2004/05, while the equilibrium line altitude (ELA) fluctuated from 990 to 1,210 m a.s.l. during the simulation period. The SnowModel meltwater retention and refreezing routines considerably reduce the amount of meltwater available as ice sheet runoff; without these routines the lakobshavn surface runoff would be overestimated by an average of 80%. From September/October through May/June no runoff events were simulated. The modeled interannual runoff variability varied from 1.81 x 10{sup 9} m{sup 3} (2001/02) to 5.21 x 10{sup 9} m{sup 3} (2004/05), yielding a cumulative runoff at the Jakobshavn glacier terminus of {approx}2.25 m w.eq. to {approx}4.5 m w.eq., respectively. The average modeled lakobshavn runoff of {approx}3.4 km{sup 3} y{sup -1} was merged with previous estimates of Jakobshavn ice discharge to quantify the freshwater flux to Illulissat Icefiord. For both runoff and ice discharge the average trends are similar, indicating increasing (insignificant) influx of freshwater to the Illulissat Icefiord for the period 2000/01-2006/07. This study suggests that surface runoff forms a minor part of the overall Jakobshavn freshwater flux to the fiord: around 7% ({approx}3.4 km{sup 3} y{sup -1}) of the average annual freshwater flux of {approx}51.0 km{sup 3} y{sup -1} originates from the surface runoff.« less

Sensitivity of desert dust emission modelling to horizontal resolution: the example of the Bodélé Depression

NASA Astrophysics Data System (ADS)

Bouet, Christel; Cautenet, Guy; Marticorena, Béatrice; Bergametti, Gilles; Minvielle, Fanny; Schmechtig, Catherine; Laurent, Benoit

2010-05-01

Atmospheric aerosols are known to play an important role in the Earth's climate system. However, the quantification of aerosol radiative impact on the Earth's radiative budget is very complex because of the high variability in space and time of aerosol mass and particle number concentrations, and optical properties as well. In many regions, like in desert regions, dust is the largest contribution to aerosol optical thickness [Tegen et al., 1997]. Consequently, it appears fundamental to well represent mineral dust emissions to reduce uncertainties concerning aerosol radiative impact on the Earth's radiative budget. Recently, several studies (e.g. Prospero et al. [2002]) underlined that the Bodélé depression, in northern Chad, is probably the most important source of mineral dust in the world. However many models fail in simulating these large dust emissions. Indeed, dust emission is a threshold phenomenon mainly driven by the intensity of surface wind velocity. Realistic estimates of dust emissions then rely on the quality and accuracy of the surface wind fields. Koren and Kaufman [2004] showed that the reanalysis data (NCEP), which can be used as input data in numerical models, underestimates surface wind velocity in the Bodélé Depression by up to 50%. Such an uncertainty on surface wind velocity cannot allow an accurate simulation of the dust emission. In mesoscale meteorological models, global reanalysis datasets are used to initialize and laterally nudge the models that compute meteorological parameters (like wind velocity) with a finer spatial and temporal resolutions. The question arises concerning the precision of the wind speeds calculated by these models. Using the Regional Atmospheric Modeling System (RAMS, Cotton et al. [2003]) coupled online with the dust production model developed by Marticorena and Bergametti [1995] and recently improved by Laurent et al. [2008] for Africa, the influence of the horizontal resolution of the mesoscale meteorological model on the simulation of dust emission in the Bodélé Depression is investigated. A one year simulation is run in order to test the capability of the model to represent the pronounced seasonal cycle of dust emission in this region. Routine measurements from meteorological stations as well as satellite imagery are used to evaluate the accuracy of the simulations.

Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors

PubMed Central

Shi, Kun; Zhang, Yunlin; Zhou, Yongqiang; Liu, Xiaohan; Zhu, Guangwei; Qin, Boqiang; Gao, Guang

2017-01-01

We developed and validated an empirical model for estimating chlorophyll a concentrations (Chla) in Lake Taihu to generate a long-term Chla and algal bloom area time series from MODIS-Aqua observations for 2003 to 2013. Then, based on the long-term time series data, we quantified the responses of cyanobacterial dynamics to nutrient enrichment and climatic conditions. Chla showed substantial spatial and temporal variability. In addition, the annual mean cyanobacterial surface bloom area exhibited an increasing trend across the entire lake from 2003 to 2013, with the exception of 2006 and 2007. High air temperature and phosphorus levels in the spring can prompt cyanobacterial growth, and low wind speeds and low atmospheric pressure levels favor cyanobacterial surface bloom formation. The sensitivity of cyanobacterial dynamics to climatic conditions was found to vary by region. Our results indicate that temperature is the most important factor controlling Chla inter-annual variability followed by phosphorus and that air pressure is the most important factor controlling cyanobacterial surface bloom formation followed by wind speeds in Lake Taihu. PMID:28074871

Controlled meteorological (CMET) balloon profiling of the Arctic atmospheric boundary layer around Spitsbergen compared to a mesoscale model

NASA Astrophysics Data System (ADS)

Roberts, T. J.; Dütsch, M.; Hole, L. R.; Voss, P. B.

2015-10-01

Observations from CMET (Controlled Meteorological) balloons are analyzed in combination with mesoscale model simulations to provide insights into tropospheric meteorological conditions (temperature, humidity, wind-speed) around Svalbard, European High Arctic. Five Controlled Meteorological (CMET) balloons were launched from Ny-Ålesund in Svalbard over 5-12 May 2011, and measured vertical atmospheric profiles above Spitsbergen Island and over coastal areas to both the east and west. One notable CMET flight achieved a suite of 18 continuous soundings that probed the Arctic marine boundary layer over a period of more than 10 h. The CMET profiles are compared to simulations using the Weather Research and Forecasting (WRF) model using nested grids and three different boundary layer schemes. Variability between the three model schemes was typically smaller than the discrepancies between the model runs and the observations. Over Spitsbergen, the CMET flights identified temperature inversions and low-level jets (LLJ) that were not captured by the model. Nevertheless, the model largely reproduced time-series obtained from the Ny-Ålesund meteorological station, with exception of surface winds during the LLJ. Over sea-ice east of Svalbard the model underestimated potential temperature and overestimated wind-speed compared to the CMET observations. This is most likely due to the full sea-ice coverage assumed by the model, and consequent underestimation of ocean-atmosphere exchange in the presence of leads or fractional coverage. The suite of continuous CMET soundings over a sea-ice free region to the northwest of Svalbard are analysed spatially and temporally, and compared to the model. The observed along-flight daytime increase in relative humidity is interpreted in terms of the diurnal cycle, and in the context of marine and terrestrial air-mass influences. Analysis of the balloon trajectory during the CMET soundings identifies strong wind-shear, with a low-level channeled flow. The study highlights the challenges of modelling the Arctic atmosphere, especially in coastal zones with varying topography, sea-ice and surface conditions. In this context, CMET balloons provide a valuable technology for profiling the free atmosphere and boundary layer in remote regions where few other observations are available for model validation.

75 FR 71029 - Approval and Promulgation of State Implementation Plans; State of Colorado; Interstate Transport...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-22

... variability in emissions and/ or meteorology), may have difficulty maintaining the standard. As explained in... year with particularly severe meteorology (weather that is conducive to ozone and/or particulate...

Assessment of ozone variations and meteorological effects in an urban area in the Mediterranean Coast.

PubMed

Dueñas, C; Fernández, M C; Cañete, S; Carretero, J; Liger, E

2002-11-01

Ozone concentrations are valuable indicators of possible health and environmental impacts. However, they are also used to monitor changes and trends in the sources of both ozone and its precursors. For this purpose, the influence of meteorological variables is a confusing factor. This study presents an analysis of a year of ozone concentrations measured in a coastal Spanish city. Firstly, the aim of this study was to perceive the daily, monthly and seasonal variation patterns of ozone concentrations. Diurnal cycles are presented by season and the fit of the data to a normal distribution is tested. In order to assess ozone behaviour under temperate weather conditions, local meteorological variables (wind direction and speed, temperature, relative humidity, pressure and rainfall) were monitored together with ozone concentrations. The main relationships we could observe in these analyses were then used to obtain a regression equation linking diurnal ozone concentrations in summer with meteorological parameters.

Evaluation of Hydrologic and Meteorological Impacts on Dengue Fever Incidences in Southern Taiwan using Time- Frequency Method

NASA Astrophysics Data System (ADS)

Tsai, Christina; Yeh, Ting-Gu

2017-04-01

Extreme weather events are occurring more frequently as a result of climate change. Recently dengue fever has become a serious issue in southern Taiwan. It may have characteristic temporal scales that can be identified. Some researchers have hypothesized that dengue fever incidences are related to climate change. This study applies time-frequency analysis to time series data concerning dengue fever and hydrologic and meteorological variables. Results of three time-frequency analytical methods - the Hilbert Huang transform (HHT), the Wavelet Transform (WT) and the Short Time Fourier Transform (STFT) are compared and discussed. A more effective time-frequency analysis method will be identified to analyze relevant time series data. The most influential time scales of hydrologic and meteorological variables that are associated with dengue fever are determined. Finally, the linkage between hydrologic/meteorological factors and dengue fever incidences can be established.

The effects of daily weather variables on psychosis admissions to psychiatric hospitals

NASA Astrophysics Data System (ADS)

McWilliams, Stephen; Kinsella, Anthony; O'Callaghan, Eadbhard

2013-07-01

Several studies have noted seasonal variations in admission rates of patients with psychotic illnesses. However, the changeable daily meteorological patterns within seasons have never been examined in any great depth in the context of admission rates. A handful of small studies have posed interesting questions regarding a potential link between psychiatric admission rates and meteorological variables such as environmental temperature (especially heat waves) and sunshine. In this study, we used simple non-parametric testing and more complex ARIMA and time-series regression analysis to examine whether daily meteorological patterns (wind speed and direction, barometric pressure, rainfall, sunshine, sunlight and temperature) exert an influence on admission rates for psychotic disorders across 12 regions in Ireland. Although there were some weak but interesting trends for temperature, barometric pressure and sunshine, the meteorological patterns ultimately did not exert a clinically significant influence over admissions for psychosis. Further analysis is needed.

Multisensor satellite data integration for sea surface wind speed and direction determination

NASA Technical Reports Server (NTRS)

Glackin, D. L.; Pihos, G. G.; Wheelock, S. L.

1984-01-01

Techniques to integrate meteorological data from various satellite sensors to yield a global measure of sea surface wind speed and direction for input to the Navy's operational weather forecast models were investigated. The sensors were launched or will be launched, specifically the GOES visible and infrared imaging sensor, the Nimbus-7 SMMR, and the DMSP SSM/I instrument. An algorithm for the extrapolation to the sea surface of wind directions as derived from successive GOES cloud images was developed. This wind veering algorithm is relatively simple, accounts for the major physical variables, and seems to represent the best solution that can be found with existing data. An algorithm for the interpolation of the scattered observed data to a common geographical grid was implemented. The algorithm is based on a combination of inverse distance weighting and trend surface fitting, and is suited to combing wind data from disparate sources.

Understanding Arctic Surface Temperature Differences in Reanalyses

NASA Technical Reports Server (NTRS)

Cullather, Richard; Zhao, Bin; Shuman, Christopher; Nowicki, Sophie

2017-01-01

Reanalyses in the Arctic are widely used for model evaluation and for understanding contemporary climate change. Nevertheless, differences among reanalyses in fundamental meteorological variables including surface air temperature are large. A review of surface temperature differences is presented with a particular focus on differences in contemporary reanalyses. An important consideration is the significant differences in Arctic surfaces, including the central Arctic Ocean, the Greenland Ice Sheet, and non-glaciated land. While there is significant correlation among reanalyses in annual time series, there is substantial disagreement in mean values. For the period 1980-2013, the trend in annual temperature ranges from 0.3 to 0.7K per decade. Over the central Arctic Ocean, differences in mean values and trends are larger. Most of the uncertainty is associated with winter months. This is likely associated with the constraint imposed by melting processes (i.e. 0 deg. Celsius), rather than seasonal changes to the observing system.

Meteorological factors affecting dengue incidence in Davao, Philippines.

PubMed

Iguchi, Jesavel A; Seposo, Xerxes T; Honda, Yasushi

2018-05-15

Dengue fever is a major public health concern in the Philippines, and has been a significant cause of hospitalizations and deaths among young children. Previous literature links climate change to dengue, and with increasingly unpredictable changing climate patterns, there is a need to understand how these meteorological variables affect dengue incidence in a highly endemic area. Weekly dengue incidences (2011-2015) in Davao Region, Philippines were obtained from the Department of Health. Same period of weekly local meteorological variables were obtained from the National Climatic Data Center (NCDC) and the National Oceanic and Atmospheric Administration (NOAA). Wavelet coherence analysis was used to determine the presence of non-stationary relationships, while a quasi-Poisson regression combined with distributed lag nonlinear model (DLNM) was used to analyze the association between meteorological variables and dengue incidences. Significant periodicity was detected in the 7 to 14-week band between the year 2011-2012 and a 26-week periodicity from the year 2013-2014. Overall cumulative risks were particularly high for rainfall at 32 mm (RR: 1.67, 95% CI: 1.07-2.62), while risks were observed to increase with increasing dew point. On the other hand, lower average temperature of 26 °C has resulted to an increased RR of dengue (RR: 1.96, 95% CI: 0.47-8.15) while higher temperature from 27 °C to 31 °C has lower RR. The observed possible threshold levels of these meteorological variables can be integrated into an early warning system to enhance dengue prediction for better vector control and management in the future.

Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

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

Kumar, Jitendra; Brooks, Bjørn-Gustaf J.; Thornton, Peter E

2012-01-01

A new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates thatmore » this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.« less

Potential solar radiation and land cover contributions to digital climate surface modeling

NASA Astrophysics Data System (ADS)

Puig, Pol; Batalla, Meritxell; Pesquer, Lluís; Ninyerola, Miquel

2016-04-01

Overview: We have designed a series of ad-hoc experiments to study the role of factors that a priori have a strong weight in developing digital models of temperature and precipitation, such as solar radiation and land cover. Empirical test beds have been designed to improve climate (mean air temperature and total precipitation) digital models using statistical general techniques (multiple regression) with residual correction (interpolated with inverse weighting distance). Aim: Understand what roles these two factors (solar radiation and land cover) play to incorporate them into the process of generating mapping of temperature and rainfall. Study area: The Iberian Peninsula and supported in this, Catalonia and the Catalan Pyrenees. Data: The dependent variables used in all experiments relate to data from meteorological stations precipitation (PL), mean temperature (MT), average temperature minimum (MN) and maximum average temperature (MX). These data were obtained monthly from the AEMET (Agencia Estatal de Meteorología). Data series of stations covers the period between 1950 to 2010. Methodology: The idea is to design ad hoc, based on a sample of more equitable space statistician, to detect the role of radiation. Based on the influence of solar radiation on the temperature of the air from a quantitative point of view, the difficulty in answering this lies in the fact that there are lots of weather stations located in areas where solar radiation is similar. This suggests that the role of the radiation variable remains "off" when, instead, we intuitively think that would strongly influence the temperature. We have developed a multiple regression analysis between these meteorological variables as the dependent ones (Temperature and rainfall), and some geographical variables: altitude (ALT), latitude (LAT), continentality (CON) and solar radiation (RAD) as the independent ones. In case of the experiment with land covers, we have used the NDVI index as a proxy of land covers and added this variable in to the independents to improve the models. Results: The role of solar radiation does not improve models only under certain conditions and areas, especially in the Pyrennes. The vegetation index NDVI and therefore the land cover on which the station is located, helps improve rainfall and temperature patterns, obtaining various degrees of improvement in terms of molded variables and months.

Simulation of Urban Heat Island Mitigation Strategies in Atlanta, GA Using High-Resolution Land Use/Land Cover Data Set to Enhance Meteorological Modeling

NASA Technical Reports Server (NTRS)

Crosson, William L.; Dembek, Scott; Estes, Maurice G., Jr.; Limaye, Ashutosh S.; Lapenta, William; Quattrochi, Dale A.; Johnson, Hoyt; Khan, Maudood

2006-01-01

The specification of land use/land cover (LULC) and associated land surface parameters in meteorological models at all scales has a major influence on modeled surface energy fluxes and boundary layer states. In urban areas, accurate representation of the land surface may be even more important than in undeveloped regions due to the large heterogeneity within the urban area. Deficiencies in the characterization of the land surface related to the spatial or temporal resolution of the data, the number of LULC classes defined, the accuracy with which they are defined, or the degree of heterogeneity of the land surface properties within each class may degrade the performance of the models. In this study, an experiment was conducted to test a new high-resolution LULC data set for meteorological simulations for the Atlanta, Georgia metropolitan area using a mesoscale meteorological model and to evaluate the effects of urban heat island (UHI) mitigation strategies on modeled meteorology for 2030. Simulation results showed that use of the new LULC data set reduced a major deficiency of the land use data used previously, specifically the poor representation of urban and suburban land use. Performance of the meteorological model improved substantially, with the overall daytime cold bias reduced by over 30%. UHI mitigation strategies were projected to offset much of a predicted urban warming between 2000 and 2030. In fact, for the urban core, the cooling due to UHI mitigation strategies was slightly greater than the warming associated with urbanization over this period. For the larger metropolitan area, cooling only partially offset the projected warming trend.

The determination of surface albedo from meteorological satellites

NASA Technical Reports Server (NTRS)

Johnson, W. T.

1977-01-01

A surface albedo was determined from visible data collected by the NOAA-4 polar orbiting meteorological satellite. To filter out the major cause of atmospheric reflectivity, namely clouds, techniques were developed and applied to the data resulting in a map of global surface albedo. Neglecting spurious surface albedos for regions with persistent cloud cover, sun glint effects, insufficient reflected light and, at this time, some unresolved influences, the surface albedos retrieved from satellite data closely matched those of a global surface albedo map produced from surface and aircraft measurements and from characteristic albedos for land type and land use.

Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

NASA Astrophysics Data System (ADS)

DY, C. Y.; Fung, J. C. H.

2016-08-01

A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the soil map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the soil moisture improves near-surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

Extension of surface data by use of meteorological satellites

NASA Technical Reports Server (NTRS)

Giddings, L. E.

1976-01-01

Ways of using meteorological satellite data to extend surface data are summarized. Temperature models are prepared from infrared data from ITOS/NOAA, NIMBUS, SMS/GOES, or future LANDSAT satellites. Using temperatures for surface meteorological stations as anchors, an adjustment is made to temperature values for each pixel in the model. The result is an image with an estimated temperature for each pixel. This provides an economical way of producing detailed temperature information for data-sparse areas, such as are found in underdeveloped countries. Related uses of these satellite data are also given, including the use of computer prepared cloud-free composites to extend climatic zones, and their use in discrimination of reflectivity-thermal regime zones.

Studies of Arctic Tropospheric Ozone Depletion Events Through Buoy-Borne Observations and Laboratory Studies

NASA Astrophysics Data System (ADS)

Halfacre, John W.

The photochemically-induced destruction of ground-level Arctic ozone in the Arctic occurs at the onset of spring, in concert with polar sunrise. Solar radiation is believed to stimulate a series of reactions that cause the production and release of molecular halogens from frozen, salty surfaces, though this mechanism is not yet well understood. The subsequent photolysis of molecular halogens produces reactive halogen atoms that remove ozone from the atmosphere in these so-called "Ozone Depletion Events" (ODEs). Given that much of the Arctic region is sunlit, meteorologically stable, and covered by saline ice and snow, it is expected that ODEs could be a phenomenon that occurs across the entire Arctic region. Indeed, an ever-growing body of evidence from coastal sites indicates that Arctic air masses devoid of O3 most often pass over sea ice-covered regions before arriving at an observation site, suggesting ODE chemistry occurs upwind over the frozen Arctic Ocean. However, outside of coastal observations, there exist very few long-term observations from the Arctic Ocean from which quantitative assessments of basic ODE characteristics can be made. This work presents the interpretation of ODEs through unique chemical and meteorological observations from several ice-tethered buoys deployed around the Arctic Ocean. These observations include detection of ozone, bromine monoxide, and measurements of temperature, relative humidity, atmospheric pressure, wind speed, and wind direction. To assess whether the O-Buoys were observing locally based depletion chemistry or the transport of ozone-poor air masses, periods of ozone decay were interpreted based on current understanding of ozone depletion kinetics, which are believed to follow a pseudo-first order rate law. In addition, the spatial extents of ODEs were estimated using air mass trajectory modeling to assess whether they are a localized or synoptic phenomenon. Results indicate that current understanding of the responsible chemical mechanisms are lacking, ODEs are observed primarily due to air mass transport (even in the Arctic Ocean), or some combination of both. Air mass trajectory modeling was also used in tandem with remote sensing observations of sea ice to determine the types of surfaces air masses were exposed to before arriving at O-Buoys. The impact of surface exposure was subsequently compared with local meteorology to assess which variables had the most effect on O 3 variability. For two observation sites, the impact of local meteorology was significantly stronger than air mass history, while a third was inconclusive. Finally, this work tests the viability of the hypothesis that initial production of molecular halogens from frozen saline surfaces results from photolytic production of the hydroxyl radical, and could be enhanced in the presence of O3. This investigation was enabled by a custom frozen-walled flow reactor coupled with chemical ionization spectrometry. It was found that hydroxyl radical could indeed promote the production and release of iodine, bromine, and chlorine, and that this production could be enhanced in the presence of ozone.

What controls springtime fine dust variability in the western United States? Implications for air quality and public health risks under future climate change.

NASA Astrophysics Data System (ADS)

Achakulwisut, P.; Mickley, L. J.; Shen, L.; Anenberg, S.

2017-12-01

Studies suggest that deposition of atmospheric dust and its surface concentrations have recently been increasing in the Southwest, and a key concern is that climate change will impact dust mobilization and transport across the western United States. Here we diagnose the dominant meteorological factors driving observed fine dust interannual variability in the western United States during 2002-2015 in March-May, and investigate the implications of our results for future dust levels. Empirical orthogonal function analysis suggests that for each spring month, 50-61% of present-day variance in fine dust can be explained by either a coherent pattern of co-variability across the West or a dipole Northwest-Southwest pattern. We identify the key drivers of fine dust variability to be regional precipitation, temperature, and soil moisture, which in turn are influenced at least in part by large-scale changes in sea surface temperature and/or atmospheric circulation patterns. Fluctuations in the trans-Pacific transport of Asian dust also contribute to fine dust variability in March and April. We find that March fine dust concentrations have increased from 2002 to 2015 in Southwest regions (0.09 ± 0.07 μg m-3 a-1). Multiple linear regression analysis suggests that these increases are associated with: (1) regionally drier and warmer conditions driven by constructive interference between El Niño Southern-Oscillation and Pacific Decadal Oscillation; (2) soil moisture reductions in areas spanning the North American deserts; and (3) stronger trans-Pacific transport. We then use observed drought-sensitivities to project future changes in annual mean fine dust during the late-21st century (2076-2095), using bias-corrected downscaled meteorological outputs from 23 CMIP5 models following two Representative Concentration Pathways (RCP2.6 and RCP8.5). Together with projections of future population and baseline incidence rates, and results from epidemiological studies of health risks due to PM2.5 exposure, we estimate the excess premature mortality and morbidity associated with projected changes in annual mean fine dust in the southwest United States.

Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture

NASA Astrophysics Data System (ADS)

Davis, Tyler W.; Prentice, I. Colin; Stocker, Benjamin D.; Thomas, Rebecca T.; Whitley, Rhys J.; Wang, Han; Evans, Bradley J.; Gallego-Sala, Angela V.; Sykes, Martin T.; Cramer, Wolfgang

2017-02-01

Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as near-surface air temperature, precipitation and cloudiness. Here we present a consolidated set of simple process-led algorithms for simulating habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant timescales. We specify equations, derivations, simplifications, and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium, and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley-Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results.

Evaluation of the ORCHIDEE ecosystem model over Africa against 25 years of satellite-based water and carbon measurements

NASA Astrophysics Data System (ADS)

Traore, Abdoul Khadre; Ciais, Philippe; Vuichard, Nicolas; Poulter, Benjamin; Viovy, Nicolas; Guimberteau, Matthieu; Jung, Martin; Myneni, Ranga; Fisher, Joshua B.

2014-08-01

Few studies have evaluated land surface models for African ecosystems. Here we evaluate the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) process-based model for the interannual variability (IAV) of the fraction of absorbed active radiation, the gross primary productivity (GPP), soil moisture, and evapotranspiration (ET). Two ORCHIDEE versions are tested, which differ by their soil hydrology parameterization, one with a two-layer simple bucket and the other a more complex 11-layer soil-water diffusion. In addition, we evaluate the sensitivity of climate forcing data, atmospheric CO2, and soil depth. Beside a very generic vegetation parameterization, ORCHIDEE simulates rather well the IAV of GPP and ET (0.5 < r < 0.9 interannual correlation) over Africa except in forestlands. The ORCHIDEE 11-layer version outperforms the two-layer version for simulating IAV of soil moisture, whereas both versions have similar performance of GPP and ET. Effects of CO2 trends, and of variable soil depth on the IAV of GPP, ET, and soil moisture are small, although these drivers influence the trends of these variables. The meteorological forcing data appear to be quite important for faithfully reproducing the IAV of simulated variables, suggesting that in regions with sparse weather station data, the model uncertainty is strongly related to uncertain meteorological forcing. Simulated variables are positively and strongly correlated with precipitation but negatively and weakly correlated with temperature and solar radiation. Model-derived and observation-based sensitivities are in agreement for the driving role of precipitation. However, the modeled GPP is too sensitive to precipitation, suggesting that processes such as increased water use efficiency during drought need to be incorporated in ORCHIDEE.

Estimation of Land Surface Temperature for the Quantitative Analysis of Land Cover of Lower Areas of Sindh to Assess the Impacts of Climate Variability

NASA Astrophysics Data System (ADS)

Qaisar, Maha

2016-07-01

Due to the present land use practices and climate variability, drastic shifts in regional climate and land covers are easily seen and their future reduction and gain are too well predicted. Therefore, there is an increasing need for data on land-cover changes at narrow and broad spatial scales. In this study, a remote sensing-based technique for land-cover-change analysis is applied to the lower Sindh areas for the last decade. Landsat satellite products were analyzed on an alternate yearly basis, from 1990 to 2016. Then Land-cover-change magnitudes were measured and mapped for alternate years. Land Surface Temperature (LST) is one of the critical elements in the natural phenomena of surface energy and water balance at local and global extent. However, LST was computed by using Landsat thermal bands via brightness temperature and a vegetation index. Normalized difference vegetation index (NDVI) was interpreted and maps were achieved. LST reflected NDVI patterns with complexity of vegetation patterns. Along with this, Object Based Image Analysis (OBIA) was done for classifying 5 major classes of water, vegetation, urban, marshy lands and barren lands with significant map layouts. Pakistan Meteorological Department provided the climate data in which rainfall, temperature and air temperature are included. Once the LST and OBIA are performed, overlay analysis was done to correlate the results of LST with OBIA and LST with meteorological data to ascertain the changes in land covers due to increasing centigrade of LST. However, satellite derived LST was also correlated with climate data for environmental analysis and to estimate Land Surface Temperature for assessing the inverse impacts of climate variability. This study's results demonstrate the land-cover changes in Lower Areas of Sindh including the Indus Delta mostly involve variations in land-cover conditions due to inter-annual climatic variability and temporary shifts in seasonality. However it is too concluded that transitory alteration of the biophysical characteristics of the surface driven by variations in rainfall is the prevailing progression. Moreover, future work will focus on finer-scale analysis and validations of patterns of changes due to rapid urbanization and population explosion in poverty stricken areas of Sindh which are posing an adverse impact on the land utilization and in turn increasing the land surface temperature and ultimately more stress on the low lying areas of Sindh i.e. Indus Delta will be losing its productivity and capacity to bear biodiversity whether the fauna or flora. Hence, this regional scale problem will become a global concern. Therefore, it is needed to stop the menace in its starting phase to mitigate the problem and to bring minds on this horrendous situation.

Assessing factors that influence deviations between measured and calculated reference evapotranspiration

NASA Astrophysics Data System (ADS)

Rodny, Marek; Nolz, Reinhard

2017-04-01

Evapotranspiration (ET) is a fundamental component of the hydrological cycle, but challenging to be quantified. Lysimeter facilities, for example, can be installed and operated to determine ET, but they are costly and represent only point measurements. Therefore, lysimeter data are traditionally used to develop, calibrate, and validate models that allow calculating reference evapotranspiration (ET0) based on meteorological data, which can be measured more easily. The standardized form of the well-known FAO Penman-Monteith equation (ASCE-EWRI) is recommended as a standard procedure for estimating ET0 and subsequently plant water requirements. Applied and validated under different climatic conditions, the Penman-Monteith equation is generally known to deliver proper results. On the other hand, several studies documented deviations between measured and calculated ET0 depending on environmental conditions. Potential reasons are, for example, differing or varying surface characteristics of the lysimeter and the location where the weather instruments are placed. Advection of sensible heat (transport of dry and hot air from surrounding areas) might be another reason for deviating ET-values. However, elaborating causal processes is complex and requires comprehensive data of high quality and specific analysis techniques. In order to assess influencing factors, we correlated differences between measured and calculated ET0 with pre-selected meteorological parameters and related system parameters. Basic data were hourly ET0-values from a weighing lysimeter (ET0_lys) with a surface area of 2.85 m2 (reference crop: frequently irrigated grass), weather data (air and soil temperature, relative humidity, air pressure, wind velocity, and solar radiation), and soil water content in different depths. ET0_ref was calculated in hourly time steps according to the standardized procedure after ASCE-EWRI (2005). Deviations between both datasets were calculated as ET0_lys-ET0_ref and separated into positive and negative values. For further interpretation, we calculated daily sums of these values. The respective daily difference (positive or negative) served as independent variable (x) in linear correlation with a selected parameter as dependent variable (y). Quality of correlation was evaluated by means of coefficients of determination (R2). When ET0_lys > ET0_ref, the differences were only weakly correlated with the selected parameters. Hence, the evaluation of the causal processes leading to underestimation of measured hourly ET0 seems to require a more rigorous approach. On the other hand, when ET0_lys < ET0_ref, the differences correlated considerably with the meteorological parameters and related system parameters. Interpreting the particular correlations in detail indicated different (or varying) surface characteristics between the irrigated lysimeter and the nearby (non-irrigated) meteorological station.

Quantifying the Influence of Near-Surface Water-Energy Budgets on Soil Thermal Properties Using a Network of Coupled Meteorological and Vadose Zone Instrument Arrays in Indiana, USA

NASA Astrophysics Data System (ADS)

Naylor, S.; Gustin, A. R.; Ellett, K. M.

2012-12-01

Weather stations that collect reliable, sustained meteorological data sets are becoming more widely distributed because of advances in both instrumentation and data server technology. However, sites collecting soil moisture and soil temperature data remain sparse with even fewer locations where complete meteorological data are collected in conjunction with soil data. Thanks to the advent of sensors that collect continuous in-situ thermal properties data for soils, we have gone a step further and incorporated thermal properties measurements as part of hydrologic instrument arrays in central and northern Indiana. The coupled approach provides insights into the variability of soil thermal conductivity and diffusivity attributable to geologic and climatological controls for various hydrogeologic settings. These data are collected to facilitate the optimization of ground-source heat pumps (GSHPs) in the glaciated Midwest by establishing publicly available data that can be used to parameterize system design models. A network of six monitoring sites was developed in Indiana. Sensors that determine thermal conductivity and diffusivity using radial differential temperature measurements around a heating wire were installed at 1.2 meters below ground surface— a typical depth for horizontal GSHP systems. Each site also includes standard meteorological sensors for calculating reference evapotranspiration following the methods by the Food and Agriculture Organization (FAO) of the United Nations. Vadose zone instrumentation includes time domain reflectometry soil-moisture and temperature sensors installed at 0.3-meter depth intervals down to a 1.8-meter depth, in addition to matric potential sensors at 0.15, 0.3, 0.6, and 1.2 meters. Cores collected at 0.3-meter intervals were analyzed in a laboratory for grain size distribution, bulk density, thermal conductivity, and thermal diffusivity. Our work includes developing methods for calibrating thermal properties sensors based on known standards and comparing measurements from transient line heat source devices. Transform equations have been developed to correct in-situ measurements of thermal conductivity and comparing these results with soil moisture data indicates that thermal conductivity can increase by as much as 25 percent during wetting front propagation. Thermal dryout curves have also been modeled based on laboratory conductivity data collected from core samples to verify field measurements, and alternatively, temperature profile data are used to calibrate near-surface temperature gradient models. We compare data collected across various spatial scales to assess the potential for upscaling near-surface thermal regimes based on available soils data. A long-term goal of the monitoring effort is to establish continuous data sets that determine the effect of climate variability on soil thermal properties such that expected ranges in thermal conductivity can be used to determine optimal ground-coupling loop lengths for GSHP systems.

Streamflow simulation for continental-scale river basins

NASA Astrophysics Data System (ADS)

Nijssen, Bart; Lettenmaier, Dennis P.; Liang, Xu; Wetzel, Suzanne W.; Wood, Eric F.

1997-04-01

A grid network version of the two-layer variable infiltration capacity (VIC-2L) macroscale hydrologic model is described. VIC-2L is a hydrologically based soil- vegetation-atmosphere transfer scheme designed to represent the land surface in numerical weather prediction and climate models. The grid network scheme allows streamflow to be predicted for large continental rivers. Off-line (observed and estimated surface meteorological and radiative forcings) applications of the model to the Columbia River (1° latitude-longitude spatial resolution) and Delaware River (0.5° resolution) are described. The model performed quite well in both applications, reproducing the seasonal hydrograph and annual flow volumes to within a few percent. Difficulties in reproducing observed streamflow in the arid portion of the Snake River basin are attributed to groundwater-surface water interactions, which are not modeled by VIC-2L.

Impacts of 2000-2050 Climate Change on Fine Particulate Matter (PM2.5) Air Quality in China Based on Statistical Projections Using an Ensemble of Global Climate Models

NASA Astrophysics Data System (ADS)

Leung, D. M.; Tai, A. P. K.; Shen, L.; Moch, J. M.; van Donkelaar, A.; Mickley, L. J.

2017-12-01

Fine particulate matter (PM2.5) air quality is strongly dependent on not only on emissions but also meteorological conditions. Here we examine the dominant synoptic circulation patterns that control day-to-day PM2.5 variability over China. We perform principal component (PC) analysis on 1998-2016 NCEP/NCAR Reanalysis I daily meteorological fields to diagnose distinct synoptic meteorological modes, and perform PC regression on spatially interpolated 2014-2016 daily mean PM2.5 concentrations in China to identify modes dominantly explaining PM2.5 variability. We find that synoptic systems, e.g., cold-frontal passages, maritime inflow and frontal precipitation, can explain up to 40% of the day-to-day PM2.5 variability in major metropolitan regions in China. We further investigate how annually changing frequencies of synoptic systems, as well as changing local meteorology, drive interannual PM2.5 variability. We apply a spectral analysis on the PC time series to obtain the 1998-2016 annual median synoptic frequency, and use a forward-selection multiple linear regression (MLR) model of satellite-derived 1998-2015 annual mean PM2.5 concentrations on local meteorology and synoptic frequency, selecting predictors that explain the highest fraction of interannual PM2.5 variability while guarding against multicollinearity. To estimate the effect of climate change on future PM2.5 air quality, we project a multimodel ensemble of 15 CMIP5 models under the RCP8.5 scenario on the PM2.5-to-meteorology sensitivities derived for the present-day from the MLR model. Our results show that climate change could be responsible for increases in PM2.5 of more than 25 μg m-3 in northwestern China and 10 mg m-3 in northeastern China by the 2050s. Increases in synoptic frequency of cold-frontal passages cause only a modest 1 μg m-3 decrease in PM2.5 in North China Plain. Our analyses show that climate change imposes a significant penalty on air quality over China and poses serious threat on human health under the RCP8.5 future.

Variability in precipitation in a watershed in the altiplano, Peru and modes of variation

NASA Astrophysics Data System (ADS)

Mazzarino, M.; Brown, C. M.

2012-12-01

This research examines system linkages between climate, water availability, pasture availability, camelids (llamas and alpacas) and indigenous herders in an Andean watershed in southern Peru. In this region, extreme meteorological events such as drought and flood, occur often and have the potential to negatively impact herding livelihoods. Predictability in the system is paramount to reducing risks associated with these events. In the altiplano, a large portion of variability in precipitation has been attributed to the influence of El Nino Southern Oscillation (ENSO). In light of climate change and observations by herders, this research returns to the question of teleconnections in the altiplano. We use December through March precipitation totals obtained from eight meteorological stations for 43 years (1964-2006) and sea surface temperatures (SSTs) in the equatorial Pacific and Atlantic to characterize the hydroclimatology in the watershed and determine modes of variability. Following principal components analysis, prevailing periodicities in regional precipitation were determined using wavelet analysis and spatial correlation and regression analysis were used to determine the relationship between SST anomalies (SSTA's) and precipitation events in the watershed. Results suggest a non-linear and non-stationary mode of variability. We draw three conclusions from the results: 1) Positive precipitation extremes are dominated by an ENSO signal in the Nino 2 region; 2) Post 1987 there is a weak relationship, if any, between anomalously dry years in the precipitation record and SSTA's in the equatorial Pacific; 3) There is a stronger relationship (inverse) between precipitation in the region and SSTA's in the tropical Atlantic than previously believed.

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data*

PubMed Central

Zhang, Li-wen; Huang, Jing-feng; Guo, Rui-fang; Li, Xin-xing; Sun, Wen-bo; Wang, Xiu-zhen

2013-01-01

The accumulation of thermal time usually represents the local heat resources to drive crop growth. Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity. To solve the critical problems of estimating air temperature (T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days (GDDs) calculation from remotely sensed data, a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed. This is a preliminary study to calculate heat accumulation, expressed in accumulative growing degree days (AGDDs) above 10 °C, from reconstructed T a based on MODIS land surface temperature (LST) data. The verification results of maximum T a, minimum T a, GDD, and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels. Overall, MODIS-derived AGDD was slightly underestimated with almost 10% relative error. However, the feasibility of employing AGDD anomaly maps to characterize the 2001–2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper. Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring, agricultural climatic regionalization, and agro-meteorological disaster detection at the regional scale. PMID:23365013

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data.

PubMed

Zhang, Li-wen; Huang, Jing-feng; Guo, Rui-fang; Li, Xin-xing; Sun, Wen-bo; Wang, Xiu-zhen

2013-02-01

The accumulation of thermal time usually represents the local heat resources to drive crop growth. Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity. To solve the critical problems of estimating air temperature (T(a)) and filling in missing pixels due to cloudy and low-quality images in growing degree days (GDDs) calculation from remotely sensed data, a novel spatio-temporal algorithm for T(a) estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed. This is a preliminary study to calculate heat accumulation, expressed in accumulative growing degree days (AGDDs) above 10 °C, from reconstructed T(a) based on MODIS land surface temperature (LST) data. The verification results of maximum T(a), minimum T(a), GDD, and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels. Overall, MODIS-derived AGDD was slightly underestimated with almost 10% relative error. However, the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper. Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring, agricultural climatic regionalization, and agro-meteorological disaster detection at the regional scale.

Performance Evaluation of PBL Schemes of ARW Model in Simulating Thermo-Dynamical Structure of Pre-Monsoon Convective Episodes over Kharagpur Using STORM Data Sets

NASA Astrophysics Data System (ADS)

Madala, Srikanth; Satyanarayana, A. N. V.; Srinivas, C. V.; Tyagi, Bhishma

2016-05-01

In the present study, advanced research WRF (ARW) model is employed to simulate convective thunderstorm episodes over Kharagpur (22°30'N, 87°20'E) region of Gangetic West Bengal, India. High-resolution simulations are conducted using 1 × 1 degree NCEP final analysis meteorological fields for initial and boundary conditions for events. The performance of two non-local [Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2)] and two local turbulence kinetic energy closures [Mellor-Yamada-Janjic (MYJ), Bougeault-Lacarrere (BouLac)] are evaluated in simulating planetary boundary layer (PBL) parameters and thermodynamic structure of the atmosphere. The model-simulated parameters are validated with available in situ meteorological observations obtained from micro-meteorological tower as well has high-resolution DigiCORA radiosonde ascents during STORM-2007 field experiment at the study location and Doppler Weather Radar (DWR) imageries. It has been found that the PBL structure simulated with the TKE closures MYJ and BouLac are in better agreement with observations than the non-local closures. The model simulations with these schemes also captured the reflectivity, surface pressure patterns such as wake-low, meso-high, pre-squall low and the convective updrafts and downdrafts reasonably well. Qualitative and quantitative comparisons reveal that the MYJ followed by BouLac schemes better simulated various features of the thunderstorm events over Kharagpur region. The better performance of MYJ followed by BouLac is evident in the lesser mean bias, mean absolute error, root mean square error and good correlation coefficient for various surface meteorological variables as well as thermo-dynamical structure of the atmosphere relative to other PBL schemes. The better performance of the TKE closures may be attributed to their higher mixing efficiency, larger convective energy and better simulation of humidity promoting moist convection relative to non-local schemes.

Validation of the RegCM4-Subgrid module for the high resolution climate simulation over Korea

NASA Astrophysics Data System (ADS)

Lee, C.; Im, E.; Chang, K.; Choi, Y.

2010-12-01

Given the discernable evidences of climate changes due to human activity, there is a growing demand for the reliable climate change scenario in response to future emission forcing. One of the most significant impacts of climate changes can be that on the hydrological process. Changes in the seasonality and the low and high rainfall extremes can influence the water balance of river basin, with several consequences for societies and ecosystems. In fact, recent studies have reported that East Asia including the Korean peninsula is regarded to be a highly vulnerability region under global warming, especially for water resources. As an attempt to accurately assess the impact of climate change over Korea, we developed the dynamical downscaling system using the RegCM4 with a mosaic-type parameterization of subgrid-scale topography and land use (Sub-BATS). The Sub-BATS system is composed of 20 km coarse-grid cell and 4 km sub-grid cell. Before a full climate change simulation is carried out, we performed the simulation spanning the 19-year periods (1989-2007) with the lateral boundary fields obtained from the ERA-Interim reanalysis. The Korean peninsula is characterized by narrow mountain systems surrounded by ocean, and covered by a relatively dense observational network (approximate 400 stations), which provides an excellent dataset to validate a finescale downscaled results over the region. The evaluation of simulated surface variables (e.g. temperature, precipitation, snow, runoff) shows the usefulness of the RegCM4-Subgrid module as a tool to produce fine scale climate information of surface processes for coupling with hydrological model over the Korean peninsula Acknowledgements This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government(MEST) (No. 2009-0085533), and by the "Advanced research on industrial meteorology" and " Development of meteorological resources for green growth." of National Institute of Meteorological Research (NIMR), funded by the Korea Meteorological Administration(KMA).

Development of an analysis tool for cloud base height and visibility

NASA Astrophysics Data System (ADS)

Umdasch, Sarah; Reinhold, Steinacker; Manfred, Dorninger; Markus, Kerschbaum; Wolfgang, Pöttschacher

2014-05-01

The meteorological variables cloud base height (CBH) and horizontal atmospheric visibility (VIS) at surface level are of vital importance for safety and effectiveness in aviation. Around 20% of all civil aviation accidents in the USA from 2003 to 2007 were due to weather related causes, around 18% of which were owing to decreased visibility or ceiling (main CBH). The aim of this study is to develop a system generating quality-controlled gridded analyses of the two parameters based on the integration of various kinds of observational data. Upon completion, the tool is planned to provide guidance for nowcasting during take-off and landing as well as for flights operated under visual flight rules. Primary input data consists of manual as well as instrumental observation of CBH and VIS. In Austria, restructuring of part of the standard meteorological stations from human observation to automatic measurement of VIS and CBH is currently in progress. As ancillary data, satellite derived products can add 2-dimensional information, e.g. Cloud Type by NWC SAF (Nowcasting Satellite Application Facilities) MSG (Meteosat Second Generation). Other useful available data are meteorological surface measurements (in particular of temperature, humidity, wind and precipitation), radiosonde, radar and high resolution topography data. A one-year data set is used to study the spatial and weather-dependent representativeness of the CBH and VIS measurements. The VERA (Vienna Enhanced Resolution Analysis) system of the Institute of Meteorology and Geophysics of the University of Vienna provides the framework for the analysis development. Its integrated "Fingerprint" technique allows the insertion of empirical prior knowledge and ancillary information in the form of spatial patterns. Prior to the analysis, a quality control of input data is performed. For CBH and VIS, quality control can consist of internal consistency checks between different data sources. The possibility of two-dimensional consistency checks has to be explored. First results in the development of quality control features and fingerprints will be shown.

Appraisal of Weather Research and Forecasting Model Downscaling of Hydro-meteorological Variables and their Applicability for Discharge Prediction: Prognostic Approach for Ungauged Basin

NASA Astrophysics Data System (ADS)

Srivastava, P. K.; Han, D.; Rico-Ramirez, M. A.; Bray, M.; Islam, T.; Petropoulos, G.; Gupta, M.

2015-12-01

Hydro-meteorological variables such as Precipitation and Reference Evapotranspiration (ETo) are the most important variables for discharge prediction. However, it is not always possible to get access to them from ground based measurements, particularly in ungauged catchments. The mesoscale model WRF (Weather Research & Forecasting model) can be used for prediction of hydro-meteorological variables. However, hydro-meteorologists would like to know how well the downscaled global data products are as compared to ground based measurements and whether it is possible to use the downscaled data for ungauged catchments. Even with gauged catchments, most of the stations have only rain and flow gauges installed. Measurements of other weather hydro-meteorological variables such as solar radiation, wind speed, air temperature, and dew point are usually missing and thus complicate the problems. In this study, for downscaling the global datasets, the WRF model is setup over the Brue catchment with three nested domains (D1, D2 and D3) of horizontal grid spacing of 81 km, 27 km and 9 km are used. The hydro-meteorological variables are downscaled using the WRF model from the National Centers for Enviromental Prediction (NCEP) reanalysis datasets and subsequently used for the ETo estimation using the Penman Monteith equation. The analysis of weather variables and precipitation are compared against the ground based datasets, which indicate that the datasets are in agreement with the observed datasets for complete monitoring period as well as during the seasons except precipitation whose performance is poorer in comparison to the measured rainfall. After a comparison, the WRF estimated precipitation and ETo are then used as a input parameter in the Probability Distributed Model (PDM) for discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimation are also taken into account for the PDM calibration and prediction following the Generalised Likelihood Uncertainty Estimation (GLUE) approach. The overall analysis suggests that the uncertainty estimates in predicted discharge using WRF downscaled ETo have comparable performance to ground based observed datasets and hence is promising for discharge prediction in the absence of ground based measurements.

Shallow soil CO2 flow along the San Andreas and Calaveras Faults, California

USGS Publications Warehouse

Lewicki, J.L.; Evans, William C.; Hilley, G.E.; Sorey, M.L.; Rogie, J.D.; Brantley, S.L.

2003-01-01

We evaluate a comprehensive soil CO2 survey along the San Andreas fault (SAF) in Parkfield, and the Calaveras fault (CF) in Hollister, California, in the context of spatial and temporal variability, origin, and transport of CO2 in fractured terrain. CO2 efflux was measured within grids with portable instrumentation and continously with meteorological parameters at a fixed station, in both faulted and unfaulted areas. Spatial and temporal variability of surface CO2 effluxes was observed to be higher at faulted SAF and CF sites, relative to comparable background areas. However, ??13C (-23.3 to - 16.4???) and ??14C (75.5 to 94.4???) values of soil CO2 in both faulted and unfaulted areas are indicative of biogenic CO2, even though CO2 effluxes in faulted areas reached values as high as 428 g m-2 d-1. Profiles of soil CO2 concentration as a function of depth were measured at multiple sites within SAF and CF grids and repeatedly at two locations at the SAF grid. Many of these profiles suggest a surprisingly high component of advective CO2 flow. Spectral and correlation analysis of SAF CO2 efflux and meteorological parameter time series indicates that effects of wind speed variations on atmospheric air flow though fractures modulate surface efflux of biogenic CO2. The resulting areal patterns in CO2 effluxes could be erroneously attributed to a deep gas source in the absence of isotopic data, a problem that must be addressed in fault zone soil gas studies.

Comparison of regional and seasonal changes and trends in daily surface temperature extremes over India and its subregions

NASA Astrophysics Data System (ADS)

Dimri, A. P.

2018-04-01

Regional changes in surface meteorological variables are one of the key issues affecting the Indian subcontinent especially in recent decades. These changes impact agriculture, health, water, etc., hence important to assess and investigate these changes. The Indian subcontinent is characterized by heterogeneous temperature regimes at regional and seasonal scales. The India Meteorological Department (IMD) observations are limited to recent decades as far as its spatial distribution is concerned. In particular, over Hilly region, these observations are sporadic. Due to variable topography and heterogeneous land use/land cover, it is complex to substantiate impacts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim (ERA-I) reanalysis not only covers a larger spatial domain but also provides a greater number of inputs than IMD. This study used ERA-I in conjunction with IMD gridded data to provide a comparative assessment of changing temperature patterns over India and its subregions at both regional and seasonal scales. Warming patterns are observed in both ERA-I and IMD data sets. Cold nights decrease during winter; warm days increase and warm spell duration increased during winter could become a cause of concern for society, agriculture, socio-economic reasons, and health. Increasing warm days over the hilly regions may affect the corresponding snow cover and thus river hydrology and glaciological dynamics. Such changes during monsoon are slower, which could be attributed to moisture availability to dampen the temperature changes. On investigation and comparison thereon, the present study provisions usages of ERA-I-based indices for various impact and adaptation studies.

Improved assessment of gross and net primary productivity of Canada's landmass

NASA Astrophysics Data System (ADS)

Gonsamo, Alemu; Chen, Jing M.; Price, David T.; Kurz, Werner A.; Liu, Jane; Boisvenue, Céline; Hember, Robbie A.; Wu, Chaoyang; Chang, Kuo-Hsien

2013-12-01

assess Canada's gross primary productivity (GPP) and net primary productivity (NPP) using boreal ecosystem productivity simulator (BEPS) at 250 m spatial resolution with improved input parameter and driver fields and phenology and nutrient release parameterization schemes. BEPS is a process-based two-leaf enzyme kinetic terrestrial ecosystem model designed to simulate energy, water, and carbon (C) fluxes using spatial data sets of meteorology, remotely sensed land surface variables, soil properties, and photosynthesis and respiration rate parameters. Two improved key land surface variables, leaf area index (LAI) and land cover type, are derived at 250 m from Moderate Resolution Imaging Spectroradiometer sensor. For diagnostic error assessment, we use nine forest flux tower sites where all measured C flux, meteorology, and ancillary data sets are available. The errors due to input drivers and parameters are then independently corrected for Canada-wide GPP and NPP simulations. The optimized LAI use, for example, reduced the absolute bias in GPP from 20.7% to 1.1% for hourly BEPS simulations. Following the error diagnostics and corrections, daily GPP and NPP are simulated over Canada at 250 m spatial resolution, the highest resolution simulation yet for the country or any other comparable region. Total NPP (GPP) for Canada's land area was 1.27 (2.68) Pg C for 2008, with forests contributing 1.02 (2.2) Pg C. The annual comparisons between measured and simulated GPP show that the mean differences are not statistically significant (p > 0.05, paired t test). The main BEPS simulation error sources are from the driver fields.

Interannual to Decadal Variability of Atlantic Water in the Nordic and Adjacent Seas

NASA Technical Reports Server (NTRS)

Carton, James A.; Chepurin, Gennady A.; Reagan, James; Haekkinen, Sirpa

2011-01-01

Warm salty Atlantic Water is the main source water for the Arctic Ocean and thus plays an important role in the mass and heat budget of the Arctic. This study explores interannual to decadal variability of Atlantic Water properties in the Nordic Seas area where Atlantic Water enters the Arctic, based on a reexamination of the historical hydrographic record for the years 1950-2009, obtained by combining multiple data sets. The analysis shows a succession of four multi-year warm events where temperature anomalies at 100m depth exceed 0.4oC, and three cold events. Three of the four warm events lasted 3-4 years, while the fourth began in 1999 and persists at least through 2009. This most recent warm event is anomalous in other ways as well, being the strongest, having the broadest geographic extent, being surface-intensified, and occurring under exceptional meteorological conditions. Three of the four warm events were accompanied by elevated salinities consistent with enhanced ocean transport into the Nordic Seas, with the exception of the event spanning July 1989-July 1993. Of the three cold events, two lasted for four years, while the third lasted for nearly 14 years. Two of the three cold events are associated with reduced salinities, but the cold event of the 1960s had elevated salinities. The relationship of these events to meteorological conditions is examined. The results show that local surface heat flux variations act in some cases to reinforce the anomalies, but are too weak to be the sole cause.

Climatic controls on the snowmelt hydrology of the northern Rocky Mountains

USGS Publications Warehouse

Pederson, G.T.; Gray, S.T.; Ault, T.; Marsh, W.; Fagre, D.B.; Bunn, A.G.; Woodhouse, C.A.; Graumlich, L.J.

2011-01-01

The northern Rocky Mountains (NRMs) are a critical headwaters region with the majority of water resources originating from mountain snowpack. Observations showing declines in western U.S. snowpack have implications for water resources and biophysical processes in high-mountain environments. This study investigates oceanic and atmospheric controls underlying changes in timing, variability, and trends documented across the entire hydroclimatic-monitoring system within critical NRM watersheds. Analyses were conducted using records from 25 snow telemetry (SNOTEL) stations, 148 1 April snow course records, stream gauge records from 14 relatively unimpaired rivers, and 37 valley meteorological stations. Over the past four decades, midelevation SNOTEL records show a tendency toward decreased snowpack with peak snow water equivalent (SWE) arriving and melting out earlier. Temperature records show significant seasonal and annual decreases in the number of frost days (days ???0??C) and changes in spring minimum temperatures that correspond with atmospheric circulation changes and surface-albedo feedbacks in March and April. Warmer spring temperatures coupled with increases in mean and variance of spring precipitation correspond strongly to earlier snowmeltout, an increased number of snow-free days, and observed changes in streamflow timing and discharge. The majority of the variability in peak and total annual snowpack and streamflow, however, is explained by season-dependent interannual-to-interdecadal changes in atmospheric circulation associated with Pacific Ocean sea surface temperatures. Over recent decades, increased spring precipitation appears to be buffering NRM total annual streamflow from what would otherwise be greater snow-related declines in hydrologic yield. Results have important implications for ecosystems, water resources, and long-lead-forecasting capabilities. ?? 2011 American Meteorological Society.

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

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

Behrang, M.A.; Assareh, E.; Ghanbarzadeh, A.

2010-08-15

The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32 16'N, 48 25'E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered: (I)Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output.more » (II)Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output. (III)Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output. (IV)Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output. (V)Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output. (VI)Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output. Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data. The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)). (author)« less

Uncertainties in Past and Future Global Water Availability

NASA Astrophysics Data System (ADS)

Sheffield, J.; Kam, J.

2014-12-01

Understanding how water availability changes on inter-annual to decadal time scales and how it may change in the future under climate change are a key part of understanding future stresses on water and food security. Historic evaluations of water availability on regional to global scales are generally based on large-scale model simulations with their associated uncertainties, in particular for long-term changes. Uncertainties are due to model errors and missing processes, parameter uncertainty, and errors in meteorological forcing data. Recent multi-model inter-comparisons and impact studies have highlighted large differences for past reconstructions, due to different simplifying assumptions in the models or the inclusion of physical processes such as CO2 fertilization. Modeling of direct anthropogenic factors such as water and land management also carry large uncertainties in their physical representation and from lack of socio-economic data. Furthermore, there is little understanding of the impact of uncertainties in the meteorological forcings that underpin these historic simulations. Similarly, future changes in water availability are highly uncertain due to climate model diversity, natural variability and scenario uncertainty, each of which dominates at different time scales. In particular, natural climate variability is expected to dominate any externally forced signal over the next several decades. We present results from multi-land surface model simulations of the historic global availability of water in the context of natural variability (droughts) and long-term changes (drying). The simulations take into account the impact of uncertainties in the meteorological forcings and the incorporation of water management in the form of reservoirs and irrigation. The results indicate that model uncertainty is important for short-term drought events, and forcing uncertainty is particularly important for long-term changes, especially uncertainty in precipitation due to reduced gauge density in recent years. We also discuss uncertainties in future projections from these models as driven by bias-corrected and downscaled CMIP5 climate projections, in the context of the balance between climate model robustness and climate model diversity.

Identifying controlling factors of ground-level ozone levels over southwestern Taiwan using a decision tree

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Lin, Chuan-Yao; Liau, Churn-Jung; Kuo, Yi-Ming

2012-12-01

Kaohsiung City and the suburban region of southwestern Taiwan have suffered from severe air pollution since becoming the largest center of heavy industry in Taiwan. The complex process of ozone (O3) formation and its precursor compounds (the volatile organic compounds (VOCs) and nitrogen oxide (NOx) emissions), accompanied by meteorological conditions, make controlling ozone difficult. Using a decision tree is especially appropriate for analyzing time series data that contain ozone levels and meteorological and explanatory variables for ozone formation. Results show that dominant variables such as temperature, wind speed, VOCs, and NOx can play vital roles in describing ozone variations among observations. That temperature and wind speed are highly correlated with ozone levels indicates that these meteorological conditions largely affect ozone variability. The results also demonstrate that spatial heterogeneity of ozone patterns are in coastal and inland areas caused by sea-land breeze and pollutant sources during high ozone episodes over southwestern Taiwan. This study used a decision tree to obtain quantitative insight into spatial distributions of precursor compound emissions and effects of meteorological conditions on ozone levels that are useful for refining monitoring plans and developing management strategies.

The Mars Pathfinder atmospheric structure investigation/meteorology (ASI/MET) experiment.

PubMed

Schofield, J T; Barnes, J R; Crisp, D; Haberle, R M; Larsen, S; Magalhães, J A; Murphy, J R; Seiff, A; Wilson, G

1997-12-05

The Mars Pathfinder atmospheric structure investigation/meteorology (ASI/MET) experiment measured the vertical density, pressure, and temperature structure of the martian atmosphere from the surface to 160 km, and monitored surface meteorology and climate for 83 sols (1 sol = 1 martian day = 24.7 hours). The atmospheric structure and the weather record are similar to those observed by the Viking 1 lander (VL-1) at the same latitude, altitude, and season 21 years ago, but there are differences related to diurnal effects and the surface properties of the landing site. These include a cold nighttime upper atmosphere; atmospheric temperatures that are 10 to 12 degrees kelvin warmer near the surface; light slope-controlled winds; and dust devils, identified by their pressure, wind, and temperature signatures. The results are consistent with the warm, moderately dusty atmosphere seen by VL-1.

Hydrologic-energy balance constraints on the Holocene lake-level history of lake Titicaca, South America

NASA Astrophysics Data System (ADS)

Rowe, H. D.; Dunbar, R. B.

2004-09-01

A basin-scale hydrologic-energy balance model that integrates modern climatological, hydrological, and hypsographic observations was developed for the modern Lake Titicaca watershed (northern Altiplano, South America) and operated under variable conditions to understand controls on post-glacial changes in lake level. The model simulates changes in five environmental variables (air temperature, cloud fraction, precipitation, relative humidity, and land surface albedo). Relatively small changes in three meteorological variables (mean annual precipitation, temperature, and/or cloud fraction) explain the large mid-Holocene lake-level decrease (˜85 m) inferred from seismic reflection profiling and supported by sediment-based paleoproxies from lake sediments. Climatic controls that shape the present-day Altiplano and the sediment-based record of Holocene lake-level change are combined to interpret model-derived lake-level simulations in terms of changes in the mean state of ENSO and its impact on moisture transport to the Altiplano.

Untangling the contribution of aspect, drainage position and elevation to the spatial variability of fine surface fuels in south east Australian forests

NASA Astrophysics Data System (ADS)

Sheridan, Gary; nyman, petter; Duff, Tom; Baillie, Craig; Bovill, William; Lane, Patrick; Tolhurst, Kevin

2015-04-01

The prediction of fuel moisture content is important for estimating the rate of spread of wildfires, the ignition probability of firebrands, and for the efficient scheduling of prescribed fire. The moisture content of fine surface fuels varies spatially at large scales (10's to 100's km) due to variation in meteorological variables (eg. temperature, relative humidity, precipitation). At smaller scales (100's of metres) in steep topography spatial variability is attributed to topographic influences that include differences in radiation due to aspect and slope, differences in precipitation, temperature and relative humidity due to elevation, and differences in soil moisture due to hillslope drainage position. Variable forest structure and canopy shading adds further to the spatial variability in surface fuel moisture. In this study we aim to combine daily 5km resolution gridded weather data with 20m resolution DEM and vegetation structure data to predict the spatial variability of fine surface fuels in steep topography. Microclimate stations were established in south east Australia to monitor surface fine fuel moisture continuously (every 15 minutes) using newly developed instrumented litter packs, in addition to temperature and relative humidity measurements inside the litter pack, and measurement of precipitation and energy inputs above and below the forest canopy. Microclimate stations were established across a gradient of aspect (5 stations), drainage position (7 stations), elevation (15 stations), and canopy cover conditions (6 stations). The data from this extensive network of microclimate stations across a broad spectrum of topographic conditions is being analysed to enable the downscaling of gridded weather data to spatial scales that are relevant to the connectivity of wildfire fuels and to the scheduling and outcome of prescribed fires. The initial results from the first year of this study are presented here.

Meteorological influence on predicting surface SO2 concentration from satellite remote sensing in Shanghai, China.

PubMed

Xue, Dan; Yin, Jingyuan

2014-05-01

In this study, we explored the potential applications of the Ozone Monitoring Instrument (OMI) satellite sensor in air pollution research. The OMI planetary boundary layer sulfur dioxide (SO2_PBL) column density and daily average surface SO2 concentration of Shanghai from 2004 to 2012 were analyzed. After several consecutive years of increase, the surface SO2 concentration finally declined in 2007. It was higher in winter than in other seasons. The coefficient between daily average surface SO2 concentration and SO2_PBL was only 0.316. But SO2_PBL was found to be a highly significant predictor of the surface SO2 concentration using the simple regression model. Five meteorological factors were considered in this study, among them, temperature, dew point, relative humidity, and wind speed were negatively correlated with surface SO2 concentration, while pressure was positively correlated. Furthermore, it was found that dew point was a more effective predictor than temperature. When these meteorological factors were used in multiple regression, the determination coefficient reached 0.379. The relationship of the surface SO2 concentration and meteorological factors was seasonally dependent. In summer and autumn, the regression model performed better than in spring and winter. The surface SO2 concentration predicting method proposed in this study can be easily adapted for other regions, especially most useful for those having no operational air pollution forecasting services or having sparse ground monitoring networks.

Decreasing reference evapotranspiration in a warming climate - A case of Changjiang (Yangtze) River catchment during 1970-2000

NASA Astrophysics Data System (ADS)

Xu, C. Y.; Gong, L. B.; Tong, J.; Chen, D. L.

2006-07-01

This study deals with temporal trends in the Penman-Monteith reference evapotranspiration estimated from standard meteorological observations, observed pan evaporation, and four related meteorological variables during 1970-2000 in the Yangtze River catchment. Relative contributions of the four meteorological variables to changes in the reference evapotranspiration are quantified. The results show that both the reference evapotranspiration and the pan evaporation have significant. decreasing trends in the upper, the middle as well as in the whole Changjiang (Yangtze) River catchment at the 5% significance level, while the air temperature shows a significant increasing trend. The decreasing trend detected in the reference evapotranspiration can be attributed to the significant decreasing trends in the net radiation and the wind speed.

Highly variable aerodynamic roughness length (z0) for a hummocky debris-covered glacier

NASA Astrophysics Data System (ADS)

Miles, Evan S.; Steiner, Jakob F.; Brun, Fanny

2017-08-01

The aerodynamic roughness length (z0) is an essential parameter in surface energy balance studies, but few literature values exist for debris-covered glaciers. We use microtopographic and aerodynamic methods to assess the spatial variability of z0 for Lirung Glacier, Nepal. We apply structure from motion to produce digital elevation models for three nested domains: five 1 m2 plots, a 21,300 m2 surface depression, and the lower 550,000 m2 of the debris-mantled tongue. Wind and temperature sensor towers were installed in the vicinity of the plots within the surface depression in October 2014. We calculate z0 according to a variety of transect-based microtopographic parameterizations for each plot, then develop a grid version of the algorithms by aggregating data from all transects. This grid approach is applied to the surface depression digital elevation model to characterize z0 spatial variability. The algorithms reproduce the same variability among transects and plots, but z0 estimates vary by an order of magnitude between algorithms. Across the study depression, results from different algorithms are strongly correlated. Using Monin-Obukov similarity theory, we derive z0 values from the meteorological data. Using different stability criteria, we derive median values of z0 between 0.03 m and 0.05 m, but with considerable uncertainty due to the glacier's complex topography. Considering estimates from these algorithms, results suggest that z0 varies across Lirung Glacier between ˜0.005 m (gravels) to ˜0.5 m (boulders). Future efforts should assess the importance of such variable z0 values in a distributed energy balance model.

Multi-platform validation of a high-resolution model in the Western Mediterranean Sea: insight into spatial-temporal variability

NASA Astrophysics Data System (ADS)

Aguiar, Eva; Mourre, Baptiste; Heslop, Emma; Juza, Mélanie; Escudier, Romain; Tintoré, Joaquín

2017-04-01

This study focuses on the validation of the high resolution Western Mediterranean Operational model (WMOP) developed at SOCIB, the Balearic Islands Coastal Observing and Forecasting System. The Mediterranean Sea is often seen as a small scale ocean laboratory where energetic eddies, fronts and circulation features have important ecological consequences. The Medclic project is a program between "La Caixa" Foundation and SOCIB which aims at characterizing and forecasting the "oceanic weather" in the Western Mediterranean Sea, specifically investigating the interactions between the general circulation and mesoscale processes. We use a WMOP 2009-2015 free run hindcast simulation and available observational datasets (altimetry, moorings and gliders) to both assess the numerical simulation and investigate the ocean variability. WMOP has a 2-km spatial resolution and uses CMEMS Mediterranean products as initial and boundary conditions, with surface forcing from the high-resolution Spanish Meteorological Agency model HIRLAM. Different aspects of the spatial and temporal variability in the model are validated from local to regional and basin scales: (1) the principal axis of variability of the surface circulation using altimetry and moorings along the Iberian coast, (2) the inter-annual changes of the surface flows incorporating also glider data, (3) the propagation of mesoscale eddies formed in the Algerian sub-basin using altimetry, and (4) the statistical properties of eddies (number, rotation, size) applying an eddy tracker detection method in the Western Mediterranean Sea. With these key points evaluated in the model, EOF analysis of sea surface height maps are used to investigate spatial patterns of variability associated with eddies, gyres and the basis-scale circulation and so gain insight into the interconnections between sub-basins, as well as the interactions between physical processes at different scales.

Simulation of mesoscale circulation in the Tatar Strait of the Japan Sea

NASA Astrophysics Data System (ADS)

Ponomarev, V. I.; Fayman, P. A.; Prants, S. V.; Budyansky, M. V.; Uleysky, M. Yu.

2018-06-01

The eddy-resolved ocean circulation model RIAMOM (Lee et al., 2003) is used to analyze seasonal variability of mesoscale circulation in the Tatar Strait of the Japan Sea. The model domain is a vast area including the northern Japan Sea, Okhotsk Sea and adjacent region in the Pacific Ocean. A numerical experiment with a horizontal 1/18° resolution has been carried out under realistic meteorological conditions from the ECMWF ERA-40 reanalysis with restoring of surface temperature and salinity. The simulated seasonal variability of both the current system and mesoscale eddy dynamics in the Tatar Strait is in a good agreement with temperature and salinity distributions of oceanographic observation data collected during various seasons and years. Two general circulation regimes in the Strait have been found. The circulation regime changes from summer to winter due to seasonal change of the North Asian Monsoon. On a synoptic time scale, the similar change of the circulation regime occurs due to change of the southeastern wind to the northwestern one when the meteorological situation with an anticyclone over the Okhotsk Sea changes to that with a strong cyclone. The Lagrangian maps illustrate seasonal changes in direction of the main currents and in polarity and location of mesoscale eddies in the Strait.

Characterizing Middle Atmospheric Dynamical Variability and its Impact on the Thermosphere/Ionosphere System During Recent Stratospheric Sudden Warmings

NASA Astrophysics Data System (ADS)

McCormack, J. P.; Sassi, F.; Hoppel, K.; Ma, J.; Eckermann, S. D.

2015-12-01

We investigate the evolution of neutral atmospheric dynamics in the 10-100 km altitude range before, during, and after recent stratospheric sudden warmings (SSWs) using a prototype high-altitude version of the Navy Global Environmental Model (NAVGEM), which combines a 4-dimensional variational (4DVAR) data assimilation system with a 3-time-level semi-Lagrangian semi-implicit global forecast model. In addition to assimilating conventional meteorological observations, NAVGEM also assimilates middle atmospheric temperature and constituent observations from both operational and research satellite platforms to provide global synoptic meteorological analyses of winds, temperatures, ozone, and water vapor from the surface to ~90 km. In this study, NAVGEM analyses are used to diagnose the spatial and temporal evolution of the main dynamical drivers in the mesosphere and lower thermosphere (MLT) before, during, and after specific SSW events during the 2009-2013 period when large disturbances were observed in the thermosphere/ionosphere (TI) region. Preliminary findings show strong modulation of the semidiurnal tide in the MLT during the onset of an SSW. To assess the impact of the neutral atmosphere dynamical variability on the TI system, NAVGEM analyses are used to constrain simulations of select SSW events using the specified dynamics (SD) configuration of the extended Whole Atmosphere Community Climate Model (WACCM-X).

Meteorological factors for PM10 concentration levels in Northern Spain

NASA Astrophysics Data System (ADS)

Santurtún, Ana; Mínguez, Roberto; Villar-Fernández, Alejandro; González Hidalgo, Juan Carlos; Zarrabeitia, María Teresa

2013-04-01

Atmospheric particulate matter (PM) is made up of a mixture of solid and aqueous species which enter the atmosphere by anthropogenic and natural pathways. The levels and composition of ambient air PM depend on the climatology and on the geography (topography, soil cover, proximity to arid zones or to the coast) of a given region. Spain has particular difficulties in achieving compliance with the limit values established by the European Union (based on recommendations from the World Health Organization) for particulate matter on the order of 10 micrometers of diameter or less (PM10), but not only antropogenical emissions are responsible for this: some studies show that PM10 concentrations originating from these kinds of sources are similar to what is found in other European countries, while some of the geographical features of the Iberian Peninsula (such as African mineral dust intrusion, soil aridity or rainfall) are proven to be a factor for higher PM concentrations. This work aims to describe PM10 concentration levels in Cantabria (Northern Spain) and their relationship with the following meteorological variables: rainfall, solar radiation, temperature, barometric pressure and wind speed. Data consists of daily series obtained from hourly data records for the 2000-2010 period, of PM10 concentrations from 4 different urban-background stations, and daily series of the meteorological variables provided by Spanish National Meteorology Agency. The method used for establishing the relationships between these variables consists of several steps: i) fitting a non-stationary probability density function for each variable accounting for long-term trends, seasonality during the year and possible seasonality during the week to distinguish between work and weekend days, ii) using the marginal distribution function obtained, transform the time series of historical values of each variable into a normalized Gaussian time series. This step allows using consistently time series models, iii) fitting of a times series model (Autoregressive moving average, ARMA) to the transformed historical values in order to eliminate the temporal autocorrelation structure of each stochastic process, obtaining a white noise for each variable, and finally, iv) the calculation of cross correlations between white noises at different time lags. These cross correlations allow characterization of the true correlation between signals, avoiding the problems induced by data scaling or autocorrelations inherent to each signal. Results provide the relationship and possible contribution to PM10 concentration levels associated with each meteorological variable. This information can be used to improve PM10 concentration levels forecasting using existing meteorological forecasts.

Meteorological variables affect fertility rate after intrauterine artificial insemination in sheep in a seasonal-dependent manner: a 7-year study.

PubMed

Palacios, C; Abecia, J A

2015-05-01

A total number of 48,088 artificial inseminations (AIs) have been controlled during seven consecutive years in 79 dairy sheep Spanish farms (41° N). Mean, maximum and minimum ambient temperatures (Ts), temperature amplitude (TA), mean relative humidity (RH), mean solar radiation (SR) and total rainfall of each insemination day and 15 days later were recorded. Temperature-humidity index (THI) and effective temperature (ET) have been calculated. A binary logistic regression model to estimate the risk of not getting pregnant compared to getting pregnant, through the odds ratio (OR), was performed. Successful winter inseminations were carried out under higher SR (P < 0.01) and summer inseminations under lower SR values (P < 0.05). Successful inseminations during the summer were performed under significantly lower maximum T (P < 0.01), while winter inseminations resulted in pregnancy when they were carried out under higher maximum (P < 0.05) and minimum Ts (P < 0.01). Up to five meteorological variables presented OR >1 (maximum T, ET and rainfall on AI day, and ET and rainfall on day 15), and two variables presented OR <1 (SR on AI day and maximum T on day 15). However, the effect of meteorological factors affected fertility in opposite ways, so T becomes a protective or risk factor on fertility depending on season. In conclusion, the percentage of pregnancy after AI in sheep is significantly affected by meteorological variables in a seasonal-dependent manner, so the parameters such as temperature reverse their effects in the hot or cold seasons. A forecast of the meteorological conditions could be a useful tool when AI dates are being scheduled.

Meteorological variables affect fertility rate after intrauterine artificial insemination in sheep in a seasonal-dependent manner: a 7-year study

NASA Astrophysics Data System (ADS)

Palacios, C.; Abecia, J. A.

2015-05-01

A total number of 48,088 artificial inseminations (AIs) have been controlled during seven consecutive years in 79 dairy sheep Spanish farms (41° N). Mean, maximum and minimum ambient temperatures ( Ts), temperature amplitude (TA), mean relative humidity (RH), mean solar radiation (SR) and total rainfall of each insemination day and 15 days later were recorded. Temperature-humidity index (THI) and effective temperature (ET) have been calculated. A binary logistic regression model to estimate the risk of not getting pregnant compared to getting pregnant, through the odds ratio (OR), was performed. Successful winter inseminations were carried out under higher SR ( P < 0.01) and summer inseminations under lower SR values ( P < 0.05). Successful inseminations during the summer were performed under significantly lower maximum T ( P < 0.01), while winter inseminations resulted in pregnancy when they were carried out under higher maximum ( P < 0.05) and minimum Ts ( P < 0.01). Up to five meteorological variables presented OR >1 (maximum T, ET and rainfall on AI day, and ET and rainfall on day 15), and two variables presented OR <1 (SR on AI day and maximum T on day 15). However, the effect of meteorological factors affected fertility in opposite ways, so T becomes a protective or risk factor on fertility depending on season. In conclusion, the percentage of pregnancy after AI in sheep is significantly affected by meteorological variables in a seasonal-dependent manner, so the parameters such as temperature reverse their effects in the hot or cold seasons. A forecast of the meteorological conditions could be a useful tool when AI dates are being scheduled.

The influence of intra- and inter-annual meteorological variability on dengue transmission: a multi-level modeling analysis

NASA Astrophysics Data System (ADS)

Wen, Tzai-Hung; Chen, Tzu-Hsin

2017-04-01

Dengue fever is one of potentially life-threatening mosquito-borne diseases and IPCC Fifth Assessment Report (AR5) has confirmed that dengue incidence is sensitive to the critical weather conditions, such as effects of temperature. However, previous literature focused on the effects of monthly or weekly average temperature or accumulative precipitation on dengue incidence. The influence of intra- and inter-annual meteorological variability on dengue outbreak is under investigated. The purpose of the study focuses on measuring the effect of the intra- and inter-annual variations of temperature and precipitation on dengue outbreaks. We developed the indices of intra-annual temperature variability are maximum continuity, intermittent, and accumulation of most suitable temperature (MST) for dengue vectors; and also the indices of intra-annual precipitation variability, including the measure of continuity of wetness or dryness during a pre-epidemic period; and rainfall intensity during an epidemic period. We used multi-level modeling to investigate the intra- and inter-annual meteorological variations on dengue outbreaks in southern Taiwan from 1998-2015. Our results indicate that accumulation and maximum continuity of MST are more significant than average temperature on dengue outbreaks. The effect of continuity of wetness during the pre-epidemic period is significantly more positive on promoting dengue outbreaks than the rainfall effect during the epidemic period. Meanwhile, extremely high or low rainfall density during an epidemic period do not promote the spread of dengue epidemics. Our study differentiates the effects of intra- and inter-annual meteorological variations on dengue outbreaks and also provides policy implications for further dengue control under the threats of climate change. Keywords: dengue fever, meteorological variations, multi-level model

Effects of spatial and temporal resolution on simulated feedbacks from polygonal tundra.

NASA Astrophysics Data System (ADS)

Coon, E.; Atchley, A. L.; Painter, S. L.; Karra, S.; Moulton, J. D.; Wilson, C. J.; Liljedahl, A.

2014-12-01

Earth system land models typically resolve permafrost regions at spatial resolutions grossly larger than the scales of topographic variation. This observation leads to two critical questions: How much error is introduced by this lack of resolution, and what is the effect of this approximation on other coupled components of the Earth system, notably the energy balance and carbon cycle? Here we use the Arctic Terrestrial Simulator (ATS) to run micro-topography resolving simulations of polygonal ground, driven by meteorological data from Barrow, AK, to address these questions. ATS couples surface and subsurface processes, including thermal hydrology, surface energy balance, and a snow model. Comparisons are made between one-dimensional "column model" simulations (similar to, for instance, CLM or other land models typically used in Earth System models) and higher-dimensional simulations which resolve micro-topography, allowing for distributed surface runoff, horizontal flow in the subsurface, and uneven snow distribution. Additionally, we drive models with meteorological data averaged over different time scales from daily to weekly moving windows. In each case, we compare fluxes important to the surface energy balance including albedo, latent and sensible heat fluxes, and land-to-atmosphere long-wave radiation. Results indicate that spatial topography variation and temporal variability are important in several ways. Snow distribution greatly affects the surface energy balance, fundamentally changing the partitioning of incoming solar radiation between the subsurface and the atmosphere. This has significant effects on soil moisture and temperature, with implications for vegetation and decomposition. Resolving temporal variability is especially important in spring, when early warm days can alter the onset of snowmelt by days to weeks. We show that high-resolution simulations are valuable in evaluating current land models, especially in areas of polygonal ground. This work was supported by LANL Laboratory Directed Research and Development Project LDRD201200068DR and by the The Next-Generation Ecosystem Experiments (NGEE Arctic) project. NGEE-Arctic is supported by the Office of Biological and Environmental Research in the DOE Office of Science. LA-UR-14-26227.

EXAMINATION OF MODEL PREDICTIONS AT DIFFERENT HORIZONTAL GRID RESOLUTIONS

EPA Science Inventory

While fluctuations in meteorological and air quality variables occur on a continuum of spatial scales, the horizontal grid spacing of coupled meteorological and photochemical models sets a lower limit on the spatial scales that they can resolve. However, both computational costs ...

Complementary system for long term measurements of radon exhalation rate from soil

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

Mazur, J.; Kozak, K., E-mail: Krzysztof.Kozak@ifj.edu.pl

A special set-up for continuous measurements of radon exhalation rate from soil is presented. It was constructed at Laboratory of Radiometric Expertise, Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN), Krakow, Poland. Radon exhalation rate was determined using the AlphaGUARD PQ2000 PRO (Genitron) radon monitor together with a special accumulation container which was put on the soil surface during the measurement. A special automatic device was built and used to raise and lower back onto the ground the accumulation container. The time of raising and putting down the container was controlled by an electronic timer. This set-up mademore » it possible to perform 4–6 automatic measurements a day. Besides, some additional soil and meteorological parameters were continuously monitored. In this way, the diurnal and seasonal variability of radon exhalation rate from soil can be studied as well as its dependence on soil properties and meteorological conditions.« less

Airborne laser ranging system for monitoring regional crustal deformation

NASA Technical Reports Server (NTRS)

Degnan, J. J.

1981-01-01

Alternate approaches for making the atmospheric correction without benefit of a ground-based meteorological network are discussed. These include (1) a two-color channel that determines the atmospheric correction by measuring the time delay induced by dispersion between pulses at two optical frequencies; (2) single-color range measurements supported by an onboard temperature sounder, pressure altimeter readings, and surface measurements by a few existing meteorological facilities; and (3) inclusion of the quadratic polynomial coefficients as variables to be solved for along with target coordinates in the reduction of the single-color range data. It is anticipated that the initial Airborne Laser Ranging System (ALRS) experiments will be carried out in Southern California in a region bounded by Santa Barbara on the norht and the Mexican border on the south. The target area will be bounded by the Pacific Ocean to the west and will extend eastward for approximately 400 km. The unique ability of the ALRS to provide a geodetic 'snapshot' of such a large area will make it a valuable geophysical tool.

Soil Water Balance and Vegetation Dynamics in two Water-limited Mediterranean Ecosystem on Sardinia under past and future climate change

NASA Astrophysics Data System (ADS)

Corona, R.; Montaldo, N.; Albertson, J. D.

2016-12-01

Water limited conditions strongly impacts soil and vegetation dynamics in Mediterranean regions, which are commonly heterogeneous ecosystems, characterized by inter-annual rainfall variability, topography variability and contrasting plant functional types (PFTs) competing for water use. Historical human influences (e.g., deforestation, urbanization) further altered these ecosystems. Sardinia island is a representative region of Mediterranean ecosystems. It is low urbanized except some plan areas close to the main cities where main agricultural activities are concentrated. Two contrasting case study sites are within the Flumendosa river basin (1700 km2). The first site is a typical grassland on an alluvial plan valley (soil depth > 2m) while the second is a patchy mixture of Mediterranean vegetation species (mainly wild olive trees and C3 herbaceous) that grow in a soil bounded from below by a rocky layer of basalt, partially fractured (soil depth 15 - 40 cm). In both sites land-surface fluxes and CO2 fluxes are estimated by the eddy correlation technique while soil moisture was continuously estimated with water content reflectometers, and periodically leaf area index (LAI) was estimated. The following objectives are addressed:1) pointing out the dynamics of land surface fluxes, soil moisture, CO2 and vegetation cover for two contrasting water-limited ecosystems; 2) assess the impact of the soil depth and type on the CO2 and water balance dynamics; 3) evaluate the impact of past and future climate change scenarios on the two contrasting ecosystems. For reaching the objectives an ecohydrologic model that couples a vegetation dynamic model (VDM), and a 3-component (bare soil, grass and woody vegetation) land surface model (LSM) has been used. Historical meteorological data are available from 1922 and hydro-meteorological scenarios are then generated using a weather generator. The VDM-LSM model predict soil water balance and vegetation dynamics for the generated hydrometeorological scenarios in the two contrasting ecosystems. Results demonstrate that vegetation dynamics are influenced by the inter-annual variability of atmospheric forcing, with vegetation density changing significantly according to seasonal rainfall amount. At the same time the vegetation dynamics affect the soil water balance.

Sensitivity of potential evapotranspiration and simulated flow to varying meteorological inputs, Salt Creek watershed, DuPage County, Illinois

USGS Publications Warehouse

Whitbeck, David E.

2006-01-01

The Lamoreux Potential Evapotranspiration (LXPET) Program computes potential evapotranspiration (PET) using inputs from four different meteorological sources: temperature, dewpoint, wind speed, and solar radiation. PET and the same four meteorological inputs are used with precipitation data in the Hydrological Simulation Program-Fortran (HSPF) to simulate streamflow in the Salt Creek watershed, DuPage County, Illinois. Streamflows from HSPF are routed with the Full Equations (FEQ) model to determine water-surface elevations. Consequently, variations in meteorological inputs have potential to propagate through many calculations. Sensitivity of PET to variation was simulated by increasing the meteorological input values by 20, 40, and 60 percent and evaluating the change in the calculated PET. Increases in temperatures produced the greatest percent changes, followed by increases in solar radiation, dewpoint, and then wind speed. Additional sensitivity of PET was considered for shifts in input temperatures and dewpoints by absolute differences of ?10, ?20, and ?30 degrees Fahrenheit (degF). Again, changes in input temperatures produced the greatest differences in PET. Sensitivity of streamflow simulated by HSPF was evaluated for 20-percent increases in meteorological inputs. These simulations showed that increases in temperature produced the greatest change in flow. Finally, peak water-surface elevations for nine storm events were compared among unmodified meteorological inputs and inputs with values predicted 6, 24, and 48 hours preceding the simulated peak. Results of this study can be applied to determine how errors specific to a hydrologic system will affect computations of system streamflow and water-surface elevations.

Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables

NASA Astrophysics Data System (ADS)

Mortensen, Eric; Wu, Shu; Notaro, Michael; Vavrus, Stephen; Montgomery, Rob; De Piérola, José; Sánchez, Carlos; Block, Paul

2018-01-01

Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

Surface wind accuracy for modeling mineral dust emissions: Comparing two regional models in a Bodélé case study

NASA Astrophysics Data System (ADS)

Laurent, B.; Heinold, B.; Tegen, I.; Bouet, C.; Cautenet, G.

2008-05-01

After a decade of research on improving the description of surface and soil features in desert regions to accurately model mineral dust emissions, we now emphasize the need for deeper evaluating the accuracy of modeled 10-m surface wind speeds U 10 . Two mesoscale models, the Lokal-Modell (LM) and the Regional Atmospheric Modeling System (RAMS), coupled with an explicit dust emission model have previously been used to simulate mineral dust events in the Bodélé region. We compare LM and RAMS U 10 , together with measurements at the Chicha site (BoDEx campaign) and Faya-Largeau meteorological station. Surface features and soil schemes are investigated to correctly simulate U 10 intensity and diurnal variability. The uncertainties in dust emissions computed with LM and RAMS U 10 and different soil databases are estimated. This sensitivity study shows the importance of accurate computation of surface winds to improve the quantification of regional dust emissions from the Bodélé

Spatiotemporal variability and assessment of drought in the Wei River basin of China

NASA Astrophysics Data System (ADS)

Cai, Siyang; Zuo, Depeng; Xu, Zongxue; Han, Xianming; Gao, Xiaoxi

2018-06-01

The temporal and spatial variations of drought in the Wei River basin (WRB) were investigated by calculating the meteorological drought Index (Standardized Precipitation Index, SPI) and the agricultural drought index (Vegetation Health Index, VHI). Monthly precipitation and air temperature were from 22 meteorological stations over the region from 1960 to 2015. Monthly Normalized Difference Vegetation Index (NDVI) and 8-days Land Surface Temperature (LST) were provided from the National Aeronautics and Space Administration (NASA) for the period 2000-2015 were also adopted. The results showed that the drought initially increased and then decreased, reaching at the maximum value in 1990s. The spatial pattern of meteorological drought showed that the drought in northern WRB was heavier than that in southern WRB before 1990s, after that, the situation had the opposite. By comparing the agricultural drought index (VHI) with crop yield, it was proved that VHI was applicable in the WRB and could well reflect the fluctuation of agricultural drought. The WRB suffered from serious agricultural drought in 2000, 2001, 2007 and 2008. Through analysis of the historical precipitation and temperature data, it was found that precipitation had a greater contribution to creating agricultural drought conditions than temperature in the Wei River basin.

Variability of O3 and NO2 profile shapes during DISCOVER-AQ: Implications for satellite observations and comparisons to model-simulated profiles

NASA Astrophysics Data System (ADS)

Flynn, Clare Marie; Pickering, Kenneth E.; Crawford, James H.; Weinheimer, Andrew J.; Diskin, Glenn; Thornhill, K. Lee; Loughner, Christopher; Lee, Pius; Strode, Sarah A.

2016-12-01

To investigate the variability of in situ profile shapes under a variety of meteorological and pollution conditions, results are presented of an agglomerative hierarchical cluster analysis of the in situ O3 and NO2 profiles for each of the four campaigns of the NASA DISCOVER-AQ mission. Understanding the observed profile variability for these trace gases is useful for understanding the accuracy of the assumed profile shapes used in satellite retrieval algorithms as well as for understanding the correlation between satellite column observations and surface concentrations. The four campaigns of the DISCOVER-AQ mission took place in Maryland during July 2011, the San Joaquin Valley of California during January-February 2013, the Houston, Texas, metropolitan region during September 2013, and the Denver-Front Range region of Colorado during July-August 2014. Several distinct profile clusters emerged for the California, Texas, and Colorado campaigns for O3, indicating significant variability of O3 profile shapes, while the Maryland campaign presented only one distinct O3 cluster. In contrast, very few distinct profile clusters emerged for NO2 during any campaign for this particular clustering technique, indicating the NO2 profile behavior was relatively uniform throughout each campaign. However, changes in NO2 profile shape were evident as the boundary layer evolved through the day, but they were apparently not significant enough to yield more clusters. The degree of vertical mixing (as indicated by temperature lapse rate) associated with each cluster exerted an important influence on the shapes of the median cluster profiles for O3, as well as impacted the correlations between the associated column and surface data for each cluster for O3. The correlation analyses suggest satellites may have the best chance to relate to surface O3 under the conditions encountered during the Maryland campaign Clusters 1 and 2, which include deep, convective boundary layers and few interruptions to this connection from complex meteorology, chemical environments, or orography. The regional CMAQ model captured the shape factors for O3, and moderately well captured the NO2 shape factors, for the conditions associated with the Maryland campaign, suggesting that a regional air quality model may adequately specify a priori profile shapes for remote sensing retrievals. CMAQ shape factor profiles were not as well represented for the other regions.

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.

Characterizing Temperature Variability and Associated Large Scale Meteorological Patterns Across South America

NASA Astrophysics Data System (ADS)

Detzer, J.; Loikith, P. C.; Mechoso, C. R.; Barkhordarian, A.; Lee, H.

2017-12-01

South America's climate varies considerably owing to its large geographic range and diverse topographical features. Spanning the tropics to the mid-latitudes and from high peaks to tropical rainforest, the continent experiences an array of climate and weather patterns. Due to this considerable spatial extent, assessing temperature variability at the continent scale is particularly challenging. It is well documented in the literature that temperatures have been increasing across portions of South America in recent decades, and while there have been many studies that have focused on precipitation variability and change, temperature has received less scientific attention. Therefore, a more thorough understanding of the drivers of temperature variability is critical for interpreting future change. First, k-means cluster analysis is used to identify four primary modes of temperature variability across the continent, stratified by season. Next, composites of large scale meteorological patterns (LSMPs) are calculated for months assigned to each cluster. Initial results suggest that LSMPs, defined using meteorological variables such as sea level pressure (SLP), geopotential height, and wind, are able to identify synoptic scale mechanisms important for driving temperature variability at the monthly scale. Some LSMPs indicate a relationship with known recurrent modes of climate variability. For example, composites of geopotential height suggest that the Southern Annular Mode is an important, but not necessarily dominant, component of temperature variability over southern South America. This work will be extended to assess the drivers of temperature extremes across South America.

Water, ice, and meteorological measurements at South Cascade Glacier, Washington, 1986-1991 balance years

USGS Publications Warehouse

Krimmel, Robert M.

2000-01-01

Mass balance and climate variables are reported for South Cascade Glacier, Washington, for the years 1986-91. These variables include air temperature, precipitation, water runoff, snow accumulation, snow and ice melt terminus position, surface level, and ice speed. Data are reduced to daily and monthly values where appropriate. The glacier-averaged values of spring snow accumulation and fall net balance given in this report differ from previous results because amore complete analysis is made. Snow accumulation values for the1986-91 period ranged from 3.54 (water equivalent) meters in 1991 to2.04 meters in 1987. Net balance values ranged from 0.07 meters in1991 to -2.06 meters in 1987. The glacier became much smaller during the 1986-91 period and retreated a cumulative 50 meters.

Exploring the relationship between meteorology and surface PM2.5 in Northern India

NASA Astrophysics Data System (ADS)

Schnell, J.; Naik, V.; Horowitz, L. W.; Paulot, F.; Ginoux, P. A.

2017-12-01

Northern India is one of the most polluted and densely populated regions in world. Accurately modeling pollution in the region is difficult due to the extreme conditions with respect to emissions, meteorology, and topography, but it is paramount in order to understand how future changes in emissions and climate may alter the region's pollution regime. We evaluate a developmental version of the new-generation NOAA GFDL Atmospheric Model, version 4 (AM4) in its ability to simulate observed wintertime PM2.5 and its relationship to meteorology over the Northern India (23°N-31°N, 68°E-90°E). We perform two simulations of the GFDL-AM4 nudged to observed meteorology for the period (1980-2016) with two emission inventories developed for CMIP5 and CMIP6 and compare results with observations from India's Central Pollution Control Board (CPCB) for the period 1 October 2015 - 31 March 2016. Overall, our results indicate that the simulation with CMIP6 emissions has substantially reduced the low model bias in the region. The AM4, albeit biased low, generally simulates the magnitude and daily variability in observed total PM2.5. Ammonium nitrate and ammonium sulfate are the primary components of PM2.5 in the model, and although not directly observed, correlations of total observed PM2.5 and meteorology with the modeled individual PM2.5 components suggest the same for the observations. The model correctly reproduces the shape and magnitude of the seasonal cycle of PM2.5; but for the diurnal cycle, it misses the early evening rise and secondary maximum found in the observations. Observed PM2.5 abundances within the densely populated Indo-Gangetic Plain are by far the highest and are closely related to boundary layer meteorology, specifically relative humidity, wind speed, boundary layer height, and inversion strength. The GFDL-AM4 reproduces the observed pollution gradient over Northern India as well as the strength of the meteorology-PM2.5 relationship in most locations.

The Sap Flow Dynamics and Response of Hedysarum scoparium to Environmental Factors in Semiarid Northwestern China.

PubMed

Deng, Jifeng; Ding, Guodong; Gao, Guanglei; Wu, Bin; Zhang, Yuqing; Qin, Shugao; Fan, Wenhui

2015-01-01

Hedysarum scoparium is an important, fast-growing and drought-resistant shrub that has been extensively used for grassland restoration and preventing desertification in semiarid regions of northwestern China. The primary objective of this study was to investigate the diurnal and seasonal variations in stem sap flow (Js) and its relation to environmental factors. The stem heat balance method was applied to plants that were approximately 17 years old (with diameters of 25, 16, 13, and 9 mm at ground level and heights of 3.1, 1.8, 1.7 and 1.4 m) and growing under natural conditions. The vertical soil temperature profile (ST), soil surface heat flux (SoilG), volumetric soil moisture content (SWC) and meteorological variables such as solar radiation (Rn), air temperature (Ta), vapour pressure deficit (VPD), wind speed (Ws) relative humidity (RH) and precipitation (P) were simultaneously measured at a meteorological station on site. Results indicated that Js varied regularly during the diurnal and seasonal term. The nocturnal Js was substantial, with a seasonal variation similar to the patterns of daytime Js. The magnitude of Js changed considerably between sunny and rainy days. Redundancy (RDA) and Kendall's tau analysis suggested that daily Js in large plants was more sensitive to environmental factors, and the variation in daily Js during the growing season could be described by a multiple linear regression against environmental variables including Ta, VPD, Ws, RH, ST, and SoilG. While the nocturnal Js in smaller plants was more sensitive to meteorological factors. Ta, VPD, and Ws were significantly correlated with nighttime Js. The hourly nighttime sap flow rate of H. scoparium corresponded closely to Ta and VPD following a non-linear pattern. The results of this study can be used to estimate the transpiration of H. scoparium.

Upper Blue Nile basin water budget from a multi-model perspective

NASA Astrophysics Data System (ADS)

Jung, Hahn Chul; Getirana, Augusto; Policelli, Frederick; McNally, Amy; Arsenault, Kristi R.; Kumar, Sujay; Tadesse, Tsegaye; Peters-Lidard, Christa D.

2017-12-01

Improved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, in-situ streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

Are atmospheric surface layer flows ergodic?

NASA Astrophysics Data System (ADS)

Higgins, Chad W.; Katul, Gabriel G.; Froidevaux, Martin; Simeonov, Valentin; Parlange, Marc B.

2013-06-01

The transposition of atmospheric turbulence statistics from the time domain, as conventionally sampled in field experiments, is explained by the so-called ergodic hypothesis. In micrometeorology, this hypothesis assumes that the time average of a measured flow variable represents an ensemble of independent realizations from similar meteorological states and boundary conditions. That is, the averaging duration must be sufficiently long to include a large number of independent realizations of the sampled flow variable so as to represent the ensemble. While the validity of the ergodic hypothesis for turbulence has been confirmed in laboratory experiments, and numerical simulations for idealized conditions, evidence for its validity in the atmospheric surface layer (ASL), especially for nonideal conditions, continues to defy experimental efforts. There is some urgency to make progress on this problem given the proliferation of tall tower scalar concentration networks aimed at constraining climate models yet are impacted by nonideal conditions at the land surface. Recent advancements in water vapor concentration lidar measurements that simultaneously sample spatial and temporal series in the ASL are used to investigate the validity of the ergodic hypothesis for the first time. It is shown that ergodicity is valid in a strict sense above uniform surfaces away from abrupt surface transitions. Surprisingly, ergodicity may be used to infer the ensemble concentration statistics of a composite grass-lake system using only water vapor concentration measurements collected above the sharp transition delineating the lake from the grass surface.

NASA Cold Land Processes Experiment (CLPX 2002/03): Ground-based and near-surface meteorological observations

Treesearch

Kelly Elder; Don Cline; Angus Goodbody; Paul Houser; Glen E. Liston; Larry Mahrt; Nick Rutter

2009-01-01

A short-term meteorological database has been developed for the Cold Land Processes Experiment (CLPX). This database includes meteorological observations from stations designed and deployed exclusively for CLPXas well as observations available from other sources located in the small regional study area (SRSA) in north-central Colorado. The measured weather parameters...

Meteorological Observations Available for the State of Utah

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

Wharton, S.

The National Weather Service’s Meteorological Assimilation Data Ingest System (MADIS) contains a large number of station networks of surface and upper air meteorological observations for the state of Utah. In addition to MADIS, observations from individual station networks may also be available. It has been confirmed that LLNL has access to the data sources listed below.

Meteorological analysis of symptom data for people with seasonal affective disorder.

PubMed

Sarran, Christophe; Albers, Casper; Sachon, Patrick; Meesters, Ybe

2017-11-01

It is thought that variation in natural light levels affect people with Seasonal Affective Disorder (SAD). Several meteorological factors related to luminance can be forecast but little is known about which factors are most indicative of worsening SAD symptoms. The aim of this meteorological analysis is to determine which factors are linked to SAD symptoms. The symptoms of 291 individuals with SAD in and near Groningen have been evaluated over the period 2003-2009. Meteorological factors linked to periods of low natural light (sunshine, global radiation, horizontal visibility, cloud cover and mist) and others (temperature, humidity and pressure) were obtained from weather observation stations. A Bayesian zero adjusted auto-correlated multilevel Poisson model was carried out to assess which variables influence the SAD symptom score BDI-II. The outcome of the study suggests that the variable sunshine duration, for both the current and previous week, and global radiation for the previous week, are significantly linked to SAD symptoms. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Assessing relationships between cattle egret migration and meteorology in the southwest Pacific: a review

NASA Astrophysics Data System (ADS)

Bridgman, H. A.; Maddock, M.; Geering, D. J.

The evolution of research into meteorological factors affecting the migration of the Cattle Egret (Ardeola ibis coromandus) in the southwestern Pacific region (Australia, New Zealand and the Tasman Sea) - from ground-based studies dependent on volunteer observers to a pilot satellite-tracking project - is reviewed and the results are related to the literature on bird migration. The predominant pattern is a seasonal migration from breeding colonies in southeast Queensland and northern New South Wales which takes place in stages along the east coastal plain under favourable meteorological conditions. Migration outward (southward) occurs in February through April and return to the breeding colonies occurs in October and November. Wintering destinations include Tasmania, southern Victoria and parts of New Zealand. Favourable meteorological conditions for migration southward include:moderate north to northwest airflow behind a high; light and variable winds in a high or col; and light and variable winds over New South Wales with moderate westerlies over Victoria and Tasmania. A satellite-tracking project helped to validate findings from the ground-based studies, provided additional information not otherwise obtainable, and demonstrated the potential of the technique to further clarify the relation between timing and staging of migration, and meteorology.

The relationship between Arabian Sea upwelling and Indian Monsoon revisited in a high resolution ocean simulation

NASA Astrophysics Data System (ADS)

Yi, Xing; Hünicke, Birgit; Tim, Nele; Zorita, Eduardo

2018-01-01

Studies based on sediment records, sea-surface temperature and wind suggest that upwelling along the western coast of Arabian Sea is strongly affected by the Indian summer Monsoon. We examine this relationship directly in an eddy-resolving global ocean simulation STORM driven by atmospheric reanalysis over the last 61 years. With its very high spatial resolution (10 km), STORM allows us to identify characteristics of the upwelling system. We analyse the co-variability between upwelling and meteorological and oceanic variables from 1950 to 2010. The analysis reveals high interannual correlations between coastal upwelling and along-shore wind-stress (r = 0.73) as well as with sea-surface temperature (r = -0.83). However, the correlation between the upwelling and the Monsoon is small. We find an atmospheric circulation pattern different from the one that drives the Monsoon as the main modulator of the upwelling variability. In spite of this, the patterns of temperature anomalies that are either linked to Arabian Sea upwelling or to the Monsoon are spatially quite similar, although the physical mechanisms of these links are different. In addition, no long-term trend is detected in our modelled upwelling in the Arabian Sea.

Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis.

PubMed

Aneja, Viney P; Pillai, Priya R; Isherwood, Aaron; Morgan, Peter; Aneja, Saurabh P

2017-04-01

This study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM 10 ), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM 2.5 ) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM 2.5 (r 2 = 0.62), and the two-variable (AOD-PM 2.5 ) model predicted PM 2.5 (r 2 = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM 2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM 2.5 . For the relevant period in 2008, in Roda, VA, the predicted PM 2.5 mass concentration is 9.11 ± 5.16 μg m -3 (mean ± 1SD). This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or "hollows," where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.

OMI NO2 in the Central US Great Plains: How Well Do We Interpret NO2 Trends?

NASA Astrophysics Data System (ADS)

Kollonige, D. E.; Duncan, B. N.; Thompson, A. M.; Lamsal, L. N.

2017-12-01

Several areas over the Central US show statistically significant increases in OMI NO2 levels of 10-30% in the last 10 years versus the generally decreasing trends over most of CONUS. Are these changes in OMI NO2 a result of human activity, meteorology, or a combination of both? To answer this, we examine regions in the Central US Great Plains that have multiple plausible sources for the observed trends, considering impacts of land surface changes, agriculture growth, oil and gas operations, and drought conditions. We find that changes to the land surface appear to contribute to some of the observed anomalies due to tree removal in the Black Hills National Forest, South Dakota, and additional livestock farming in the Sandhills of Nebraska. However, increasing OMI NO2 also corresponds to several areas with growing agriculture business (ex. South Dakota and Nebraska) and oil and gas activity (ex. Williston Basin in North Dakota and Permian Basin in TX). To understand the relationship between the observed NO2 variability and the regional meteorological conditions over the last decade, we analyze the time series and correlations between OMI NO2, NH3 (an agriculture tracer), surface temperature, normalized difference vegetation index (NDVI) from Landsat, and the Palmer Drought Severity Index (PDSI). In 2012, drought conditions affect NO2, NH3 and NDVI observations across the Central US. Areas where dryland farming and livestock grazing are predominant (Central SD, ND, KS, and NE) are less sensitive to drought and changes in temperature. This suggests positive OMI NO2 trends are caused by increased production in wheats and livestock in the Northern Great Plains. These study regions in the Central US, impacted by local emissions and meteorology, are valuable for evaluating future trend analyses including the continuation of OMI-type NO2 retrievals from the TROPOMI and TEMPO satellite instruments.

Emission factors for fugitive dust from bulldozers working on a coal pile.

PubMed

Mueller, Stephen F; Mallard, Jonathan W; Mao, Qi; Shaw, Stephanie L

2015-01-01

A study of a Powder River Basin (PRB) coal pile found that fugitive emissions from natural and human activity each produced similar levels of downwind fine + coarse (i.e., smaller than 10 µm, or PM10) particle mass concentrations. Natural impacts were statistically removed from downwind measurements to estimate emission factor Ev for bulldozers working on the pile. The Ev determined here was similar in magnitude to emission factors (EFs) computed using a U.S. Environmental Protection Agency (EPA) formulation for unpaved surfaces at industrial sites, even though the latter was not based on data for coal piles. EF formulations from this study and those in the EPA guidance yield values of similar magnitude but differ in the variables used to compute Ev variations. EPA studies included effects of surface silt fraction and vehicle weight, while the present study captured the influence of coal moisture. Our data indicate that the relationship between PRB coal fugitive dust Ev (expressed as mass of PM10 emitted per minute of bulldozer operation) and coal moisture content Mc (in percent) at the study site is best expressed as Ev =10(f(Mc())) where f(Mc) is a function of moisture. This function was determined by statistical regression between log10(Ev) and Mc where both Ev and Mc are expressed as daily averages of observations based on 289 hours sampled during 44 days from late June through mid-November of 2012. A methodology is described that estimates Mc based on available meteorological data (precipitation amount and solar radiation flux). An example is given of computed variations in daily Ev for an entire year. This illustrates the sensitivity of the daily average particulate EF to meteorological variability at one location. Finally, a method is suggested for combining the moisture-sensitive formulation for Ev with the EPA formulation to accommodate a larger number of independent variables that influence fugitive emissions.

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.

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.

A simple lightning assimilation technique for improving ...

EPA Pesticide Factsheets

Convective rainfall is often a large source of error in retrospective modeling applications. In particular, positive rainfall biases commonly exist during summer months due to overactive convective parameterizations. In this study, lightning assimilation was applied in the Kain-Fritsch (KF) convective scheme to improve retrospective simulations using the Weather Research and Forecasting (WRF) model. The assimilation method has a straightforward approach: force KF deep convection where lightning is observed and, optionally, suppress deep convection where lightning is absent. WRF simulations were made with and without lightning assimilation over the continental United States for July 2012, July 2013, and January 2013. The simulations were evaluated against NCEP stage-IV precipitation data and MADIS near-surface meteorological observations. In general, the use of lightning assimilation considerably improves the simulation of summertime rainfall. For example, the July 2012 monthly averaged bias of 6 h accumulated rainfall is reduced from 0.54 to 0.07 mm and the spatial correlation is increased from 0.21 to 0.43 when lightning assimilation is used. Statistical measures of near-surface meteorological variables also are improved. Consistent improvements also are seen for the July 2013 case. These results suggest that this lightning assimilation technique has the potential to substantially improve simulation of warm-season rainfall in retrospective WRF applications. The

A Simple Lightning Assimilation Technique For Improving ...

EPA Pesticide Factsheets

Convective rainfall is often a large source of error in retrospective modeling applications. In particular, positive rainfall biases commonly exist during summer months due to overactive convective parameterizations. In this study, lightning assimilation was applied in the Kain-Fritsch (KF) convective scheme to improve retrospective simulations using the Weather Research and Forecasting (WRF) model. The assimilation method has a straightforward approach: Force KF deep convection where lightning is observed and, optionally, suppress deep convection where lightning is absent. WRF simulations were made with and without lightning assimilation over the continental United States for July 2012, July 2013, and January 2013. The simulations were evaluated against NCEP stage-IV precipitation data and MADIS near-surface meteorological observations. In general, the use of lightning assimilation considerably improves the simulation of summertime rainfall. For example, the July 2012 monthly-averaged bias of 6-h accumulated rainfall is reduced from 0.54 mm to 0.07 mm and the spatial correlation is increased from 0.21 to 0.43 when lightning assimilation is used. Statistical measures of near-surface meteorological variables also are improved. Consistent improvements also are seen for the July 2013 case. These results suggest that this lightning assimilation technique has the potential to substantially improve simulation of warm-season rainfall in retrospective WRF appli

An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

NASA Astrophysics Data System (ADS)

Mailhot, J.; Milbrandt, J. A.; Giguère, A.; McTaggart-Cowan, R.; Erfani, A.; Denis, B.; Glazer, A.; Vallée, M.

2014-01-01

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM-LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.

BOREAS TF-10 NSA-YJP Tower Flux, Meteorological, and Porometry Data

NASA Technical Reports Server (NTRS)

McCaughey, J. Harry; Liblik, Laura; Hall, Forrest G. (Editor); Huemmrich, K. (Editor)

2000-01-01

The BOREAS TF-10 team collected tower flux and meteorological data at two sites, a fen and a young jack pine forest, near Thompson, Manitoba, Canada, as part of BOREAS. A preliminary data set was assembled in August 1993 while field testing the instrument packages, and at both sites data were collected from 15-Aug to 31-Aug. The main experimental period was in 1994, when continuous data were collected from the young jack pine site from 23-May to 20-Sep. Upon examination of the 1994 data set, it became clear that the behavior of the heat, water, and carbon dioxide fluxes throughout the whole growing season was an important scientific question, and that the 1994 data record was not sufficiently long to capture the character of the seasonal behavior of the fluxes. Thus, the young jack pine site was operated from 08-May to 07-Nov in 1996 in order to collect data from spring melt to autumn freeze-up. All variables are presented as 30-minute averages. Supporting data were also collected to describe the surface#s state and to provide the information, in association with the flux data, to build SVAT models. For the young jack pine site, these supporting data included stomatal conductance measurements. The data are stored in tabular ASCII files.

Extended T-index models for glacier surface melting: a case study from Chorabari Glacier, Central Himalaya, India

NASA Astrophysics Data System (ADS)

Karakoti, Indira; Kesarwani, Kapil; Mehta, Manish; Dobhal, D. P.

2016-10-01

Two enhanced temperature-index (T-index) models are proposed by incorporating meteorological parameters viz. relative humidity, wind speed and net radiation. The models are an attempt to explore different climatic variables other than temperature affecting glacier surface melting. Weather data were recorded at Chorabari Glacier using an automatic weather station during the summers of 2010 (July 10 to September 10) and 2012 (June 10 to October 25). The modelled surface melt is validated against the measured point surface melting at the snout. Performance of the developed models is evaluated by comparing with basic temperature-index model and is quantified through different efficiency criteria. The results suggest that proposed models yield considerable improvement in surface melt simulation . Consequently, the study reveals that glacier surface melt depends not only on temperature but also on weather parameters viz. relative humidity, wind speed and net radiation play a significant role in glacier surface melting. This approach provides a major improvement on basic temperature-index method and offers an alternative to energy balance model.

Surface microlayer enrichment of polycyclic aromatic hydrocarbons in lower Chesapeake Bay

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

Liu, K.; Dickhut, R.M.

1995-12-31

Surface microlayer samples were collected with a rotating cylinder sampler in the York River and Elizabeth River tributaries of lower Chesapeake Bay every other month from May 1994 to June, 1995. Spatial and temporal variabilities were also investigated over an annual cycle as well as shorter periods (i.e. days). All the samples were analyzed for 17 polycyclic aromatic hydrocarbons, total suspended particulate matter (TSP), particular organic carbon (POC), total nitrogen(TN) and dissolved organic carbon (DOC), and selected samples for chlorophyll. TSP in the surface microlayer was 10 to 100 times higher than that in the related bulk water. Particle associatedmore » PAH concentrations were 20--50 times those in bulk surface water, whereas PAH concentrations in freely dissolved phase of the surface microlayer were 5--60 times higher than dissolved concentrations in the bulk water. Particulate PAH concentrations increase with TSP in the surface microlayer and dissolved PAH concentrations increase with DOC. Overall, surface microlayer characteristics were found to be significantly affected by hydrological and meteorological parameters.« less

Quantifying PM2.5-Meteorology Sensitivities in a Global Climate Model

NASA Technical Reports Server (NTRS)

Westervelt, D. M.; Horowitz, L. W.; Naik, V.; Tai, A. P. K.; Fiore, A. M.; Mauzerall, D. L.

2016-01-01

Climate change can influence fine particulate matter concentrations (PM2.5) through changes in air pollution meteorology. Knowledge of the extent to which climate change can exacerbate or alleviate air pollution in the future is needed for robust climate and air pollution policy decision-making. To examine the influence of climate on PM2.5, we use the Geophysical Fluid Dynamics Laboratory Coupled Model version 3 (GFDL CM3), a fully-coupled chemistry-climate model, combined with future emissions and concentrations provided by the four Representative Concentration Pathways (RCPs). For each of the RCPs, we conduct future simulations in which emissions of aerosols and their precursors are held at 2005 levels while other climate forcing agents evolve in time, such that only climate (and thus meteorology) can influence PM2.5 surface concentrations. We find a small increase in global, annual mean PM2.5 of about 0.21 micro-g/cu m3 (5%) for RCP8.5, a scenario with maximum warming. Changes in global mean PM2.5 are at a maximum in the fall and are mainly controlled by sulfate followed by organic aerosol with minimal influence of black carbon. RCP2.6 is the only scenario that projects a decrease in global PM2.5 with future climate changes, albeit only by -0.06 micro-g/cu m (1.5%) by the end of the 21st century. Regional and local changes in PM2.5 are larger, reaching upwards of 2 micro-g/cu m for polluted (eastern China) and dusty (western Africa) locations on an annually averaged basis in RCP8.5. Using multiple linear regression, we find that future PM2.5 concentrations are most sensitive to local temperature, followed by surface wind and precipitation. PM2.5 concentrations are robustly positively associated with temperature, while negatively related with precipitation and wind speed. Present-day (2006-2015) modeled sensitivities of PM2.5 to meteorological variables are evaluated against observations and found to agree reasonably well with observed sensitivities (within 10e50% over the eastern United States for several variables), although the modeled PM2.5 is less sensitive to precipitation than in the observations due to weaker convective scavenging. We conclude that the hypothesized "climate penalty" of future increases in PM2.5 is relatively minor on a global scale compared to the influence of emissions on PM2.5 concentrations.

Forward Modeling of Atmospheric Carbon Dioxide in GEOS-5: Uncertainties Related to Surface Fluxes and Sub-Grid Transport

NASA Technical Reports Server (NTRS)

Pawson, Steven; Ott, Lesley E.; Zhu, Zhengxin; Bowman, Kevin; Brix, Holger; Collatz, G. James; Dutkiewicz, Stephanie; Fisher, Joshua B.; Gregg, Watson W.; Hill, Chris;

New directions: Time for a new approach to modeling surface-atmosphere exchanges in air quality models?

NASA Astrophysics Data System (ADS)

Saylor, Rick D.; Hicks, Bruce B.

2016-03-01

Just as the exchange of heat, moisture and momentum between the Earth's surface and the atmosphere are critical components of meteorological and climate models, the surface-atmosphere exchange of many trace gases and aerosol particles is a vitally important process in air quality (AQ) models. Current state-of-the-art AQ models treat the emission and deposition of most gases and particles as separate model parameterizations, even though evidence has accumulated over time that the emission and deposition processes of many constituents are often two sides of the same coin, with the upward (emission) or downward (deposition) flux over a landscape depending on a range of environmental, seasonal and biological variables. In this note we argue that the time has come to integrate the treatment of these processes in AQ models to provide biological, physical and chemical consistency and improved predictions of trace gases and particles.

The Hyperspectral Infrared Imager (HyspIRI) Public Health and Air Quality Applications

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Hook, Simon J.

2014-01-01

The neglected tropical diseases (NTDs), a group of chronic, debilitating, and poverty-promoting parasitic, bacterial, and some viral and fungal infections, are among the most common causes of illness of the poorest people living in developing countries. Abiotic environmental factors are important in determining the distribution of disease-causing vectors and their life-cycles. HyspIRI observations can be merged through a Land Data Assimilation System (LDAS) be used to drive spatially-explicit ecological models of NTD vectors distribution and life cycles. Assimilations will be driven by observational data LDAS and satellite-derived meteorological forcing data, parameter datasets, and assimilation observations. HyspIRI hyperspectral measurements would provide global measurements of surface mineralogy and biotic crusts important in accessing the impact of dust in human health. HyspIRI surface thermal measurements would also help identify the variability of dust sources due to surface moisture conditions and map mineralogy.

The Hyperspectral Infrared Imager (HyspIRI) Public Health and Air Quality Applications

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Hook, Simon J.

2013-01-01

The neglected tropical diseases (NTDs), a group of chronic, debilitating, and poverty-promoting parasitic, bacterial, and some viral and fungal infections, are among the most common causes of illness of the poorest people living in developing countries. Abiotic environmental factors are important in determining the distribution of disease-causing vectors and their life-cycles. HyspIRI observations can be merged through a Land Data Assimilation System (LDAS) be used to drive spatially-explicit ecological models of NTD vectors distribution & life cycles. Assimilations will be driven by observational data LDAS and satellite-derived meteorological forcing data, parameter datasets, and assimilation observations. HyspIRI hyperspectral measurements would provide global measurements of surface mineralogy and biotic crusts important in accessing the impact of dust in human health. HyspIRI surface thermal measurements would also help identify the variability of dust sources due to surface moisture conditions and map mineralogy.

The Hyperspectral Infrared Imager (HyspIRI) Public Health & Air Quality Applications

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Hook, Simon J.

2013-01-01

The neglected tropical diseases (NTDs), a group of chronic, debilitating, and poverty-promoting parasitic, bacterial, and some viral and fungal infections, are among the most common causes of illness of the poorest people living in developing countries. Abiotic environmental factors are important in determining the distribution of disease-causing vectors and their life-cycles. HyspIRI observations can be merged through a Land Data Assimilation System (LDAS) be used to drive spatially-explicit ecological models of NTD vectors distribution & life cycles. Assimilations will be driven by observational data LDAS and satellite-derived meteorological forcing data, parameter datasets, and assimilation observations. HyspIRI hyperspectral measurements would provide global measurements of surface mineralogy and biotic crusts important in accessing the impact of dust in human health. HyspIRI surface thermal measurements would also help identify the variability of dust sources due to surface moisture conditions and map mineralogy.

USING METEOROLOGICAL MODEL OUTPUT AS A SURROGATE FOR ON-SITE OBSERVATIONS TO PREDICT DEPOSITION VELOCITY

EPA Science Inventory

The National Oceanic and Atmospheric Administration's Multi-Layer Model (NOAA-MLM) is used by several operational dry deposition networks for estimating the deposition velocity of O , SO , HNO , and particles. The NOAA-MLM requires hourly values of meteorological variables and...

Impact of regional ventilation changes on surface particulate matter concentrations in South Korea

NASA Astrophysics Data System (ADS)

Kim, H. C.; Stein, A. F.; Chai, T.; Ngan, F.; Kim, B. U.; Jin, C. S.; Hong, S. Y.; Park, R.; Son, S. W.; Bae, C.; Bae, M.; Song, C. K.; Kim, S.

2017-12-01

The recent increase in surface particulate matter (PM) concentrations in South Korea is intriguing due to its disagreement with current intensive emission reduction efforts. The long-term trend of surface PM concentrations in South Korea declined in the 2000s, but since 2012 its concentrations have tended to increase, resulting in frequent severe haze events in the region. This study demonstrates that the interannual variation of surface PM concentrations in South Korea is not only affected by changes in local or regional emission sources, but also closely linked with the interannual variations in regional ventilation. Using EPA Community Multiscale Air Quality modeling system, a 12-year (2004-2015) regional air quality simulation was conducted to assess the impact of the meteorological conditions under constant anthropogenic emissions. In addition, NOAA HYSPLIT dispersion model was utilized to estimate the strength of regional ventilation that dissipates local pollutions. Simulated PM concentrations show a strong negative correlation (i.e. R=-0.86) with regional wind speed, implying that reduced regional ventilation is likely associated with more stagnant conditions that cause severe pollutant episodes in South Korea. We conclude that the current PM concentration trend in South Korea is a combination of long-term decline by emission control efforts and short-term fluctuations in regional wind speed interannual variability. When the meteorology-driven variations are removed, PM concentrations in South Korea have declined continuously even after 2012, with -1.45±0.12, -1.41±0.16, and -1.09±0.16 mg/m3 per year in Seoul, the Seoul Metropolitan Area, and South Korea, respectively.

The influence of weather on migraine – are migraine attacks predictable?

PubMed Central

Hoffmann, Jan; Schirra, Tonio; Lo, Hendra; Neeb, Lars; Reuter, Uwe; Martus, Peter

2015-01-01

Objective The study aimed at elucidating a potential correlation between specific meteorological variables and the prevalence and intensity of migraine attacks as well as exploring a potential individual predictability of a migraine attack based on meteorological variables and their changes. Methods Attack prevalence and intensity of 100 migraineurs were correlated with atmospheric pressure, relative air humidity, and ambient temperature in 4-h intervals over 12 consecutive months. For each correlation, meteorological parameters at the time of the migraine attack as well as their variation within the preceding 24 h were analyzed. For migraineurs showing a positive correlation, logistic regression analysis was used to assess the predictability of a migraine attack based on meteorological information. Results In a subgroup of migraineurs, a significant weather sensitivity could be observed. In contrast, pooled analysis of all patients did not reveal a significant association. An individual prediction of a migraine attack based on meteorological data was not possible, mainly as a result of the small prevalence of attacks. Interpretation The results suggest that only a subgroup of migraineurs is sensitive to specific weather conditions. Our findings may provide an explanation as to why previous studies, which commonly rely on a pooled analysis, show inconclusive results. The lack of individual attack predictability indicates that the use of preventive measures based on meteorological conditions is not feasible. PMID:25642431

Meteorological Processors and Accessory Programs

EPA Pesticide Factsheets

Surface and upper air data, provided by NWS, are important inputs for air quality models. Before these data are used in some of the EPA dispersion models, meteorological processors are used to manipulate the data.

Possible rainfall reduction through reduced surface temperatures due to overgrazing

NASA Technical Reports Server (NTRS)

Otterman, J.

1975-01-01

Surface temperature reduction in terrain denuded of vegetation (as by overgrazing) is postulated to decrease air convection, reducing cloudiness and rainfall probability during weak meteorological disturbances. By reducing land-sea daytime temperature differences, the surface temperature reduction decreases daytime circulation of thermally driven local winds. The described desertification mechanism, even when limited to arid regions, high albedo soils, and weak meteorological disturbances, can be an effective rainfall reducing process in many areas including most of the Mediterranean lands.

Wind effect on salt transport variability in the Bay of Bengal

NASA Astrophysics Data System (ADS)

Sandeep, K. K.; Pant, V.

2017-12-01

The Bay of Bengal (BoB) exhibits large spatial variability in sea surface salinity (SSS) pattern caused by its unique hydrological, meteorological and oceanographical characteristics. This SSS variability is largely controlled by the seasonally reversing monsoon winds and the associated currents. Further, the BoB receives substantial freshwater inputs through excess precipitation over evaporation and river discharge. Rivers like Ganges, Brahmaputra, Mahanadi, Krishna, Godavari, and Irawwady discharge annually a freshwater volume in range between 1.5 x 1012 and 1.83 x 1013 m3 into the bay. A major volume of this freshwater input to the bay occurs during the southwest monsoon (June-September) period. In the present study, a relative role of winds in the SSS variability in the bay is investigated by using an eddy-resolving three dimensional Regional Ocean Modeling System (ROMS) numerical model. The model is configured with realistic bathymetry, coastline of study region and forced with daily climatology of atmospheric variables. River discharges from the major rivers are distributed in the model grid points representing their respective geographic locations. Salt transport estimate from the model simulation for realistic case are compared with the standard reference datasets. Further, different experiments were carried out with idealized surface wind forcing representing the normal, low, high, and very high wind speed conditions in the bay while retaining the realistic daily varying directions for all the cases. The experimental simulations exhibit distinct dispersal patterns of the freshwater plume and SSS in different experiments in response to the idealized winds. Comparison of the meridional and zonal surface salt transport estimated for each experiment showed strong seasonality with varying magnitude in the bay with a maximum spatial and temporal variability in the western and northern parts of the BoB.

Remote sensing of atmosphere and oceans; Proceedings of Symposium 1 and of the Topical Meeting of the 27th COSPAR Plenary Meeting, Espoo, Finland, July 18-29, 1988

NASA Technical Reports Server (NTRS)

Raschke, E. (Editor); Ghazi, A. (Editor); Gower, J. F. R. (Editor); Mccormick, P. (Editor); Gruber, A. (Editor); Hasler, A. F. (Editor)

1989-01-01

Papers are presented on the contribution of space remote sensing observations to the World Climate Research Program and the Global Change Program, covering topics such as space observations for global environmental monitoring, experiments related to land surface fluxes, studies of atmospheric composition, structure, motions, and precipitation, and remote sensing for oceanography, observational studies of the atmosphere, clouds, and the earth radiation budget. Also, papers are given on results from space observations for meteorology, oceanography, and mesoscale atmospheric and ocean processes. The topics include vertical atmospheric soundings, surface water temperature determination, sea level variability, data on the prehurricane atmosphere, linear and circular mesoscale convective systems, Karman vortex clouds, and temporal patterns of phytoplankton abundance.

One-dimensional soil temperature assimilation experiment based on unscented particle filter and Common Land Model

NASA Astrophysics Data System (ADS)

Fu, Xiao Lei; Jin, Bao Ming; Jiang, Xiao Lei; Chen, Cheng

2018-06-01

Data assimilation is an efficient way to improve the simulation/prediction accuracy in many fields of geosciences especially in meteorological and hydrological applications. This study takes unscented particle filter (UPF) as an example to test its performance at different two probability distribution, Gaussian and Uniform distributions with two different assimilation frequencies experiments (1) assimilating hourly in situ soil surface temperature, (2) assimilating the original Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) once per day. The numerical experiment results show that the filter performs better when increasing the assimilation frequency. In addition, UPF is efficient for improving the soil variables (e.g., soil temperature) simulation/prediction accuracy, though it is not sensitive to the probability distribution for observation error in soil temperature assimilation.

Bio-optical and physical variability in the subarctic North Atlantic Ocean during the spring of 1989

NASA Technical Reports Server (NTRS)

Dickey, T.; Marra, J.; Stramska, M.; Langdon, C.; Granata, T.; Plueddemann, A.; Weller, R.; Yoder, J.

1994-01-01

A unique set of physical, bio-optical, and meteorological observations were made from a mooring located in the open ocean south of Iceland (59 deg 29.5 min N, 20 deg 49.8 min W) from April 13 to June 12 1989. The present measurements are apparently the first to resolve the rapid transition to springtime physical and biological conditions at such a high latitude site. Our data were collected with bio-optical and physical moored systems every few minutes. The abrupt onset of springtime stratification was observed with the mixed layer shoaling from approximately 550 m to approximately 50 m in approximately 5 days. During this period a major phytoplankton bloom occurred with a tenfold increase in near-surface chlorophyll concentration in less than 3 weeks. Our statistical analysis indicates that the velocity shear in the upper layer is driven primarily by local wind stress. Mesoscale variability is also apparent from these and concurrent airborne oceangraphic lidar observations. Our complementary modeling results suggest that the near-surface layer may be reasonably well described by a one-dimensional model and that the spring bloom was initiated during incipient near-surface restratification.

Impact of WRF model PBL schemes on air quality simulations over Catalonia, Spain.

PubMed

Banks, R F; Baldasano, J M

2016-12-01

Here we analyze the impact of four planetary boundary-layer (PBL) parametrization schemes from the Weather Research and Forecasting (WRF) numerical weather prediction model on simulations of meteorological variables and predicted pollutant concentrations from an air quality forecast system (AQFS). The current setup of the Spanish operational AQFS, CALIOPE, is composed of the WRF-ARW V3.5.1 meteorological model tied to the Yonsei University (YSU) PBL scheme, HERMES v2 emissions model, CMAQ V5.0.2 chemical transport model, and dust outputs from BSC-DREAM8bv2. We test the performance of the YSU scheme against the Assymetric Convective Model Version 2 (ACM2), Mellor-Yamada-Janjic (MYJ), and Bougeault-Lacarrère (BouLac) schemes. The one-day diagnostic case study is selected to represent the most frequent synoptic condition in the northeast Iberian Peninsula during spring 2015; regional recirculations. It is shown that the ACM2 PBL scheme performs well with daytime PBL height, as validated against estimates retrieved using a micro-pulse lidar system (mean bias=-0.11km). In turn, the BouLac scheme showed WRF-simulated air and dew point temperature closer to METAR surface meteorological observations. Results are more ambiguous when simulated pollutant concentrations from CMAQ are validated against network urban, suburban, and rural background stations. The ACM2 scheme showed the lowest mean bias (-0.96μgm -3 ) with respect to surface ozone at urban stations, while the YSU scheme performed best with simulated nitrogen dioxide (-6.48μgm -3 ). The poorest results were with simulated particulate matter, with similar results found with all schemes tested. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

When the Fog Clears: Long-Term Monitoring of Fog and Fog-Dependent Biota in the Namib Desert

NASA Astrophysics Data System (ADS)

Logan, J. R. V.

2014-12-01

The Gobabeb Research and Training Centre in western Namibia is currently undertaking several efforts to enhance long-term atmospheric and fog monitoring in the central Namib Desert and to measure how fog-dependent biota are responding to global change. In an environment that receives regular sea fog and a mean annual rainfall of only 25 mm, Gobabeb is ideally situated to study the drivers and ecological role of fog in arid environments. Currently more than ten meteorological projects perform measurements at or close to Gobabeb. These projects include continuous trace gas measurements, fog isotope sampling, in situ surface radiation measurements, land surface temperature and other satellite validation studies, and multiple aerosol/dust monitoring projects; most of these projects are also components in other global monitoring networks. To these projects, Gobabeb has recently added a network of nine autonomous weather stations spanning the central Namib that will continuously collect basic meteorological data over an area of approximately 70x70 km. Using this data in conjunction with modeling efforts will expand our understanding of fog formation and the linkages between fog and the Benguela Current off Namibia's coast. Historical weather data from previous meteorological stations and satellite observations will also enable development of a fog time series for the last 50 years to determine climate variability driven by possible changes in the Benguela Current system. To complement these efforts, Gobabeb is also expanding its decades-old ecological research programs to explore the impacts of the fog on the region's biota at various time and spatial scales. Gobabeb's long-term, multidisciplinary projects can serve as a prototype for monitoring in other fog-affected systems, together increasing our understanding of coastal fog dynamics, land-atmosphere-ocean connections, and the impacts of fog-related global change.

Circulation and thermohaline structure of the Aral Sea in the last three years

NASA Astrophysics Data System (ADS)

Izhitskiy, A. S.; Zavialov, P. O.

2012-04-01

The results of the 3 latest expeditions (2009 - 2011) of the Shirshov Institute to the Aral Sea are reported. We analyze the interannual variability of the basin circulation together with the thermohaline structure in order to identify the underlying mechanisms. The study is based on the results of the field surveys of August, 2009, September, 2010, and November, 2011. The vertical profiles of temperature and salinity were obtained using a CTD profiler at 6 stations across the deepest part of the western basin in 2009 and 2010, and 3 stations in 2011. Additionally, during each of the surveys, mooring stations equipped with current meters and pressure gauges were deployed for 3-5 days in the deepest portion of the western basin. A portable automatic meteorological station, continuously recording the wind stress and the principal meteorological parameters, was installed near the mooring sites. The vertical stratification exhibited a 3-layered pattern, with local salinity maxima in the upper mixed layer and near the bottom, while the intermediate layer was characterized by a core of minimum salinity and temperature. Such a pattern persisted throughout the 3 years of observations. Analysis of the current measurements data along with the meteorological data records demonstrated that the mean basin-scale surface circulation of the Large Aral Sea is likely to have remained anticyclonic, whilst the near-bottom circulation appears to be cyclonic. The current velocity and level anomalies responded energetically to winds. Correlation analysis of the velocity and surface level series versus the wind stress allowed to quantify the response of the system to the wind forcing as well as to formulate a conceptual scheme of the lake's response to wind forcing at synoptic temporal scales.

Global Scale Remote Sensing Monitoring of Endorheic Lake Systems

NASA Astrophysics Data System (ADS)

Scuderi, L. A.

2010-12-01

Semi-arid regions of the world contain thousands of endorheic lakes in large shallow basins. Due to their generally remote locations few are continuously monitored. Documentation of recent variability is essential to assessing how endorheic lakes respond to short-term meteorological conditions and longer-term decadal-scale climatic variability and is critical in determining future disturbance of hydrological regimes with respect to predicted warming and drying in the mid-latitudes. Short- and long-term departures from climatic averages, rapid environmental shifts and increased population pressures may result in significant fluctuations in the hydrologic budgets of these lakes and adversely impact endorheic lake/basin ecosystems. Information on flooding variability is also critical in estimating changes in P/E balances and on the production of exposed and easily deflated surfaces that may impact dust loading locally and regionally. In order to provide information on how these lakes respond we need to understand how entire systems respond hydrologically to different climatic inputs. This requires monitoring and analysis of regional to continental-scale systems. To date, this level of monitoring has not been achieved in an operational system. In order to assess the possibility of creating a global-scale lake inundation database we analyzed two contrasting lake systems in western North America (Mexico and New Mexico, USA) and China (Inner Mongolia). We asked two major questions: 1) is it possible to quickly and accurately quantify current lake inundation events in near real time using remote sensing? and, 2) is it possible to differentiate variable meteorological sources and resultant lake inundation responses using this type of database? With respect to these results we outline an automated lake monitoring approach using MODIS data and real-time processing systems that may provide future global monitoring capabilities.

Monitoring of airborne biological particles in outdoor atmosphere. Part 1: Importance, variability and ratios.

PubMed

Núñez, Andrés; Amo de Paz, Guillermo; Rastrojo, Alberto; García, Ana M; Alcamí, Antonio; Gutiérrez-Bustillo, A Montserrat; Moreno, Diego A

2016-03-01

The first part of this review ("Monitoring of airborne biological particles in outdoor atmosphere. Part 1: Importance, variability and ratios") describes the current knowledge on the major biological particles present in the air regarding their global distribution, concentrations, ratios and influence of meteorological factors in an attempt to provide a framework for monitoring their biodiversity and variability in such a singular environment as the atmosphere. Viruses, bacteria, fungi, pollen and fragments thereof are the most abundant microscopic biological particles in the air outdoors. Some of them can cause allergy and severe diseases in humans, other animals and plants, with the subsequent economic impact. Despite the harsh conditions, they can be found from land and sea surfaces to beyond the troposphere and have been proposed to play a role also in weather conditions and climate change by acting as nucleation particles and inducing water vapour condensation. In regards to their global distribution, marine environments act mostly as a source for bacteria while continents additionally provide fungal and pollen elements. Within terrestrial environments, their abundances and diversity seem to be influenced by the land-use type (rural, urban, coastal) and their particularities. Temporal variability has been observed for all these organisms, mostly triggered by global changes in temperature, relative humidity, et cetera. Local fluctuations in meteorological factors may also result in pronounced changes in the airbiota. Although biological particles can be transported several hundreds of meters from the original source, and even intercontinentally, the time and final distance travelled are strongly influenced by factors such as wind speed and direction. [Int Microbiol 2016; 19(1):1-1 3]. Copyright© by the Spanish Society for Microbiology and Institute for Catalan Studies.

Impact of Uncertainties in Meteorological Forcing Data and Land Surface Parameters on Global Estimates of Terrestrial Water Balance Components

NASA Astrophysics Data System (ADS)

Nasonova, O. N.; Gusev, Ye. M.; Kovalev, Ye. E.

2009-04-01

Global estimates of the components of terrestrial water balance depend on a technique of estimation and on the global observational data sets used for this purpose. Land surface modelling is an up-to-date and powerful tool for such estimates. However, the results of modelling are affected by the quality of both a model and input information (including meteorological forcing data and model parameters). The latter is based on available global data sets containing meteorological data, land-use information, and soil and vegetation characteristics. Now there are a lot of global data sets, which differ in spatial and temporal resolution, as well as in accuracy and reliability. Evidently, uncertainties in global data sets will influence the results of model simulations, but to which extent? The present work is an attempt to investigate this issue. The work is based on the land surface model SWAP (Soil Water - Atmosphere - Plants) and global 1-degree data sets on meteorological forcing data and the land surface parameters, provided within the framework of the Second Global Soil Wetness Project (GSWP-2). The 3-hourly near-surface meteorological data (for the period from 1 July 1982 to 31 December 1995) are based on reanalyses and gridded observational data used in the International Satellite Land-Surface Climatology Project (ISLSCP) Initiative II. Following the GSWP-2 strategy, we used a number of alternative global forcing data sets to perform different sensitivity experiments (with six alternative versions of precipitation, four versions of radiation, two pure reanalysis products and two fully hybridized products of meteorological data). To reveal the influence of model parameters on simulations, in addition to GSWP-2 parameter data sets, we produced two alternative global data sets with soil parameters on the basis of their relationships with the content of clay and sand in a soil. After this the sensitivity experiments with three different sets of parameters were performed. As a result, 16 variants of global annual estimates of water balance components were obtained. Application of alternative data sets on radiation, precipitation, and soil parameters allowed us to reveal the influence of uncertainties in input data on global estimates of water balance components.

Ocean Mixed Layer responses to intense meteorological events during HyMeX-SOP1 from a high-resolution ocean simulation

NASA Astrophysics Data System (ADS)

Lebeaupin Brossier, Cindy; Arsouze, Thomas; Béranger, Karine; Bouin, Marie-Noëlle; Bresson, Emilie; Ducrocq, Véronique; Giordani, Hervé; Nuret, Mathieu; Rainaud, Romain; Taupier-Letage, Isabelle

2014-12-01

The western Mediterranean Sea is a source of heat and humidity for the atmospheric low-levels in autumn. Large exchanges take place at the air-sea interface, especially during intense meteorological events, such as heavy precipitation and/or strong winds. The Ocean Mixed Layer (OML), which is quite thin at this time of year (∼ 20 m-depth), evolves rapidly under such intense fluxes. This study investigates the ocean responses under intense meteorological events that occurred during HyMeX SOP1 (5 September-6 November 2012). The OML conditions and tendencies are derived from a high-resolution ocean simulation using the sub-regional eddy-resolving NEMO-WMED36 model (1/36°-resolution), driven at the surface by hourly air-sea fluxes from the AROME-WMED forecasts (2.5 km-resolution). The high space-time resolution of the atmospheric forcing allows the highly variable surface fluxes, which induce rapid changes in the OML, to be well represented and linked to small-scale atmospheric processes. First, the simulation results are compared to ocean profiles from several platforms obtained during the campaign. Then, this study focuses on the short-term OML evolution during three events. In particular, we examine the OML cooling and mixing under strong wind events, potentially associated with upwelling, as well as the surface freshening under heavy precipitation events, producing low-salinity lenses. Tendencies demonstrate the major role of the surface forcing in the temperature and/or salinity anomaly formation. At the same time, mixing [restratification] rapidly occurs. As expected, the sign of this tendency term is very dependent on the local vertical stratification which varies at fine scale in the Mediterranean. It also controls [disables] the vertical propagation. In the Alboran Sea, the strong dynamics redistribute the OML anomalies, sometimes up to 7 days after their formation. Elsewhere, despite local amplitude modulations due to internal wave excitation by strong winds, the integrated effect of the horizontal advection is almost null on the anomalies' spread and decay. Finally, diffusion has a small contribution.

On the factors governing water vapor turbulence mixing in the convective boundary layer over land: Concept and data analysis technique using ground-based lidar measurements.

PubMed

Pal, Sandip

2016-06-01

The convective boundary layer (CBL) turbulence is the key process for exchanging heat, momentum, moisture and trace gases between the earth's surface and the lower part of the troposphere. The turbulence parameterization of the CBL is a challenging but important component in numerical models. In particular, correct estimation of CBL turbulence features, parameterization, and the determination of the contribution of eddy diffusivity are important for simulating convection initiation, and the dispersion of health hazardous air pollutants and Greenhouse gases. In general, measurements of higher-order moments of water vapor mixing ratio (q) variability yield unique estimates of turbulence in the CBL. Using the high-resolution lidar-derived profiles of q variance, third-order moment, and skewness and analyzing concurrent profiles of vertical velocity, potential temperature, horizontal wind and time series of near-surface measurements of surface flux and meteorological parameters, a conceptual framework based on bottom up approach is proposed here for the first time for a robust characterization of the turbulent structure of CBL over land so that our understanding on the processes governing CBL q turbulence could be improved. Finally, principal component analyses will be applied on the lidar-derived long-term data sets of q turbulence statistics to identify the meteorological factors and the dominant physical mechanisms governing the CBL turbulence features. Copyright © 2016 Elsevier B.V. All rights reserved.

Hydrologic impacts of land cover variability and change at seasonal to decadal time scales over North America, 1992-2016

NASA Astrophysics Data System (ADS)

Bohn, T. J.; Vivoni, E. R.

2017-12-01

Land cover variability and change have been shown to influence the terrestrial hydrologic cycle by altering the partitioning of moisture and energy fluxes. However, the magnitude and directionality of the relationship between land cover and surface hydrology has been shown to vary substantially across regions. Here, we provide an assessment of the impacts of land cover change on hydrologic processes at seasonal (vegetation phenology) to decadal scales (land cover conversion) in the United States and Mexico. To this end, we combine time series of remotely-sensed land surface characteristics with land cover maps for different decades as input to the Variable Infiltration Capacity hydrologic model. Land surface characteristics (leaf area index, surface albedo, and canopy fraction derived from normalized difference vegetation index) were obtained from the Moderate Resolution Imaging Spectrometer (MODIS) at 8-day intervals over the period 2000-2016. Land cover maps representing conditions in 1992, 2001, and 2011 were derived by homogenizing the National Land Cover Database over the US and the INEGI Series I through V maps over Mexico. An additional map covering all of North America was derived from the most frequent land cover class observed in each pixel of the MODIS MOD12Q1 product during 2001-2013. Land surface characteristics were summarized over land cover fractions at 1/16 degree (6 km) resolution. For each land cover map, hydrologic simulations were conducted that covered the period 1980-2013, using the best-available, hourly meteorological forcings at a similar spatial resolution. Based on these simulations, we present a comparison of the contributions of land cover change and climate variability at seasonal to decadal scales on the hydrologic and energy budgets, identifying the dominant components through time and space. This work also offers a valuable dataset on land cover variability and its hydrologic response for continental-scale assessments and modeling.

Recent changes and drivers of the atmospheric evaporative demand in the Canary Islands

NASA Astrophysics Data System (ADS)

Vicente-Serrano, Sergio M.; Azorin-Molina, Cesar; Sanchez-Lorenzo, Arturo; El Kenawy, Ahmed; Martín-Hernández, Natalia; Peña-Gallardo, Marina; Beguería, Santiago; Tomas-Burguera, Miquel

2016-08-01

We analysed recent evolution and meteorological drivers of the atmospheric evaporative demand (AED) in the Canary Islands for the period 1961-2013. We employed long and high-quality time series of meteorological variables to analyse current AED changes in this region and found that AED has increased during the investigated period. Overall, the annual ETo, which was estimated by means of the FAO-56 Penman-Monteith equation, increased significantly by 18.2 mm decade-1 on average, with a stronger trend in summer (6.7 mm decade-1). In this study we analysed the contribution of (i) the aerodynamic (related to the water vapour that a parcel of air can store) and (ii) radiative (related to the available energy to evaporate a quantity of water) components to the decadal variability and trends of ETo. More than 90 % of the observed ETo variability at the seasonal and annual scales can be associated with the variability in the aerodynamic component. The variable that recorded more significant changes in the Canary Islands was relative humidity, and among the different meteorological factors used to calculate ETo, relative humidity was the main driver of the observed ETo trends. The observed trend could have negative consequences in a number of water-depending sectors if it continues in the future.

Effects of future land use and ecosystem changes on boundary-layer meteorology and air quality

NASA Astrophysics Data System (ADS)

Tai, A. P. K.; Wang, L.; Sadeke, M.

2017-12-01

Land vegetation plays key roles shaping boundary-layer meteorology and air quality via various pathways. Vegetation can directly affect surface ozone via dry deposition and biogenic emissions of volatile organic compounds (VOCs). Transpiration from land plants can also influence surface temperature, soil moisture and boundary-layer mixing depth, thereby indirectly affecting surface ozone. Future changes in the distribution, density and physiology of vegetation are therefore expected to have major ramifications for surface ozone air quality. In our study, we examine two aspects of potential vegetation changes using the Community Earth System Model (CESM) in the fully coupled land-atmosphere configuration, and evaluate their implications on meteorology and air quality: 1) land use change, which alters the distribution of plant functional types and total leaf density; and 2) ozone damage on vegetation, which alters leaf density and physiology (e.g., stomatal resistance). We find that, following the RCP8.5 scenario for 2050, global cropland expansion induces only minor changes in surface ozone in tropical and subtropical regions, but statistically significant changes by up to +4 ppbv in midlatitude North America and East Asia, mostly due to higher surface temperature that enhances biogenic VOC emissions, and reduced dry deposition to a lesser degree. These changes are in turn to driven mostly by meteorological changes that include a shift from latent to sensible heat in the surface energy balance and reduced soil moisture, reflecting not only local responses but also a northward expansion of the Hadley Cell. On the other hand, ozone damage on vegetation driven by rising anthropogenic emissions is shown to induce a further enhancement of ozone by up to +6 ppbv in midlatitude regions by 2050. This reflects a strong localized positive feedback, with severe ozone damage in polluted regions generally inducing stomatal closure, which in turn reduces transpiration, increases surface temperature, and thus enhances biogenic VOC emissions and surface ozone. Our findings demonstrate the importance of considering meteorological responses to vegetation changes in future air quality assessment, and call for greater coordination among land use, ecosystem and air quality management efforts.

Synoptic meteorological modes of variability for fine particulate matter (PM2.5) air quality in major metropolitan regions of China

NASA Astrophysics Data System (ADS)

Leung, Danny M.; Tai, Amos P. K.; Mickley, Loretta J.; Moch, Jonathan M.; van Donkelaar, Aaron; Shen, Lu; Martin, Randall V.

2018-05-01

In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ˜ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998-2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing-Tianjin-Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of ˜ 0.5 µg m-3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small climate benefit, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.

Seasonal variation of air temperature at the Mendel Station, James Ross Island in the period of 2006-2009

NASA Astrophysics Data System (ADS)

Laska, Kamil; Prošek, Pavel; Budík, Ladislav

2010-05-01

Key words: air temperature, seasonal variation, James Ross Island, Antarctic Peninsula Recently, significant role of the atmospheric and oceanic circulation variation on positive trend of near surface air temperature along the Antarctic Peninsula has been reported by many authors. However, small number of the permanent meteorological stations located on the Peninsula coast embarrasses a detail analysis. It comprises analysis of spatiotemporal variability of climatic conditions and validation of regional atmospheric climate models. However, geographical location of the Czech Johann Gregor Mendel Station (hereafter Mendel Station) newly established on the northern ice-free part of the James Ross Island provides an opportunity to fill the gap. There are recorded important meteorological characteristics which allow to evaluate specific climatic regime of the region and their impact on the ice-shelf disintegration and glacier retreat. Mendel Station (63°48'S, 57°53'W) is located on marine terrace at the altitude of 7 m. In 2006, a monitoring network of several automatic weather stations was installed at different altitudes ranging from the seashore level up to mesas and tops of glaciers (514 m a.s.l.). In this contribution, a seasonal variation of near surface air temperature at the Mendel Station in the period of 2006-2009 is presented. Annual mean air temperature was -7.2 °C. Seasonal mean temperature ranged from +1.4 °C (December-February) to -17.7 °C (June-August). Frequently, the highest temperature occurred in the second half of January. It reached maximum of +8.1 °C. Sudden changes of atmospheric circulation pattern during winter caused a large interdiurnal variability of air temperature with the amplitude of 30 °C.

The seasonality and geographic dependence of ENSO impacts on U.S. surface ozone variability

NASA Astrophysics Data System (ADS)

Xu, Li; Yu, Jin-Yi; Schnell, Jordan L.; Prather, Michael J.

2017-04-01

We examine the impact of El Niño-Southern Oscillation (ENSO) on surface ozone abundance observed over the continental United States (U.S.) during 1993-2013. The monthly ozone decreases (increases) during El Niño (La Niña) years with amplitude up to 1.8 ppb per standard deviation of Niño 3.4 index. The largest ENSO influences occur over two southern U.S. regions during fall when the ENSO develops and over two western U.S. regions during the winter to spring after the ENSO decays. ENSO affects surface ozone via chemical processes during warm seasons in southern regions, where favorable meteorological conditions occur, but via dynamic transport during cold seasons in western regions, where the ENSO-induced circulation variations are large. The geographic dependence and seasonality of the ENSO impacts imply that regulations regarding air quality and its exceedance need to be adjusted for different seasons and U.S. regions to account for the ENSO-driven patterns in surface ozone.

Gaseous Elemental Mercury (GEM) Emissions from Snow Surfaces in Northern New York

PubMed Central

Maxwell, J. Alexander; Holsen, Thomas M.; Mondal, Sumona

2013-01-01

Snow surface-to-air exchange of gaseous elemental mercury (GEM) was measured using a modified Teflon fluorinated ethylene propylene (FEP) dynamic flux chamber (DFC) in a remote, open site in Potsdam, New York. Sampling was conducted during the winter months of 2011. The inlet and outlet of the DFC were coupled with a Tekran Model 2537A mercury (Hg) vapor analyzer using a Tekran Model 1110 two port synchronized sampler. The surface GEM flux ranged from −4.47 ng m−2 hr−1 to 9.89 ng m−2 hr−1. For most sample periods, daytime GEM flux was strongly correlated with solar radiation. The average nighttime GEM flux was slightly negative and was not well correlated with any of the measured meteorological variables. Preliminary, empirical models were developed to estimate GEM emissions from snow surfaces in northern New York. These models suggest that most, if not all, of the Hg deposited with and to snow is reemitted to the atmosphere. PMID:23874951

Gaseous elemental mercury (GEM) emissions from snow surfaces in northern New York.

PubMed

Maxwell, J Alexander; Holsen, Thomas M; Mondal, Sumona

2013-01-01

Snow surface-to-air exchange of gaseous elemental mercury (GEM) was measured using a modified Teflon fluorinated ethylene propylene (FEP) dynamic flux chamber (DFC) in a remote, open site in Potsdam, New York. Sampling was conducted during the winter months of 2011. The inlet and outlet of the DFC were coupled with a Tekran Model 2537A mercury (Hg) vapor analyzer using a Tekran Model 1110 two port synchronized sampler. The surface GEM flux ranged from -4.47 ng m(-2) hr(-1) to 9.89 ng m(-2) hr(-1). For most sample periods, daytime GEM flux was strongly correlated with solar radiation. The average nighttime GEM flux was slightly negative and was not well correlated with any of the measured meteorological variables. Preliminary, empirical models were developed to estimate GEM emissions from snow surfaces in northern New York. These models suggest that most, if not all, of the Hg deposited with and to snow is reemitted to the atmosphere.

[Individual tree diameter increment model for natural Betula platyphylla forests based on meteorological factors].

PubMed

Zhang, Hai Ping; Li, Feng Ri; Dong, Li Hu; Liu, Qiang

2017-06-18

Based on the 212 re-measured permanent plots for natural Betula platyphylla fore-sts in Daxing'an Mountains and Xiaoxing'an Mountains and 30 meteorological stations data, an individual tree growth model based on meteorological factors was constructed. The differences of stand and meteorological factors between Daxing'an Mountains and Xiaoxing'an Mountains were analyzed and the diameter increment model including the regional effects was developed by dummy variable approach. The results showed that the minimum temperature (T g min ) and mean precipitation (P g m ) in growing season were the main meteorological factors which affected the diameter increment in the two study areas. T g min and P g m were positively correlated with the diameter increment, but the influence strength of T g min was obviously different between the two research areas. The adjusted coefficient of determination (R a 2 ) of the diameter increment model with meteorological factors was 0.56 and had an 11% increase compared to the one without meteorological factors. It was concluded that meteorological factors could well explain the diameter increment of B. platyphylla. R a 2 of the model with regional effects was 0.59, and increased by 18% compared to the one without regional effects, and effectively solved the incompatible problem of parameters between the two research areas. The validation results showed that the individual tree diameter growth model with regional effect had the best prediction accuracy in estimating the diameter increment of B. platyphylla. The mean error, mean absolute error, mean error percent and mean prediction error percent were 0.0086, 0.4476, 5.8% and 20.0%, respectively. Overall, dummy variable model of individual tree diameter increment based on meteorological factors could well describe the diameter increment process of natural B. platyphylla in Daxing'an Mountains and Xiaoxing'an Mountains.

Instantaneous-to-daily GPP upscaling schemes based on a coupled photosynthesis-stomatal conductance model: correcting the overestimation of GPP by directly using daily average meteorological inputs.

PubMed

Wang, Fumin; Gonsamo, Alemu; Chen, Jing M; Black, T Andrew; Zhou, Bin

2014-11-01

Daily canopy photosynthesis is usually temporally upscaled from instantaneous (i.e., seconds) photosynthesis rate. The nonlinear response of photosynthesis to meteorological variables makes the temporal scaling a significant challenge. In this study, two temporal upscaling schemes of daily photosynthesis, the integrated daily model (IDM) and the segmented daily model (SDM), are presented by considering the diurnal variations of meteorological variables based on a coupled photosynthesis-stomatal conductance model. The two models, as well as a simple average daily model (SADM) with daily average meteorological inputs, were validated using the tower-derived gross primary production (GPP) to assess their abilities in simulating daily photosynthesis. The results showed IDM closely followed the seasonal trend of the tower-derived GPP with an average RMSE of 1.63 g C m(-2) day(-1), and an average Nash-Sutcliffe model efficiency coefficient (E) of 0.87. SDM performed similarly to IDM in GPP simulation but decreased the computation time by >66%. SADM overestimated daily GPP by about 15% during the growing season compared to IDM. Both IDM and SDM greatly decreased the overestimation by SADM, and improved the simulation of daily GPP by reducing the RMSE by 34 and 30%, respectively. The results indicated that IDM and SDM are useful temporal upscaling approaches, and both are superior to SADM in daily GPP simulation because they take into account the diurnally varying responses of photosynthesis to meteorological variables. SDM is computationally more efficient, and therefore more suitable for long-term and large-scale GPP simulations.

Evaluation of the effects of climate and man intervention on ground waters and their dependent ecosystems using time series analysis

NASA Astrophysics Data System (ADS)

Gemitzi, Alexandra; Stefanopoulos, Kyriakos

2011-06-01

SummaryGroundwaters and their dependent ecosystems are affected both by the meteorological conditions as well as from human interventions, mainly in the form of groundwater abstractions for irrigation needs. This work aims at investigating the quantitative effects of meteorological conditions and man intervention on groundwater resources and their dependent ecosystems. Various seasonal Auto-Regressive Integrated Moving Average (ARIMA) models with external predictor variables were used in order to model the influence of meteorological conditions and man intervention on the groundwater level time series. Initially, a seasonal ARIMA model that simulates the abstraction time series using as external predictor variable temperature ( T) was prepared. Thereafter, seasonal ARIMA models were developed in order to simulate groundwater level time series in 8 monitoring locations, using the appropriate predictor variables determined for each individual case. The spatial component was introduced through the use of Geographical Information Systems (GIS). Application of the proposed methodology took place in the Neon Sidirochorion alluvial aquifer (Northern Greece), for which a 7-year long time series (i.e., 2003-2010) of piezometric and groundwater abstraction data exists. According to the developed ARIMA models, three distinct groups of groundwater level time series exist; the first one proves to be dependent only on the meteorological parameters, the second group demonstrates a mixed dependence both on meteorological conditions and on human intervention, whereas the third group shows a clear influence from man intervention. Moreover, there is evidence that groundwater abstraction has affected an important protected ecosystem.

A joint modelling exercise designed to assess the respective impact of emission changes and meteorological variability on the observed air quality trends in major urban hotspots.

NASA Astrophysics Data System (ADS)

Colette, Augustin; Bessagnet, Bertrand; Dangiola, Ariela; D'Isidoro, Massimo; Gauss, Michael; Granier, Claire; Hodnebrog, Øivind; Jakobs, Hermann; Kanakidou, Maria; Khokhar, Fahim; Law, Kathy; Maurizi, Alberto; Meleux, Frederik; Memmesheimer, Michael; Nyiri, Agnes; Rouil, Laurence; Stordal, Frode; Tampieri, Francesco

2010-05-01

With the growth of urban agglomerations, assessing the drivers of variability of air quality in and around the main anthropogenic emission hotspots has become a major societal concern as well as a scientific challenge. These drivers include emission changes and meteorological variability; both of them can be investigated by means of numerical modelling of trends over the past few years. A collaborative effort has been developed in the framework of the CityZen European project to address this question. Several chemistry and transport models (CTMs) are deployed in this activity: four regional models (BOLCHEM, CHIMERE, EMEP and EURAD) and three global models (CTM2, MOZART, and TM4). The period from 1998 to 2007 has been selected for the historic reconstruction. The focus for the present preliminary presentation is Europe. A consistent set of emissions is used by all partners (EMEP for the European domain and IPCC-AR5 beyond) while a variety of meteorological forcing is used to gain robustness in the ensemble spread amongst models. The results of this experiment will be investigated to address the following questions: - Is the envelope of models able to reproduce the observed trends of the key chemical constituents? - How the variability amongst models changes in time and space and what does it tell us about the processes driving the observed trends? - Did chemical regimes and aerosol formation processes changed in selected hotspots? Answering the above questions will contribute to fulfil the ultimate goal of the present study: distinguishing the respective contribution of meteorological variability and emissions changes on air quality trends in major anthropogenic emissions hotspots.

GLACIOCLIM-SAMBA: A Terre Adelie / Wilkes Land Antarctic surface mass balance observatory

NASA Astrophysics Data System (ADS)

Genthon, C.; Frezzotti, M.; Le Meur, E.; Magand, O.; Six, D.; Wagnon, P.

2005-12-01

While local measurements at hundreds of sites are now available (although sometimes questionable, e.g. Magand et al., this volume) to verify how large-scale models reproduce the spatial distribution of the surface mass balance (SMB) of Antarctica, few field observations yet make it possible to verify current intra- and inter-annual variability and trends of the SMB in the models, and to evaluate the processes that relate this variability with that of climate. It is a major aim of the GLACIOCLIM-SAMBA observatory (http://lgge.obs.ujf-grenoble.fr/~christo/glacioclim/samba/), initiated in 2004, to provide such observations in the Terre Adelie and Wilkes Land area. Recognizing that the largest absolute changes (and thus contribution to sea-level) of Antarctic SMB are expected where the current mean SMB is largest, that is in the coastal regions, SAMBA is largely focused on ice sheet margin. To sample spatial scales compatible with the scales resolved by models used to predict climate and SMB changes, a 150 km accumulation stakes line is being set up from the coast near the French Dumont d'Urville station, towards to Antarctic plateau in the general direction of the Italy/France Concordia station. Ground penetrating radar survey will provide snap-shot SMB interpolation along the stakes line. A blue ice stretch at the coast is being monitored by a 50-stake ablation network. Three 50-stakes networks are being set up near Concordia station to relate coastal and plateau SMB variability and change. An automatic weather station (AWS, including radiation) deployed at the coast, and the D-10, D-47 and DCII Antarctic Meteorological Research Center (http://amrc.ssec.wisc.edu/) AWSs, provide meteorological information to relate observed SMB and climate. Italian meteorology and radiation programs at Concordia, planned micrometeorology special campaigns at the margin, and precipitation monitoring at both sites, should help decipher the processes that relate SMB and climate variability. As a summary of results on the existing observatory as of Jan; 2005: i) The first year mean SMB along a 50 km stakes line was ~60 cm water equivalent (we), which qualifies 2004 as a very high accumulation year in the Terre Adelie area; ii) Spatial variability along the stakes line is high, ranging from 16 to 125 cm (we), confirming the need for spatial sampling consistent with the scales resolved by climate models; iii) At the coastal blue ice, ablation occurs in summer only while the winter SMB is close to 0. The SAMBA observatory is scheduled to operate for at least 10 years, hopefully more if successful, with main support by the French (IPEV) and Italian (PNRA) Polar Institutes. The French ministry of research and Institut National des Sciences de l'univers (Climate Change and Cryosphere and ORE-GLACIOCLIM programs) also contribute support. All SAMBA observations will be distributed and freely available on the internet as soon as the observatory is fully operational and validated.

Weather analysis and interpretation procedures developed for the US/Canada wheat and barley exploratory experiment

NASA Technical Reports Server (NTRS)

Trenchard, M. H. (Principal Investigator)

1980-01-01

Procedures and techniques for providing analyses of meteorological conditions at segments during the growing season were developed for the U.S./Canada Wheat and Barley Exploratory Experiment. The main product and analysis tool is the segment-level climagraph which depicts temporally meteorological variables for the current year compared with climatological normals. The variable values for the segment are estimates derived through objective analysis of values obtained at first-order station in the region. The procedures and products documented represent a baseline for future Foreign Commodity Production Forecasting experiments.

Quantifying the quality of precipitation data from different sources

NASA Astrophysics Data System (ADS)

Leijnse, Hidde; Wauben, Wiel; Overeem, Aart; de Haij, Marijn

2015-04-01

There is an increasing demand for high-resolution rainfall data. The current manual and automatic networks of climate and meteorological stations provide high quality rainfall data, but they cannot provide the high spatial and temporal resolution required for many applications. This can only partly be solved by using remotely sensed data. It is therefore necessary to consider third-party data, such as rain gauges operated by amateurs and rainfall intensities from commercial cellular communication links. The quality of such third-party data is highly variable and generally lower than that of dedicated networks. Often, such data quality information is missing for third party data. In order to be able to use data from various sources it is vital that quantitative knowledge of the data quality is available. This holds for all data sources, including the rain gauges in the reference networks of climate and meteorological stations. Data quality information is generally either not available or very limited for third-party data sources. For most dedicated climate meteorological networks, this information is only available for the sensor in laboratory conditions. In many cases, however, a significant part of the measurement errors and uncertainties is determined by the siting and maintenance of the sensor, for which generally only qualitative information is available. Furthermore sensors may have limitations under specific conditions. We aim to quantify data quality for different data sources by performing analyses on collocated data sets. Here we present an intercomparison of two years of precipitation data from six different sources (manual rain gauge, automatic rain gauge, present weather sensor, weather radar, commercial cellular communication links, and Meteosat) at three different locations in the Netherlands. We use auxiliary meteorological data to determine if the quality is influenced by other variables (e.g. the temperature influencing the evaporation from the rain gauge). We use three techniques to compare the data sets: 1) direct comparison; 2) triple collocation (see Stoffelen, 1998); and 3) comparison of statistics. Stoffelen, A. (1998). Toward the true near-surface wind speed: Error modeling and calibration using triple collocation. Journal of Geophysical Research: Oceans (1978-2012), 103(C4), 7755-7766.

Evaluating the Capacity of Global CO2 Flux and Atmospheric Transport Models to Incorporate New Satellite Observations

NASA Technical Reports Server (NTRS)

Kawa, S. R.; Collatz, G. J.; Erickson, D. J.; Denning, A. S.; Wofsy, S. C.; Andrews, A. E.

2007-01-01

As we enter the new era of satellite remote sensing for CO2 and other carbon cyclerelated quantities, advanced modeling and analysis capabilities are required to fully capitalize on the new observations. Model estimates of CO2 surface flux and atmospheric transport are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, and ultimately for future projections of carbon-climate interactions. For application to current, planned, and future remotely sensed CO2 data, it is desirable that these models are accurate and unbiased at time scales from less than daily to multi-annual and at spatial scales from several kilometers or finer to global. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 1998 through 2006. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at lxi degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-2), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to in situ observations at sites in Northern mid latitudes and the continental tropics. The influence of key process representations is inferred. We find that the model can resolve much of the hourly to synoptic variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The seasonal cycle and its interannual variations generally respond adequately, but discrepancies in the tropics suggest the need for a refinement of the soil moisture dependence of the respiration flux in CASA. Examples and inferences for interpretation of satellite data will be discussed. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in the terrestrial CO2 sink and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

Comparing Global Atmospheric CO2 Flux and Transport Models with Remote Sensing (and Other) Observations

NASA Technical Reports Server (NTRS)

Kawa, S. R.; Collatz, G. J.; Pawson, S.; Wennberg, P. O.; Wofsy, S. C.; Andrews, A. E.

2010-01-01

We report recent progress derived from comparison of global CO2 flux and transport models with new remote sensing and other sources of CO2 data including those from satellite. The overall objective of this activity is to improve the process models that represent our understanding of the workings of the atmospheric carbon cycle. Model estimates of CO2 surface flux and atmospheric transport processes are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, to provide the basic framework for carbon data assimilation, and ultimately for future projections of carbon-climate interactions. Models can also be used to test consistency within and between CO2 data sets under varying geophysical states. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 2000 through 2009. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at 1x1 degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-3), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to remote sensing observations from TCCON, GOSAT, and AIRS as well as relevant in situ observations. Examples of the influence of key process representations are shown from both forward and inverse model comparisons. We find that the model can resolve much of the synoptic, seasonal, and interannual variability in the observations, although reasons for persistent discrepancies in northern hemisphere vegetation uptake are examined. At this time, we do not find any serious shortcomings in the model transport representation, but this is still the subject of close scrutiny. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in CO2 fluxes and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

Physical and Chemical Properties of Seasonal Snow and the Impacts on Albedo in New Hampshire, USA

NASA Astrophysics Data System (ADS)

Adolph, A. C.; Albert, M. R.; Amante, J.; Dibb, J. E.

2014-12-01

Snow albedo is critical to surface energy budgets and thus to the timing of mid-winter and vernal melt events in seasonal snow packs. Timing of these melt events is important in predicting flooding, understanding plant and animal phenology, and the availability of winter recreational activity. The state of New Hampshire experiences large spatial and temporal variability in snow albedo as a result of differences in meteorological conditions, physical snow structure, and chemical impurities in the snow, particularly highly absorptive black carbon (BC) and dust particles. This work focuses on the winters of 2012-2013 and 2013-2014, comparing three intensive study sites. Data collected at these sites include sub-hourly meteorological data, near daily measurements of snow depth, snow density, surface IR temperature, specific surface area (SSA) from contact spectroscopy, and spectrally resolved snow albedo using an ASD FieldSpec4 throughout the winter season. Additionally, snow samples were analyzed for black carbon content and other chemical impurities including Cl-, NO3-, NH4 , K , Na , Mg2+ , Ca2+ and SO42-. For each storm event at the three intensive sites, moisture sources and paths were determined using HYPLIT back trajectory modeling to determine potential sources of black carbon and other impurities in the snow. Storms with terrestrial-based paths across the US Midwest and Canada resulted in higher BC content than storms with ocean-based paths and sources. In addition to the variable storm path between sites and between years, the second year of study was on average 2.5°C colder than the first year, impacting duration of snow cover at each site and the SSA of surface snow which is sensitive to frequency of snow events and relies on cold temperatures to reduce grain metamorphism. Combining an understanding of storm frequency and path with physical and chemical attributes of the snow allows us to investigate snow albedo sensitivities with implications for understanding the impacts of future climate change on snow albedo in the Northeastern US.

Large and local-scale influences on physical and chemical characteristics of coastal waters of Western Europe during winter

NASA Astrophysics Data System (ADS)

Tréguer, Paul; Goberville, Eric; Barrier, Nicolas; L'Helguen, Stéphane; Morin, Pascal; Bozec, Yann; Rimmelin-Maury, Peggy; Czamanski, Marie; Grossteffan, Emilie; Cariou, Thierry; Répécaud, Michel; Quéméner, Loic

2014-11-01

There is now a strong scientific consensus that coastal marine systems of Western Europe are highly sensitive to the combined effects of natural climate variability and anthropogenic climate change. However, it still remains challenging to assess the spatial and temporal scales at which climate influence operates. While large-scale hydro-climatic indices, such as the North Atlantic Oscillation (NAO) or the East Atlantic Pattern (EAP) and the weather regimes such as the Atlantic Ridge (AR), are known to be relevant predictors of physical processes, changes in coastal waters can also be related to local hydro-meteorological and geochemical forcing. Here, we study the temporal variability of physical and chemical characteristics of coastal waters located at about 48°N over the period 1998-2013 using (1) sea surface temperature, (2) sea surface salinity and (3) nutrient concentration observations for two coastal sites located at the outlet of the Bay of Brest and off Roscoff, (4) river discharges of the major tributaries close to these two sites and (5) regional and local precipitation data over the region of interest. Focusing on the winter months, we characterize the physical and chemical variability of these coastal waters and document changes in both precipitation and river runoffs. Our study reveals that variability in coastal waters is connected to the large-scale North Atlantic atmospheric circulation but is also partly explained by local river influences. Indeed, while the NAO is strongly related to changes in sea surface temperature at the Brest and Roscoff sites, the EAP and the AR have a major influence on precipitations, which in turn modulate river discharges that impact sea surface salinity at the scale of the two coastal stations.

Integrating Meteorology into Research on Migration

PubMed Central

Shamoun-Baranes, Judy; Bouten, Willem; van Loon, E. Emiel

2010-01-01

Atmospheric dynamics strongly influence the migration of flying organisms. They affect, among others, the onset, duration and cost of migration, migratory routes, stop-over decisions, and flight speeds en-route. Animals move through a heterogeneous environment and have to react to atmospheric dynamics at different spatial and temporal scales. Integrating meteorology into research on migration is not only challenging but it is also important, especially when trying to understand the variability of the various aspects of migratory behavior observed in nature. In this article, we give an overview of some different modeling approaches and we show how these have been incorporated into migration research. We provide a more detailed description of the development and application of two dynamic, individual-based models, one for waders and one for soaring migrants, as examples of how and why to integrate meteorology into research on migration. We use these models to help understand underlying mechanisms of individual response to atmospheric conditions en-route and to explain emergent patterns. This type of models can be used to study the impact of variability in atmospheric dynamics on migration along a migratory trajectory, between seasons and between years. We conclude by providing some basic guidelines to help researchers towards finding the right modeling approach and the meteorological data needed to integrate meteorology into their own research. PMID:20811515

A Synoptic Weather Typing Approach to Assess Climate Change Impacts on Meteorological and Hydrological Risks at Local Scale in South-Central Canada

NASA Astrophysics Data System (ADS)

Cheng, Chad Shouquan; Li, Qian; Li, Guilong

2010-05-01

The synoptic weather typing approach has become popular in evaluating the impacts of climate change on a variety of environmental problems. One of the reasons is its ability to categorize a complex set of meteorological variables as a coherent index, which can facilitate analyses of local climate change impacts. The weather typing method has been applied in Environment Canada to analyze climatic change impacts on various meteorological/hydrological risks, such as freezing rain, heavy rainfall, high-/low-flow events, air pollution, and human health. These studies comprise of three major parts: (1) historical simulation modeling to verify the hazardous events, (2) statistical downscaling to provide station-scale future climate information, and (3) estimates of changes in frequency and magnitude of future hazardous meteorological/hydrological events in this century. To achieve these goals, in addition to synoptic weather typing, the modeling conceptualizations in meteorology and hydrology and various linear/nonlinear regression techniques were applied. Furthermore, a formal model result verification process has been built into the entire modeling exercise. The results of the verification, based on historical observations of the outcome variables predicted by the models, showed very good agreement. This paper will briefly summarize these research projects, focusing on the modeling exercise and results.

Algorithm of regional surface evaporation using remote sensing: A case study of Haihe basin, China

NASA Astrophysics Data System (ADS)

Xiong, Jun; Wu, Bingfang; Yan, Nana; Hu, Minggang

2007-11-01

Evapotranspiration (ET, or latent heat flux) is the most essential and uncertain factor in water resource management. Remote sensing is a promising tool for estimation of spatial distribution of ET at regional scale with limited ground observations. We developed an algorithm for estimating regional evapotranspiration from MODIS 1b data and ancillary meteorological data. The algorithm is an integration of Penman-Monteith equation and SEBS (Surface Energy Balance System) model. The former is a combination of the energy balance theory and the mass transfer method to compute the evaporation from cropped surfaces from standard climatological records of sunshine, temperature, humidity and wind speed by introducing resistance factors, and the latter determines the spatio-temporal variability of regional evaporative condition. First, we characterized key land surface parameters on satellite over passing days, including fractional vegetation cover (fc), roughness height for momentum (z0m), net radiation (Rn) and soil heat flux (G0); Second, SEBS was applied to partition the sensible heat (H) from latent heat (LE) in combination with Planetary Boundary Layer (PBL) information from seven meteorological stations. A parameterization of surface roughness was applied at mountainous area considering topographic influence; third, we chose available surface resistance (RS) as the temporal-scaling factor. With bulk surface resistance is properly defined, P-M methods is valid for both soil and vegetation canopy. We validated ET from this algorithm with limited actual observations of ET including 2 eddy covariance system dataset and 1 lysimeter sites. Water balance equation is used as a trend-analysis tool to show the consistency between rainfall and ET on four drainage area. As a result, the prototype products showed different accuracy and applicability on different underlying and time scale, which demonstrates the potential of this approach for estimating ET from 1-km to regional spatial scale in North China Plain.

Rainfall pattern variability as climate change impact in The Wallacea Region

NASA Astrophysics Data System (ADS)

Pujiastuti, I.; Nurjani, E.

2018-04-01

The objective of the study is to observe the characteristic variability of rainfall pattern in the city located in every rainfall type, local (Kendari), monsoon (Manado), and equatorial (Palu). The result will be compared to determine which has the most significantly precipitation changing due to climate change impact. Rainfall variability in Indonesia illustrates precipitation variation thus the important variability is the variability of monthly rainfall. Monthly precipitation data for the period of 1961-2010 are collected from Indonesian Agency for Meteorological, Climatological, and Geophysical Agency. This data is calculated with the normal test statistical method to analyze rainfall variability. The result showed the pattern of trend and variability of rainfall in every city with the own characteristic which determines the rainfall type. Moreover, there is comparison of rainfall pattern changing between every rainfall type. This information is useful for climate change mitigation and adaptation strategies especially in water resource management form precipitation as well as the occurrence of meteorological disasters.

Meteorological Factors for Dengue Fever Control and Prevention in South China.

PubMed

Gu, Haogao; Leung, Ross Ka-Kit; Jing, Qinlong; Zhang, Wangjian; Yang, Zhicong; Lu, Jiahai; Hao, Yuantao; Zhang, Dingmei

2016-08-31

Dengue fever (DF) is endemic in Guangzhou and has been circulating for decades, causing significant economic loss. DF prevention mainly relies on mosquito control and change in lifestyle. However, alert fatigue may partially limit the success of these countermeasures. This study investigated the delayed effect of meteorological factors, as well as the relationships between five climatic variables and the risk for DF by boosted regression trees (BRT) over the period of 2005-2011, to determine the best timing and strategy for adapting such preventive measures. The most important meteorological factor was daily average temperature. We used BRT to investigate the lagged relationship between dengue clinical burden and climatic variables, with the 58 and 62 day lag models attaining the largest area under the curve. The climatic factors presented similar patterns between these two lag models, which can be used as references for DF prevention in the early stage. Our results facilitate the development of the Mosquito Breeding Risk Index for early warning systems. The availability of meteorological data and modeling methods enables the extension of the application to other vector-borne diseases endemic in tropical and subtropical countries.

Precipitation Interpolation by Multivariate Bayesian Maximum Entropy Based on Meteorological Data in Yun- Gui-Guang region, Mainland China

NASA Astrophysics Data System (ADS)

Wang, Chaolin; Zhong, Shaobo; Zhang, Fushen; Huang, Quanyi

2016-11-01

Precipitation interpolation has been a hot area of research for many years. It had close relation to meteorological factors. In this paper, precipitation from 91 meteorological stations located in and around Yunnan, Guizhou and Guangxi Zhuang provinces (or autonomous region), Mainland China was taken into consideration for spatial interpolation. Multivariate Bayesian maximum entropy (BME) method with auxiliary variables, including mean relative humidity, water vapour pressure, mean temperature, mean wind speed and terrain elevation, was used to get more accurate regional distribution of annual precipitation. The means, standard deviations, skewness and kurtosis of meteorological factors were calculated. Variogram and cross- variogram were fitted between precipitation and auxiliary variables. The results showed that the multivariate BME method was precise with hard and soft data, probability density function. Annual mean precipitation was positively correlated with mean relative humidity, mean water vapour pressure, mean temperature and mean wind speed, negatively correlated with terrain elevation. The results are supposed to provide substantial reference for research of drought and waterlog in the region.

Surface Meteorology and Solar Energy (SSE) Data Release 5.1

NASA Technical Reports Server (NTRS)

Stackhouse, Paul W. (Principal Investigator)

The Surface meteorology and Solar Energy (SSE) data set contains over 200 parameters formulated for assessing and designing renewable energy systems.The SSE data set is formulated from NASA satellite- and reanalysis-derived insolation and meteorological data for the 10-year period July 1983 through June 1993. Results are provided for 1 degree latitude by 1 degree longitude grid cells over the globe. Average daily and monthly measurements for 1195 World Radiation Data Centre ground sites are also available. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1983-07-01; Stop_Date=1993-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree].

Benchmarking of Typical Meteorological Year datasets dedicated to Concentrated-PV systems

NASA Astrophysics Data System (ADS)

Realpe, Ana Maria; Vernay, Christophe; Pitaval, Sébastien; Blanc, Philippe; Wald, Lucien; Lenoir, Camille

2016-04-01

Accurate analysis of meteorological and pyranometric data for long-term analysis is the basis of decision-making for banks and investors, regarding solar energy conversion systems. This has led to the development of methodologies for the generation of Typical Meteorological Years (TMY) datasets. The most used method for solar energy conversion systems was proposed in 1978 by the Sandia Laboratory (Hall et al., 1978) considering a specific weighted combination of different meteorological variables with notably global, diffuse horizontal and direct normal irradiances, air temperature, wind speed, relative humidity. In 2012, a new approach was proposed in the framework of the European project FP7 ENDORSE. It introduced the concept of "driver" that is defined by the user as an explicit function of the pyranometric and meteorological relevant variables to improve the representativeness of the TMY datasets with respect the specific solar energy conversion system of interest. The present study aims at comparing and benchmarking different TMY datasets considering a specific Concentrated-PV (CPV) system as the solar energy conversion system of interest. Using long-term (15+ years) time-series of high quality meteorological and pyranometric ground measurements, three types of TMY datasets generated by the following methods: the Sandia method, a simplified driver with DNI as the only representative variable and a more sophisticated driver. The latter takes into account the sensitivities of the CPV system with respect to the spectral distribution of the solar irradiance and wind speed. Different TMY datasets from the three methods have been generated considering different numbers of years in the historical dataset, ranging from 5 to 15 years. The comparisons and benchmarking of these TMY datasets are conducted considering the long-term time series of simulated CPV electric production as a reference. The results of this benchmarking clearly show that the Sandia method is not suitable for CPV systems. For these systems, the TMY datasets obtained using dedicated drivers (DNI only or more precise one) are more representative to derive TMY datasets from limited long-term meteorological dataset.

Meteorological and urban landscape factors on severe air pollution in Beijing.

PubMed

Han, Lijian; Zhou, Weiqi; Li, Weifeng; Meshesha, Derege T; Li, Li; Zheng, Mingqing

2015-07-01

Air pollution gained special attention with the rapid development in Beijing. In January 2013, Beijing experienced extreme air pollution, which was not well examined. We thus examine the magnitude of air quality in the particular month by applying the air quality index (AQI), which is based on the newly upgraded Chinese environmental standard. Our finding revealed that (1) air quality has distinct spatial heterogeneity and relatively better air quality was observed in the northwest while worse quality happened in the southeast part of the city; (2) the wind speed is the main determinant of air quality in the city-when wind speed is greater than 4 m/sec, air quality can be significantly improved; and (3) urban impervious surface makes a contribution to the severity of air pollution-that is, with an increase in the fraction of impervious surface in a given area, air pollution is more severe. The results from our study demonstrated the severe pollution in Beijing and its meteorological and landscape factors. Also, the results of this work suggest that very strict air quality management should be conducted when wind speed less than 4 m/sec, especially at places with a large fraction of urban impervious surface. Prevention of air pollution is rare among methods with controls on meteorological and urban landscape conditions. We present research that utilizes the latest air quality index (AQI) to compare air pollution with meteorological and landscape conditions. We found that wind is the major meteorological factor that determines the air quality. For a given wind speed greater than 4 m/sec, the air quality improved significantly. Urban impervious surface also contributes to the severe air pollution: that is, when the fraction of impervious surface increases, there is more severe air pollution. These results suggest that air quality management should be conducted when wind speed is less than 4 m/sec, especially at places with a larger fraction of urban impervious surface.

Evaluation of a simple, point-scale hydrologic model in simulating soil moisture using the Delaware environmental observing system

NASA Astrophysics Data System (ADS)

Legates, David R.; Junghenn, Katherine T.

2018-04-01

Many local weather station networks that measure a number of meteorological variables (i.e. , mesonetworks) have recently been established, with soil moisture occasionally being part of the suite of measured variables. These mesonetworks provide data from which detailed estimates of various hydrological parameters, such as precipitation and reference evapotranspiration, can be made which, when coupled with simple surface characteristics available from soil surveys, can be used to obtain estimates of soil moisture. The question is Can meteorological data be used with a simple hydrologic model to estimate accurately daily soil moisture at a mesonetwork site? Using a state-of-the-art mesonetwork that also includes soil moisture measurements across the US State of Delaware, the efficacy of a simple, modified Thornthwaite/Mather-based daily water balance model based on these mesonetwork observations to estimate site-specific soil moisture is determined. Results suggest that the model works reasonably well for most well-drained sites and provides good qualitative estimates of measured soil moisture, often near the accuracy of the soil moisture instrumentation. The model exhibits particular trouble in that it cannot properly simulate the slow drainage that occurs in poorly drained soils after heavy rains and interception loss, resulting from grass not being short cropped as expected also adversely affects the simulation. However, the model could be tuned to accommodate some non-standard siting characteristics.

A longitudinal study of mortality and air pollution for São Paulo, Brazil.

PubMed

Botter, Denise A; Jørgensen, Bent; Peres, Antonieta A Q

2002-09-01

We study the effects of various air-pollution variables on the daily death counts for people over 65 years in São Paulo, Brazil, from 1991 to 1993, controlling for meteorological variables. We use a state space model where the air-pollution variables enter via the latent process, and the meteorological variables via the observation equation. The latent process represents the potential mortality due to air pollution, and is estimated by Kalman filter techniques. The effect of air pollution on mortality is found to be a function of the variation in the sulphur dioxide level for the previous 3 days, whereas the other air-pollution variables (total suspended particulates, nitrogen dioxide, carbon monoxide, ozone) are not significant when sulphur dioxide is in the equation. There are significant effects of humidity and up to lag 3 of temperature, and a significant seasonal variation.

Understanding surface-water availability in the Central Valley as a means to projecting future groundwater storage with climate variability

NASA Astrophysics Data System (ADS)

Goodrich, J. P.; Cayan, D. R.

2017-12-01

California's Central Valley (CV) relies heavily on diverted surface water and groundwater pumping to supply irrigated agriculture. However, understanding the spatiotemporal character of water availability in the CV is difficult because of the number of individual farms and local, state, and federal agencies involved in using and managing water. Here we use the Central Valley Hydrologic Model (CVHM), developed by the USGS, to understand the relationships between climatic variability, surface water inputs, and resulting groundwater use over the historical period 1970-2013. We analyzed monthly surface water diversion data from >500 CV locations. Principle components analyses were applied to drivers constructed from meteorological data, surface reservoir storage, ET, land use cover, and upstream inflows, to feed multiple regressions and identify factors most important in predicting surface water diversions. Two thirds of the diversion locations ( 80% of total diverted water) can be predicted to within 15%. Along with monthly inputs, representations of cumulative precipitation over the previous 3 to 36 months can explain an additional 10% of variance, depending on location, compared to results that excluded this information. Diversions in the southern CV are highly sensitive to inter-annual variability in precipitation (R2 = 0.8), whereby more surface water is used during wet years. Until recently, this was not the case in the northern and mid-CV, where diversions were relatively constant annually, suggesting relative insensitivity to drought. In contrast, this has important implications for drought response in southern regions (eg. Tulare Basin) where extended dry conditions can severely limit surface water supplies and lead to excess groundwater pumping, storage loss, and subsidence. In addition to fueling our understanding of spatiotemporal variability in diversions, our ability to predict these water balance components allows us to update CVHM predictions before surface water data are compiled. We can then develop groundwater pumping and storage predictions in real time, and make them available to water managers. In addition, we are working toward future projections by coupling the regional CVHM to downscaled GCM output to assess future scenarios of water availability in this critical region.

Evaluating the Governing Factors of Variability in Nocturnal Boundary Layer Height Based on Elastic Lidar in Wuhan.

PubMed

Wang, Wei; Mao, Feiyue; Gong, Wei; Pan, Zengxin; Du, Lin

2016-11-01

The atmospheric boundary layer (ABL), an atmospheric region near the Earth's surface, is affected by surface forcing and is important for studying air quality, climate, and weather forecasts. In this study, long-term urban nocturnal boundary layers (NBLs) were estimated by an elastic backscatter light detection and ranging (LiDAR) with various methods in Wuhan (30.5° N, 114.4° E), a city in Central China. This study aims to explore two ABL research topics: (1) the relationship between NBL height (NBLH) and near-surface parameters (e.g., sensible heat flux, temperature, wind speed, and relative humidity) to elucidate meteorological processes governing NBL variability; and (2) the influence of NBLH variations in surface particulate matter (PM) in Wuhan. We analyzed the nocturnal ABL-dilution/ABL-accumulation effect on surface particle concentration by using a typical case. A long-term analysis was then performed from 5 December 2012-17 June 2016. Results reveal that the seasonal averages of nocturnal (from 20:00 to 05:00 next day, Chinese standard time) NBLHs are 386 ± 161 m in spring, 473 ± 154 m in summer, 383 ± 137 m in autumn, and 309 ± 94 m in winter. The seasonal variations in NBLH, AOD, and PM 2.5 display a deep (shallow) seasonal mean NBL, consistent with a small (larger) seasonal mean PM 2.5 near the surface. Seasonal variability of NBLH is partly linearly correlated with sensible heat flux at the surface (R = 0.72). Linear regression analyses between NBLH and other parameters show the following: (1) the positive correlation (R = 0.68) between NBLH and surface temperature indicates high (low) NBLH corresponding to warm (cool) conditions; (2) the slight positive correlation (R = 0.52) between NBLH and surface relative humidity in Wuhan; and (3) the weak positive correlation (R = 0.38) between NBLH and wind speed inside the NBL may imply that the latter is not an important direct driver that governs the seasonal variability of NBLH.

Preliminary Interpretation of the MSL REMS Pressure Data

NASA Astrophysics Data System (ADS)

Haberle, Robert; Gómez-Elvira, Javier; de la Torre Juárez, Manuel; Harri, Ari-Matti; Hollingsworth, Jeffery; Kahanpää, Henrik; Kahre, Melinda; Martin-Torres, Javier; Mischna, Michael; Newman, Claire; Rafkin, Scot; Rennó, Nilton; Richardson, Mark; Rodríguez-Manfredi, Jose; Vasavada, Ashwin; Zorzano, Maria-Paz; REMS/MSL Science Teams

2013-04-01

The Rover Environmental Monitoring Station (REMS) on the Mars Science Laboratory (MSL) Curiosity rover consists of a suite of meteorological instruments that measure pressure, temperature (air and ground), wind (speed and direction), relative humidity, and the UV flux. A detailed description of the REMS sensors and their performance can be found in Gómez-Elvira et al. [2012, Space Science Reviews, 170(1-4), 583-640]. Here we focus on interpreting the first 100 sols of REMS operations with a particular emphasis on the pressure data. A unique feature of pressure data is that they reveal information on meteorological phenomena with time scales from seconds to years and spatial scales from local to global. From a single station we can learn about dust devils, regional circulations, thermal tides, synoptic weather systems, the CO2 cycle, dust storms, and interannual variability. Thus far MSL's REMS pressure sensor, provided by the Finnish Meteorological Institute and integrated into the REMS payload by Centro de Astrobiología, is performing flawlessly and our preliminary interpretation of its data includes the discovery of relatively dust-free convective vortices; a regional circulation system significantly modified by Gale crater and its central mound; the strongest thermal tides yet measured from the surface of Mars whose amplitudes and phases are very sensitive to fluctuations in global dust loading; and the classical signature of the seasonal cycling of carbon dioxide into and out of the polar caps.

Residential Ventilation: A Review of Established Systems and a Laboratory Investigation of the Fine Wire Heat Recovery Ventilator

NASA Astrophysics Data System (ADS)

von Hippel, Matthew Hans Benjamin

A novel vehicle concept is introduced and its feasibility as an autonomous, self-propelled weather buoy for use in violent storm systems is analyzed. The vehicle concept is a spar sailboat -- consisting of only a deep keel and a sailing rig; no hull -- a design which is intended to improve longevity in rough seas as well as provide ideal placement opportunities for meteorological sensors. To evaluate the hypothetical locomotive and meteorological observation capabilities of the concept sailing spar in hurricane-like conditions, several relevant oceanographic phenomena are analyzed with the performance of the concept vehicle in mind. Enthalpy transfer from the ocean to the air is noted as the primary driving force of tropical storms and therefore becomes the measuring objective of the sailing spar. A discrete, iterative process for optimizing driving force while achieving equilibrium between the four airfoil surfaces is used to steer the sailing spar towards any objective despite variable and opposing simulated winds. Based on the limitations of sailing theory, logic is developed to autonomously navigate the sailing spar between human-selected waypoints on a digitized geographic map. Due the perceived inability to measure air-sea enthalpy exchange because the nature of tropical storms and due to its small scale, the sailing spar is deemed infeasible as a hurricane-capable meteorological observation platform.

[Application of artificial neural networks on the prediction of surface ozone concentrations].

PubMed

Shen, Lu-Lu; Wang, Yu-Xuan; Duan, Lei

2011-08-01

Ozone is an important secondary air pollutant in the lower atmosphere. In order to predict the hourly maximum ozone one day in advance based on the meteorological variables for the Wanqingsha site in Guangzhou, Guangdong province, a neural network model (Multi-Layer Perceptron) and a multiple linear regression model were used and compared. Model inputs are meteorological parameters (wind speed, wind direction, air temperature, relative humidity, barometric pressure and solar radiation) of the next day and hourly maximum ozone concentration of the previous day. The OBS (optimal brain surgeon) was adopted to prune the neutral work, to reduce its complexity and to improve its generalization ability. We find that the pruned neural network has the capacity to predict the peak ozone, with an agreement index of 92.3%, the root mean square error of 0.0428 mg/m3, the R-square of 0.737 and the success index of threshold exceedance 77.0% (the threshold O3 mixing ratio of 0.20 mg/m3). When the neural classifier was added to the neural network model, the success index of threshold exceedance increased to 83.6%. Through comparison of the performance indices between the multiple linear regression model and the neural network model, we conclud that that neural network is a better choice to predict peak ozone from meteorological forecast, which may be applied to practical prediction of ozone concentration.

Effect of meteorological variables on the incidence of hand, foot, and mouth disease in children: a time-series analysis in Guangzhou, China.

PubMed

Huang, Yong; Deng, Te; Yu, Shicheng; Gu, Jing; Huang, Cunrui; Xiao, Gexin; Hao, Yuantao

2013-03-13

Over the last decade, major outbreaks of hand, foot, and mouth disease (HFMD) have been reported in Asian countries, resulting in thousands of deaths among children. However, less is known regarding the effect of meteorological variables on the incidence of HFMD in children. This study aims at quantifying the relationship between meteorological variables and the incidence of HFMD among children in Guangzhou, China. The association between weekly HFMD cases in children aged <15 years and meteorological variables in Guangzhou from 2008 to 2011 were analyzed using the generalized additive model (GAM) and time-series method, after controlling for long-term trend and seasonality, holiday effects, influenza period and delayed effects. Temperature and relative humidity with one week lag were significantly associated with HFMD infection among children. We found that a 1°C increase in temperature led to an increase of 1.86% (95% CI: 0.92, 2.81%) in the weekly number of cases in the 0-14 years age group. A one percent increase in relative humidity may lead to an increase of 1.42% (95% CI: 0.97, 1.87%) in the weekly number of cases in the 0-14 years age group. This study provides quantitative evidence that the incidence of HFMD in children was associated with high average temperature and high relative humidity. The one-week delay in the effects of temperature and relative humidity on HFMD is consistent with the enterovirus incubation period and the potential time lag between onset of children's sickness and parental awareness and response.

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 Role of Surface Energy Exchange for Simulating Wind Inflow: An Evaluation of Multiple Land Surface Models in WRF for the Southern Great Plains Site Field Campaign Report

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

Wharton, Sonia; Simpson, Matthew; Osuna, Jessica

The Weather Research and Forecasting (WRF) model is used to investigate choice of land surface model (LSM) on the near-surface wind profile, including heights reached by multi-megawatt wind turbines. Simulations of wind profiles and surface energy fluxes were made using five LSMs of varying degrees of sophistication in dealing with soil-plant-atmosphere feedbacks for the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Southern Great Plains (SGP) Central Facility in Oklahoma. Surface-flux and wind-profile measurements were available for validation. The WRF model was run for three two-week periods during which varying canopy and meteorological conditions existed. Themore » LSMs predicted a wide range of energy-flux and wind-shear magnitudes even during the cool autumn period when we expected less variability. Simulations of energy fluxes varied in accuracy by model sophistication, whereby LSMs with very simple or no soil-plant-atmosphere feedbacks were the least accurate; however, the most complex models did not consistently produce more accurate results. Errors in wind shear also were sensitive to LSM choice and were partially related to the accuracy of energy flux data. The variability of LSM performance was relatively high, suggesting that LSM representation of energy fluxes in the WRF model remains a significant source of uncertainty for simulating wind turbine inflow conditions.« less

Urban Surface Radiative Energy Budgets Determined Using Aircraft Scanner Data

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Quattrochi, Dale A.; Rickman, Doug L.; Estes, Maury G.; Arnold, James E. (Technical Monitor)

2002-01-01

It is estimated that by the year 2025, 80% of the world's population will live in cities. The extent of these urban areas across the world can be seen in an image of city lights from the Defense Meteorological Satellite Program. In many areas of North America and Europe, it is difficult to separate individual cities because of the dramatic growth and sprawl of urbanized areas. This conversion of the natural landscape vegetation into man-made urban structures such as roads and buildings drastically alter the regional surface energy budgets, hydrology, precipitation patterns, and meteorology. One of the earliest recognized and measured phenomena of urbanization is the urban heat island (UHI) which was reported as early as 1833 for London and 1862 for Paris. The urban heat island results from the energy that is absorbed by man-made materials during the day and is released at night resulting in the heating of the air within the urban area. The magnitude of the air temperature difference between the urban and surrounding countryside is highly dependent on the structure of the urban area, amount of solar immolation received during the day, and atmospheric conditions during the night. These night time air temperature differences can be in the range of 2 to 5 C. or greater. Although day time air temperature differences between urban areas and the countryside exists during the day, atmospheric mixing and stability reduce the magnitude. This phenomena is not limited to large urban areas, but also occurs in smaller metropolitan areas. The UHI has significant impacts on the urban air quality, meteorology, energy use, and human health. The UPI can be mitigated through increasing the amount of vegetation and modification of urban surfaces using high albedo materials for roofs and paved surfaces. To understand why the urban heat island phenomenon exists it is useful to define the surface in terms of the surface energy budget. Surface temperature and albedo is a major component of the surface energy budget. Knowledge of it is important in any attempt to describe the radiative and mass fluxes which occur at the surface. Use of energy terms in modeling surface energy budgets allows the direct comparison of various land surfaces encountered in a urban landscape, from vegetated (forest and herbaceous) to non-vegetated (bare soil, roads, and buildings). These terms are also easily measured using remote sensing from aircraft or satellite platforms allowing one to examine the spacial variability. The partitioning of energy budget terms depends on the surface type. In natural landscapes, the partitioning is dependent on canopy biomass, leaf area index, aerodynamic roughness, and moisture status, all of which are influenced by the development stage of the ecosystem. In urban landscapes, coverage by man-made materials substantially alters the surface face energy budget. The remotely sensed data obtained from aircraft and satellites, when properly calibrated allows the measurement of important terms in the radiative surface energy budget a urban landscape scale.

Wind regimes and their relation to synoptic variables using self-organizing maps

NASA Astrophysics Data System (ADS)

Berkovic, Sigalit

2018-01-01

This study exemplifies the ability of the self-organizing maps (SOM) method to directly define well known wind regimes over Israel during the entire year, except summer period, at 12:00 UTC. This procedure may be applied at other hours and is highly relevant to future automatic climatological analysis and applications. The investigation is performed by analysing surface wind measurements from 53 Israel Meteorological Service stations. The relation between the synoptic variables and the wind regimes is revealed from the averages of ECMWF ERA-INTERIM reanalysis variables for each SOM wind regime. The inspection of wind regimes and their average geopotential anomalies has shown that wind regimes relate to the gradient of the pressure anomalies, rather than to the specific isobars pattern. Two main wind regimes - strong western and the strong eastern or northern - are well known over this region. The frequencies of the regimes according to seasons is verified. Strong eastern regimes are dominant during winter, while strong western regimes are frequent in all seasons.

How well do meteorological indicators represent agricultural and forest drought across Europe?

NASA Astrophysics Data System (ADS)

Bachmair, S.; Tanguy, M.; Hannaford, J.; Stahl, K.

2018-03-01

Drought monitoring and early warning (M&EW) systems are an important component of agriculture/silviculture drought risk assessment. Many operational information systems rely mostly on meteorological indicators, and a few incorporate vegetation state information. However, the relationships between meteorological drought indicators and agricultural/silvicultural drought impacts vary across Europe. The details of this variability have not been elucidated sufficiently on a continental scale in Europe to inform drought risk management at administrative scales. The objective of this study is to fill this gap and evaluate how useful the variety of meteorological indicators are to assess agricultural/silvicultural drought across Europe. The first part of the analysis systematically linked meteorological drought indicators to remote sensing based vegetation indices (VIs) for Europe at NUTs3 administrative regions scale using correlation analysis for crops and forests. In a second step, a stepwise multiple linear regression model was deployed to identify variables explaining the spatial differences observed. Finally, corn crop yield in Germany was chosen as a case study to verify VIs’ representativeness of agricultural drought impacts. Results show that short accumulation periods of SPI and SPEI are best linked to crop vegetation stress in most cases, which further validates the use of SPI3 in existing operational drought monitors. However, large regional differences in correlations are also revealed. Climate (temperature and precipitation) explained the largest proportion of variance, suggesting that meteorological indices are less informative of agricultural/silvicultural drought in colder/wetter parts of Europe. These findings provide important context for interpreting meteorological indices on widely used national to continental M&EW systems, leading to a better understanding of where/when such M&EW tools can be indicative of likely agricultural stress and impacts.

Local Adaptive Calibration of the GLASS Surface Incident Shortwave Radiation Product Using Smoothing Spline

NASA Astrophysics Data System (ADS)

Zhang, X.; Liang, S.; Wang, G.

2015-12-01

Incident solar radiation (ISR) over the Earth's surface plays an important role in determining the Earth's climate and environment. Generally, can be obtained from direct measurements, remotely sensed data, or reanalysis and general circulation models (GCMs) data. Each type of product has advantages and limitations: the surface direct measurements provide accurate but sparse spatial coverage, whereas other global products may have large uncertainties. Ground measurements have been normally used for validation and occasionally calibration, but transforming their "true values" spatially to improve the satellite products is still a new and challenging topic. In this study, an improved thin-plate smoothing spline approach is presented to locally "calibrate" the Global LAnd Surface Satellite (GLASS) ISR product using the reconstructed ISR data from surface meteorological measurements. The influences of surface elevation on ISR estimation was also considered in the proposed method. The point-based surface reconstructed ISR was used as the response variable, and the GLASS ISR product and the surface elevation data at the corresponding locations as explanatory variables to train the thin plate spline model. We evaluated the performance of the approach using the cross-validation method at both daily and monthly time scales over China. We also evaluated estimated ISR based on the thin-plate spline method using independent ground measurements at 10 sites from the Coordinated Enhanced Observation Network (CEON). These validation results indicated that the thin plate smoothing spline method can be effectively used for calibrating satellite derived ISR products using ground measurements to achieve better accuracy.

Influence of ozone and meteorological parameters on levels of polycyclic aromatic hydrocarbons in the air

NASA Astrophysics Data System (ADS)

Pehnec, Gordana; Jakovljević, Ivana; Šišović, Anica; Bešlić, Ivan; Vađić, Vladimira

2016-04-01

Concentrations of ten polycyclic aromatic hydrocarbons (PAHs) in the PM10 particle fraction were measured together with ozone and meteorological parameters at an urban site (Zagreb, Croatia) over a one-year period. Data were subjected to regression analysis in order to determine the relationship between the measured pollutants and selected meteorological variables. All of the PAHs showed seasonal variations with high concentrations in winter and autumn and very low concentrations during summer and spring. All of the ten PAHs concentrations also correlated well with each other. A statistically significant negative correlation was found between the concentrations of PAHs and ozone concentrations and concentrations of PAHs and temperature, as well as a positive correlation between concentrations of PAHs and PM10 mass concentration and relative humidity. Multiple regression analysis showed that concentrations of PM10 and ozone, temperature, relative humidity and pressure accounted for 43-70% of PAHs variability. Concentrations of PM10 and temperature were significant variables for all of the measured PAH's concentrations in all seasons. Ozone concentrations were significant for only some of the PAHs, particularly 6-ring PAHs.

Changes in the type of precipitation and associated cloud types in Eastern Romania (1961-2008)

NASA Astrophysics Data System (ADS)

Manea, Ancuta; Birsan, Marius-Victor; Tudorache, George; Cărbunaru, Felicia

2016-03-01

Recent climate change is characterized (among other things) by changes in the frequency of some meteorological phenomena. This paper deals with the long-term changes in various precipitation types, and the connection between their variability and cloud type frequencies, at 11 meteorological stations from Eastern Romania over 1961-2008. These stations were selected with respect to data record completeness for all considered variables (weather phenomena and cloud type). The meteorological variables involved in the present study are: monthly number of days with rain, snowfall, snow showers, rain and snow (sleet), sleet showers and monthly frequency of the Cumulonimbus, Nimbostratus and Stratus clouds. Our results show that all stations present statistically significant decreasing trends in the number of days with rain in the warm period of the year. Changes in the frequency of days for each precipitation type show statistically significant decreasing trends for non-convective (stratiform) precipitation - rain, drizzle, sleet and snowfall -, while the frequencies of rain shower and snow shower (convective precipitation) are increasing. Cloud types show decreasing trends for Nimbostratus and Stratus, and increasing trends for Cumulonimbus.

Mapping near-surface air temperature, pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

NASA Astrophysics Data System (ADS)

Li, Tao; Zheng, Xiaogu; Dai, Yongjiu; Yang, Chi; Chen, Zhuoqi; Zhang, Shupeng; Wu, Guocan; Wang, Zhonglei; Huang, Chengcheng; Shen, Yan; Liao, Rongwei

2014-09-01

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal resolution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km×1 km. The approach comprises two steps: (1) fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; (2) apply a simple kriging procedure to the residual for trend surface correction. The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.

[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.

Climate Products and Services to Meet the Challenges of Extreme Events

NASA Astrophysics Data System (ADS)

McCalla, M. R.

2008-12-01

The 2002 Office of the Federal Coordinator for Meteorological Services and Supporting Research (OFCM1)-sponsored report, Weather Information for Surface Transportation: National Needs Assessment Report, addressed meteorological needs for six core modes of surface transportation: roadway, railway, transit, marine transportation/operations, pipeline, and airport ground operations. The report's goal was to articulate the weather information needs and attendant surface transportation weather products and services for those entities that use, operate, and manage America's surface transportation infrastructure. The report documented weather thresholds and associated impacts which are critical for decision-making in surface transportation. More recently, the 2008 Climate Change Science Program's (CCSP) Synthesis and Assessment Product (SAP) 4.7 entitled, Impacts of Climate Change and Variability on Transportation Systems and Infrastructure: Gulf Coast Study, Phase I, included many of the impacts from the OFCM- sponsored report in Table 1.1 of this SAP.2 The Intergovernmental Panel on Climate Change (IPCC) reported that since 1950, there has been an increase in the number of heat waves, heavy precipitation events, and areas of drought. Moreover, the IPCC indicated that greater wind speeds could accompany more severe tropical cyclones.3 Taken together, the OFCM, CCSP, and IPCC reports indicate not only the significance of extreme events, but also the potential increasing significance of many of the weather thresholds and associated impacts which are critical for decision-making in surface transportation. Accordingly, there is a real and urgent need to understand what climate products and services are available now to address the weather thresholds within the surface transportation arena. It is equally urgent to understand what new climate products and services are needed to address these weather thresholds, and articulate what can be done to fill the gap between the existing federal climate products and services and the needed federal climate products and services which will address these weather thresholds. Just as important, as we work to meet the needs, a robust education and outreach program is essential to take full advantage of new products, services and capabilities. To ascertain what climate products and services currently exist to address weather thresholds relative to surface transportation, what climate products and services are needed to address these weather thresholds, and how to bridge the gap between what is available and what is needed, the OFCM surveyed the federal meteorological community. Consistent with the extreme events highlighted in the IPCC report, the OFCM survey categorized the weather thresholds associated with surface transportation into the following extreme event areas: (a) excessive heat, (b) winter precipitation, (c) summer precipitation, (d) high winds, and (e) flooding and coastal inundation. The survey results, the gap analysis, as well as OFCM's planned, follow-on activities with additional categories (i.e., in addition to surface transportation) and weather thresholds will be shared with meeting participants. 1 The OFCM is an interdepartmental office established in response to Public Law 87-843 with the mission to ensure the effective use of federal meteorological resources by leading the systematic coordination of operational weather and climate requirements, products, services, and supporting research among the federal agencies. 2 http://www.climatescience.gov/Library/sap/sap4-7/final-report/sap4-7-final-ch1.pdf 3 http://www.gcrio.org/ipcc/ar4/wg1/faq/ar4wg1faq-3-3.pdf

Exploiting diurnal variations to evaluate the ISCCP-FD flux calculations and radiative-flux-analysis-processed surface observations from BSRN, ARM, and SURFRAD

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

Zhang, Yuanchong; Long, Charles N.; Rossow, William B.

2010-01-01

Based on monthly-3-hourly and 3-hourly mean surface radiative fluxes and their associated meteorological parameters for 2004 from the International Satellite Cloud Climatology Project-FD (ISCCP-FD) and the Radiative Flux Analysis method-Produced Surface Observations (RFA-PSO) for 15 high-quality-controlled surface stations, operated by the Baseline Surface Radiation Network (BSRN), the Atmospheric Radiation Measurement (ARM) and the National Oceanic and Atmospheric Administration's Surface Radiation budget network (SURFRAD), this work, goes beyond the previous validation for FD against surface observation by introducing the Meteorological Similarity Comparison Method (MSCM) to make a more precise, mutual evaluation of both FD and PSO products. The comparison results inmore » substantial uncertainty reduction and provides reasonable physical explanations for the flux differences. This approach compares fluxes for cases where the atmospheric and surface physical properties (specifically, the input parameters for radiative transfer model) are as close as possible to the values determined at the observational sites by matching the RFA-produced cloud fraction (CF) and/or optical thickness (Tau), etc., or alternatively, by directly changing the model input variables for FD to match PSO values, and using such-produced matched sub-datasets to make more accurate comparisons based on more similar meteorological environments between FD and PSO. The crucial part is the availability of flux-associated meteorological parameters from RFA-PSO, which was only recently made available that makes this work possible. For surface downwelling shortwave(SW) flux (SWdn) and its two components, diffuse (Dif) and direct (Dir), uncertainty for monthly mean is 15, 15 and 17 W/m 2, respectively, smaller than the separately estimated uncertainty values from both FD and PSO. When applying MSCM by reducing their CF difference, the differences can be reduced by a factor of 2. The strength of MSCM is particularly shown in the comparisons of diurnal variations. For clear sky, reducing the FD values of aerosol optical depth (AOD) by 50% to approximately match the PSO values brings all downward SW flux components into substantial agreement. For cloudy scenes, when both CF and Tau are matched to within 0.1 – 0.25 and ~10, respectively, the majority of the SW flux components have nearly-perfect agreement between FD and PSO. The best restriction differences are not zero indicates the influence of other parameters that are not accounted for yet. For longwave (LW) fluxes, general evaluation also confirms uncertainty values for FD and PSO less than separately estimated. When applying MSCM to CF and surface air temperature, the agreement is substantially improved. For downwelling LW diurnal variation comparison, FD shows good agreement with PSO for both RFA-defined or true clear sky but overestimates the amplitude for cloudy sky by 3-7 W/m 2, which may be caused by different sensitivities to cirrus clouds. For upwelling LW diurnal cycle, the situation is reversed; FD now underestimates the diurnal amplitude for all and clear sky but generally agrees for overcast (CF > 0.7). The combined effect of downwelling and upwelling LW fluxes results in FD's underestimates of the diurnal variation of the net-LW-loss for all the scenes by up to 10 W/m 2, although the daily mean net loss is more accurate. Therefore, in terms of amplitude and phase, both FD and PSO seem to have caught correct diurnal variations.« less

Computer simulations of space-borne meteorological systems on the CYBER 205

NASA Technical Reports Server (NTRS)

Halem, M.

1984-01-01

Because of the extreme expense involved in developing and flight testing meteorological instruments, an extensive series of numerical modeling experiments to simulate the performance of meteorological observing systems were performed on CYBER 205. The studies compare the relative importance of different global measurements of individual and composite systems of the meteorological variables needed to determine the state of the atmosphere. The assessments are made in terms of the systems ability to improve 12 hour global forecasts. Each experiment involves the daily assimilation of simulated data that is obtained from a data set called nature. This data is obtained from two sources: first, a long two-month general circulation integration with the GLAS 4th Order Forecast Model and second, global analysis prepared by the National Meteorological Center, NOAA, from the current observing systems twice daily.

The Future of Wind Energy in California: Future Projections in Variable-Resolution CESM

NASA Astrophysics Data System (ADS)

Wang, M.; Ullrich, P. A.; Millstein, D.; Collier, C.

2017-12-01

This study focuses on the wind energy characterization and future projection at five primary wind turbine sites in California. Historical (1980-2000) and mid-century (2030-2050) simulations were produced using the Variable-Resolution Community Earth System Model (VR-CESM) to analyze the trends and variations in wind energy under climate change. Datasets from Det Norske Veritas Germanischer Llyod (DNV GL), MERRA-2, CFSR, NARR, as well as surface observational data were used for model validation and comparison. Significant seasonal wind speed changes under RCP8.5 were detected from several wind farm sites. Large-scale patterns were then investigated to analyze the synoptic-scale impact on localized wind change. The agglomerative clustering method was applied to analyze and group different wind patterns. The associated meteorological background of each cluster was investigated to analyze the drivers of different wind patterns. This study improves the characterization of uncertainty around the magnitude and variability in space and time of California's wind resources in the near future, and also enhances understanding of the physical mechanisms related to the trends in wind resource variability.

New insights into the use of stable water isotopes at the northern Antarctic Peninsula as a tool for regional climate studies

NASA Astrophysics Data System (ADS)

Fernandoy, Francisco; Tetzner, Dieter; Meyer, Hanno; Gacitúa, Guisella; Hoffmann, Kirstin; Falk, Ulrike; Lambert, Fabrice; MacDonell, Shelley

2018-03-01

Due to recent atmospheric and oceanic warming, the Antarctic Peninsula is one of the most challenging regions of Antarctica to understand in terms of both local- and regional-scale climate signals. Steep topography and a lack of long-term and in situ meteorological observations complicate the extrapolation of existing climate models to the sub-regional scale. Therefore, new techniques must be developed to better understand processes operating in the region. Isotope signals are traditionally related mainly to atmospheric conditions, but a detailed analysis of individual components can give new insight into oceanic and atmospheric processes. This paper aims to use new isotopic records collected from snow and firn cores in conjunction with existing meteorological and oceanic datasets to determine changes at the climatic scale in the northern extent of the Antarctic Peninsula. In particular, a discernible effect of sea ice cover on local temperatures and the expression of climatic modes, especially the Southern Annular Mode (SAM), is demonstrated. In years with a large sea ice extension in winter (negative SAM anomaly), an inversion layer in the lower troposphere develops at the coastal zone. Therefore, an isotope-temperature relationship (δ-T) valid for all periods cannot be obtained, and instead the δ-T depends on the seasonal variability of oceanic conditions. Comparatively, transitional seasons (autumn and spring) have a consistent isotope-temperature gradient of +0.69 ‰ °C-1. As shown by firn core analysis, the near-surface temperature in the northern-most portion of the Antarctic Peninsula shows a decreasing trend (-0.33 °C year-1) between 2008 and 2014. In addition, the deuterium excess (dexcess) is demonstrated to be a reliable indicator of seasonal oceanic conditions, and therefore suitable to improve a firn age model based on seasonal dexcess variability. The annual accumulation rate in this region is highly variable, ranging between 1060 and 2470 kg m-2 year-1 from 2008 to 2014. The combination of isotopic and meteorological data in areas where data exist is key to reconstruct climatic conditions with a high temporal resolution in polar regions where no direct observations exist.

Classification of rainfall events for weather forecasting purposes in andean region of Colombia

NASA Astrophysics Data System (ADS)

Suárez Hincapié, Joan Nathalie; Romo Melo, Liliana; Vélez Upegui, Jorge Julian; Chang, Philippe

2016-04-01

This work presents a comparative analysis of the results of applying different methodologies for the identification and classification of rainfall events of different duration in meteorological records of the Colombian Andean region. In this study the work area is the urban and rural area of Manizales that counts with a monitoring hydro-meteorological network. This network is composed of forty-five (45) strategically located stations, this network is composed of forty-five (45) strategically located stations where automatic weather stations record seven climate variables: air temperature, relative humidity, wind speed and direction, rainfall, solar radiation and barometric pressure. All this information is sent wirelessly every five (5) minutes to a data warehouse located at the Institute of Environmental Studies-IDEA. With obtaining the series of rainfall recorded by the hydrometeorological station Palogrande operated by the National University of Colombia in Manizales (http://froac.manizales.unal.edu.co/bodegaIdea/); it is with this information that we proceed to perform behavior analysis of other meteorological variables, monitored at surface level and that influence the occurrence of such rainfall events. To classify rainfall events different methodologies were used: The first according to Monjo (2009) where the index n of the heavy rainfall was calculated through which various types of precipitation are defined according to the intensity variability. A second methodology that permitted to produce a classification in terms of a parameter β introduced by Rice and Holmberg (1973) and adapted by Llasat and Puigcerver, (1985, 1997) and the last one where a rainfall classification is performed according to the value of its intensity following the issues raised by Linsley (1977) where the rains can be considered light, moderate and strong fall rates to 2.5 mm / h; from 2.5 to 7.6 mm / h and above this value respectively for the previous classifications. The main contribution which is done with this research is the obtainment elements to optimize and to improve the spatial resolution of the results obtained with mesoscale models such as the Weather Research & Forecasting Model- WRF, used in Colombia for the purposes of weather forecasting and that in addition produces other tools used in current issues such as risk management.

Ensemble projections of wildfire activity and carbonaceous aerosol concentrations over the western United States in the mid-21st century

PubMed Central

Yue, Xu; Mickley, Loretta J.; Logan, Jennifer A.; Kaplan, Jed O.

2013-01-01

We estimate future wildfire activity over the western United States during the mid-21st century (2046–2065), based on results from 15 climate models following the A1B scenario. We develop fire prediction models by regressing meteorological variables from the current and previous years together with fire indexes onto observed regional area burned. The regressions explain 0.25–0.60 of the variance in observed annual area burned during 1980–2004, depending on the ecoregion. We also parameterize daily area burned with temperature, precipitation, and relative humidity. This approach explains ~0.5 of the variance in observed area burned over forest ecoregions but shows no predictive capability in the semi-arid regions of Nevada and California. By applying the meteorological fields from 15 climate models to our fire prediction models, we quantify the robustness of our wildfire projections at mid-century. We calculate increases of 24–124% in area burned using regressions and 63–169% with the parameterization. Our projections are most robust in the southwestern desert, where all GCMs predict significant (p<0.05) meteorological changes. For forested ecoregions, more GCMs predict significant increases in future area burned with the parameterization than with the regressions, because the latter approach is sensitive to hydrological variables that show large inter-model variability in the climate projections. The parameterization predicts that the fire season lengthens by 23 days in the warmer and drier climate at mid-century. Using a chemical transport model, we find that wildfire emissions will increase summertime surface organic carbon aerosol over the western United States by 46–70% and black carbon by 20–27% at midcentury, relative to the present day. The pollution is most enhanced during extreme episodes: above the 84th percentile of concentrations, OC increases by ~90% and BC by ~50%, while visibility decreases from 130 km to 100 km in 32 Federal Class 1 areas in Rocky Mountains Forest. PMID:24015109

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.

Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting.

PubMed

Pires, J C M; Gonçalves, B; Azevedo, F G; Carneiro, A P; Rego, N; Assembleia, A J B; Lima, J F B; Silva, P A; Alves, C; Martins, F G

2012-09-01

This study proposes three methodologies to define artificial neural network models through genetic algorithms (GAs) to predict the next-day hourly average surface ozone (O(3)) concentrations. GAs were applied to define the activation function in hidden layer and the number of hidden neurons. Two of the methodologies define threshold models, which assume that the behaviour of the dependent variable (O(3) concentrations) changes when it enters in a different regime (two and four regimes were considered in this study). The change from one regime to another depends on a specific value (threshold value) of an explanatory variable (threshold variable), which is also defined by GAs. The predictor variables were the hourly average concentrations of carbon monoxide (CO), nitrogen oxide, nitrogen dioxide (NO(2)), and O(3) (recorded in the previous day at an urban site with traffic influence) and also meteorological data (hourly averages of temperature, solar radiation, relative humidity and wind speed). The study was performed for the period from May to August 2004. Several models were achieved and only the best model of each methodology was analysed. In threshold models, the variables selected by GAs to define the O(3) regimes were temperature, CO and NO(2) concentrations, due to their importance in O(3) chemistry in an urban atmosphere. In the prediction of O(3) concentrations, the threshold model that considers two regimes was the one that fitted the data most efficiently.

Effects of Meteorological Variables on the Incidence of Rupture of Intracranial Aneurysms in Central New Jersey.

PubMed

Kellogg, Marissa; Petrov, Dimitriy; Agarwal, Nitin; Patel, Nitesh V; Hansberry, David Richard; Agarwal, Prateek; Brimacombe, Michael; Gandhi, Chirag D; Prestigiacomo, Charles

2017-05-01

Introduction  Previous studies have suggested relationships between the rupture of intracranial aneurysms and meteorological variables such as season, barometric pressure, and temperature. Our objective was to examine the relationship between the incidence of hospital admissions secondary to aneurysmal subarachnoid hemorrhage (aSAH) and meteorological variables in central New Jersey. Methods  The study population consisted of 312 patients who presented to University Hospital in Newark, New Jersey, between January 1, 2003, and December 31, 2008, with aSAH. Days in the 6-year period were classified as nonbleed days (no aSAH), bleed days (one or more aSAHs within 1 calendar day), cluster days (two or more aSAHs within 2 calendar days), and multiple-bleed days (two or more aSAHs within 1 calendar day). Results  The only significant meteorological risk factor for the occurrence of multiple-bleed days was high barometric pressure (1018.5 versus 1016.5 millibars [mbars]; p <  0.04), but an increase in barometric pressure (+ 2.8 mbars) over the 2 days prior to the multiple-bleed day, although not statistically significant, may be a risk factor ( p <  0.09). Barometric pressure was also noted to be increased on bleed days (1017.2 versus 1016.5 mbars) and cluster days (1017.7 versus 1016.5 mbars), but this relationship was not significant ( p <  0.1 and p <  0.1, respectively). Although aSAH days demonstrated consistently lower temperatures than non-aSAH days and dropping temperatures were consistently found in the days preceding the aSAH, these relationships were not significant. Conclusion  Among meteorological factors, high barometric pressure and low temperature may be risk factors for the onset of aSAH. Georg Thieme Verlag KG Stuttgart · New York.

Uncertainties in Episodic Ozone Modeling Stemming from Uncertainties in the Meteorological Fields.

NASA Astrophysics Data System (ADS)

Biswas, Jhumoor; Trivikrama Rao, S.

2001-02-01

This paper examines the uncertainty associated with photochemical modeling using the Variable-Grid Urban Airshed Model (UAM-V) with two different prognostic meteorological models. The meteorological fields for ozone episodes that occurred during 17-20 June, 12-15 July, and 30 July-2 August in the summer of 1995 were derived from two meteorological models, the Regional Atmospheric Modeling System (RAMS) and the Fifth-Generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). The simulated ozone concentrations from the two photochemical modeling systems, namely, RAMS/UAM-V and MM5/UAM-V, are compared with each other and with ozone observations from several monitoring sites in the eastern United States. The overall results indicate that neither modeling system performs significantly better than the other in reproducing the observed ozone concentrations. The results reveal that there is a significant variability, about 20% at the 95% level of confidence, in the modeled 1-h ozone concentration maxima from one modeling system to the other for a given episode. The model-to-model variability in the simulated ozone levels is for most part attributable to the unsystematic type of errors. The directionality for emission controls (i.e., NOx versus VOC sensitivity) is also evaluated with UAM-V using hypothetical emission reductions. The results reveal that not only the improvement in ozone but also the VOC-sensitive and NOx-sensitive regimes are influenced by the differences in the meteorological fields. Both modeling systems indicate that a large portion of the eastern United States is NOx limited, but there are model-to-model and episode-to-episode differences at individual grid cells regarding the efficacy of emission reductions.

A meteorological distribution system for high-resolution terrestrial modeling (MicroMet)

Treesearch

Glen E. Liston; Kelly Elder

2006-01-01

An intermediate-complexity, quasi-physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are...

Mesoscale numerical modeling of meteorological events in a strong topographic gradient in the northeastern part of Mexico

NASA Astrophysics Data System (ADS)

Pineda-Martinez, Luis F.; Carbajal, Noel

2009-08-01

A series of numerical experiments were carried out to study the effect of meteorological events such as warm and cold air masses on climatic features and variability of a understudied region with strong topographic gradients in the northeastern part of Mexico. We applied the mesoscale model MM5. We investigated the influence of soil moisture availability in the performance of the model under two representative events for winter and summer. The results showed that a better resolution in land use cover improved the agreement among observed and calculated data. The topography induces atmospheric circulation patterns that determine the spatial distribution of climate and seasonal behavior. The numerical experiments reveal regions favorable to forced convection on the eastern side of the mountain chains Eastern Sierra Madre and Sierra de Alvarez. These processes affect the vertical and horizontal structure of the meteorological variables along the topographic gradient.

Meteorological Influences on the Seasonality of Lyme Disease in the United States

PubMed Central

Moore, Sean M.; Eisen, Rebecca J.; Monaghan, Andrew; Mead, Paul

2014-01-01

Lyme disease (Borrelia burgdorferi infection) is the most common vector-transmitted disease in the United States. The majority of human Lyme disease (LD) cases occur in the summer months, but the timing of the peak occurrence varies geographically and from year to year. We calculated the beginning, peak, end, and duration of the main LD season in 12 highly endemic states from 1992 to 2007 and then examined the association between the timing of these seasonal variables and several meteorological variables. An earlier beginning to the LD season was positively associated with higher cumulative growing degree days through Week 20, lower cumulative precipitation, a lower saturation deficit, and proximity to the Atlantic coast. The timing of the peak and duration of the LD season were also associated with cumulative growing degree days, saturation deficit, and cumulative precipitation, but no meteorological predictors adequately explained the timing of the end of the LD season. PMID:24470565

Validation and statistical analysis of temperature, humidity profiles and Integrated Water Vapor (IWV) from microwave measurements over Granada (Spain)

NASA Astrophysics Data System (ADS)

Bedoya, Andres; Navas-Guzmán, Francisco; Guerrero-Rascado, Juan Luis; Alados-Arboledas, Lucas

2017-04-01

Profiles of meteorological variables such as temperature, relative humidity and integrated water vapor derived from a ground-based microwave radiometer (MWR, RPG-HATPRO) are continuously monitored since 2012 at Granada station (Southeastern Spain). During this period up to 210 collocated meteorological balloons, equipped with a radiosonde DFM-09 (GRAWMET), were launched. This study is carried out with a twofold goal. On one hand, a validation of the MWR products such as temperature and water vapor mixing ratio profiles and the IWV from MWR is carried out comparing with radiosonde measurements. The behavior of MWR retrievals under clear and cloudy conditions and for special situations such as inversions has been analyzed. On the other hand, the whole period with continuous measurements is used for a statistical evaluation of the meteorological variables derived from MWR in order to thermodynamically characterize the atmosphere over Granada.

Selected meteorological data for an arid climate over bare soil near Beatty, Nye County, Nevada, November 1977 through May 1980

USGS Publications Warehouse

Brown, Robin G.; Nichols, William D.

1990-01-01

Meteorological data were collected over bare soil at a site for low-level radioactive-waste burial near Beatty, Nevada, from November 1977 to May 1980. The data include precipitation, windspeed, wind direction, incident solar radiation, reflected solar radiation, net radiation, dry- and wet-bulb air temperatures at three heights, soil temperature at five depths, and soil-heat flux at three depths. Mean relative humidity was computed for each day of the collection period for which data are available.A discussion is presented of the study site and the instrumentation and procedures used for collecting and processing the data. Selected data from November 1977 to May 1980 are presented in tabular form. Diurnal fluctuations of selected meteorological variables for representative summer and winter periods are graphically presented. The effects on selected variables of a partial solar eclipse are also discussed

Numerical experiments on short-term meteorological effects on solar variability

NASA Technical Reports Server (NTRS)

Somerville, R. C. J.; Hansen, J. E.; Stone, P. H.; Quirk, W. J.; Lacis, A. A.

1975-01-01

A set of numerical experiments was conducted to test the short-range sensitivity of a large atmospheric general circulation model to changes in solar constant and ozone amount. On the basis of the results of 12-day sets of integrations with very large variations in these parameters, it is concluded that realistic variations would produce insignificant meteorological effects. Any causal relationships between solar variability and weather, for time scales of two weeks or less, rely upon changes in parameters other than solar constant or ozone amounts, or upon mechanisms not yet incorporated in the model.

Comparing interpolation techniques for annual temperature mapping across Xinjiang region

NASA Astrophysics Data System (ADS)

Ren-ping, Zhang; Jing, Guo; Tian-gang, Liang; Qi-sheng, Feng; Aimaiti, Yusupujiang

2016-11-01

Interpolating climatic variables such as temperature is challenging due to the highly variable nature of meteorological processes and the difficulty in establishing a representative network of stations. In this paper, based on the monthly temperature data which obtained from the 154 official meteorological stations in the Xinjiang region and surrounding areas, we compared five spatial interpolation techniques: Inverse distance weighting (IDW), Ordinary kriging, Cokriging, thin-plate smoothing splines (ANUSPLIN) and Empirical Bayesian kriging(EBK). Error metrics were used to validate interpolations against independent data. Results indicated that, the ANUSPLIN performed best than the other four interpolation methods.

Effects of Northern Hemisphere Sea Surface Temperature Changes on the Global Air Quality

NASA Astrophysics Data System (ADS)

Yi, K.; Liu, J.

2017-12-01

The roles of regional sea surface temperature (SST) variability on modulating the climate system and consequently the air quality are investigated using the Community Earth System Model (CESM). Idealized, spatially uniform SST anomalies of +/- 1 °C are superimposed onto the North Pacific, North Atlantic, and North Indian Oceans individually. Ignoring the response of natural emissions, our simulations suggest large seasonal and regional variability of surface O3 and PM2.5 concentrations in response to SST anomalies, especially during boreal summers. Increasing the SST by 1 °C in one of the oceans generally decreases the surface O3 concentrations from 1 to 5 ppbv while increases the anthropogenic PM2.5 concentrations from 0.5 to 3 µg m-3. We implement the integrated process rate (IPR) analysis in CESM and find that meteorological transport in response to SST changes is the key process causing air pollutant perturbations in most cases. During boreal summers, the increase in tropical SST over different ocean basins enhances deep convection, which significantly increases the air temperature over the upper troposphere and trigger large-scale subsidence over nearby and remote regions. These processes tend to increase tropospheric stability and suppress rainfall at lower mid-latitudes. Consequently, it reduces the vertical transport of O3 to the surface while facilitating the accumulation of PM2.5 concentrations over most regions. In addition, this regional SST warming may also considerably suppress intercontinental transport of air pollution as confirmed with idealized CO-like tracers. Our findings indicate a robust linkage between basin-scale SST variability and regional air quality, which can help local air quality management.

Evaluating MTCLIM for incident daily solar radiation and humidity in diverse meteorological and topographical environments in the main Hawaiian Islands

NASA Astrophysics Data System (ADS)

Giambelluca, T. W.; Needham, H.; Longman, R. J.

2017-12-01

Continuous and high resolution climatologies are important inputs in determining future scenarios for land processes. In Hawaíi, a lack of continuous meteorological data has been a problem for both ecological and hydrological research of land-surface processes at daily time scales. For downward shortwave radiation (SWdown) and relative humidity (RH) climate variables, the number of surface stations which record daily values are limited and tend to be situated at city airports or in convenient locations leaving large sections of the islands underrepresented. The aim of this study is to evaluate the rationale behind using the mountain microclimate simulator MTCLIM to obtain a gridded observation based ensemble of SWdown and RH data at a daily increment for the period of 1990-2014 for the main Hawaiian Islands. Preliminary results, testing model output with observed data, show mean bias errors (%MBE) of 1.15 W/m2 for SWdown and -0.8% for RH. Mean absolute errors (%MAE) of 32.83 W/m2 SWdown and 14.96% RH, with root mean square errors (%RMSE) of 40.17 W/m2 SWdown and 11.75% RH. Further optimization of the model and additional methods to reduce errors are being investigated to improve the model's functionality with Hawaíi's extreme climate gradients.

A Mulitivariate Statistical Model Describing the Compound Nature of Soil Moisture Drought

NASA Astrophysics Data System (ADS)

Manning, Colin; Widmann, Martin; Bevacqua, Emanuele; Maraun, Douglas; Van Loon, Anne; Vrac, Mathieu

2017-04-01

Soil moisture in Europe acts to partition incoming energy into sensible and latent heat fluxes, thereby exerting a large influence on temperature variability. Soil moisture is predominantly controlled by precipitation and evapotranspiration. When these meteorological variables are accumulated over different timescales, their joint multivariate distribution and dependence structure can be used to provide information of soil moisture. We therefore consider soil moisture drought as a compound event of meteorological drought (deficits of precipitation) and heat waves, or more specifically, periods of high Potential Evapotraspiration (PET). We present here a statistical model of soil moisture based on Pair Copula Constructions (PCC) that can describe the dependence amongst soil moisture and its contributing meteorological variables. The model is designed in such a way that it can account for concurrences of meteorological drought and heat waves and describe the dependence between these conditions at a local level. The model is composed of four variables; daily soil moisture (h); a short term and a long term accumulated precipitation variable (Y1 and Y_2) that account for the propagation of meteorological drought to soil moisture drought; and accumulated PET (Y_3), calculated using the Penman Monteith equation, which can represent the effect of a heat wave on soil conditions. Copula are multivariate distribution functions that allow one to model the dependence structure of given variables separately from their marginal behaviour. PCCs then allow in theory for the formulation of a multivariate distribution of any dimension where the multivariate distribution is decomposed into a product of marginal probability density functions and two-dimensional copula, of which some are conditional. We apply PCC here in such a way that allows us to provide estimates of h and their uncertainty through conditioning on the Y in the form h=h|y_1,y_2,y_3 (1) Applying the model to various Fluxnet sites across Europe, we find the model has good skill and can particularly capture periods of low soil moisture well. We illustrate the relevance of the dependence structure of these Y variables to soil moisture and show how it may be generalised to offer information of soil moisture on a widespread scale where few observations of soil moisture exist. We then present results from a validation study of a selection of EURO CORDEX climate models where we demonstrate the skill of these models in representing these dependencies and so offer insight into the skill seen in the representation of soil moisture in these models.

Computer-generated imagery for 4-D meteorological data

NASA Technical Reports Server (NTRS)

Hibbard, William L.

1986-01-01

The University of Wisconsin-Madison Space Science and Engineering Center is developing animated stereo display terminals for use with McIDAS (Man-computer Interactive Data Access System). This paper describes image-generation techniques which have been developed to take maximum advantage of these terminals, integrating large quantities of four-dimensional meteorological data from balloon and satellite soundings, satellite images, Doppler and volumetric radar, and conventional surface observations. The images have been designed to use perspective, shading, hidden-surface removal, and transparency to augment the animation and stereo-display geometry. They create an illusion of a moving three-dimensional model of the atmosphere. This paper describes the design of these images and a number of rules of thumb for generating four-dimensional meteorological displays.

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.

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.

Integrating meteorology into research on migration.

PubMed

Shamoun-Baranes, Judy; Bouten, Willem; van Loon, E Emiel

2010-09-01

Atmospheric dynamics strongly influence the migration of flying organisms. They affect, among others, the onset, duration and cost of migration, migratory routes, stop-over decisions, and flight speeds en-route. Animals move through a heterogeneous environment and have to react to atmospheric dynamics at different spatial and temporal scales. Integrating meteorology into research on migration is not only challenging but it is also important, especially when trying to understand the variability of the various aspects of migratory behavior observed in nature. In this article, we give an overview of some different modeling approaches and we show how these have been incorporated into migration research. We provide a more detailed description of the development and application of two dynamic, individual-based models, one for waders and one for soaring migrants, as examples of how and why to integrate meteorology into research on migration. We use these models to help understand underlying mechanisms of individual response to atmospheric conditions en-route and to explain emergent patterns. This type of models can be used to study the impact of variability in atmospheric dynamics on migration along a migratory trajectory, between seasons and between years. We conclude by providing some basic guidelines to help researchers towards finding the right modeling approach and the meteorological data needed to integrate meteorology into their own research. © The Author 2010. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved.

Dynamical Downscaling of Meteorology from a Global Model by WRF towards Resolving US PM2.5 Distributions for the Mid 21st Century

NASA Astrophysics Data System (ADS)

Kunwar, S.; Bowden, J.; Milly, G.; Previdi, M. J.; Fiore, A. M.; West, J. J.

2017-12-01

In the coming decades, anthropogenically induced climate change will likely impact PM2.5 through both changing meteorology and feedback in natural emissions. A major goal of our project is to assess changes in PM2.5 levels over the continental US due to climate variability and change for the period 2005-2065. We will achieve this by using regional models to dynamically downscale coarse resolution (20 × 20) meteorology and air chemistry from a global model to finer spatial resolution (12 km), improving air quality projections for regions and subregions of the US (NE, SE, SW, NW, Midwest, Intermountain West). We downscale from GFDL CM3 simulations of the RCP8.5 scenario for the years 2006-2100 with aerosol and ozone precursor emissions fixed at 2005 levels. We carefully select model years from the global simulations that sample the range of PM2.5 distributions for different US regions at mid 21st century (2050-2065). Here we will show results for the meteorological downscaling (using WRF version 3.8.1) for this project, including a performance evaluation for meteorological variables with respect to the global model. In the future, the downscaled meteorology presented here will be used to drive air quality downscaling in CMAQ (version 5.2). Analysis of the resulting PM2.5 statistics for US regions, as well as the drivers for PM2.5 changes, will be important in supporting informed policies for air quality (also health and visibility) planning for different US regions for the next five decades.

Characterizing UT/LS O3 from Global Ozonesonde Profiles Using a Clustering Technique and Meteorological Reanalyses

NASA Astrophysics Data System (ADS)

Stauffer, R. M.; Thompson, A. M.

2017-12-01

Previous studies employing the self-organizing map (SOM) clustering technique to US ozonesonde data proved valuable for quantifying UT/LS O3 variability, and linking meteorological and chemical drivers to the shape of the ozone (O3) profile from the troposphere to the lower stratosphere. Focus has thus far been limited to specific geographical regions, but SOM has demonstrated the advantages of clustering over monthly climatological O3 averages, which mask day-to-day variability in the O3 profile and the correspondence between O3 and meteorology. We expand SOM to a global set of ozonesonde profiles, mostly from WOUDC, spanning 1980-present from 30 sites to evaluate global O3 climatologies and quantify links to geophysical processes for various meteorological regimes. Four clusters of O3 mixing ratio profiles are generated for each site, which show dominant profile shapes that correspond to site latitude. Offsets among O3 profile clusters and monthly O3 climatologies are 100s of ppbv in the UT/LS at higher latitude sites with active dynamics. Examination of meteorological reanalyses reveals a clear relationship among SOM clusters and covarying meteorological fields (geopotential height, potential vorticity, and tropopause height) for most sites. Tropical SOM clusters show marked dependence on velocity potential anomalies calculated from reanalysis winds, with low UT/LS O3 amounts corresponding to enhanced upper-level divergence, and vice versa. In addition to creating SOM cluster-based O3 climatologies, these results are meant to inform future approaches to validation of chemical transport models and satellite retrievals, which often struggle in the UT/LS region.

GEWEX Water and Energy Budget Study

NASA Technical Reports Server (NTRS)

Roads, J.; Bainto, E.; Masuda, K.; Rodell, Matthew; Rossow, W. B.

2008-01-01

Closing the global water and energy budgets has been an elusive Global Energy and Water-cycle Experiment (GEWEX) goal. It has been difficult to gather many of the needed global water and energy variables and processes, although, because of GEWEX, we now have globally gridded observational estimates for precipitation and radiation and many other relevant variables such as clouds and aerosols. Still, constrained models are required to fill in many of the process and variable gaps. At least there are now several atmospheric reanalyses ranging from the early National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and NCEP/Department of Energy (DOE) reanalyses to the more recent ERA40 and JRA-25 reanalyses. Atmospheric constraints include requirements that the models state variables remain close to in situ observations or observed satellite radiances. This is usually done by making short-term forecasts from an analyzed initial state; these short-term forecasts provide the next guess, which is corrected by comparison to available observations. While this analysis procedure is likely to result in useful global descriptions of atmospheric temperature, wind and humidity, there is no guarantee that relevant hydroclimate processes like precipitation, which we can observe and evaluate, and evaporation over land, which we cannot, have similar verisimilitude. Alternatively, the Global Land Data Assimilation System (GLDAS), drives uncoupled land surface models with precipitation, surface solar radiation, and surface meteorology (from bias-corrected reanalyses during the study period) to simulate terrestrial states and surface fluxes. Further constraints are made when a tuned water balance model is used to characterize the global runoff observational estimates. We use this disparate mix of observational estimates, reanalyses, GLDAS and calibrated water balance simulations to try to characterize and close global and terrestrial atmospheric and surface water and energy budgets to within 10-20% for long term (1986-1995), large-scale global to regional annual means.

Techniques for Improved Retrospective Fine-scale Meteorology

EPA Science Inventory

Pleim-Xiu Land-Surface model (PX LSM) was developed for retrospective meteorological simulations to drive chemical transport models. One of the key features of the PX LSM is the indirect soil moisture and temperature nudging. The idea is to provide a three hourly 2-m temperature ...

Surface Meteorological Station - SWiFT southwest - METa1 - Reviewed Data

DOE Data Explorer

Herges, Thomas

2017-10-23

Scaled Wind Farm Technology (SWiFT) Facility meteorological tower (MET), turbine, and Technical University of Denmark (DTU) SpinnerLidar data acquired on 20161216 UTC during a neutral atmospheric boundary layer inflow at a single focus distance of 2.5 D (D=27 m).