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.

Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

NASA Astrophysics Data System (ADS)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.

Development of the TACOM (Tank Automotive Command) Thermal Imaging Model (TTIM). Volume 1. Technical Guide and User’s Manual.

DTIC Science & Technology

1984-12-01

BLOCK DATA Default values for variables input by menus. LIBR Interface with frame I/O routines. SNSR Interface with sensor routines. ATMOS Interface with...Routines Included in Frame I/O Interface Routine Description LIBR Selects options for input or output to a data library. FRREAD Reads frame from file and/or...Layer", Journal of Applied Meteorology 20, pp. 242-249, March 1981. 15 L.J. Harding, Numerical Analysis and Applications Software Abstracts, Computing

How sensitive are estimates of carbon fixation in agricultural models to input data?

PubMed Central

2012-01-01

Background Process based vegetation models are central to understand the hydrological and carbon cycle. To achieve useful results at regional to global scales, such models require various input data from a wide range of earth observations. Since the geographical extent of these datasets varies from local to global scale, data quality and validity is of major interest when they are chosen for use. It is important to assess the effect of different input datasets in terms of quality to model outputs. In this article, we reflect on both: the uncertainty in input data and the reliability of model results. For our case study analysis we selected the Marchfeld region in Austria. We used independent meteorological datasets from the Central Institute for Meteorology and Geodynamics and the European Centre for Medium-Range Weather Forecasts (ECMWF). Land cover / land use information was taken from the GLC2000 and the CORINE 2000 products. Results For our case study analysis we selected two different process based models: the Environmental Policy Integrated Climate (EPIC) and the Biosphere Energy Transfer Hydrology (BETHY/DLR) model. Both process models show a congruent pattern to changes in input data. The annual variability of NPP reaches 36% for BETHY/DLR and 39% for EPIC when changing major input datasets. However, EPIC is less sensitive to meteorological input data than BETHY/DLR. The ECMWF maximum temperatures show a systematic pattern. Temperatures above 20°C are overestimated, whereas temperatures below 20°C are underestimated, resulting in an overall underestimation of NPP in both models. Besides, BETHY/DLR is sensitive to the choice and accuracy of the land cover product. Discussion This study shows that the impact of input data uncertainty on modelling results need to be assessed: whenever the models are applied under new conditions, local data should be used for both input and result comparison. PMID:22296931

Impact of inherent meteorology uncertainty on air quality ...

EPA Pesticide Factsheets

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short-Range Ensemble Forecast system to drive the four-dimensional data assimilation in the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone-mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone-mixing ratios of the ensemble can vary as much as 10–20 ppb

NCEP-ECPC monthly to seasonal US fire danger forecasts

Treesearch

J. Roads; P. Tripp; H. Juang; J. Wang; F. Fujioka; S. Chen

2010-01-01

Five National Fire Danger Rating System indices (including the Ignition Component, Energy Release Component, Burning Index, Spread Component, and the Keetch–Byram Drought Index) and the Fosberg Fire Weather Index are used to characterise US fire danger. These fire danger indices and input meteorological variables, including temperature, relative humidity, precipitation...

Estimating water temperatures in small streams in western Oregon using neural network models

USGS Publications Warehouse

Risley, John C.; Roehl, Edwin A.; Conrads, Paul

2003-01-01

Artificial neural network models were developed to estimate water temperatures in small streams using data collected at 148 sites throughout western Oregon from June to September 1999. The sites were located on 1st-, 2nd-, or 3rd-order streams having undisturbed or minimally disturbed conditions. Data collected at each site for model development included continuous hourly water temperature and description of riparian habitat. Additional data pertaining to the landscape characteristics of the basins upstream of the sites were assembled using geographic information system (GIS) techniques. Hourly meteorological time series data collected at 25 locations within the study region also were assembled. Clustering analysis was used to partition 142 sites into 3 groups. Separate models were developed for each group. The riparian habitat, basin characteristic, and meteorological time series data were independent variables and water temperature time series were dependent variables to the models, respectively. Approximately one-third of the data vectors were used for model training, and the remaining two-thirds were used for model testing. Critical input variables included riparian shade, site elevation, and percentage of forested area of the basin. Coefficient of determination and root mean square error for the models ranged from 0.88 to 0.99 and 0.05 to 0.59 oC, respectively. The models also were tested and validated using temperature time series, habitat, and basin landscape data from 6 sites that were separate from the 142 sites that were used to develop the models. The models are capable of estimating water temperatures at locations along 1st-, 2nd-, and 3rd-order streams in western Oregon. The model user must assemble riparian habitat and basin landscape characteristics data for a site of interest. These data, in addition to meteorological data, are model inputs. Output from the models include simulated hourly water temperatures for the June to September period. Adjustments can be made to the shade input data to simulate the effects of minimum or maximum shade on water temperatures.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Long-term hydrometeorological trends in the Midwest region based on a century long gridded hydrometeorological dataset and simulations from a macro-scale hydrology model

NASA Astrophysics Data System (ADS)

Chiu, C. M.; Hamlet, A. F.

2014-12-01

Climate change is likely to impact the Great Lakes region and Midwest region via changes in Great Lakes water levels, agricultural impacts, river flooding, urban stormwater impacts, drought, water temperature, and impacts to terrestrial and aquatic ecosystems. Self-consistent and temporally homogeneous long-term data sets of precipitation and temperature over the entire Great Lakes region and Midwest regions are needed to provide inputs to hydrologic models, assess historical trends in hydroclimatic variables, and downscale global and regional-scale climate models. To support these needs a new hybrid gridded meteorological forcing dataset at 1/16 degree resolution based on data from co-op station records, the U. S Historical Climatology Network (HCN) , the Historical Canadian Climate Database (HCCD), and Precipitation Regression on Independent Slopes Method (PRISM) has been assembled over the Great Lakes and Midwest region from 1915-2012 at daily time step. These data were then used as inputs to the macro-scale Variable Infiltration Capacity (VIC) hydrology model, implemented over the Midwest and Great Lakes region at 1/16 degree resolution, to produce simulated hydrologic variables that are amenable to long-term trend analysis. Trends in precipitation and temperature from the new meteorological driving data sets, as well as simulated hydrometeorological variables such as snowpack, soil moisture, runoff, and evaporation over the 20th century are presented and discussed.

The U.S. Geological Survey Monthly Water Balance Model Futures Portal

USGS Publications Warehouse

Bock, Andrew R.; Hay, Lauren E.; Markstrom, Steven L.; Emmerich, Christopher; Talbert, Marian

2017-05-03

The U.S. Geological Survey Monthly Water Balance Model Futures Portal (https://my.usgs.gov/mows/) is a user-friendly interface that summarizes monthly historical and simulated future conditions for seven hydrologic and meteorological variables (actual evapotranspiration, potential evapotranspiration, precipitation, runoff, snow water equivalent, atmospheric temperature, and streamflow) at locations across the conterminous United States (CONUS).The estimates of these hydrologic and meteorological variables were derived using a Monthly Water Balance Model (MWBM), a modular system that simulates monthly estimates of components of the hydrologic cycle using monthly precipitation and atmospheric temperature inputs. Precipitation and atmospheric temperature from 222 climate datasets spanning historical conditions (1952 through 2005) and simulated future conditions (2020 through 2099) were summarized for hydrographic features and used to drive the MWBM for the CONUS. The MWBM input and output variables were organized into an open-access database. An Open Geospatial Consortium, Inc., Web Feature Service allows the querying and identification of hydrographic features across the CONUS. To connect the Web Feature Service to the open-access database, a user interface—the Monthly Water Balance Model Futures Portal—was developed to allow the dynamic generation of summary files and plots  based on plot type, geographic location, specific climate datasets, period of record, MWBM variable, and other options. Both the plots and the data files are made available to the user for download 

figure1.nc

EPA Pesticide Factsheets

NetCDF file of the SREF standard deviation of wind speed and direction that was used to inject variability in the FDDA input.variable U_NDG_OLD contains standard deviation of wind speed (m/s)variable V_NDG_OLD contains the standard deviation of wind direction (deg)This dataset is associated with the following publication:Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

A GIS Procedure to Monitor PWV During Severe Meteorological Events

NASA Astrophysics Data System (ADS)

Ferrando, I.; Federici, B.; Sguerso, D.

2016-12-01

As widely known, the observation of GNSS signal's delay can improve the knowledge of meteorological phenomena. The local Precipitable Water Vapour (PWV), which can be easily derived from Zenith Total Delay (ZTD), Pressure (P) and Temperature (T) (Bevis et al., 1994), is not a satisfactory parameter to evaluate the occurrence of severe meteorological events. Hence, a GIS procedure, called G4M (GNSS for Meteorology), has been conceived to produce 2D PWV maps with high spatial and temporal resolution (1 km and 6 minutes respectively). The input data are GNSS, P and T observations not necessarily co-located coming from existing infrastructures, combined with a simplified physical model, owned by the research group.On spite of the low density and the different configurations of GNSS, P and T networks, the procedure is capable to detect severe meteorological events with reliable results. The procedure has already been applied in a wide and orographically complex area covering approximately the north-west of Italy and the French-Italian border region, to study two severe meteorological events occurred in Genoa (Italy) and other meteorological alert cases. The P, T and PWV 2D maps obtained by the procedure have been compared with the ones coming from meteorological re-analysis models, used as reference to obtain statistics on the goodness of the procedure in representing these fields. Additionally, the spatial variability of PWV was taken into account as indicator for representing potential critical situations; this index seems promising in highlighting remarkable features that precede intense precipitations. The strength and originality of the procedure lie into the employment of existing infrastructures, the independence from meteorological models, the high adaptability to different networks configurations, and the ability to produce high-resolution 2D PWV maps even from sparse input data. In the next future, the procedure could also be set up for near real-time applications.

Barrier island forest ecosystem: role of meteorologic nutrient inputs.

PubMed

Art, H W; Bormann, F H; Voigt, G K; Woodwell, G M

1974-04-05

The Sunken Forest, located on Fire Island, a barrier island in the Atlantic Ocean off Long Island, New York, is an ecosystem in which most of the basic cation input is in the form of salt spray. This meteorologic input is sufficient to compensate for the lack of certain nutrients in the highly weathered sandy soils. In other ecosystems these nutrients are generally supplied by weathering of soil particles. The compensatory effect of meteorologic input allows for primary production rates in the Sunken Forest similar to those of inland temperate forests.

Effects of Meteorological Data Quality on Snowpack Modeling

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Robertson, M.; Hedrick, A. R.; Johnson, M.

2017-12-01

Detailed quality control of meteorological inputs is the most time-intensive component of running the distributed, physically-based iSnobal snow model, and the effect of data quality of the inputs on the model is unknown. The iSnobal model has been run operationally since WY2013, and is currently run in several basins in Idaho and California. The largest amount of user input during modeling is for the quality control of precipitation, temperature, relative humidity, solar radiation, wind speed and wind direction inputs. Precipitation inputs require detailed user input and are crucial to correctly model the snowpack mass. This research applies a range of quality control methods to meteorological input, from raw input with minimal cleaning, to complete user-applied quality control. The meteorological input cleaning generally falls into two categories. The first is global minimum/maximum and missing value correction that could be corrected and/or interpolated with automated processing. The second category is quality control for inputs that are not globally erroneous, yet are still unreasonable and generally indicate malfunctioning measurement equipment, such as temperature or relative humidity that remains constant, or does not correlate with daily trends observed at nearby stations. This research will determine how sensitive model outputs are to different levels of quality control and guide future operational applications.

The Gaussian atmospheric transport model and its sensitivity to the joint frequency distribution and parametric variability.

PubMed

Hamby, D M

2002-01-01

Reconstructed meteorological data are often used in some form of long-term wind trajectory models for estimating the historical impacts of atmospheric emissions. Meteorological data for the straight-line Gaussian plume model are put into a joint frequency distribution, a three-dimensional array describing atmospheric wind direction, speed, and stability. Methods using the Gaussian model and joint frequency distribution inputs provide reasonable estimates of downwind concentration and have been shown to be accurate to within a factor of four. We have used multiple joint frequency distributions and probabilistic techniques to assess the Gaussian plume model and determine concentration-estimate uncertainty and model sensitivity. We examine the straight-line Gaussian model while calculating both sector-averaged and annual-averaged relative concentrations at various downwind distances. The sector-average concentration model was found to be most sensitive to wind speed, followed by horizontal dispersion (sigmaZ), the importance of which increases as stability increases. The Gaussian model is not sensitive to stack height uncertainty. Precision of the frequency data appears to be most important to meteorological inputs when calculations are made for near-field receptors, increasing as stack height increases.

Interannual Variability in Intercontinental Transport

NASA Technical Reports Server (NTRS)

Gupta, Mohan; Douglass, Anne; Kawa, S. Randy; Pawson, Steven

2003-01-01

We have investigated the importance of intercontinental transport using source-receptor relationship. A global radon-like and seven regional tracers were used in three-dimensional model simulations to quantify their contributions to column burdens and vertical profiles at world-wide receptors. Sensitivity of these contributions to meteorological input was examined using different years of meteorology in two atmospheric simulations. Results show that Asian emission influences tracer distributions in its eastern downwind regions extending as far as Europe with major contributions in mid- and upper troposphere. On the western and eastern sides of the US, Asian contribution to annual average column burdens are 37% and 5% respectively with strong monthly variations. At an altitude of 10 km, these contributions are 75% and 25% respectively. North American emissions contribute more than 15% to annual average column burden and about 50% at 8 km altitude over the European region. Contributions from tropical African emissions are wide-spread in both the hemispheres. Differences in meteorological input cause non-uniform redistribution of tracer mass throughout the troposphere at all receptors. We also show that in model-model and model-data comparison, correlation analysis of tracer's spatial gradients provides an added measure of model's performance.

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

Response of winter and spring wheat grain yields to meteorological variation

NASA Technical Reports Server (NTRS)

Feyerherm, A. M.; Kanemasu, E. T.; Paulsen, G. M.

1977-01-01

Mathematical models which quantify the relation of wheat yield to selected weather-related variables are presented. Other sources of variation (amount of applied nitrogen, improved varieties, cultural practices) have been incorporated in the models to explain yield variation both singly and in combination with weather-related variables. Separate models were developed for fall-planted (winter) and spring-planted (spring) wheats. Meteorological variation is observed, basically, by daily measurements of minimum and maximum temperatures, precipitation, and tabled values of solar radiation at the edge of the atmosphere and daylength. Two different soil moisture budgets are suggested to compute simulated values of evapotranspiration; one uses the above-mentioned inputs, the other uses the measured temperatures and precipitation but replaces the tabled values (solar radiation and daylength) by measured solar radiation and satellite-derived multispectral scanner data to estimate leaf area index. Weather-related variables are defined by phenological stages, rather than calendar periods, to make the models more universally applicable.

Alpine Ecohydrology Across Scales: Propagating Fine-scale Heterogeneity to the Catchment and Beyond

NASA Astrophysics Data System (ADS)

Mastrotheodoros, T.; Pappas, C.; Molnar, P.; Burlando, P.; Hadjidoukas, P.; Fatichi, S.

2017-12-01

In mountainous ecosystems, complex topography and landscape heterogeneity govern ecohydrological states and fluxes. Here, we investigate topographic controls on water, energy and carbon fluxes across different climatic regimes and vegetation types representative of the European Alps. We use an ecohydrological model to perform fine-scale numerical experiments on a synthetic domain that comprises a symmetric mountain with eight catchments draining along the cardinal and intercardinal directions. Distributed meteorological model input variables are generated using observations from Switzerland. The model computes the incoming solar radiation based on the local topography. We implement a multivariate statistical framework to disentangle the impact of landscape heterogeneity (i.e., elevation, aspect, flow contributing area, vegetation type) on the simulated water, carbon, and energy dynamics. This allows us to identify the sensitivities of several ecohydrological variables (including leaf area index, evapotranspiration, snow-cover and net primary productivity) to topographic and meteorological inputs at different spatial and temporal scales. We also use an alpine catchment as a real case study to investigate how the natural variability of soil and land cover affects the idealized relationships that arise from the synthetic domain. In accordance with previous studies, our analysis shows a complex pattern of vegetation response to radiation. We find also different patterns of ecosystem sensitivity to topography-driven heterogeneity depending on the hydrological regime (i.e., wet vs. dry conditions). Our results suggest that topography-driven variability in ecohydrological variables (e.g. transpiration) at the fine spatial scale can exceed 50%, but it is substantially reduced ( 5%) when integrated at the catchment scale.

Gsflow-py: An integrated hydrologic model development tool

NASA Astrophysics Data System (ADS)

Gardner, M.; Niswonger, R. G.; Morton, C.; Henson, W.; Huntington, J. L.

2017-12-01

Integrated hydrologic modeling encompasses a vast number of processes and specifications, variable in time and space, and development of model datasets can be arduous. Model input construction techniques have not been formalized or made easily reproducible. Creating the input files for integrated hydrologic models (IHM) requires complex GIS processing of raster and vector datasets from various sources. Developing stream network topology that is consistent with the model resolution digital elevation model is important for robust simulation of surface water and groundwater exchanges. Distribution of meteorologic parameters over the model domain is difficult in complex terrain at the model resolution scale, but is necessary to drive realistic simulations. Historically, development of input data for IHM models has required extensive GIS and computer programming expertise which has restricted the use of IHMs to research groups with available financial, human, and technical resources. Here we present a series of Python scripts that provide a formalized technique for the parameterization and development of integrated hydrologic model inputs for GSFLOW. With some modifications, this process could be applied to any regular grid hydrologic model. This Python toolkit automates many of the necessary and laborious processes of parameterization, including stream network development and cascade routing, land coverages, and meteorological distribution over the model domain.

A Comprehensive Estimation of the Economic Effects of Meteorological Services Based on the Input-Output Method

PubMed Central

Wu, Xianhua; Yang, Lingjuan; Guo, Ji; Lu, Huaguo; Chen, Yunfeng; Sun, Jian

2014-01-01

Concentrating on consuming coefficient, partition coefficient, and Leontief inverse matrix, relevant concepts and algorithms are developed for estimating the impact of meteorological services including the associated (indirect, complete) economic effect. Subsequently, quantitative estimations are particularly obtained for the meteorological services in Jiangxi province by utilizing the input-output method. It is found that the economic effects are noticeably rescued by the preventive strategies developed from both the meteorological information and internal relevance (interdependency) in the industrial economic system. Another finding is that the ratio range of input in the complete economic effect on meteorological services is about 1 : 108.27–1 : 183.06, remarkably different from a previous estimation based on the Delphi method (1 : 30–1 : 51). Particularly, economic effects of meteorological services are higher for nontraditional users of manufacturing, wholesale and retail trades, services sector, tourism and culture, and art and lower for traditional users of agriculture, forestry, livestock, fishery, and construction industries. PMID:24578666

A comprehensive estimation of the economic effects of meteorological services based on the input-output method.

PubMed

Wu, Xianhua; Wei, Guo; Yang, Lingjuan; Guo, Ji; Lu, Huaguo; Chen, Yunfeng; Sun, Jian

2014-01-01

Concentrating on consuming coefficient, partition coefficient, and Leontief inverse matrix, relevant concepts and algorithms are developed for estimating the impact of meteorological services including the associated (indirect, complete) economic effect. Subsequently, quantitative estimations are particularly obtained for the meteorological services in Jiangxi province by utilizing the input-output method. It is found that the economic effects are noticeably rescued by the preventive strategies developed from both the meteorological information and internal relevance (interdependency) in the industrial economic system. Another finding is that the ratio range of input in the complete economic effect on meteorological services is about 1 : 108.27-1 : 183.06, remarkably different from a previous estimation based on the Delphi method (1 : 30-1 : 51). Particularly, economic effects of meteorological services are higher for nontraditional users of manufacturing, wholesale and retail trades, services sector, tourism and culture, and art and lower for traditional users of agriculture, forestry, livestock, fishery, and construction industries.

Exploring the full natural variability of eruption sizes within probabilistic hazard assessment of tephra dispersal

NASA Astrophysics Data System (ADS)

Selva, Jacopo; Sandri, Laura; Costa, Antonio; Tonini, Roberto; Folch, Arnau; Macedonio, Giovanni

2014-05-01

The intrinsic uncertainty and variability associated to the size of next eruption strongly affects short to long-term tephra hazard assessment. Often, emergency plans are established accounting for the effects of one or a few representative scenarios (meant as a specific combination of eruptive size and vent position), selected with subjective criteria. On the other hand, probabilistic hazard assessments (PHA) consistently explore the natural variability of such scenarios. PHA for tephra dispersal needs the definition of eruptive scenarios (usually by grouping possible eruption sizes and vent positions in classes) with associated probabilities, a meteorological dataset covering a representative time period, and a tephra dispersal model. PHA results from combining simulations considering different volcanological and meteorological conditions through a weight given by their specific probability of occurrence. However, volcanological parameters, such as erupted mass, eruption column height and duration, bulk granulometry, fraction of aggregates, typically encompass a wide range of values. Because of such a variability, single representative scenarios or size classes cannot be adequately defined using single values for the volcanological inputs. Here we propose a method that accounts for this within-size-class variability in the framework of Event Trees. The variability of each parameter is modeled with specific Probability Density Functions, and meteorological and volcanological inputs are chosen by using a stratified sampling method. This procedure allows avoiding the bias introduced by selecting single representative scenarios and thus neglecting most of the intrinsic eruptive variability. When considering within-size-class variability, attention must be paid to appropriately weight events falling within the same size class. While a uniform weight to all the events belonging to a size class is the most straightforward idea, this implies a strong dependence on the thresholds dividing classes: under this choice, the largest event of a size class has a much larger weight than the smallest event of the subsequent size class. In order to overcome this problem, in this study, we propose an innovative solution able to smoothly link the weight variability within each size class to the variability among the size classes through a common power law, and, simultaneously, respect the probability of different size classes conditional to the occurrence of an eruption. Embedding this procedure into the Bayesian Event Tree scheme enables for tephra fall PHA, quantified through hazard curves and maps representing readable results applicable in planning risk mitigation actions, and for the quantification of its epistemic uncertainties. As examples, we analyze long-term tephra fall PHA at Vesuvius and Campi Flegrei. We integrate two tephra dispersal models (the analytical HAZMAP and the numerical FALL3D) into BET_VH. The ECMWF reanalysis dataset are used for exploring different meteorological conditions. The results obtained clearly show that PHA accounting for the whole natural variability significantly differs from that based on a representative scenarios, as in volcanic hazard common practice.

Predicting Low Flow Conditions from Climatic Indices - Indicator-Based Modeling for Climate Change Impact Assessment

NASA Astrophysics Data System (ADS)

Fangmann, Anne; Haberlandt, Uwe

2014-05-01

In the face of climate change, the assessment of future hydrological regimes has become indispensable in the field of water resources management. Investigation of potential change is vital for proper planning, especially with regard to hydrological extremes. Commonly, projection of future streamflow is done applying process-based hydrological models, using climate model data as input, whose complex model structures generally require excessive amounts of time and effort for set-up and computation. This study aims at identifying practical alternatives to the employment of sophisticated models by considering simpler, yet sufficiently accurate methods for modeling rainfall-runoff relations with regard to hydrological extremes. The focus is thereby put on the prediction of low flow periods, which are, in contrast to flood events, characterized by extended durations and spatial dimensions. The models to be established in this study base on indicators, which characterize both meteorological and hydrological conditions within dry periods. This approach makes direct use of the coupling between atmospheric driving forces and streamflow response with the underlying presumption that low-precipitation and high-evaporation periods result in diminished flow, implying that relationships exist between the properties of both meteorological and hydrological events (duration, volume, severity etc.). Eventually, optimal combinations of meteorological indicators are sought that are suitable to predict various low flow characteristics with satisfactory accuracy. Two approaches for model specification are tested: a) multiple linear regression, and b) Fuzzy logic. The data used for this study are daily time series of mean discharge obtained from 294 gauges with variable record length situated in the federal state of Lower Saxony, Germany, as well as interpolated climate variables available for a period from 1951 to 2011. After extraction of a variety of indicators from the available discharge and climate time series on a bi-annual basis, regression and Fuzzy models are fit. Fitting is done in two variations: locally at each of the watersheds in the study area, and regionally, yielding one specific model expression for the entire study area. Models for the individual stations perform well using only the meteorological indicators as predictor variables, while the regional models require the additional input of catchment descriptors to account for the variability of the rainfall-runoff translation processes between the catchments.

Statistical Approaches for Spatiotemporal Prediction of Low Flows

NASA Astrophysics Data System (ADS)

Fangmann, A.; Haberlandt, U.

2017-12-01

An adequate assessment of regional climate change impacts on streamflow requires the integration of various sources of information and modeling approaches. This study proposes simple statistical tools for inclusion into model ensembles, which are fast and straightforward in their application, yet able to yield accurate streamflow predictions in time and space. Target variables for all approaches are annual low flow indices derived from a data set of 51 records of average daily discharge for northwestern Germany. The models require input of climatic data in the form of meteorological drought indices, derived from observed daily climatic variables, averaged over the streamflow gauges' catchments areas. Four different modeling approaches are analyzed. Basis for all pose multiple linear regression models that estimate low flows as a function of a set of meteorological indices and/or physiographic and climatic catchment descriptors. For the first method, individual regression models are fitted at each station, predicting annual low flow values from a set of annual meteorological indices, which are subsequently regionalized using a set of catchment characteristics. The second method combines temporal and spatial prediction within a single panel data regression model, allowing estimation of annual low flow values from input of both annual meteorological indices and catchment descriptors. The third and fourth methods represent non-stationary low flow frequency analyses and require fitting of regional distribution functions. Method three is subject to a spatiotemporal prediction of an index value, method four to estimation of L-moments that adapt the regional frequency distribution to the at-site conditions. The results show that method two outperforms successive prediction in time and space. Method three also shows a high performance in the near future period, but since it relies on a stationary distribution, its application for prediction of far future changes may be problematic. Spatiotemporal prediction of L-moments appeared highly uncertain for higher-order moments resulting in unrealistic future low flow values. All in all, the results promote an inclusion of simple statistical methods in climate change impact assessment.

On the influence of meteorological input on photochemical modelling of a severe episode over a coastal area

NASA Astrophysics Data System (ADS)

Pirovano, G.; Coll, I.; Bedogni, M.; Alessandrini, S.; Costa, M. P.; Gabusi, V.; Lasry, F.; Menut, L.; Vautard, R.

The modelling reconstruction of the processes determining the transport and mixing of ozone and its precursors in complex terrain areas is a challenging task, particularly when local-scale circulations, such as sea breeze, take place. Within this frame, the ESCOMPTE European campaign took place in the vicinity of Marseille (south-east of France) in summer 2001. The main objectives of the field campaign were to document several photochemical episodes, as well as to constitute a detailed database for chemistry transport models intercomparison. CAMx model has been applied on the largest intense observation periods (IOP) (June 21-26, 2001) in order to evaluate the impacts of two state-of-the-art meteorological models, RAMS and MM5, on chemical model outputs. The meteorological models have been used as best as possible in analysis mode, thus allowing to identify the spread arising in pollutant concentrations as an indication of the intrinsic uncertainty associated to the meteorological input. Simulations have been deeply investigated and compared with a considerable subset of observations both at ground level and along vertical profiles. The analysis has shown that both models were able to reproduce the main circulation features of the IOP. The strongest discrepancies are confined to the Planetary Boundary Layer, consisting of a clear tendency to underestimate or overestimate wind speed over the whole domain. The photochemical simulations showed that variability in circulation intensity was crucial mainly for the representation of the ozone peaks and of the shape of ozone plumes at the ground that have been affected in the same way over the whole domain and all along the simulated period. As a consequence, such differences can be thought of as a possible indicator for the uncertainty related to the definition of meteorological fields in a complex terrain area.

Sixth Annual Workshop on Meteorological and Environmental Inputs to Aviation Systems, 26-28 October 1982, Tullahoma, Tenn

NASA Technical Reports Server (NTRS)

Camp, D. W.; Frost, W.; Coons, F.; Evanich, P.; Sprinkle, C. H.

1984-01-01

The six workshops whose proceedings are presently reported considered the subject of meteorological and environmental information inputs to aviation, in order to satisfy workshop-sponsoring agencies' requirements for (1) greater knowledge of the interaction of the atmosphere with aircraft and airport operators, (2) a better definition and implementation of meteorological services to operators, and (3) the collection and interpretation of data useful in establishing operational criteria that relate the atmospheric science input to aviation community operations. Workshop topics included equipment and instrumentation, forecasts and information updates, training and simulation facilities, and severe weather, icing and wind shear.

Throughfall under a teak plantation in Thailand: a multifactorial analysis on the effects of canopy phenology and meteorological conditions

NASA Astrophysics Data System (ADS)

Tanaka, N.; Levia, D. F., Jr.; Igarashi, Y.; Nanko, K.; Yoshifuji, N.; Tanaka, K.; Chatchai, T.; Suzuki, M.; Kumagai, T.

2014-12-01

Teak (Tectona grandis Linn. f.) plantations cover vast areas throughout Southeast Asia and are of great economic importance. This study has sought to increase our understanding of throughfall inputs under teak by analyzing the abiotic and biotic factors governing throughfall amounts and throughfall ratios in relation to three canopy phenophases (leafless, leafing, and leafed). There is no rain during the brief leaf senescence phenophase. Daily data was available for both throughfall volumes and depths as well as leaf area index. Detailed meteorological data were available in situ every ten minutes. Leveraging this high-resolution field data, we employed boosted regression trees (BRT) analysis to identify the primary controls on throughfall amount and ratio during each of the three canopy phenophases. Whereas throughfall amounts were always dominated by the magnitude of rainfall (as expected), throughfall ratios were governed by a suite of predictor variables during each phenophase. The BRT analysis demonstrated that throughfall ratio in the leafless phase was most influenced (in descending order of importance) by air temperature, rainfall amount, maximum wind speed, and rainfall intensity. Throughfall ratio in the leafed phenophase was dominated by rainfall amount which exerted 54.0% of the relative influence. The leafing phenophase was an intermediate case where rainfall amount, air temperature, and vapor pressure deficit were most important. Our results highlight the fact that throughfall ratios are differentially influenced by a suite of meteorological variables during leafless, leafing, and leafed phenophases. Abiotic variables (rainfall amount, air temperature, vapor pressure deficit, and maximum wind speed) trumped leaf area index and stand density in their effect on throughfall ratio. The leafing phenophase, while transitional in nature and short in duration, has a detectable and unique impact on water inputs to teak plantations. Further work is clearly needed to better gauge the importance of the leaf emergence period to the stemflow hydrology and forest biogeochemistry of teak plantations.

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

Spatial and temporal variability of reference evapotranspiration and influenced meteorological factors in the Jialing River Basin, China

NASA Astrophysics Data System (ADS)

Herath, Imali Kaushalya; Ye, Xuchun; Wang, Jianli; Bouraima, Abdel-Kabirou

2018-02-01

Reference evapotranspiration (ETr) is one of the important parameters in the hydrological cycle. The spatio-temporal variation of ETr and other meteorological parameters that influence ETr were investigated in the Jialing River Basin (JRB), China. The ETr was estimated using the CROPWAT 8.0 computer model based on the Penman-Montieth equation for the period 1964-2014. Mean temperature (MT), relative humidity (RH), sunshine duration (SD), and wind speed (WS) were the main input parameters of CROPWAT while 12 meteorological stations were evaluated. Linear regression and Mann-Kendall methods were applied to study the spatio-temporal trends while the inverse distance weighted (IDW) method was used to identify the spatial distribution of ETr. Stepwise regression and partial correlation methods were used to identify the meteorological variables that most significantly influenced the changes in ETr. The highest annual ETr was found in the northern part of the basin, whereas the lowest rate was recorded in the western part. In the autumn, the highest ETr was recorded in the southeast part of JRB. The annual ETr reflected neither significant increasing nor decreasing trends. Except for the summer, ETr is slightly increasing in other seasons. The MT significantly increased whereas SD and RH were significantly decreased during the 50-year period. Partial correlation and stepwise regression methods found that the impact of meteorological parameters on ETr varies on an annual and seasonal basis while SD, MT, and RH contributed to the changes of annual and seasonal ETr in the JRB.

Proceedings: Sixth Annual Workshop on Meteorological and Environmental Inputs to Aviation Systems

NASA Technical Reports Server (NTRS)

Frost, W. (Editor); Camp, D. W. (Editor); Hershman, L. W. (Editor)

1983-01-01

The topics of interaction of the atmosphere with aviation systems, the better definition and implementation of services to operators, and the collection and interpretation of data for establishing operational criteria relating the total meteorological inputs from the atmospheric sciences to the needs of aviation communities were addressed.

On quality control procedures for solar radiation and meteorological measures, from subhourly to montly average time periods

NASA Astrophysics Data System (ADS)

Espinar, B.; Blanc, P.; Wald, L.; Hoyer-Klick, C.; Schroedter-Homscheidt, M.; Wanderer, T.

2012-04-01

Meteorological data measured by ground stations are often a key element in the development and validation of methods exploiting satellite images. These data are considered as a reference against which satellite-derived estimates are compared. Long-term radiation and meteorological measurements are available from a large number of measuring stations. However, close examination of the data often reveals a lack of quality, often for extended periods of time. This lack of quality has been the reason, in many cases, of the rejection of large amount of available data. The quality data must be checked before their use in order to guarantee the inputs for the methods used in modelling, monitoring, forecast, etc. To control their quality, data should be submitted to several conditions or tests. After this checking, data that are not flagged by any of the test is released as a plausible data. In this work, it has been performed a bibliographical research of quality control tests for the common meteorological variables (ambient temperature, relative humidity and wind speed) and for the usual solar radiometrical variables (horizontal global and diffuse components of the solar radiation and the beam normal component). The different tests have been grouped according to the variable and the average time period (sub-hourly, hourly, daily and monthly averages). The quality test may be classified as follows: • Range checks: test that verify values are within a specific range. There are two types of range checks, those based on extrema and those based on rare observations. • Step check: test aimed at detecting unrealistic jumps or stagnation in the time series. • Consistency checks: test that verify the relationship between two or more time series. The gathered quality tests are applicable for all latitudes as they have not been optimized regionally nor seasonably with the aim of being generic. They have been applied to ground measurements in several geographic locations, what result in the detection of some control tests that are no longer adequate, due to different reasons. After the modification of some test, based in our experience, a set of quality control tests is now presented, updated according to technology advances and classified. The presented set of quality tests allows radiation and meteorological data to be tested in order to know their plausibility to be used as inputs in theoretical or empirical methods for scientific research. The research leading to those results has partly receive funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement no. 262892 (ENDORSE project).

[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.

Improvement of fog predictability in a coupled system of PAFOG and WRF

NASA Astrophysics Data System (ADS)

Kim, Wonheung; Yum, Seong Soo; Kim, Chang Ki

2017-04-01

Fog is difficult to predict because of the multi-scale nature of its formation mechanism: not only the synoptic conditions but also the local meteorological conditions crucially influence fog formation. Coarse vertical resolution and parameterization errors in fog prediction models are also critical reasons for low predictability. In this study, we use a coupled model system of a 3D mesoscale model (WRF) and a single column model with a fine vertical resolution (PAFOG, PArameterized FOG) to simulate fogs formed over the southern coastal region of the Korean Peninsula, where National Center for Intensive Observation of Severe Weather (NCIO) is located. NCIO is unique in that it has a 300 m meteorological tower built at the location to measure basic meteorological variables (temperature, dew point temperature and winds) at eleven different altitudes, and comprehensive atmospheric physics measurements are made with the various remote sensing instruments such as visibility meter, cloud radar, wind profiler, microwave radiometer, and ceilometer. These measurement data are used as input data to the model system and for evaluating the results. Particularly the data for initial and external forcings, which are tightly connected to the predictability of coupled model system, are derived from the tower measurement. This study aims at finding out the most important factors that influence fog predictability of the coupled system for NCIO. Nudging of meteorological tower data and soil moisture variability are found to be critically influencing fog predictability. Detailed results will be discussed at the conference.

Effect of spatial averaging on multifractal properties of meteorological time series

NASA Astrophysics Data System (ADS)

Hoffmann, Holger; Baranowski, Piotr; Krzyszczak, Jaromir; Zubik, Monika

2016-04-01

Introduction The process-based models for large-scale simulations require input of agro-meteorological quantities that are often in the form of time series of coarse spatial resolution. Therefore, the knowledge about their scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice-versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and non-stationarities. Here we assess how spatially aggregating meteorological data to coarser resolutions affects the data's temporal scaling properties. While it is known that spatial aggregation may affect spatial data properties (Hoffmann et al., 2015), it is unknown how it affects temporal data properties. Therefore, the objective of this study was to characterize the aggregation effect (AE) with regard to both temporal and spatial input data properties considering scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological time series through multifractal detrended fluctuation analysis (MFDFA). Materials and Methods Time series coming from years 1982-2011 were spatially averaged from 1 to 10, 25, 50 and 100 km resolution to assess the impact of spatial aggregation. Daily minimum, mean and maximum air temperature (2 m), precipitation, global radiation, wind speed and relative humidity (Zhao et al., 2015) were used. To reveal the multifractal structure of the time series, we used the procedure described in Baranowski et al. (2015). The diversity of the studied multifractals was evaluated by the parameters of time series spectra. In order to analyse differences in multifractal properties to 1 km resolution grids, data of coarser resolutions was disaggregated to 1 km. Results and Conclusions Analysing the spatial averaging on multifractal properties we observed that spatial patterns of the multifractal spectrum (MS) of all meteorological variables differed from 1 km grids and MS-parameters were biased by -29.1 % (precipitation; width of MS) up to >4 % (min. Temperature, Radiation; asymmetry of MS). Also, the spatial variability of MS parameters was strongly affected at the highest aggregation (100 km). Obtained results confirm that spatial data aggregation may strongly affect temporal scaling properties. This should be taken into account when upscaling for large-scale studies. Acknowledgements The study was conducted within FACCE MACSUR. Please see Baranowski et al. (2015) for details on funding. References Baranowski, P., Krzyszczak, J., Sławiński, C. et al. (2015). Climate Research 65, 39-52. Hoffman, H., G. Zhao, L.G.J. Van Bussel et al. (2015). Climate Research 65, 53-69. Zhao, G., Siebert, S., Rezaei E. et al. (2015). Agricultural and Forest Meteorology 200, 156-171.

On the impact of using downscaled reanalysis data instead of direct measurements for modeling the mass balance of a tropical glacier (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Galos, Stephan; Hofer, Marlis; Marzeion, Ben; Mölg, Thomas; Großhauser, Martin

2013-04-01

Due to their setting, tropical glaciers are sensitive indicators of mid-tropospheric meteorological variability and climate change. Furthermore these glaciers are of particular interest because they respond faster to climatic changes than glaciers located in mid- or high-latitudes. As long-term direct meteorological measurements in such remote environments are scarce, reanalysis data (e.g. ERA-Interim) provide a highly valuable source of information. Reanalysis datasets (i) enable a temporal extension of data records gained by direct measurements and (ii) provide information from regions where direct measurements are not available. In order to properly derive the physical exchange processes between glaciers and atmosphere from reanalysis data, downscaling procedures are required. In the present study we investigate if downscaled atmospheric variables (air temperature and relative humidity) from a reanalysis dataset can be used as input for a physically based, high resolution energy and mass balance model. We apply a well validated empirical-statistical downscaling model, fed with ERA-Interim data, to an automated weather station (AWS) on the surface of Glaciar Artesonraju (8.96° S | 77.63° W). The downscaled data is then used to replace measured air temperature and relative humidity in the input for the energy and mass balance model, which was calibrated using ablation data from stakes and a sonic ranger. In order to test the sensitivity of the modeled mass balance to the downscaled data, the results are compared to a reference model run driven solely with AWS data as model input. We finally discuss the results and present future perspectives for further developing this method.

A multi-source probabilistic hazard assessment of tephra dispersal in the Neapolitan area

NASA Astrophysics Data System (ADS)

Sandri, Laura; Costa, Antonio; Selva, Jacopo; Folch, Arnau; Macedonio, Giovanni; Tonini, Roberto

2015-04-01

In this study we present the results obtained from a long-term Probabilistic Hazard Assessment (PHA) of tephra dispersal in the Neapolitan area. Usual PHA for tephra dispersal needs the definition of eruptive scenarios (usually by grouping eruption sizes and possible vent positions in a limited number of classes) with associated probabilities, a meteorological dataset covering a representative time period, and a tephra dispersal model. PHA then results from combining simulations considering different volcanological and meteorological conditions through weights associated to their specific probability of occurrence. However, volcanological parameters (i.e., erupted mass, eruption column height, eruption duration, bulk granulometry, fraction of aggregates) typically encompass a wide range of values. Because of such a natural variability, single representative scenarios or size classes cannot be adequately defined using single values for the volcanological inputs. In the present study, we use a method that accounts for this within-size-class variability in the framework of Event Trees. The variability of each parameter is modeled with specific Probability Density Functions, and meteorological and volcanological input values are chosen by using a stratified sampling method. This procedure allows for quantifying hazard without relying on the definition of scenarios, thus avoiding potential biases introduced by selecting single representative scenarios. Embedding this procedure into the Bayesian Event Tree scheme enables the tephra fall PHA and its epistemic uncertainties. We have appied this scheme to analyze long-term tephra fall PHA from Vesuvius and Campi Flegrei, in a multi-source paradigm. We integrate two tephra dispersal models (the analytical HAZMAP and the numerical FALL3D) into BET_VH. The ECMWF reanalysis dataset are used for exploring different meteorological conditions. The results obtained show that PHA accounting for the whole natural variability are consistent with previous probabilities maps elaborated for Vesuvius and Campi Flegrei on the basis of single representative scenarios, but show significant differences. In particular, the area characterized by a 300 kg/m2-load exceedance probability larger than 5%, accounting for the whole range of variability (that is, from small violent strombolian to plinian eruptions), is similar to that displayed in the maps based on the medium magnitude reference eruption, but it is of a smaller extent. This is due to the relatively higher weight of the small magnitude eruptions considered in this study, but neglected in the reference scenario maps. On the other hand, in our new maps the area characterized by a 300 kg/m2-load exceedance probability larger than 1% is much larger than that of the medium magnitude reference eruption, due to the contribution of plinian eruptions at lower probabilities, again neglected in the reference scenario maps.

GEMPAK5 user's guide, version 5.0

NASA Technical Reports Server (NTRS)

Desjardins, Mary L.; Brill, Keith F.; Schotz, Steven S.

1991-01-01

GEMPAK is a general meteorological software package used to analyze and display conventional meteorological data as well as satellite derived parameters. The User's Guide describes the GEMPAK5 programs and input parameters and details the algorithms used for the meteorological computations.

Modeling Circulation along the Vietnamese Coast Influenced by Monsoon Variability in Meteorology, River Discharge and Interactions with the Vietnamese East Sea

DTIC Science & Technology

2012-09-30

input data and making choices on the configuration of the regional VES model. Details on the domain for the nested/ downscaled shelf model with follow...microwave satellite radiometer data (which are unaffected by cloud) blended with high resolution infrared imagery and found this a useful data set for...variational methods for the assimilation of satellite and in situ observations to achieve improved state estimation and subsequent forecast skill in real

Performance assessment of retrospective meteorological inputs for use in air quality modeling during TexAQS 2006

NASA Astrophysics Data System (ADS)

Ngan, Fong; Byun, Daewon; Kim, Hyuncheol; Lee, Daegyun; Rappenglück, Bernhard; Pour-Biazar, Arastoo

2012-07-01

To achieve more accurate meteorological inputs than was used in the daily forecast for studying the TexAQS 2006 air quality, retrospective simulations were conducted using objective analysis and 3D/surface analysis nudging with surface and upper observations. Model ozone using the assimilated meteorological fields with improved wind fields shows better agreement with the observation compared to the forecasting results. In the post-frontal conditions, important factors for ozone modeling in terms of wind patterns are the weak easterlies in the morning for bringing in industrial emissions to the city and the subsequent clockwise turning of the wind direction induced by the Coriolis force superimposing the sea breeze, which keeps pollutants in the urban area. Objective analysis and nudging employed in the retrospective simulation minimize the wind bias but are not able to compensate for the general flow pattern biases inherited from large scale inputs. By using an alternative analyses data for initializing the meteorological simulation, the model can re-produce the flow pattern and generate the ozone peak location closer to the reality. The inaccurate simulation of precipitation and cloudiness cause over-prediction of ozone occasionally. Since there are limitations in the meteorological model to simulate precipitation and cloudiness in the fine scale domain (less than 4-km grid), the satellite-based cloud is an alternative way to provide necessary inputs for the retrospective study of air quality.

Assessing Fire Weather Index using statistical downscaling and spatial interpolation techniques in Greece

NASA Astrophysics Data System (ADS)

Karali, Anna; Giannakopoulos, Christos; Frias, Maria Dolores; Hatzaki, Maria; Roussos, Anargyros; Casanueva, Ana

2013-04-01

Forest fires have always been present in the Mediterranean ecosystems, thus they constitute a major ecological and socio-economic issue. The last few decades though, the number of forest fires has significantly increased, as well as their severity and impact on the environment. Local fire danger projections are often required when dealing with wild fire research. In the present study the application of statistical downscaling and spatial interpolation methods was performed to the Canadian Fire Weather Index (FWI), in order to assess forest fire risk in Greece. The FWI is used worldwide (including the Mediterranean basin) to estimate the fire danger in a generalized fuel type, based solely on weather observations. The meteorological inputs to the FWI System are noon values of dry-bulb temperature, air relative humidity, 10m wind speed and precipitation during the previous 24 hours. The statistical downscaling methods are based on a statistical model that takes into account empirical relationships between large scale variables (used as predictors) and local scale variables. In the framework of the current study the statistical downscaling portal developed by the Santander Meteorology Group (https://www.meteo.unican.es/downscaling) in the framework of the EU project CLIMRUN (www.climrun.eu) was used to downscale non standard parameters related to forest fire risk. In this study, two different approaches were adopted. Firstly, the analogue downscaling technique was directly performed to the FWI index values and secondly the same downscaling technique was performed indirectly through the meteorological inputs of the index. In both cases, the statistical downscaling portal was used considering the ERA-Interim reanalysis as predictands due to the lack of observations at noon. Additionally, a three-dimensional (3D) interpolation method of position and elevation, based on Thin Plate Splines (TPS) was used, to interpolate the ERA-Interim data used to calculate the index. Results from this method were compared with the statistical downscaling results obtained from the portal. Finally, FWI was computed using weather observations obtained from the Hellenic National Meteorological Service, mainly in the south continental part of Greece and a comparison with the previous results was performed.

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.

EVALUATING THE USE OF OUTPUTS FROM COMPREHENSIVE METEOROLOGICAL MODELS IN AIR QUALITY MODELING APPLICATIONS

EPA Science Inventory

Currently used dispersion models, such as the AMS/EPA Regulatory Model (AERMOD), process routinely available meteorological observations to construct model inputs. Thus, model estimates of concentrations depend on the availability and quality of Meteorological observations, as we...

Assessing risk based on uncertain avalanche activity patterns

NASA Astrophysics Data System (ADS)

Zeidler, Antonia; Fromm, Reinhard

2015-04-01

Avalanches may affect critical infrastructure and may cause great economic losses. The planning horizon of infrastructures, e.g. hydropower generation facilities, reaches well into the future. Based on the results of previous studies on the effect of changing meteorological parameters (precipitation, temperature) and the effect on avalanche activity we assume that there will be a change of the risk pattern in future. The decision makers need to understand what the future might bring to best formulate their mitigation strategies. Therefore, we explore a commercial risk software to calculate risk for the coming years that might help in decision processes. The software @risk, is known to many larger companies, and therefore we explore its capabilities to include avalanche risk simulations in order to guarantee a comparability of different risks. In a first step, we develop a model for a hydropower generation facility that reflects the problem of changing avalanche activity patterns in future by selecting relevant input parameters and assigning likely probability distributions. The uncertain input variables include the probability of avalanches affecting an object, the vulnerability of an object, the expected costs for repairing the object and the expected cost due to interruption. The crux is to find the distribution that best represents the input variables under changing meteorological conditions. Our focus is on including the uncertain probability of avalanches based on the analysis of past avalanche data and expert knowledge. In order to explore different likely outcomes we base the analysis on three different climate scenarios (likely, worst case, baseline). For some variables, it is possible to fit a distribution to historical data, whereas in cases where the past dataset is insufficient or not available the software allows to select from over 30 different distribution types. The Monte Carlo simulation uses the probability distribution of uncertain variables using all valid combinations of the values of input variables to simulate all possible outcomes. In our case the output is the expected risk (Euro/year) for each object (e.g. water intake) considered and the entire hydropower generation system. The output is again a distribution that is interpreted by the decision makers as the final strategy depends on the needs and requirements of the end-user, which may be driven by personal preferences. In this presentation, we will show a way on how we used the uncertain information on avalanche activity in future to subsequently use it in a commercial risk software and therefore bringing the knowledge of natural hazard experts to decision makers.

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).

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.

An algorithm to generate input data from meteorological and space shuttle observations to validate a CH4-CO model

NASA Technical Reports Server (NTRS)

Peters, L. K.; Yamanis, J.

1981-01-01

Objective procedures to analyze data from meteorological and space shuttle observations to validate a three dimensional model were investigated. The transport and chemistry of carbon monoxide and methane in the troposphere were studied. Four aspects were examined: (1) detailed evaluation of the variational calculus procedure, with the equation of continuity as a strong constraint, for adjustment of global tropospheric wind fields; (2) reduction of the National Meteorological Center (NMC) data tapes for data input to the OSTA-1/MAPS Experiment; (3) interpolation of the NMC Data for input to the CH4-CO model; and (4) temporal and spatial interpolation procedures of the CO measurements from the OSTA-1/MAPS Experiment to generate usable contours of the data.

Dispersion Modeling Using Ensemble Forecasts Compared to ETEX Measurements.

NASA Astrophysics Data System (ADS)

Straume, Anne Grete; N'dri Koffi, Ernest; Nodop, Katrin

1998-11-01

Numerous numerical models are developed to predict long-range transport of hazardous air pollution in connection with accidental releases. When evaluating and improving such a model, it is important to detect uncertainties connected to the meteorological input data. A Lagrangian dispersion model, the Severe Nuclear Accident Program, is used here to investigate the effect of errors in the meteorological input data due to analysis error. An ensemble forecast, produced at the European Centre for Medium-Range Weather Forecasts, is then used as model input. The ensemble forecast members are generated by perturbing the initial meteorological fields of the weather forecast. The perturbations are calculated from singular vectors meant to represent possible forecast developments generated by instabilities in the atmospheric flow during the early part of the forecast. The instabilities are generated by errors in the analyzed fields. Puff predictions from the dispersion model, using ensemble forecast input, are compared, and a large spread in the predicted puff evolutions is found. This shows that the quality of the meteorological input data is important for the success of the dispersion model. In order to evaluate the dispersion model, the calculations are compared with measurements from the European Tracer Experiment. The model manages to predict the measured puff evolution concerning shape and time of arrival to a fairly high extent, up to 60 h after the start of the release. The modeled puff is still too narrow in the advection direction.

The effect of changes in space shuttle parameters on the NASA/MSFC multilayer diffusion model predictions of surface HCl concentrations

NASA Technical Reports Server (NTRS)

Glasser, M. E.; Rundel, R. D.

1978-01-01

A method for formulating these changes into the model input parameters using a preprocessor program run on a programed data processor was implemented. The results indicate that any changes in the input parameters are small enough to be negligible in comparison to meteorological inputs and the limitations of the model and that such changes will not substantially increase the number of meteorological cases for which the model will predict surface hydrogen chloride concentrations exceeding public safety levels.

Improvement of Meteorological Inputs for TexAQS-II Air Quality Simulations

NASA Astrophysics Data System (ADS)

Ngan, F.; Byun, D.; Kim, H.; Cheng, F.; Kim, S.; Lee, D.

2008-12-01

An air quality forecasting system (UH-AQF) for Eastern Texas, which is in operation by the Institute for Multidimensional Air Quality Studies (IMAQS) at the University of Houston, uses the Fifth-Generation PSU/NCAR Mesoscale Model MM5 model as the meteorological driver for modeling air quality with the Community Multiscale Air Quality (CMAQ) model. While the forecasting system was successfully used for the planning and implementation of various measurement activities, evaluations of the forecasting results revealed a few systematic problems in the numerical simulations. From comparison with observations, we observe some times over-prediction of northerly winds caused by inaccurate synoptic inputs and other times too strong southerly winds caused by local sea breeze development. Discrepancies in maximum and minimum temperature are also seen for certain days. Precipitation events, as well as clouds, are simulated at the incorrect locations and times occasionally. Model simulatednrealistic thunderstorms are simulated, causing sometimes cause unrealistically strong outflows. To understand physical and chemical processes influencing air quality measures, a proper description of real world meteorological conditions is essential. The objective of this study is to generate better meteorological inputs than the AQF results to support the chemistry modeling. We utilized existing objective analysis and nudging tools in the MM5 system to develop the MUltiscale Nest-down Data Assimilation System (MUNDAS), which incorporates extensive meteorological observations available in the simulated domain for the retrospective simulation of the TexAQS-II period. With the re-simulated meteorological input, we are able to better predict ozone events during TexAQS-II period. In addition, base datasets in MM5 such as land use/land cover, vegetation fraction, soil type and sea surface temperature are updated by satellite data to represent the surface features more accurately. They are key physical parameters inputs affecting transfer of heat, momentum and soil moisture in land-surface process in MM5. Using base the accurate input datasets, we are able to have improved see the differences of predictions of ground temperatures, winds and even thunderstorm activities within boundary layer.

Perils of correlating CUSUM-transformed variables to infer ecological relationships (Breton et al. 2006; Glibert 2010)

USGS Publications Warehouse

Cloern, James E.; Jassby, Alan D.; Carstensen, Jacob; Bennett, William A.; Kimmerer, Wim; Mac Nally, Ralph; Schoellhamer, David H.; Winder, Monika

2012-01-01

We comment on a nonstandard statistical treatment of time-series data first published by Breton et al. (2006) in Limnology and Oceanography and, more recently, used by Glibert (2010) in Reviews in Fisheries Science. In both papers, the authors make strong inferences about the underlying causes of population variability based on correlations between cumulative sum (CUSUM) transformations of organism abundances and environmental variables. Breton et al. (2006) reported correlations between CUSUM-transformed values of diatom biomass in Belgian coastal waters and the North Atlantic Oscillation, and between meteorological and hydrological variables. Each correlation of CUSUM-transformed variables was judged to be statistically significant. On the basis of these correlations, Breton et al. (2006) developed "the first evidence of synergy between climate and human-induced river-based nitrate inputs with respect to their effects on the magnitude of spring Phaeocystis colony blooms and their dominance over diatoms."

WRF-CMAQ Two-way Coupled System with Aerosol Feedback: Software Development and Preliminary Results

EPA Science Inventory

Air quality models such as the EPA Community Multiscale Air Quality (CMAQ) require meteorological data as part of the input to drive the chemistry and transport simulation. The Meteorology-Chemistry Interface Processor (MCIP) is used to convert meteorological data into CMAQ-ready...

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.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Multimodel hydrological ensemble forecasts for the Baskatong catchment in Canada using the TIGGE database.

NASA Astrophysics Data System (ADS)

Tito Arandia Martinez, Fabian

2014-05-01

Adequate uncertainty assessment is an important issue in hydrological modelling. An important issue for hydropower producers is to obtain ensemble forecasts which truly grasp the uncertainty linked to upcoming streamflows. If properly assessed, this uncertainty can lead to optimal reservoir management and energy production (ex. [1]). The meteorological inputs to the hydrological model accounts for an important part of the total uncertainty in streamflow forecasting. Since the creation of the THORPEX initiative and the TIGGE database, access to meteorological ensemble forecasts from nine agencies throughout the world have been made available. This allows for hydrological ensemble forecasts based on multiple meteorological ensemble forecasts. Consequently, both the uncertainty linked to the architecture of the meteorological model and the uncertainty linked to the initial condition of the atmosphere can be accounted for. The main objective of this work is to show that a weighted combination of meteorological ensemble forecasts based on different atmospheric models can lead to improved hydrological ensemble forecasts, for horizons from one to ten days. This experiment is performed for the Baskatong watershed, a head subcatchment of the Gatineau watershed in the province of Quebec, in Canada. Baskatong watershed is of great importance for hydro-power production, as it comprises the main reservoir for the Gatineau watershed, on which there are six hydropower plants managed by Hydro-Québec. Since the 70's, they have been using pseudo ensemble forecast based on deterministic meteorological forecasts to which variability derived from past forecasting errors is added. We use a combination of meteorological ensemble forecasts from different models (precipitation and temperature) as the main inputs for hydrological model HSAMI ([2]). The meteorological ensembles from eight of the nine agencies available through TIGGE are weighted according to their individual performance and combined to form a grand ensemble. Results show that the hydrological forecasts derived from the grand ensemble perform better than the pseudo ensemble forecasts actually used operationally at Hydro-Québec. References: [1] M. Verbunt, A. Walser, J. Gurtz et al., "Probabilistic flood forecasting with a limited-area ensemble prediction system: Selected case studies," Journal of Hydrometeorology, vol. 8, no. 4, pp. 897-909, Aug, 2007. [2] N. Evora, Valorisation des prévisions météorologiques d'ensemble, Institu de recherceh d'Hydro-Québec 2005. [3] V. Fortin, Le modèle météo-apport HSAMI: historique, théorie et application, Institut de recherche d'Hydro-Québec, 2000.

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

NASA Astrophysics Data System (ADS)

Fang, G. H.; Yang, J.; Chen, Y. N.; Zammit, C.

2015-06-01

Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River basin, northwestern China, and expected to be vulnerable to climate change. It has been demonstrated that regional climate models (RCMs) provide more reliable results for a regional impact study of climate change (e.g., on water resources) than general circulation models (GCMs). However, due to their considerable bias it is still necessary to apply bias correction before they are used for water resources research. In this paper, after a sensitivity analysis on input meteorological variables based on the Sobol' method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin. Precipitation correction methods applied include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and quantile mapping (QM), while temperature correction methods are LS, variance scaling (VARI) and DM. The corrected precipitation and temperature were compared to the observed meteorological data, prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on streamflow. The results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM simulations are heavily biased from observed meteorological data, and its use for streamflow simulations results in large biases from observed streamflow, and all bias correction methods effectively improved these simulations; (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g., standard deviation, percentile values) while the LOCI method performed best in terms of the time-series-based indices (e.g., Nash-Sutcliffe coefficient, R2); (4) for temperature, all correction methods performed equally well in correcting raw temperature; and (5) for simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with those of corrected precipitation; i.e., the PT and QM methods performed equally best in correcting flow duration curve and peak flow while the LOCI method performed best in terms of the time-series-based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other areas and models.

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.

Sources of variability of evapotranspiration in California

USGS Publications Warehouse

Hidalgo, H.G.; Cayan, D.R.; Dettinger, M.D.

2005-01-01

The variability (1990-2002) of potential evapotranspiration estimates (ETo) and related meteorological variables from a set of stations from the California Irrigation Management System (CIMIS) is studied. Data from the National Climatic Data Center (NCDC) and from the Department of Energy from 1950 to 2001 were used to validate the results. The objective is to determine the characteristics of climatological ETo and to identify factors controlling its variability (including associated atmospheric circulations). Daily ETo anomalies are strongly correlated with net radiation (Rn) anomalies, relative humidity (RH), and cloud cover, and less with average daily temperature (Tavg). The highest intraseasonal variability of ETo daily anomalies occurs during the spring, mainly caused by anomalies below the high ETo seasonal values during cloudy days. A characteristic circulation pattern is associated with anomalies of ETo and its driving meteorological inputs, Rn, RH, and Tavg, at daily to seasonal time scales. This circulation pattern is dominated by 700-hPa geopotential height (Z700) anomalies over a region off the west coast of North America, approximately between 32?? and 44?? latitude, referred to as the California Pressure Anomaly (CPA). High cloudiness and lower than normal ETo are associated with the lowheight (pressure) phase of the CPA pattern. Higher than normal ETo anomalies are associated with clear skies maintained through anomalously high Z700 anomalies offshore of the North American coast. Spring CPA, cloudiness, maximum temperature (Tmax), pan evaporation (Epan), and ETo conditions have not trended significantly or consistently during the second half of the twentieth century in California. Because it is not known how cloud cover and humidity will respond to climate change, the response of ETo in California to increased greenhouse-gas concentrations is essentially unknown; however, to retain the levels of ETo in the current climate, a decline of Rn by about 6% would be required to compensate for a warming of +3??C. ?? 2005 American Meteorological Society.

High-resolution, regional-scale crop yield simulations for the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D. H.; Kafatos, M.; Medvigy, D.; El-Askary, H. M.; Hatzopoulos, N.; Kim, J.; Kim, S.; Prasad, A. K.; Tremback, C.; Walko, R. L.; Asrar, G. R.

2012-12-01

Over the past few decades, there have been many process-based crop models developed with the goal of better understanding the impacts of climate, soils, and management decisions on crop yields. These models simulate the growth and development of crops in response to environmental drivers. Traditionally, process-based crop models have been run at the individual farm level for yield optimization and management scenario testing. Few previous studies have used these models over broader geographic regions, largely due to the lack of gridded high-resolution meteorological and soil datasets required as inputs for these data intensive process-based models. In particular, assessment of regional-scale yield variability due to climate change requires high-resolution, regional-scale, climate projections, and such projections have been unavailable until recently. The goal of this study was to create a framework for extending the Agricultural Production Systems sIMulator (APSIM) crop model for use at regional scales and analyze spatial and temporal yield changes in the Southwestern United States (CA, AZ, and NV). Using the scripting language Python, an automated pipeline was developed to link Regional Climate Model (RCM) output with the APSIM crop model, thus creating a one-way nested modeling framework. This framework was used to combine climate, soil, land use, and agricultural management datasets in order to better understand the relationship between climate variability and crop yield at the regional-scale. Three different RCMs were used to drive APSIM: OLAM, RAMS, and WRF. Preliminary results suggest that, depending on the model inputs, there is some variability between simulated RCM driven maize yields and historical yields obtained from the United States Department of Agriculture (USDA). Furthermore, these simulations showed strong non-linear correlations between yield and meteorological drivers, with critical threshold values for some of the inputs (e.g. minimum and maximum temperature), beyond which the yields were negatively affected. These results are now being used for further regional-scale yield analysis as the aforementioned framework is adaptable to multiple geographic regions and crop types.

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.

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

NASA Astrophysics Data System (ADS)

Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

2016-05-01

Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

Sensitivity of modeled estuarine circulation to spatial and temporal resolution of input meteorological forcing of a cold frontal passage

NASA Astrophysics Data System (ADS)

Weaver, Robert J.; Taeb, Peyman; Lazarus, Steven; Splitt, Michael; Holman, Bryan P.; Colvin, Jeffrey

2016-12-01

In this study, a four member ensemble of meteorological forcing is generated using the Weather Research and Forecasting (WRF) model in order to simulate a frontal passage event that impacted the Indian River Lagoon (IRL) during March 2015. The WRF model is run to provide high and low, spatial (0.005° and 0.1°) and temporal (30 min and 6 h) input wind and pressure fields. The four member ensemble is used to force the Advanced Circulation model (ADCIRC) coupled with Simulating Waves Nearshore (SWAN) and compute the hydrodynamic and wave response. Results indicate that increasing the spatial resolution of the meteorological forcing has a greater impact on the results than increasing the temporal resolution in coastal systems like the IRL where the length scales are smaller than the resolution of the operational meteorological model being used to generate the forecast. Changes in predicted water elevations are due in part to the upwind and downwind behavior of the input wind forcing. The significant wave height is more sensitive to the meteorological forcing, exhibited by greater ensemble spread throughout the simulation. It is important that the land mask, seen by the meteorological model, is representative of the geography of the coastal estuary as resolved by the hydrodynamic model. As long as the temporal resolution of the wind field captures the bulk characteristics of the frontal passage, computational resources should be focused so as to ensure that the meteorological model resolves the spatial complexities, such as the land-water interface, that drive the land use responsible for dynamic downscaling of the winds.

Improved meteorology from an updated WRF/CMAQ modeling ...

EPA Pesticide Factsheets

Realistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) that employs the Pleim-Xiu land surface model (PX LSM). Recently, PX LSM WRF/CMAQ has been updated in vegetation, soil, and boundary layer processes resulting in improved 2 m temperature (T) and mixing ratio (Q), 10 m wind speed, and surface ozone simulations across the domain compared to the previous version for a period around August 2006. Yearlong meteorology simulations with the updated system demonstrate that MODIS input helps reduce bias of the 2 m Q estimation during the growing season from April to September. Improvements follow the green-up in the southeast from April and move toward the west and north through August. From October to March, MODIS input does not have much influence on the system because vegetation is not as active. The greatest effects of MODIS input include more accurate phenology, better representation of leaf area index (LAI) for various forest ecosystems and agricultural areas, and realistically sparse vegetation coverage in the western drylands. Despite the improved meteorology, MODIS input causes higher bias for the surface O3 simulation in April, August, and October in areas where MODIS LAI is much less than the base LAI. Thus, improvement

FLUXCOM - Overview and First Synthesis

NASA Astrophysics Data System (ADS)

Jung, M.; Ichii, K.; Tramontana, G.; Camps-Valls, G.; Schwalm, C. R.; Papale, D.; Reichstein, M.; Gans, F.; Weber, U.

2015-12-01

We present a community effort aiming at generating an ensemble of global gridded flux products by upscaling FLUXNET data using an array of different machine learning methods including regression/model tree ensembles, neural networks, and kernel machines. We produced products for gross primary production, terrestrial ecosystem respiration, net ecosystem exchange, latent heat, sensible heat, and net radiation for two experimental protocols: 1) at a high spatial and 8-daily temporal resolution (5 arc-minute) using only remote sensing based inputs for the MODIS era; 2) 30 year records of daily, 0.5 degree spatial resolution by incorporating meteorological driver data. Within each set-up, all machine learning methods were trained with the same input data for carbon and energy fluxes respectively. Sets of input driver variables were derived using an extensive formal variable selection exercise. The performance of the extrapolation capacities of the approaches is assessed with a fully internally consistent cross-validation. We perform cross-consistency checks of the gridded flux products with independent data streams from atmospheric inversions (NEE), sun-induced fluorescence (GPP), catchment water balances (LE, H), satellite products (Rn), and process-models. We analyze the uncertainties of the gridded flux products and for example provide a breakdown of the uncertainty of mean annual GPP originating from different machine learning methods, different climate input data sets, and different flux partitioning methods. The FLUXCOM archive will provide an unprecedented source of information for water, energy, and carbon cycle 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.

Assessing the Influence of Precipitation Variability on the Vegetation Dynamics of the Mediterranean Rangelands using NDVI and Machine Learning

NASA Astrophysics Data System (ADS)

Daliakopoulos, Ioannis; Tsanis, Ioannis

2017-04-01

Mitigating the vulnerability of Mediterranean rangelands against degradation is limited by our ability to understand and accurately characterize those impacts in space and time. The Normalized Difference Vegetation Index (NDVI) is a radiometric measure of the photosynthetically active radiation absorbed by green vegetation canopy chlorophyll and is therefore a good surrogate measure of vegetation dynamics. On the other hand, meteorological indices such as the drought assessing Standardised Precipitation Index (SPI) are can be easily estimated from historical and projected datasets at the global scale. This work investigates the potential of driving Random Forest (RF) models with meteorological indices to approximate NDVI-based vegetation dynamics. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The updated E-OBS-v13.1 dataset of the ENSEMBLES EU FP6 program provides observed monthly meteorological input to estimate SPI over the Mediterranean rangelands. RF models are trained to depict vegetation dynamics using the latest version (3g.v1) of the third generation GIMMS NDVI generated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensors. Analysis is conducted for the period 1981-2015 at a gridded spatial resolution of 25 km. Preliminary results demonstrate the potential of machine learning algorithms to effectively mimic the underlying physical relationship of drought and Earth Observation vegetation indices to provide estimates based on precipitation variability.

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

A comparison between modeled and measured permafrost temperatures at Ritigraben borehole, Switzerland

NASA Astrophysics Data System (ADS)

Mitterer-Hoinkes, Susanna; Lehning, Michael; Phillips, Marcia; Sailer, Rudolf

2013-04-01

The area-wide distribution of permafrost is sparsely known in mountainous terrain (e.g. Alps). Permafrost monitoring can only be based on point or small scale measurements such as boreholes, active rock glaciers, BTS measurements or geophysical measurements. To get a better understanding of permafrost distribution, it is necessary to focus on modeling permafrost temperatures and permafrost distribution patterns. A lot of effort on these topics has been already expended using different kinds of models. In this study, the evolution of subsurface temperatures over successive years has been modeled at the location Ritigraben borehole (Mattertal, Switzerland) by using the one-dimensional snow cover model SNOWPACK. The model needs meteorological input and in our case information on subsurface properties. We used meteorological input variables of the automatic weather station Ritigraben (2630 m) in combination with the automatic weather station Saas Seetal (2480 m). Meteorological data between 2006 and 2011 on an hourly basis were used to drive the model. As former studies showed, the snow amount and the snow cover duration have a great influence on the thermal regime. Low snow heights allow for deeper penetration of low winter temperatures into the ground, strong winters with a high amount of snow attenuate this effect. In addition, variations in subsurface conditions highly influence the temperature regime. Therefore, we conducted sensitivity runs by defining a series of different subsurface properties. The modeled subsurface temperature profiles of Ritigraben were then compared to the measured temperatures in the Ritigraben borehole. This allows a validation of the influence of subsurface properties on the temperature regime. As expected, the influence of the snow cover is stronger than the influence of sub-surface material properties, which are significant, however. The validation presented here serves to prepare a larger spatial simulation with the complex hydro-meteorological 3-dimensional model Alpine 3D, which is based on a distributed application of SNOWPACK.

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.

Using Meteorological Analogues for Reordering Postprocessed Precipitation Ensembles in Hydrological Forecasting

NASA Astrophysics Data System (ADS)

Bellier, Joseph; Bontron, Guillaume; Zin, Isabella

2017-12-01

Meteorological ensemble forecasts are nowadays widely used as input of hydrological models for probabilistic streamflow forecasting. These forcings are frequently biased and have to be statistically postprocessed, using most of the time univariate techniques that apply independently to individual locations, lead times and weather variables. Postprocessed ensemble forecasts therefore need to be reordered so as to reconstruct suitable multivariate dependence structures. The Schaake shuffle and ensemble copula coupling are the two most popular methods for this purpose. This paper proposes two adaptations of them that make use of meteorological analogues for reconstructing spatiotemporal dependence structures of precipitation forecasts. Performances of the original and adapted techniques are compared through a multistep verification experiment using real forecasts from the European Centre for Medium-Range Weather Forecasts. This experiment evaluates not only multivariate precipitation forecasts but also the corresponding streamflow forecasts that derive from hydrological modeling. Results show that the relative performances of the different reordering methods vary depending on the verification step. In particular, the standard Schaake shuffle is found to perform poorly when evaluated on streamflow. This emphasizes the crucial role of the precipitation spatiotemporal dependence structure in hydrological ensemble forecasting.

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.

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.

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.

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.

Linking the M&Rfi Weather Generator with Agrometeorological Models

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin; Trnka, Miroslav

2015-04-01

Realistic meteorological inputs (representing the present and/or future climates) for the agrometeorological model simulations are often produced by stochastic weather generators (WGs). This contribution presents some methodological issues and results obtained in our recent experiments. We also address selected questions raised in the synopsis of this session. The input meteorological time series for our experiments are produced by the parametric single site weather generator (WG) Marfi, which is calibrated from the available observational data (or interpolated from surrounding stations). To produce meteorological series representing the future climate, the WG parameters are modified by climate change scenarios, which are prepared by the pattern scaling method: the standardised scenarios derived from Global or Regional Climate Models are multiplied by the change in global mean temperature (ΔTG) determined by the simple climate model MAGICC. The presentation will address following questions: (i) The dependence of the quality of the synthetic weather series and impact results on the WG settings. An emphasis will be put on an effect of conditioning the daily WG on monthly WG (presently being one of our hot topics), which aims at improvement of the reproduction of the low-frequency weather variability. Comparison of results obtained with various WG settings is made in terms of climatic and agroclimatic indices (including extreme temperature and precipitation characteristics and drought indices). (ii) Our methodology accounts for the uncertainties coming from various sources. We will show how the climate change impact results are affected by 1. uncertainty in climate modelling, 2. uncertainty in ΔTG, and 3. uncertainty related to the complexity of the climate change scenario (focusing on an effect of inclusion of changes in variability into the climate change scenarios). Acknowledgements: This study was funded by project "Building up a multidisciplinary scientific team focused on drought" No. CZ.1.07/2.3.00/20.0248. The weather generator is being developed within the frame of WG4VALUE project (LD12029), which is supported by Ministry of Education, Youth and Sports and linked to the COST action ES1102 VALUE.

Impact of inherent meteorology uncertainty on air quality model predictions

EPA Science Inventory

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is impor...

Spatial differences in drought vulnerability

NASA Astrophysics Data System (ADS)

Perčec Tadić, M.; Cindić, K.; Gajić-Čapka, M.; Zaninović, K.

2012-04-01

Drought causes the highest economic losses among all hydro-meteorological events in Croatia. It is the most frequent hazard, which produces the highest damages in the agricultural sector. The climate assessment in Croatia according to the aridity index (defined as the ratio of precipitation and potential evapotranspiration) shows that the susceptibility to desertification is present in the warm part of the year and it is mostly pronounced in the Adriatic region and the eastern Croatia lowland. The evidence of more frequent extreme drought events in the last decade is apparent. These facts were motivation to study the drought risk assessment in Croatia. One step in this issue is the construction of the vulnerability map. This map is a complex combination of the geomorphologic and climatological inputs (maps) that are presumed to be natural factors which modify the amount of moisture in the soil. In this study, the first version of the vulnerability map is followed by the updated one that additionally includes the soil types and the land use classes. The first input considered is the geomorphologic slope angle calculated from the digital elevation model (DEM). The SRTM DEM of 100 m resolution is used. The steeper slopes are more likely to lose water and to become dryer. The second climatological parameter, the solar irradiation map, gives for the territory of Croatia the maximum irradiation on the coast. The next meteorological parameter that influences the drought vulnerability is precipitation which is in this assessment included through the precipitation variability expressed by the coefficient of variation. Larger precipitation variability is related with the higher drought vulnerability. The preliminary results for Croatia, according to the recommended procedure in the framework of Drought Management Centre for Southeastern Europe (DMCSEE project), show the most sensitive areas to drought in the southern Adriatic coast and eastern continental lowland.

Measuring progress of the global sea level observing system

NASA Astrophysics Data System (ADS)

Woodworth, Philip L.; Aarup, Thorkild; Merrifield, Mark; Mitchum, Gary T.; Le Provost, Christian

Sea level is such a fundamental parameter in the sciences of oceanography geophysics, and climate change, that in the mid-1980s, the Intergovernmental Oceanographic Commission (IOC) established the Global Sea Level Observing System (GLOSS). GLOSS was to improve the quantity and quality of data provided to the Permanent Service for Mean Sea Level (PSMSL), and thereby, data for input to studies of long-term sea level change by the Intergovernmental Panel on Climate Change (IPCC). It would also provide the key data needed for international programs, such as the World Ocean Circulation Experiment (WOCE) and later, the Climate Variability and Predictability Programme (CLIVAR).GLOSS is now one of the main observation components of the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM) of IOC and the World Meteorological Organization (WMO). Progress and deficiencies in GLOSS were presented in July to the 22nd IOC Assembly at UNESCO in Paris and are contained in the GLOSS Assessment Report (GAR) [IOC, 2003a].

The model SIRANE for atmospheric urban pollutant dispersion. PART III: Validation against NO2 yearly concentration measurements in a large urban agglomeration

NASA Astrophysics Data System (ADS)

Soulhac, L.; Nguyen, C. V.; Volta, P.; Salizzoni, P.

2017-10-01

We present a validation study of an updated version of the SIRANE model, whose results have been systematically compared to concentrations of nitrogen dioxide collected over the whole urban agglomeration of Lyon. We model atmospheric dispersion of nitrogen oxides emitted by road traffic, industries and domestic heating. The meteorological wind field is computed by a pre-processor using data collected at a ground level monitoring station. Model results are compared with hourly concentrations measured at 15 monitoring stations over the whole year (2008). Further 75 passive diffusion samplers were used during 3 periods of 2 weeks to get a detailed spatial distribution over the west part of the city. An analysis of the model results depending on the variability of the meteorological input allows us to identify the causes for peculiar bad performances of the model and to identify possible improvements of the parameterisations implemented in it.

Using the Quantile Mapping to improve a weather generator

NASA Astrophysics Data System (ADS)

Chen, Y.; Themessl, M.; Gobiet, A.

2012-04-01

We developed a weather generator (WG) by using statistical and stochastic methods, among them are quantile mapping (QM), Monte-Carlo, auto-regression, empirical orthogonal function (EOF). One of the important steps in the WG is using QM, through which all the variables, no matter what distribution they originally are, are transformed into normal distributed variables. Therefore, the WG can work on normally distributed variables, which greatly facilitates the treatment of random numbers in the WG. Monte-Carlo and auto-regression are used to generate the realization; EOFs are employed for preserving spatial relationships and the relationships between different meteorological variables. We have established a complete model named WGQM (weather generator and quantile mapping), which can be applied flexibly to generate daily or hourly time series. For example, with 30-year daily (hourly) data and 100-year monthly (daily) data as input, the 100-year daily (hourly) data would be relatively reasonably produced. Some evaluation experiments with WGQM have been carried out in the area of Austria and the evaluation results will be presented.

Preliminary validation of WRF model in two Arctic fjords, Hornsund and Porsanger

NASA Astrophysics Data System (ADS)

Aniskiewicz, Paulina; Stramska, Małgorzata

2017-04-01

Our research is focused on development of efficient modeling system for arctic fjords. This tool should include high-resolution meteorological data derived using downscaling approach. In this presentation we have focused on modeling, with high spatial resolution, of the meteorological conditions in two Arctic fjords: Hornsund (H), located in the western part of Svalbard archipelago and Porsanger (P) located in the coastal waters of the Barents Sea. The atmospheric downscaling is based on The Weather Research and Forecasting Model (WRF, www.wrf-model.org) with polar stereographic projection. We have created two parent domains with grid point distances of about 3.2 km (P) and 3.0 km (H) and with nested domains (almost 5 times higher resolution than parent domains). We tested what is the impact of the spatial resolution of the model on derived meteorological quantities. For both fjords the input topography data resolution is 30 sec. To validate the results we have used meteorological data from the Norwegian Meteorological Institute for stations Lakselv (L) and Honningsvåg (Ho) located in the inner and outer parts of the Porsanger fjord as well as from station in the outer part of the Hornsund fjord. We have estimated coefficients of determination (r2), statistical errors (St) and systematic errors (Sy) between measured and modelled air temperature and wind speed at each station. This approach will allow us to create high resolution spatially variable meteorological fields that will serve as forcing for numerical models of the fjords. We will investigate the role of different meteorological quantities (e. g. wind, solar insolation, precipitation) on hydrohraphic processes in fjords. The project has been financed from the funds of the Leading National Research Centre (KNOW) received by the Centre for Polar Studies for the period 2014-2018. This work was also funded by the Norway Grants (NCBR contract No. 201985, project NORDFLUX). Partial support comes from the Institute of Oceanology (IO PAN).

Municipality Level Simulations of Dengue Fever Incidence in Puerto Rico Using Ground Based and Remotely Sensed Climate Data

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Morin, Cory

2015-01-01

Dengue fever (DF) is caused by a virus transmitted between humans and Aedes genus mosquitoes through blood feeding. In recent decades incidence of the disease has drastically increased in the tropical Americas, culminating with the Pan American outbreak in 2010 which resulted in 1.7 million reported cases. In Puerto Rico dengue is endemic, however, there is significant inter-annual, intraannual, and spatial variability in case loads. Variability in climate and the environment, herd immunity and virus genetics, and demographic characteristics may all contribute to differing patterns of transmission both spatially and temporally. Knowledge of climate influences on dengue incidence could facilitate development of early warning systems allowing public health workers to implement appropriate transmission intervention strategies. In this study, we simulate dengue incidence in several municipalities in Puerto Rico using population and meteorological data derived from ground based stations and remote sensing instruments. This data was used to drive a process based model of vector population development and virus transmission. Model parameter values for container composition, vector characteristics, and incubation period were chosen by employing a Monte Carlo approach. Multiple simulations were performed for each municipality and the results were compared with reported dengue cases. The best performing simulations were retained and their parameter values and meteorological input were compared between years and municipalities. Parameter values varied by municipality and year illustrating the complexity and sensitivity of the disease system. Local characteristics including the natural and built environment impact transmission dynamics and produce varying responses to meteorological conditions.

Probabilistic estimation of residential air exchange rates for ...

EPA Pesticide Factsheets

Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER measurements. An algorithm for probabilistically estimating AER was developed based on the Lawrence Berkley National Laboratory Infiltration model utilizing housing characteristics and meteorological data with adjustment for window opening behavior. The algorithm was evaluated by comparing modeled and measured AERs in four US cities (Los Angeles, CA; Detroit, MI; Elizabeth, NJ; and Houston, TX) inputting study-specific data. The impact on the modeled AER of using publically available housing data representative of the region for each city was also assessed. Finally, modeled AER based on region-specific inputs was compared with those estimated using literature-based distributions. While modeled AERs were similar in magnitude to the measured AER they were consistently lower for all cities except Houston. AERs estimated using region-specific inputs were lower than those using study-specific inputs due to differences in window opening probabilities. The algorithm produced more spatially and temporally variable AERs compared with literature-based distributions reflecting within- and between-city differences, helping reduce error in estimates of air pollutant exposure. Published in the Journal of

Proceedings: Third Annual Workshop on Meteorological and Environmental Inputs to Aviation Systems

NASA Technical Reports Server (NTRS)

Camp, D. W. (Editor); Frost, W. (Editor)

1979-01-01

The proceedings of a workshop on meteorological and environmental inputs to aviation systems are reported. The major objectives of the workshop are to satisfy such needs of the sponsoring agencies as the expansion of our understanding and knowledge of the interaction of the atmosphere with aviation systems, the better definition and implementation of services to operators, and the collection and interpretation of data for establishing operational criteria, relating the total meteorological inputs from the atmospheric sciences to the needs of aviation communities. The unique aspect of the workshop was the achievement of communication across the interface of the boundaries between pilots, meteorologists, training personnel, accident investigators, traffic controllers, flight operation personnel from military, civil, general aviation, and commercial interests alike. Representatives were in attendance from government, airlines, private agencies, aircraft manufacturers, Department of Defense, industries, research institutes, and universities. Full-length papers addressed the topics of training, flight operations, accident investigation, air traffic control, and airports. Winds and wind shear; icing and frost; atmospheric electricity and lightning; fog, visibility and ceilings; and turbulence were discussed.

User's guide for MAGIC-Meteorologic and hydrologic genscn (generate scenarios) input converter

USGS Publications Warehouse

Ortel, Terry W.; Martin, Angel

2010-01-01

Meteorologic and hydrologic data used in watershed modeling studies are collected by various agencies and organizations, and stored in various formats. Data may be in a raw, un-processed format with little or no quality control, or may be checked for validity before being made available. Flood-simulation systems require data in near real-time so that adequate flood warnings can be made. Additionally, forecasted data are needed to operate flood-control structures to potentially mitigate flood damages. Because real-time data are of a provisional nature, missing data may need to be estimated for use in floodsimulation systems. The Meteorologic and Hydrologic GenScn (Generate Scenarios) Input Converter (MAGIC) can be used to convert data from selected formats into the Hydrologic Simulation System-Fortran hourly-observations format for input to a Watershed Data Management database, for use in hydrologic modeling studies. MAGIC also can reformat the data to the Full Equations model time-series format, for use in hydraulic modeling studies. Examples of the application of MAGIC for use in the flood-simulation system for Salt Creek in northeastern Illinois are presented in this report.

An Evaluation System for the Online Training Programs in Meteorology and Hydrology

ERIC Educational Resources Information Center

Wang, Yong; Zhi, Xiefei

2009-01-01

This paper studies the current evaluation system for the online training program in meteorology and hydrology. CIPP model that includes context evaluation, input evaluation, process evaluation and product evaluation differs from Kirkpatrick model including reactions evaluation, learning evaluation, transfer evaluation and results evaluation in…

NASA Earth Science Research Results for Improved Regional Crop Yield Prediction

NASA Astrophysics Data System (ADS)

Mali, P.; O'Hara, C. G.; Shrestha, B.; Sinclair, T. R.; G de Goncalves, L. G.; Salado Navarro, L. R.

2007-12-01

National agencies such as USDA Foreign Agricultural Service (FAS), Production Estimation and Crop Assessment Division (PECAD) work specifically to analyze and generate timely crop yield estimates that help define national as well as global food policies. The USDA/FAS/PECAD utilizes a Decision Support System (DSS) called CADRE (Crop Condition and Data Retrieval Evaluation) mainly through an automated database management system that integrates various meteorological datasets, crop and soil models, and remote sensing data; providing significant contribution to the national and international crop production estimates. The "Sinclair" soybean growth model has been used inside CADRE DSS as one of the crop models. This project uses Sinclair model (a semi-mechanistic crop growth model) for its potential to be effectively used in a geo-processing environment with remote-sensing-based inputs. The main objective of this proposed work is to verify, validate and benchmark current and future NASA earth science research results for the benefit in the operational decision making process of the PECAD/CADRE DSS. For this purpose, the NASA South American Land Data Assimilation System (SALDAS) meteorological dataset is tested for its applicability as a surrogate meteorological input in the Sinclair model meteorological input requirements. Similarly, NASA sensor MODIS products is tested for its applicability in the improvement of the crop yield prediction through improving precision of planting date estimation, plant vigor and growth monitoring. The project also analyzes simulated Visible/Infrared Imager/Radiometer Suite (VIIRS, a future NASA sensor) vegetation product for its applicability in crop growth prediction to accelerate the process of transition of VIIRS research results for the operational use of USDA/FAS/PECAD DSS. The research results will help in providing improved decision making capacity to the USDA/FAS/PECAD DSS through improved vegetation growth monitoring from high spatial and temporal resolution remote sensing datasets; improved time-series meteorological inputs required for crop growth models; and regional prediction capability through geo-processing-based yield modeling.

Performance evaluation of the national early warning system for shallow landslides in Norway

NASA Astrophysics Data System (ADS)

Dahl, Mads-Peter; Piciullo, Luca; Devoli, Graziella; Colleuille, Hervé; Calvello, Michele

2017-04-01

As a consequence of the increased number of rainfall-and snowmelt-induced landslides (debris flows, debris slides, debris avalanches and slush flows) occurring in Norway, a national landslide early warning system (EWS) has been developed for monitoring and forecasting the hydro-meteorological conditions potentially necessary of triggering slope failures. The system, operational since 2013, is managed by the Norwegian Water Resources and Energy Directorate (NVE) and has been designed in cooperation with the Norwegian Public Road Administration (SVV), the Norwegian National Rail Administration (JBV) and the Norwegian Meteorological Institute (MET). Decision-making in the EWS is based upon hazard threshold levels, hydro-meteorological and real-time landslide observations as well as landslide inventory and susceptibility maps. Hazard threshold levels have been obtained through statistical analyses of historical landslides and modelled hydro-meteorological parameters. Daily hydro-meteorological conditions such as rainfall, snowmelt, runoff, soil saturation, groundwater level and frost depth have been derived from a distributed version of the hydrological HBV-model. Two different landslide susceptibility maps are used as supportive data in deciding daily warning levels. Daily alerts are issued throughout the country considering variable warning zones. Warnings are issued once per day for the following 3 days with an update possibility later during the day according to the information gathered by the monitoring variables. The performance of the EWS has been evaluated applying the EDuMaP method. In particular, the performance of warnings issued in Western Norway, in the period 2013-2014 has been evaluated using two different landslide datasets. The best performance is obtained for the smallest and more accurate dataset. Different performance results may be observed as a function of changing the landslide density criterion, Lden(k), (i.e., thresholds considered to differentiate among classes of landslide events) used as an input parameter within the EDuMaP method. To investigate this issue, a parametric analysis has been conducted; the results of the analysis show clear differences among computed performances when absolute or relative landslide density criteria are considered.

Meteorological Influences on Nitrogen Dynamics of a Coastal Onsite Wastewater Treatment System

PubMed Central

O’Driscoll, M.A.; Humphrey, C. P.; Deal, N.E.; Lindbo, D.L.; Zarate-Bermudez, M.A.

2016-01-01

Onsite wastewater treatment systems (OWTS) can contribute nitrogen (N) to coastal waters. In coastal areas with shallow groundwater, OWTS are likely affected by meteorological events. However, the meteorological influences on temporal variability of N exports from OWTS are not well documented. Hydrogeological characterization and seasonal monitoring of wastewater and groundwater quality were conducted at a residence adjacent to the Pamlico River Estuary, North Carolina during a two-year field study (October 2009–2011). Rainfall was elevated during the first study year, relative to the annual mean. In the second year, drought was followed by extreme precipitation from Hurricane Irene. Recent meteorological conditions influenced N speciation and concentrations in groundwater. Groundwater total dissolved nitrogen (TDN) beneath the OWTS drainfield was dominated by nitrate during the drought; during wetter periods ammonium and organic N were common. Effective precipitation (P-ET) affected OWTS TDN exports because of its influence on groundwater recharge and discharge. Groundwater nitrate-N concentrations beneath the drainfield were typically higher than 10 mg/l when total bi-weekly precipitation was less than evapotranspiration (precipitation deficit: P15 m downgradient of the drainfield. Although OWTS nitrate inputs caused elevated groundwater nitrate concentrations between the drainfield and the estuary, the majority of nitrate was attenuated via denitrification between the OWTS and 48 m to the estuary. However, DON originating from the OWTS was mobile and contributed to elevated TDN concentrations along the groundwater flowpath to the estuary. PMID:25602204

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.)

Long-term solar UV radiation reconstructed by ANN modelling with emphasis on spatial characteristics of input data

NASA Astrophysics Data System (ADS)

Feister, U.; Junk, J.; Woldt, M.; Bais, A.; Helbig, A.; Janouch, M.; Josefsson, W.; Kazantzidis, A.; Lindfors, A.; den Outer, P. N.; Slaper, H.

2008-06-01

Artificial Neural Networks (ANN) are efficient tools to derive solar UV radiation from measured meteorological parameters such as global radiation, aerosol optical depths and atmospheric column ozone. The ANN model has been tested with different combinations of data from the two sites Potsdam and Lindenberg, and used to reconstruct solar UV radiation at eight European sites by more than 100 years into the past. Special emphasis will be given to the discussion of small-scale characteristics of input data to the ANN model. Annual totals of UV radiation derived from reconstructed daily UV values reflect interannual variations and long-term patterns that are compatible with variabilities and changes of measured input data, in particular global dimming by about 1980/1990, subsequent global brightening, volcanic eruption effects such as that of Mt. Pinatubo, and the long-term ozone decline since the 1970s. Patterns of annual erythemal UV radiation are very similar at sites located at latitudes close to each other, but different patterns occur between UV radiation at sites in different latitude regions.

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

NASA Astrophysics Data System (ADS)

Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun; Baatz, Roland; Montzka, Carsten; Schmidt, Marius; Vereecken, Harry

2018-01-01

Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C3 grass and C3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ ∑ NEE) for the different ensemble members from ˜ 2 to 3 g C m-2 yr-1 (with uncertain parameters) to ˜ 45 g C m-2 yr-1 (C3 grass) and ˜ 75 g C m-2 yr-1 (C3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ ∑ NEE ˜ 4.0-13.5 g C m-2 yr-1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

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.

Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.

Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms.

PubMed

Motamarri, Srinivas; Boccelli, Dominic L

2012-09-15

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ)--a direct classification approach--for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards. Copyright © 2012 Elsevier Ltd. All rights reserved.

Assessing the competing roles of model resolution and meteorological forcing fidelity in hyperresolution simulations of snowpack and streamflow in the southern Rocky Mountains

NASA Astrophysics Data System (ADS)

Gochis, D. J.; Dugger, A. L.; Karsten, L. R.; Barlage, M. J.; Sampson, K. M.; Yu, W.; Pan, L.; McCreight, J. L.; Howard, K.; Busto, J.; Deems, J. S.

2017-12-01

Hydrometeorological processes vary over comparatively short length scales in regions of complex terrain such as the southern Rocky Mountains. Changes in temperature, precipitation, wind and solar radiation can vary significantly across elevation gradients, terrain landform and land cover conditions throughout the region. Capturing such variability in hydrologic models can necessitate the utilization of so-called `hyper-resolution' spatial meshes with effective element spacings of less than 100m. However, it is often difficult to obtain meteorological forcings of high quality in such regions at those resolutions which can result in significant uncertainty in fundamental in hydrologic model inputs. In this study we examine the comparative influences of meteorological forcing data fidelity and spatial resolution on seasonal simulations of snowpack evolution, runoff and streamflow in a set of high mountain watersheds in southern Colorado. We utilize the operational, NOAA National Water Model configuration of the community WRF-Hydro system as a baseline and compare against it, additional model scenarios with differing specifications of meteorological forcing data, with and without topographic downscaling adjustments applied, with and without experimental high resolution radar derived precipitation estimates and with WRF-Hydro configurations of progressively finer spatial resolution. The results suggest significant influence from and importance of meteorological downscaling techniques in controlling spatial distributions of meltout and runoff timing. The use of radar derived precipitation exhibits clear sensitivity on hydrologic simulation skill compared with the use of coarser resolution, background precipitation analyses. Advantages and disadvantages of the utilization of progressively higher resolution model configurations both in terms of computational requirements and model fidelity are also discussed.

Improving fungal disease forecasts in winter wheat: a critical role of intra-day variations of meteorological conditions

USDA-ARS?s Scientific Manuscript database

Meteorological conditions are important factors in the development of fungal diseases in winter wheat and are the main inputs of the decision support systems used to forecast disease and thus determine timing for efficacious fungicide application. This study uses the Fourier transform method (FTM) t...

A Simple Model of Cirrus Horizontal Inhomogeneity and Cloud Fraction

NASA Technical Reports Server (NTRS)

Smith, Samantha A.; DelGenio, Anthony D.

1998-01-01

A simple model of horizontal inhomogeneity and cloud fraction in cirrus clouds has been formulated on the basis that all internal horizontal inhomogeneity in the ice mixing ratio is due to variations in the cloud depth, which are assumed to be Gaussian. The use of such a model was justified by the observed relationship between the normalized variability of the ice water mixing ratio (and extinction) and the normalized variability of cloud depth. Using radar cloud depth data as input, the model reproduced well the in-cloud ice water mixing ratio histograms obtained from horizontal runs during the FIRE2 cirrus campaign. For totally overcast cases the histograms were almost Gaussian, but changed as cloud fraction decreased to exponential distributions which peaked at the lowest nonzero ice value for cloud fractions below 90%. Cloud fractions predicted by the model were always within 28% of the observed value. The predicted average ice water mixing ratios were within 34% of the observed values. This model could be used in a GCM to produce the ice mixing ratio probability distribution function and to estimate cloud fraction. It only requires basic meteorological parameters, the depth of the saturated layer and the standard deviation of cloud depth as input.

Anvil Forecast Tool in the Advanced Weather Interactive Processing System, Phase II

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III

2008-01-01

Meteorologists from the 45th Weather Squadron (45 WS) and Spaceflight Meteorology Group have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Light Rules. As a result, the Applied Meteorology Unit (AMU) created a graphical overlay tool for the Meteorological Interactive Data Display Systems (MIDDS) to indicate the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input.

Sensitivity analysis of radionuclides atmospheric dispersion following the Fukushima accident

NASA Astrophysics Data System (ADS)

Girard, Sylvain; Korsakissok, Irène; Mallet, Vivien

2014-05-01

Atmospheric dispersion models are used in response to accidental releases with two purposes: - minimising the population exposure during the accident; - complementing field measurements for the assessment of short and long term environmental and sanitary impacts. The predictions of these models are subject to considerable uncertainties of various origins. Notably, input data, such as meteorological fields or estimations of emitted quantities as function of time, are highly uncertain. The case studied here is the atmospheric release of radionuclides following the Fukushima Daiichi disaster. The model used in this study is Polyphemus/Polair3D, from which derives IRSN's operational long distance atmospheric dispersion model ldX. A sensitivity analysis was conducted in order to estimate the relative importance of a set of identified uncertainty sources. The complexity of this task was increased by four characteristics shared by most environmental models: - high dimensional inputs; - correlated inputs or inputs with complex structures; - high dimensional output; - multiplicity of purposes that require sophisticated and non-systematic post-processing of the output. The sensitivities of a set of outputs were estimated with the Morris screening method. The input ranking was highly dependent on the considered output. Yet, a few variables, such as horizontal diffusion coefficient or clouds thickness, were found to have a weak influence on most of them and could be discarded from further studies. The sensitivity analysis procedure was also applied to indicators of the model performance computed on a set of gamma dose rates observations. This original approach is of particular interest since observations could be used later to calibrate the input variables probability distributions. Indeed, only the variables that are influential on performance scores are likely to allow for calibration. An indicator based on emission peaks time matching was elaborated in order to complement classical statistical scores which were dominated by deposit dose rates and almost insensitive to lower atmosphere dose rates. The substantial sensitivity of these performance indicators is auspicious for future calibration attempts and indicates that the simple perturbations used here may be sufficient to represent an essential part of the overall uncertainty.

Decadal trend of precipitation and temperature patterns and impacts on snow-related variables in a semiarid region, Sierra Nevada, Spain.

NASA Astrophysics Data System (ADS)

José Pérez-Palazón, María; Pimentel, Rafael; Herrero, Javier; José Polo, María

2016-04-01

In the current context of global change, mountainous areas constitute singular locations in which these changes can be traced. Early detection of significant shifts of snow state variables in semiarid regions can help assess climate variability impacts and future snow dynamics in northern latitudes. The Sierra Nevada mountain range, in southern Spain, is a representative example of snow areas in Mediterranean-climate regions and both monitoring and modelling efforts have been performed to assess this variability and its significant scales. This work presents a decadal trend analysis throughout the 50-yr period 1960-2010 performed on some snow-related variables over Sierra Nevada, in Spain, which is included in the global climate change observatories network around the world. The study area comprises 4583 km2 distributed throughout the five head basins influenced by these mountains, with altitude values ranging from 140 to 3479 m.a.s.l., just 40 km from the Mediterranean coastline. Meteorological variables obtained from 44 weather stations from the National Meteorological Agency were studied and further used as input to the distributed hydrological model WiMMed (Polo et al., 2010), operational at the study area, to obtain selected snow variables. Decadal trends were obtained, together with their statistical significance, over the following variables, averaged over the whole study area: (1) annual precipitation; (2) annual snowfall; annual (3) mean, (4) maximum and (5) minimum daily temperature; annual (6) mean and (7) maximum daily fraction of snow covered areas; (8) annual number of days with snow cover; (9) mean and (10) maximum daily snow water equivalent; (11) annual number of extreme precipitation events; and (12) mean intensity of the annual extreme precipitation events. These variables were also studied over each of the five regions associated to each basin in the range. Globally decreasing decadal trends were obtained for all the meteorological variables, with the exception of the average annual mean and maximum daily temperature. In the case of the snow-related variables, no significant trends are observed at this time scale; nonetheless, a global decreasing rate is predominant in most of the variables. The torrential events are more frequent in the last decades of the study period, with an apparently increasing associated dispersion. This study constitutes a first sound analysis of the long-term observed trends of the snow regime in this area under the context of increasing temperature and decreasing precipitation regimes. The results highlight the complexity of non-linearity in environmental processes in Mediterranean regions, and point out to a significant shift in the precipitation and temperature regime, and thus on the snow-affected hydrological variables in the study area.

Influence of seasonal and inter-annual hydro-meteorological variability on surface water fecal coliform concentration under varying land-use composition.

PubMed

St Laurent, Jacques; Mazumder, Asit

2014-01-01

Quantifying the influence of hydro-meteorological variability on surface source water fecal contamination is critical to the maintenance of safe drinking water. Historically, this has not been possible due to the scarcity of data on fecal indicator bacteria (FIB). We examined the relationship between hydro-meteorological variability and the most commonly measured FIB, fecal coliform (FC), concentration for 43 surface water sites within the hydro-climatologically complex region of British Columbia. The strength of relationship was highly variable among sites, but tended to be stronger in catchments with nival (snowmelt-dominated) hydro-meteorological regimes and greater land-use impacts. We observed positive relationships between inter-annual FC concentration and hydro-meteorological variability for around 50% of the 19 sites examined. These sites are likely to experience increased fecal contamination due to the projected intensification of the hydrological cycle. Seasonal FC concentration variability appeared to be driven by snowmelt and rainfall-induced runoff for around 30% of the 43 sites examined. Earlier snowmelt in nival catchments may advance the timing of peak contamination, and the projected decrease in annual snow-to-precipitation ratio is likely to increase fecal contamination levels during summer, fall, and winter among these sites. Safeguarding drinking water quality in the face of such impacts will require increased monitoring of FIB and waterborne pathogens, especially during periods of high hydro-meteorological variability. This data can then be used to develop predictive models, inform source water protection measures, and improve drinking water treatment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Quality assurance of weather data for agricultural system model input

USDA-ARS?s Scientific Manuscript database

It is well known that crop production and hydrologic variation on watersheds is weather related. Rarely, however, is meteorological data quality checks reported for agricultural systems model research. We present quality assurance procedures for agricultural system model weather data input. Problems...

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.

A meteorologically-driven yield reduction model for spring and winter wheat

NASA Technical Reports Server (NTRS)

Ravet, F. W.; Cremins, W. J.; Taylor, T. W.; Ashburn, P.; Smika, D.; Aaronson, A. (Principal Investigator)

1983-01-01

A yield reduction model for spring and winter wheat was developed for large-area crop condition assessment. Reductions are expressed in percentage from a base yield and are calculated on a daily basis. The algorithm contains two integral components: a two-layer soil water budget model and a crop calendar routine. Yield reductions associated with hot, dry winds (Sukhovey) and soil moisture stress are determined. Input variables include evapotranspiration, maximum temperature and precipitation; subsequently crop-stage, available water holding percentage and stress duration are evaluated. No specific base yield is required and may be selected by the user; however, it may be generally characterized as the maximum likely to be produced commercially at a location.

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).

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.

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.

Multivariate Statistical Postprocessing of Ensemble Forcasts of Precipitation and Temperature over four River Basins in California

NASA Astrophysics Data System (ADS)

Scheuerer, Michael; Hamill, Thomas M.; Whitin, Brett; He, Minxue; Henkel, Arthur

2017-04-01

Hydrological forecasts strongly rely on predictions of precipitation amounts and temperature as meteorological inputs to hydrological models. Ensemble weather predictions provide a number of different scenarios that reflect the uncertainty about these meteorological inputs, but are often biased and underdispersive, and therefore require statistical postprocessing. In hydrological applications it is crucial that spatial and temporal (i.e. between different forecast lead times) dependencies as well as dependence between the two weather variables is adequately represented by the recalibrated forecasts. We present a study with temperature and precipitation forecasts over four river basins over California that are postprocessed with a variant of the nonhomogeneous Gaussian regression method (Gneiting et al., 2005) and the censored, shifted gamma distribution approach (Scheuerer and Hamill, 2015) respectively. For modelling spatial, temporal and inter-variable dependence we propose a variant of the Schaake Shuffle (Clark et al., 2005) that uses spatio-temporal trajectories of observed temperture and precipitation as a dependence template, and chooses the historic dates in such a way that the divergence between the marginal distributions of these trajectories and the univariate forecast distributions is minimized. For the four river basins considered in our study, this new multivariate modelling technique consistently improves upon the Schaake Shuffle and yields reliable spatio-temporal forecast trajectories of temperature and precipitation that can be used to force hydrological forecast systems. References: Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., Wilby, R., 2004. The Schaake Shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology, 5, pp.243-262. Gneiting, T., Raftery, A.E., Westveld, A.H., Goldman, T., 2005. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS. Monthly Weather Review, 133, pp.1098-1118. Scheuerer, M., Hamill, T.M., 2015. Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Monthly Weather Review, 143, pp.4578-4596. Scheuerer, M., Hamill, T.M., Whitin, B., He, M., and Henkel, A., 2016: A method for preferential selection of dates in the Schaake shuffle approach to constructing spatio-temporal forecast fields of temperature and precipitation. Water Resources Research, submitted.

Sensitivity of the Community Multiscale Air Quality (CMAQ) Model v4.7 Results for the Eastern United States to MM5 and WRF Meteorological Drivers

EPA Science Inventory

This paper presents a comparison of the operational performance of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th generation Mesoscale Model MM5 and the Weather Research and Forecasting (WRF) meteorological models.

Meteorological Input to General Aviation Pilot Training

NASA Technical Reports Server (NTRS)

Colomy, J. R.

1979-01-01

The meteorological education of general aviation pilots is discussed in terms of the definitions and concepts of learning and good educational procedures. The effectiveness of the metoeorological program in the training of general aviations pilots is questioned. It is suggested that flight instructors provide real experience during low ceilings and visibilities, and that every pilot receiving an instrument rating should experience real instrument flight.

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.

2013-12-01

Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.

A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.

PubMed

Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng

2015-02-01

Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Adding Four- Dimensional Data Assimilation (aka grid ...

EPA Pesticide Factsheets

Adding four-dimensional data assimilation (a.k.a. grid nudging) to MPAS.The U.S. Environmental Protection Agency is investigating the use of MPAS as the meteorological driver for its next-generation air quality model. To function as such, MPAS needs to operate in a diagnostic mode in much the same manner as the current meteorological driver, the Weather Research and Forecasting (WRF) model. The WRF operates in diagnostic mode using Four-Dimensional Data Assimilation, also known as "grid nudging". MPAS version 4.0 has been modified with the addition of an FDDA routine to the standard physics drivers to nudge the state variables for wind, temperature and water vapor towards MPAS initialization fields defined at 6-hour intervals from GFS-derived data. The results to be shown demonstrate the ability to constrain MPAS simulations to known historical conditions and thus provide the U.S. EPA with a practical meteorological driver for global-scale air quality simulations. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use bo

[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.

A quantitative sensitivity analysis on the behaviour of common thermal indices under hot and windy conditions in Doha, Qatar

NASA Astrophysics Data System (ADS)

Fröhlich, Dominik; Matzarakis, Andreas

2016-04-01

Human thermal perception is best described through thermal indices. The most popular thermal indices applied in human bioclimatology are the perceived temperature (PT), the Universal Thermal Climate Index (UTCI), and the physiologically equivalent temperature (PET). They are analysed focusing on their sensitivity to single meteorological input parameters under the hot and windy meteorological conditions observed in Doha, Qatar. It can be noted, that the results for the three indices are distributed quite differently. Furthermore, they respond quite differently to modifications in the input conditions. All of them show particular limitations and shortcomings that have to be considered and discussed. While the results for PT are unevenly distributed, UTCI shows limitations concerning the input data accepted. PET seems to respond insufficiently to changes in vapour pressure. The indices should therefore be improved to be valid for several kinds of climates.

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...

Surface Meteorological Station - Astoria, OR (AST) - Raw Data

DOE Data Explorer

Gottas, Daniel

2017-10-23

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Bonneville - Raw Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology and precipitation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Condon - Reviewed Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Troutdale - Reviewed Data

DOE Data Explorer

Gottas, Daniel

2017-12-11

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Prineville - Raw Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Troutdale - Raw Data

DOE Data Explorer

Gottas, Daniel

2017-12-11

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Prineville - Reviewed Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Bonneville - Reviewed Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology and precipitation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - North Bend, OR (OTH) - Raw Data

DOE Data Explorer

Gottas, Daniel

2017-10-23

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Condon - Raw Data

DOE Data Explorer

McCaffrey, Katherine

2017-10-23

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - Forks, WA (FKS) - Raw Data

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

Gottas, Daniel

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - Forks, WA (FKS) - Reviewed Data

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

Gottas, Daniel

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Proceedings of the 2nd Annual Workshop on Meteorological and Environmental Inputs to Aviation Systems

NASA Technical Reports Server (NTRS)

Frost, W. (Editor); Camp, D. W. (Editor); Durham, D. E. (Editor)

1978-01-01

The proceedings of a workshop held at the University of Tennessee Space Institute, Tullahoma, Tennessee, March 28-30, 1978, are reported. The workshop was jointly sponsored by NASA, NOAA, FAA, and brought together many disciplines of the aviation communities in round table discussions. The major objectives of the workshop are to satisfy such needs of the sponsoring agencies as the expansion of our understanding and knowledge of the interactions of the atmosphere with aviation systems, as the better definition and implementation of services to operators, and as the collection and interpretation of data for establishing operational criteria, relating the total meteorological inputs from the atmospheric sciences to the needs of aviation communities.

Seasonal and spatial variability of rainfall redistribution under Scots pine and Downy oak forests in Mediterranean conditions

NASA Astrophysics Data System (ADS)

Garcia-Estringana, Pablo; Latron, Jérôme; Molina, Antonio J.; Llorens, Pilar

2013-04-01

The large degree of temporal and spatial variability of throughfall input patterns may lead to significant changes in the volume of water that reach the soil in each location, and beyond in the hydrological response of forested hillslopes. To explore the role of vegetation in the temporal and spatial redistribution of rainfall in Mediterranean climatic conditions two contrasted stands were monitored. One is a Downy oak forest (Quercus pubescens) and the other is a Scots pine forest (Pinus sylvestris), both are located in the Vallcebre research catchments (NE Spain, 42° 12'N, 1° 49'E). These plots are representative of Mediterranean mountain areas with spontaneous afforestation by Scots pine as a consequence of the abandonment of agricultural terraces, formerly covered by Downy oaks. The monitoring design of each plot consists of a set of 20 automatic rain recorders and 40 automatic soil moisture probes located below the canopy. 100 hemispheric photographs of the canopy were used to place the instruments at representative locations (in terms of canopy cover) within the plot. Bulk rainfall, stemflow and meteorological conditions above the forest cover are also automatically recorded. Canopy cover as well as biometric characteristics of the plots are also regularly measured. This work presents the first results describing the variability of throughfall beneath each forest stand and compares the persistence of temporal patterns among stands, and for the oaks stand among the leafed and the leafless period. Furthermore, canopy structure, rainfall characteristics and meteorological conditions of rainfall events are evaluated as main drivers of throughfall redistribution.

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.

Eco-hydrological Modeling in the Framework of Climate Change

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Ivanov, Valeriy Y.; Caporali, Enrica

2010-05-01

A blueprint methodology for studying climate change impacts, as inferred from climate models, on eco-hydrological dynamics at the plot and small catchment scale is presented. Input hydro-meteorological variables for hydrological and eco-hydrological models for present and future climates are reproduced using a stochastic downscaling technique and a weather generator, "AWE-GEN". The generated time series of meteorological variables for the present climate and an ensemble of possible future climates serve as input to a newly developed physically-based eco-hydrological model "Tethys-Chloris". An application of the proposed methodology is realized reproducing the current (1961-2000) and multiple future (2081-2100) climates for the location of Tucson (Arizona). A general reduction of precipitation and a significant increase of air temperature are inferred. The eco-hydrological model is successively applied to detect changes in water recharge and vegetation dynamics for a desert shrub ecosystem, typical of the semi-arid climate of south Arizona. Results for the future climate account for uncertainties in the downscaling and are produced in terms of probability density functions. A comparison of control and future scenarios is discussed in terms of changes in the hydrological balance components, energy fluxes, and indices of vegetation productivity. An appreciable effect of climate change can be observed in metrics of vegetation performance. The negative impact on vegetation due to amplification of water stress in a warmer and dryer climate is offset by a positive effect of carbon dioxide augment. This implies a positive shift in plant capabilities to exploit water. Consequently, the plant water use efficiency and rain use efficiency are expected to increase. Interesting differences in the long-term vegetation productivity are also observed for the ensemble of future climates. The reduction of precipitation and the substantial maintenance of vegetation cover ultimately leads to the depletion of soil moisture and recharge to deeper layers. Such an outcome can affect the long-tem water availability in semi-arid systems and expose plants to more severe and frequent periods of stress.

Key drivers of precipitation isotopes in Windhoek, Namibia (2012-2016)

NASA Astrophysics Data System (ADS)

Kaseke, K. F.; Wang, L.; Wanke, H.

2017-12-01

Southern African climate is characterized by large variability with precipitation model estimates varying by as much as 70% during summer. This difference between model estimates is partly because most models associate precipitation over Southern Africa with moisture inputs from the Indian Ocean while excluding inputs from the Atlantic Ocean. However, growing evidence suggests that the Atlantic Ocean may also contribute significant amounts of moisture to the region. This four-year (2012-2016) study investigates the isotopic composition (δ18O, δ2H and δ17O) of event-scale precipitation events, the key drivers of isotope variations and the origins of precipitation experienced in Windhoek, Namibia. Results indicate large storm-to-storm isotopic variability δ18O (25‰), δ2H (180‰) and δ17O (13‰) over the study period. Univariate analysis showed significant correlations between event precipitation isotopes and local meteorological parameters; lifted condensation level, relative humidity (RH), precipitation amount, average wind speed, surface and air temperature (p < 0.05). The number of significant correlations between local meteorological parameters and monthly isotopes was much lower suggesting loss of information through data aggregation. Nonetheless, the most significant isotope driver at both event and monthly scales was RH, consistent with the semi-arid classification of the site. Multiple linear regression analysis suggested RH, precipitation amount and air temperature were the most significant local drivers of precipitation isotopes accounting for about 50% of the variation implying that about 50% could be attributed to source origins. HYSLPIT trajectories indicated that 78% of precipitation originated from the Indian Ocean while 21% originated from the Atlantic Ocean. Given that three of the four study years were droughts while two of the three drought years were El Niño related, our data also suggests that δ'17O-δ'18O could be a useful tool to differentiate local vs synoptic (El Niño) droughts.

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.

Surface Meteorological Station - ESRL Short Tower, Wasco Airport - Raw Data

DOE Data Explorer

Gottas, Daniel

2017-12-11

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Surface Meteorological Station - ESRL Short Tower, Wasco Airport - Reviewed Data

DOE Data Explorer

Gottas, Daniel

2017-12-11

A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

Sensitivity analysis of the near-road dispersion model RLINE - An evaluation at Detroit, Michigan

NASA Astrophysics Data System (ADS)

Milando, Chad W.; Batterman, Stuart A.

2018-05-01

The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies.

Sustainable management of agriculture activity on areas with soil vulnerability to compaction trough a developed decision support system (DSS)

NASA Astrophysics Data System (ADS)

Moretto, Johnny; Fantinato, Luciano; Rasera, Roberto

2017-04-01

One of the main environmental effects of agriculture is the negative impacts on areas with soil vulnerability to compaction and undersurface water derived from inputs and treatment distributions. A solution may represented from the "Precision Farming". Precision Farming refers to a management concept focusing on (near-real time) observation, measurement and responses to inter- and intra-variability in crops, fields and animals. Potential benefits may include increasing crop yields and animal performance, cost and labour reduction and optimisation of process inputs, all of which would increase profitability. At the same time, Precision Farming should increase work safety and reduce the environmental impacts of agriculture and farming practices, thus contributing to the sustainability of agricultural production. The concept has been made possible by the rapid development of ICT-based sensor technologies and procedures along with dedicated software that, in the case of arable farming, provides the link between spatially-distributed variables and appropriate farming practices such as tillage, seeding, fertilisation, herbicide and pesticide application, and harvesting. Much progress has been made in terms of technical solutions, but major steps are still required for the introduction of this approach over the common agricultural practices. There are currently a large number of sensors capable of collecting data for various applications (e.g. Index of vegetation vigor, soil moisture, Digital Elevation Models, meteorology, etc.). The resulting large volumes of data need to be standardised, processed and integrated using metadata analysis of spatial information, to generate useful input for decision-support systems. In this context, a user-friendly IT applications has been developed, for organizing and processing large volumes of data from different types of remote sensing and meteorological sensors, and for integrating these data into user-friendly farm management support systems able to support the farm manager. In this applications will be possible to implement numerical models to support the farm manager on the best time to work in field and/or the best trajectory to follow with a GPS navigation system on soil vulnerability to compaction. In addition to provide "as applied map" to indicate in each part of the field the exact needed quantity of inputs and treatments. This new working models for data management will allow to a most efficient resource usage contributing in a more sustainable agriculture both for a more economic benefits for the farmers and for reduction of environmental soil and undersurface water impacts.

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.

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

USGS Publications Warehouse

Cook, B.D.; Bolstad, P.V.; Naesset, E.; Anderson, R. Scott; Garrigues, S.; Morisette, J.T.; Nickeson, J.; Davis, K.J.

2009-01-01

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Using LIDAR and Quickbird Data to Model Plant Production and Quantify Uncertainties Associated with Wetland Detection and Land Cover Generalizations

NASA Technical Reports Server (NTRS)

Cook, Bruce D.; Bolstad, Paul V.; Naesset, Erik; Anderson, Ryan S.; Garrigues, Sebastian; Morisette, Jeffrey T.; Nickeson, Jaime; Davis, Kenneth J.

2009-01-01

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Investigation of Effects of Varying Model Inputs on Mercury Deposition Estimates in the Southwest US

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model version 4.7.1 was used to simulate mercury wet and dry deposition for a domain covering the continental United States (US). The simulations used MM5-derived meteorological input fields and the US Environmental Protection Agency (E...

Meteorological and Environmental Inputs to Aviation Systems

NASA Technical Reports Server (NTRS)

Camp, Dennis W. (Editor); Frost, Walter (Editor)

1988-01-01

Reports on aviation meteorology, most of them informal, are presented by representatives of the National Weather Service, the Bracknell (England) Meteorological Office, the NOAA Wave Propagation Lab., the Fleet Numerical Oceanography Center, and the Aircraft Owners and Pilots Association. Additional presentations are included on aircraft/lidar turbulence comparison, lightning detection and locating systems, objective detection and forecasting of clear air turbulence, comparative verification between the Generalized Exponential Markov (GEM) Model and official aviation terminal forecasts, the evaluation of the Prototype Regional Observation and Forecast System (PROFS) mesoscale weather products, and the FAA/MIT Lincoln Lab. Doppler Weather Radar Program.

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.

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.

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.

A Lagrangian particle model to predict the airborne spread of foot-and-mouth disease virus

NASA Astrophysics Data System (ADS)

Mayer, D.; Reiczigel, J.; Rubel, F.

Airborne spread of bioaerosols in the boundary layer over a complex terrain is simulated using a Lagrangian particle model, and applied to modelling the airborne spread of foot-and-mouth disease (FMD) virus. Two case studies are made with study domains located in a hilly region in the northwest of the Styrian capital Graz, the second largest town in Austria. Mountainous terrain as well as inhomogeneous and time varying meteorological conditions prevent from application of so far used Gaussian dispersion models, while the proposed model can handle these realistically. In the model, trajectories of several thousands of particles are computed and the distribution of virus concentration near the ground is calculated. This allows to assess risk of infection areas with respect to animal species of interest, such as cattle, swine or sheep. Meteorological input data like wind field and other variables necessary to compute turbulence were taken from the new pre-operational version of the non-hydrostatic numerical weather prediction model LMK ( Lokal-Modell-Kürzestfrist) running at the German weather service DWD ( Deutscher Wetterdienst). The LMK model provides meteorological parameters with a spatial resolution of about 2.8 km. To account for the spatial resolution of 400 m used by the Lagrangian particle model, the initial wind field is interpolated upon the finer grid by a mass consistent interpolation method. Case studies depict a significant influence of local wind systems on the spread of virus. Higher virus concentrations at the upwind side of the hills and marginal concentrations in the lee are well observable, as well as canalization effects by valleys. The study demonstrates that the Lagrangian particle model is an appropriate tool for risk assessment of airborne spread of virus by taking into account the realistic orographic and meteorological conditions.

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...

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.

Two-Dimensional Model Simulations of Interannual Variability in the Tropical Stratosphere

NASA Technical Reports Server (NTRS)

Fleming, Eric L.; Jackman, Charles H.; Considine, David B.; Rosenfeld, Joan; Bhartia, P. K. (Technical Monitor)

2001-01-01

Meteorological data from the United Kingdom Meteorological Office (UKMO) and constituent data from the Upper Atmospheric Research Satellite (UARS) are used to construct yearly zonal mean dynamical fields for the 1990s for use in the GSFC 2-D chemistry and transport model. This allows for interannual dynamical variability to be included in the model constituent simulations. In this study, we focus on the tropical stratosphere. We find that the phase of quasi-biennial oscillation (QBO) signals in equatorial CH4, and profile and total column 03 data is resolved quite well using this empirically- based 2-D model transport framework. However. the QBO amplitudes in the model constituents are systematically underestimated relative to the observations at most levels. This deficiency is probably due in part to the limited vertical resolutions of the 2-D model and the UKMO and UARS input data sets. We find that using different heating rate calculations in the model affects the interannual and QBO amplitudes in the constituent fields, but has little impact on the phase. Sensitivity tests reveal that the QBO in transport dominates the ozone interannual variability in the lower stratosphere. with the effect of the temperature QBO being dominant in the tipper stratosphere via the strong temperature dependence of the ozone loss reaction rates. We also find that the QBO in odd nitrogen radicals, which is caused by the QBO modulated transport of NOy, plays a significant but not dominant role in determining the ozone QBO variability in the middle stratosphere. The model mean age of air is in good overall agreement with that determined from tropical lower,middle stratospheric OMS balloon observations of SF6 and CO2. The interannual variability of tile equatorial mean age in the model increases with altitude and maximizes near 40 km, with a range, of 4-5 years over the 1993-2000 time period.

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.

Atmospheric inputs of organic matter to a forested watershed: Variations from storm to storm over the seasons

NASA Astrophysics Data System (ADS)

Iavorivska, Lidiia; Boyer, Elizabeth W.; Miller, Matthew P.; Brown, Michael G.; Vasilopoulos, Terrie; Fuentes, Jose D.; Duffy, Christopher J.

2016-12-01

The objectives of this study were to determine the quantity and chemical composition of precipitation inputs of dissolved organic carbon (DOC) to a forested watershed; and to characterize the associated temporal variability. We sampled most precipitation that occurred from May 2012 through August 2013 at the Susquehanna Shale Hills Critical Zone Observatory (Pennsylvania, USA). Sub-event precipitation samples (159) were collected sequentially during 90 events; covering various types of synoptic meteorological conditions in all climatic seasons. Precipitation DOC concentrations and rates of wet atmospheric DOC deposition were highly variable from storm to storm, ranging from 0.3 to 5.6 mg C L-1 and from 0.5 to 32.8 mg C m-2 h-1, respectively. Seasonally, storms in spring and summer had higher concentrations of DOC and more optically active organic matter than in winter. Higher DOC concentrations resulted from weather types that favor air advection, where cold frontal systems, on average, delivered more than warm/stationary fronts and northeasters. A mixed modeling statistical approach revealed that factors related to storm properties, emission sources, and to the chemical composition of the atmosphere could explain more than 60% of the storm to storm variability in DOC concentrations. This study provided observations on changes in dissolved organic matter that can be useful in modeling of atmospheric oxidative chemistry, exploring relationships between organics and other elements of precipitation chemistry, and in considering temporal changes in ecosystem nutrient balances and microbial activity.

Diffusion from a line source

NASA Technical Reports Server (NTRS)

Burns, R. E.

1973-01-01

The problem with predicting pollutant diffusion from a line source of arbitrary geometry is treated. The concentration at the line source may be arbitrarily varied with time. Special attention is given to the meteorological inputs which act as boundary conditions for the problem, and a mixing layer of arbitrary depth is assumed. Numerical application of the derived theory indicates the combinations of meteorological parameters that may be expected to result in high pollution concentrations.

The Air Force Interactive Meteorological System: A Research Tool for Satellite Meteorology

DTIC Science & Technology

1992-12-02

NFARnet itself is a subnet to the global computer network INTERNET that links nearly all U.S. government research facilities and universi- ties along...required input to a generalized mathematical solution to the satellite/earth coordinate transform used for earth location of GOES sensor data. A direct...capability also exists to convert absolute coordinates to relative coordinates for transformations associated with gridded fields. 3. Spatial objective

Evaluation of near surface ozone and particulate matter in air ...

EPA Pesticide Factsheets

In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher-resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000–2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method’s use for future air quality projections. This paper shows that if emissions inputs and coarse-scale meteorological inputs are reasonably accurate, then air quality can be simulated with acceptable accuracy even wi

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.

OpenDrift - an open source framework for ocean trajectory modeling

NASA Astrophysics Data System (ADS)

Dagestad, Knut-Frode; Breivik, Øyvind; Ådlandsvik, Bjørn

2016-04-01

We will present a new, open source tool for modeling the trajectories and fate of particles or substances (Lagrangian Elements) drifting in the ocean, or even in the atmosphere. The software is named OpenDrift, and has been developed at Norwegian Meteorological Institute in cooperation with Institute of Marine Research. OpenDrift is a generic framework written in Python, and is openly available at https://github.com/knutfrode/opendrift/. The framework is modular with respect to three aspects: (1) obtaining input data, (2) the transport/morphological processes, and (3) exporting of results to file. Modularity is achieved through well defined interfaces between components, and use of a consistent vocabulary (CF conventions) for naming of variables. Modular input implies that it is not necessary to preprocess input data (e.g. currents, wind and waves from Eulerian models) to a particular file format. Instead "reader modules" can be written/used to obtain data directly from any original source, including files or through web based protocols (e.g. OPeNDAP/Thredds). Modularity of processes implies that a model developer may focus on the geophysical processes relevant for the application of interest, without needing to consider technical tasks such as reading, reprojecting, and colocating input data, rotation and scaling of vectors and model output. We will show a few example applications of using OpenDrift for predicting drifters, oil spills, and search and rescue objects.

Improving Water Resources System Operation by Direct Use of Hydroclimatic Information

NASA Astrophysics Data System (ADS)

Castelletti, A.; Pianosi, F.

2011-12-01

It is generally agreed that more information translates into better decisions. For instance, the availability of inflow predictions can improve reservoir operation; soil moisture data can be exploited to increase irrigation efficiency; etc. However, beyond this general statement, many theoretical and practical questions remain open. Provided that not all information sources are equally relevant, how does their value depend on the physical features of the water system and on the purposes of the system operation? What is the minimum lead time needed for anticipatory management to be effective? How does uncertainty in the information propagates through the modelling chain from hydroclimatic data through descriptive and decision models, and finally affect the decision? Is the data-predictions-decision paradigm truly effective or would it be better to directly use hydroclimatic data to take optimal decisions, skipping the intermediate step of hydrological forecasting? In this work we investigate these issues by application to the management of a complex water system in Northern Vietnam, characterized by multiple, conflicting objectives including hydropower production, flood control and water supply. First, we quantify the value of hydroclimatic information as the improvement in the system performances that could be attained under the (ideal) assumption of perfect knowledge of all future meteorological and hydrological input. Then, we assess and compare the relevance of different candidate information (meteorological or hydrological observations; ground or remote data; etc.) for the purpose of system operation by novel Input Variable Selection techniques. Finally, we evaluate the performance improvement made possible by the use of such information in re-designing the system operation.

Generation and evaluation of typical meteorological year datasets for greenhouse and external conditions on the Mediterranean coast.

PubMed

Fernández, M D; López, J C; Baeza, E; Céspedes, A; Meca, D E; Bailey, B

2015-08-01

A typical meteorological year (TMY) represents the typical meteorological conditions over many years but still contains the short term fluctuations which are absent from long-term averaged data. Meteorological data were measured at the Experimental Station of Cajamar 'Las Palmerillas' (Cajamar Foundation) in Almeria, Spain, over 19 years at the meteorological station and in a reference greenhouse which is typical of those used in the region. The two sets of measurements were subjected to quality control analysis and then used to create TMY datasets using three different methodologies proposed in the literature. Three TMY datasets were generated for the external conditions and two for the greenhouse. They were assessed by using each as input to seven horticultural models and comparing the model results with those obtained by experiment in practical trials. In addition, the models were used with the meteorological data recorded during the trials. A scoring system was used to identify the best performing TMY in each application and then rank them in overall performance. The best methodology was that of Argiriou for both greenhouse and external conditions. The average relative errors between the seasonal values estimated using the 19-year dataset and those using the Argiriou greenhouse TMY were 2.2 % (reference evapotranspiration), -0.45 % (pepper crop transpiration), 3.4 % (pepper crop nitrogen uptake) and 0.8 % (green bean yield). The values obtained using the Argiriou external TMY were 1.8 % (greenhouse reference evapotranspiration), 0.6 % (external reference evapotranspiration), 4.7 % (greenhouse heat requirement) and 0.9 % (loquat harvest date). Using the models with the 19 individual years in the historical dataset showed that the year to year weather variability gave results which differed from the average values by ± 15 %. By comparison with results from other greenhouses it was shown that the greenhouse TMY is applicable to greenhouses which have a solar radiation transmission of approximately 65 % and rely on manual control of ventilation which constitute the majority in the south-east of Spain and in most Mediterranean greenhouse areas.

Generation and evaluation of typical meteorological year datasets for greenhouse and external conditions on the Mediterranean coast

NASA Astrophysics Data System (ADS)

Fernández, M. D.; López, J. C.; Baeza, E.; Céspedes, A.; Meca, D. E.; Bailey, B.

2015-08-01

A typical meteorological year (TMY) represents the typical meteorological conditions over many years but still contains the short term fluctuations which are absent from long-term averaged data. Meteorological data were measured at the Experimental Station of Cajamar `Las Palmerillas' (Cajamar Foundation) in Almeria, Spain, over 19 years at the meteorological station and in a reference greenhouse which is typical of those used in the region. The two sets of measurements were subjected to quality control analysis and then used to create TMY datasets using three different methodologies proposed in the literature. Three TMY datasets were generated for the external conditions and two for the greenhouse. They were assessed by using each as input to seven horticultural models and comparing the model results with those obtained by experiment in practical trials. In addition, the models were used with the meteorological data recorded during the trials. A scoring system was used to identify the best performing TMY in each application and then rank them in overall performance. The best methodology was that of Argiriou for both greenhouse and external conditions. The average relative errors between the seasonal values estimated using the 19-year dataset and those using the Argiriou greenhouse TMY were 2.2 % (reference evapotranspiration), -0.45 % (pepper crop transpiration), 3.4 % (pepper crop nitrogen uptake) and 0.8 % (green bean yield). The values obtained using the Argiriou external TMY were 1.8 % (greenhouse reference evapotranspiration), 0.6 % (external reference evapotranspiration), 4.7 % (greenhouse heat requirement) and 0.9 % (loquat harvest date). Using the models with the 19 individual years in the historical dataset showed that the year to year weather variability gave results which differed from the average values by ± 15 %. By comparison with results from other greenhouses it was shown that the greenhouse TMY is applicable to greenhouses which have a solar radiation transmission of approximately 65 % and rely on manual control of ventilation which constitute the majority in the south-east of Spain and in most Mediterranean greenhouse areas.

Assessing the impacts of seasonal and vertical atmospheric conditions on air quality over the Pearl River Delta region

NASA Astrophysics Data System (ADS)

Tong, Cheuk Hei Marcus; Yim, Steve Hung Lam; Rothenberg, Daniel; Wang, Chien; Lin, Chuan-Yao; Chen, Yongqin David; Lau, Ngar Cheung

2018-05-01

Air pollution is an increasingly concerning problem in many metropolitan areas due to its adverse public health and environmental impacts. Vertical atmospheric conditions have strong effects on vertical mixing of air pollutants, which directly affects surface air quality. The characteristics and magnitude of how vertical atmospheric conditions affect surface air quality, which are critical to future air quality projections, have not yet been fully understood. This study aims to enhance understanding of the annual and seasonal sensitivities of air pollution to both surface and vertical atmospheric conditions. Based on both surface and vertical meteorological characteristics provided by 1994-2003 monthly dynamic downscaling data from the Weather and Research Forecast Model, we develop generalized linear models (GLMs) to study the relationships between surface air pollutants (ozone, respirable suspended particulates, and sulfur dioxide) and atmospheric conditions in the Pearl River Delta (PRD) region. Applying Principal Component Regression (PCR) to address multi-collinearity, we study the contributions of various meteorological variables to pollutants' concentration levels based on the loading and model coefficient of major principal components. Our results show that relatively high pollutant concentration occurs under relatively low mid-level troposphere temperature gradients, low relative humidity, weak southerly wind (or strong northerly wind) and weak westerly wind (or strong easterly wind). Moreover, the correlations vary among pollutant species, seasons, and meteorological variables at various altitudes. In general, pollutant sensitivity to meteorological variables is found to be greater in winter than in other seasons, and the sensitivity of ozone to meteorology differs from that of the other two pollutants. Applying our GLMs to anomalous air pollution episodes, we find that meteorological variables up to mid troposphere (∼700 mb) play an important role in influencing surface air quality, pinpointing the significant and unique associations between meteorological variables at higher altitudes and surface air quality.

Reference evapotranspiration forecasting based on local meteorological and global climate information screened by partial mutual information

NASA Astrophysics Data System (ADS)

Fang, Wei; Huang, Shengzhi; Huang, Qiang; Huang, Guohe; Meng, Erhao; Luan, Jinkai

2018-06-01

In this study, reference evapotranspiration (ET0) forecasting models are developed for the least economically developed regions subject to meteorological data scarcity. Firstly, the partial mutual information (PMI) capable of capturing the linear and nonlinear dependence is investigated regarding its utility to identify relevant predictors and exclude those that are redundant through the comparison with partial linear correlation. An efficient input selection technique is crucial for decreasing model data requirements. Then, the interconnection between global climate indices and regional ET0 is identified. Relevant climatic indices are introduced as additional predictors to comprise information regarding ET0, which ought to be provided by meteorological data unavailable. The case study in the Jing River and Beiluo River basins, China, reveals that PMI outperforms the partial linear correlation in excluding the redundant information, favouring the yield of smaller predictor sets. The teleconnection analysis identifies the correlation between Nino 1 + 2 and regional ET0, indicating influences of ENSO events on the evapotranspiration process in the study area. Furthermore, introducing Nino 1 + 2 as predictors helps to yield more accurate ET0 forecasts. A model performance comparison also shows that non-linear stochastic models (SVR or RF with input selection through PMI) do not always outperform linear models (MLR with inputs screen by linear correlation). However, the former can offer quite comparable performance depending on smaller predictor sets. Therefore, efforts such as screening model inputs through PMI and incorporating global climatic indices interconnected with ET0 can benefit the development of ET0 forecasting models suitable for data-scarce regions.

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.

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/.

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.

Causes of Interannual Variability over the Southern Hemispheric Tropospheric Ozone Maximum

NASA Technical Reports Server (NTRS)

Liu, Junhua; Rodriguez, Jose M.; Steenrod, Stephen D.; Douglass, Anne R.; Logan, Jennifer A.; Olsen, Mark A.; Wargan, Krzysztog; Ziemke, Jerald R.

2017-01-01

We examine the relative contribution of processes controlling the interannual variability (IAV) of tropospheric ozone over four sub-regions of the southern hemispheric tropospheric ozone maximum (SHTOM) over a 20-year period. Our study is based on hindcast simulations from the National Aeronautics and Space Administration Global Modeling Initiative chemistry transport model (NASA GMI-CTM) of tropospheric and stratospheric chemistry, driven by assimilated Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological fields. Our analysis shows that over SHTOM region, the IAV of the stratospheric contribution is the most important factor driving the IAV of upper tropospheric ozone (270 hectopascals), where ozone has a strong radiative effect. Over the South Atlantic region, the contribution from surface emissions to the IAV of ozone exceeds that from stratospheric input at and below 430 hectopascals. Over the South Indian Ocean, the IAV of stratospheric ozone makes the largest contribution to the IAV of ozone with little or no influence from surface emissions at 270 and 430 hectopascals in austral winter. Over the tropical South Atlantic region, the contribution from IAV of stratospheric input dominates in austral winter at 270 hectopascals and drops to less than half but is still significant at 430 hectopascals. Emission contributions are not significant at these two levels. The IAV of lightning over this region also contributes to the IAV of ozone in September and December. Over the tropical southeastern Pacific, the contribution of the IAV of stratospheric input is significant at 270 and 430 hectopascals in austral winter, and emissions have little influence.

Causes of interannual variability over the southern hemispheric tropospheric ozone maximum

NASA Astrophysics Data System (ADS)

Liu, Junhua; Rodriguez, Jose M.; Steenrod, Stephen D.; Douglass, Anne R.; Logan, Jennifer A.; Olsen, Mark A.; Wargan, Krzysztof; Ziemke, Jerald R.

2017-03-01

We examine the relative contribution of processes controlling the interannual variability (IAV) of tropospheric ozone over four sub-regions of the southern hemispheric tropospheric ozone maximum (SHTOM) over a 20-year period. Our study is based on hindcast simulations from the National Aeronautics and Space Administration Global Modeling Initiative chemistry transport model (NASA GMI-CTM) of tropospheric and stratospheric chemistry, driven by assimilated Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological fields. Our analysis shows that over SHTOM region, the IAV of the stratospheric contribution is the most important factor driving the IAV of upper tropospheric ozone (270 hPa), where ozone has a strong radiative effect. Over the South Atlantic region, the contribution from surface emissions to the IAV of ozone exceeds that from stratospheric input at and below 430 hPa. Over the South Indian Ocean, the IAV of stratospheric ozone makes the largest contribution to the IAV of ozone with little or no influence from surface emissions at 270 and 430 hPa in austral winter. Over the tropical South Atlantic region, the contribution from IAV of stratospheric input dominates in austral winter at 270 hPa and drops to less than half but is still significant at 430 hPa. Emission contributions are not significant at these two levels. The IAV of lightning over this region also contributes to the IAV of ozone in September and December. Over the tropical southeastern Pacific, the contribution of the IAV of stratospheric input is significant at 270 and 430 hPa in austral winter, and emissions have little influence.

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.

Agrometeorological services for smallholder farmers in West Africa

NASA Astrophysics Data System (ADS)

Tarchiani, Vieri; Camacho, José; Coulibaly, Hamidou; Rossi, Federica; Stefanski, Robert

2018-04-01

Climate variability and change are recognised as a major threat for West African agriculture, particularly for smallholder farmers. Moreover, population pressure, poverty, and food insecurity, are worsening the vulnerability of production systems to climate risks. Application of Climate Services in agriculture, specifically Agrometeorological Services, is acknowledged as a valuable innovation to assist decision-making and develop farmers' specific adaptive capacities. In West Africa, the World Meteorological Organisation and National Meteorological Services deployed considerable efforts in the development of Agrometeorological Services. Nevertheless, the impacts of such services on West African farming communities are still largely unknown. This paper aims to delineate the added value of agrometeorological services for farmers within the Agriculture Innovation System of Mauritania. The results of this quali-quantitative assessment demonstrate that farmers use agrometeorological information for a variety of choices: making strategic choice on the seed variety and on the geographical distribution of plots, choosing the most appropriate planting date, better tuning crop development cycle with the rhythm of the rains and choosing favourable periods for different cultural operations. Globally, the effects of all these good practices can be summarized by an increase of crops productivity and a decrease of cropping costs (including opportunity cost) in terms of inputs and working time.

Proceedings: Fourth Annual Workshop on Meteorological and Environmental Inputs to Aviation Systems

NASA Technical Reports Server (NTRS)

Frost, Walter (Editor); Camp, Dennis W. (Editor)

1980-01-01

The proceedings of a workshop on meteorological and environmental inputs to aviation systems held at The University of Tennessee Space Institute, Tullahoma, Tennessee, March 25-27, 1980, are reported. The workshop was jointly sponsored by NASA, NOAA, and FAA and brought together many disciplines of the aviation communities in round table discussions. The major objectives of the workshop are to satisfy such needs of the sponsoring agencies as the expansion of our understanding and knowledge of the interaction of the atmosphere with aviation systems, the better definition and implementation of services to operators, and the collection and interpretation of data for establishing operational criteria relating the total meteorological inputs from the atmospheric sciences to the needs of aviation communities. The unique aspects of the workshop were the diversity of the participants and the achievement of communication across the interface of the boundaries between pilots, meteorologists, training personnel, accident investigators, traffic controllers, flight operation personnel from military, civil, general aviation, and commercial interests alike. Representatives were in attendance from government, airlines, private agencies, aircraft manufacturers, Department of Defense, industries, research institutes, and universities. Full-length papers from invited speakers addressed topics on icing, turbulence, wind and wind shear, ceilings and visibility, lightning, and atmospheric electricity. These papers are contained in the proceedings together with the committee chairmen's reports on the results and conclusions of their efforts on similar subjects.

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…

Modeling Study for Tangier Island Jetties, Tangier Island, Virginia

DTIC Science & Technology

2015-03-01

meteorological and oceanographic (metocean) inputs used as forcing conditions. CENAO provided survey data available for Tangier Is- land from a...and 5 ft or 1.5 m). Wave direction is meteorological (e.g., direction waves coming from). Figure 55. Ten selected locations (black squares) in Alt...given in the previous sections. The Hud- son equation is well known and has been used for years to determine ar- mor stability ( Hudson 1959; Shore

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

What does industry need in the way of meteorology for air pollution problems

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

Crow, L.W.

1978-01-01

A discussion covers information which should be supplied to the consulting meteorologist; typical requests made by industrial concerns of various consultants; feeding meteorological data to models; the use of meteorological information in developing prevention of significant deterioration requirements; sources of error, e.g., assuming that digital values have a higher validity than the original analog records; the need for industrial concerns to develop sets of site-specific data; and the greater liability of air flow and stability frequencies estimated by professional meteorologists than transposed historical data from the nearest airport station for use as model input to make preliminary planning decisions.

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.

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.

Towards an integrated set of surface meterological observations for climate science and applications

NASA Astrophysics Data System (ADS)

Dunn, Robert; Thorne, Peter

2017-04-01

We cannot predict what is not observed, and we cannot analyse what is not archived. To meet current scientific and societal demands, as well as future requirements for climate services, it is vital that the management and curation of land-based meteorological data holdings is improved. A comprehensive global set of data holdings, of known provenance, integrated across both climate variable and timescale are required to meet the wide range of user needs. Presently, the land-based holdings are highly fractured into global, region and national holdings for different variables and timescales, from a variety of sources, and in a mixture of formats. We present a high level overview, based on broad community input, of the steps that are required to bring about this integration and progress towards such a database. Any long-term, international, program creating such an integrated database will transform the our collective ability to provide societally relevant research, analysis and predictions across the globe.

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...

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.

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.

Assessment of NASA's Physiographic and Meteorological Datasets as Input to HSPF and SWAT Hydrological Models

NASA Technical Reports Server (NTRS)

Alacron, Vladimir J.; Nigro, Joseph D.; McAnally, William H.; OHara, Charles G.; Engman, Edwin Ted; Toll, David

2011-01-01

This paper documents the use of simulated Moderate Resolution Imaging Spectroradiometer land use/land cover (MODIS-LULC), NASA-LIS generated precipitation and evapo-transpiration (ET), and Shuttle Radar Topography Mission (SRTM) datasets (in conjunction with standard land use, topographical and meteorological datasets) as input to hydrological models routinely used by the watershed hydrology modeling community. The study is focused in coastal watersheds in the Mississippi Gulf Coast although one of the test cases focuses in an inland watershed located in northeastern State of Mississippi, USA. The decision support tools (DSTs) into which the NASA datasets were assimilated were the Soil Water & Assessment Tool (SWAT) and the Hydrological Simulation Program FORTRAN (HSPF). These DSTs are endorsed by several US government agencies (EPA, FEMA, USGS) for water resources management strategies. These models use physiographic and meteorological data extensively. Precipitation gages and USGS gage stations in the region were used to calibrate several HSPF and SWAT model applications. Land use and topographical datasets were swapped to assess model output sensitivities. NASA-LIS meteorological data were introduced in the calibrated model applications for simulation of watershed hydrology for a time period in which no weather data were available (1997-2006). The performance of the NASA datasets in the context of hydrological modeling was assessed through comparison of measured and model-simulated hydrographs. Overall, NASA datasets were as useful as standard land use, topographical , and meteorological datasets. Moreover, NASA datasets were used for performing analyses that the standard datasets could not made possible, e.g., introduction of land use dynamics into hydrological simulations

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.

"Development of an interactive crop growth web service architecture to review and forecast agricultural sustainability"

NASA Astrophysics Data System (ADS)

Seamon, E.; Gessler, P. E.; Flathers, E.; Walden, V. P.

2014-12-01

As climate change and weather variability raise issues regarding agricultural production, agricultural sustainability has become an increasingly important component for farmland management (Fisher, 2005, Akinci, 2013). Yet with changes in soil quality, agricultural practices, weather, topography, land use, and hydrology - accurately modeling such agricultural outcomes has proven difficult (Gassman et al, 2007, Williams et al, 1995). This study examined agricultural sustainability and soil health over a heterogeneous multi-watershed area within the Inland Pacific Northwest of the United States (IPNW) - as part of a five year, USDA funded effort to explore the sustainability of cereal production systems (Regional Approaches to Climate Change for Pacific Northwest Agriculture - award #2011-68002-30191). In particular, crop growth and soil erosion were simulated across a spectrum of variables and time periods - using the CropSyst crop growth model (Stockle et al, 2002) and the Water Erosion Protection Project Model (WEPP - Flanagan and Livingston, 1995), respectively. A preliminary range of historical scenarios were run, using a high-resolution, 4km gridded dataset of surface meteorological variables from 1979-2010 (Abatzoglou, 2012). In addition, Coupled Model Inter-comparison Project (CMIP5) global climate model (GCM) outputs were used as input to run crop growth model and erosion future scenarios (Abatzoglou and Brown, 2011). To facilitate our integrated data analysis efforts, an agricultural sustainability web service architecture (THREDDS/Java/Python based) is under development, to allow for the programmatic uploading, sharing and processing of variable input data, running model simulations, as well as downloading and visualizing output results. The results of this study will assist in better understanding agricultural sustainability and erosion relationships in the IPNW, as well as provide a tangible server-based tool for use by researchers and farmers - for both small scale field examination, or more regionalized scenarios.

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.

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.

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).

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.

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).

Efficient Prediction of Low-Visibility Events at Airports Using Machine-Learning Regression

NASA Astrophysics Data System (ADS)

Cornejo-Bueno, L.; Casanova-Mateo, C.; Sanz-Justo, J.; Cerro-Prada, E.; Salcedo-Sanz, S.

2017-11-01

We address the prediction of low-visibility events at airports using machine-learning regression. The proposed model successfully forecasts low-visibility events in terms of the runway visual range at the airport, with the use of support-vector regression, neural networks (multi-layer perceptrons and extreme-learning machines) and Gaussian-process algorithms. We assess the performance of these algorithms based on real data collected at the Valladolid airport, Spain. We also propose a study of the atmospheric variables measured at a nearby tower related to low-visibility atmospheric conditions, since they are considered as the inputs of the different regressors. A pre-processing procedure of these input variables with wavelet transforms is also described. The results show that the proposed machine-learning algorithms are able to predict low-visibility events well. The Gaussian process is the best algorithm among those analyzed, obtaining over 98% of the correct classification rate in low-visibility events when the runway visual range is {>}1000 m, and about 80% under this threshold. The performance of all the machine-learning algorithms tested is clearly affected in extreme low-visibility conditions ({<}500 m). However, we show improved results of all the methods when data from a neighbouring meteorological tower are included, and also with a pre-processing scheme using a wavelet transform. Also presented are results of the algorithm performance in daytime and nighttime conditions, and for different prediction time horizons.

Estimation of Monthly Near Surface Air Temperature Using Geographically Weighted Regression in China

NASA Astrophysics Data System (ADS)

Wang, M. M.; He, G. J.; Zhang, Z. M.; Zhang, Z. J.; Liu, X. G.

2018-04-01

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58 °C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

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.

Operational applications of a process-based runoff generation module on the Swiss Plateau and Prealps

NASA Astrophysics Data System (ADS)

Horat, Christoph; Antonetti, Manuel; Wernli, Heini; Zappa, Massimiliano

2017-04-01

Flash floods evolve rapidly during and after heavy precipitation events and represent a risk for society, especially in mountainous areas. Knowledge on meteorological variables and their temporal development is often not sufficient to predict their occurrence. Therefore, information about the state of the hydrological system derived from hydrological models is used. These models rely however on strong simplifying assumptions and need therefore to be calibrated. This prevents their application on catchments, where no runoff data is available. Here we present a flash-flood forecasting chain including: (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip), (ii) meteorological data from numerical weather prediction models at currently finest available resolution (COSMO-1, COSMO-E), (iii) operationally available soil moisture estimations from the PREVAH hydrological model, and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes (DRPs) which can be derived with different mapping approaches, and is parameterised a priori based on expert knowledge. First, we compared the performance of RGM-PRO with the one of a traditional conceptual runoff generation module for several events on Swiss Emme catchment, as well as on their nested catchments. Different DRP-maps are furthermore tested to evaluate the sensitivity of the forecasting chain to the mapping approaches. Then, we benchmarked the new forecasting chain with the traditional chain used on the Swiss Verzasca catchment. The results show that RGM-PRO performs similarly or even better than the traditional calibrated conceptual module on the investigated catchments. The use of strongly simplified DRP mapping approaches still leads to satisfying results, due mainly to the fact that the largest uncertainty source is represented by the meteorological input data. On the Verzasca catchment, RGM-PRO outperformed the traditional forecast chain in terms of mean absolute error, independently from the lead time and threshold quantile, whereas the Brier Skill Score did not show any clear preference. Probabilistic input data led generally to better results compared with those obtained with deterministic forecasts.

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

Using self-organizing maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin.

PubMed

Nkiaka, E; Nawaz, N R; Lovett, J C

2016-07-01

Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydro-meteorological time series, many of these methods require inputs from neighbouring stations, which are often not available, while other methods are computationally demanding. Computing techniques such as artificial intelligence can be used to address this challenge. Self-organizing maps (SOMs), which are a type of artificial neural network, were used for infilling gaps in a hydro-meteorological time series in a Sudano-Sahel catchment. The coefficients of determination obtained were all above 0.75 and 0.65 while the average topographic error was 0.008 and 0.02 for rainfall and river discharge time series, respectively. These results further indicate that SOMs are a robust and efficient method for infilling missing gaps in hydro-meteorological time series.

Atmospheric inputs of organic matter to a forested watershed: Variations from storm to storm over the seasons

USGS Publications Warehouse

Iavorivska , Lidiia; Boyer, Elizabeth W.; Miller, Matthew P.; Brown, Michael G.; Vasilopoulos , Terrie; Fuentes, Jose D.; Duffy, Christopher J.

2016-01-01

The objectives of this study were to determine the quantity and chemical composition of precipitation inputs of dissolved organic carbon (DOC) to a forested watershed; and to characterize the associated temporal variability. We sampled most precipitation that occurred from May 2012 through August 2013 at the Susquehanna Shale Hills Critical Zone Observatory (Pennsylvania, USA). Sub-event precipitation samples (159) were collected sequentially during 90 events; covering various types of synoptic meteorological conditions in all climatic seasons. Precipitation DOC concentrations and rates of wet atmospheric DOC deposition were highly variable from storm to storm, ranging from 0.3 to 5.6 mg C L−1 and from 0.5 to 32.8 mg C m−2 h−1, respectively. Seasonally, storms in spring and summer had higher concentrations of DOC and more optically active organic matter than in winter. Higher DOC concentrations resulted from weather types that favor air advection, where cold frontal systems, on average, delivered more than warm/stationary fronts and northeasters. A mixed modeling statistical approach revealed that factors related to storm properties, emission sources, and to the chemical composition of the atmosphere could explain more than 60% of the storm to storm variability in DOC concentrations. This study provided observations on changes in dissolved organic matter that can be useful in modeling of atmospheric oxidative chemistry, exploring relationships between organics and other elements of precipitation chemistry, and in considering temporal changes in ecosystem nutrient balances and microbial activity.

Overview of meteorological measurements for aerial spray modeling.

PubMed

Rafferty, J E; Biltoft, C A; Bowers, J F

1996-06-01

The routine meteorological observations made by the National Weather Service have a spatial resolution on the order of 1,000 km, whereas the resolution needed to conduct or model aerial spray applications is on the order of 1-10 km. Routinely available observations also do not include the detailed information on the turbulence and thermal structure of the boundary layer that is needed to predict the transport, dispersion, and deposition of aerial spray releases. This paper provides an overview of the information needed to develop the meteorological inputs for an aerial spray model such as the FSCBG and discusses the different types of instruments that are available to make the necessary measurements.

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean.

PubMed

de Gennaro, Gianluigi; Trizio, Livia; Di Gilio, Alessia; Pey, Jorge; Pérez, Noemi; Cusack, Michael; Alastuey, Andrés; Querol, Xavier

2013-10-01

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANN's forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies. © 2013 Elsevier B.V. All rights reserved.

Effects of Land Use Change on Evapotranspiration and Water Yield in the Great Lakes Region

NASA Astrophysics Data System (ADS)

Mao, D.; Cherkauer, K. A.

2005-12-01

Human activities have affected the exchange of energy and water between atmosphere and land surface through land use change. Conversion of large regions of pre-settlement forest and grassland to a majority cropland cover in the Great Lakes region has resulted in regional scale changes to hydrologic responses. Understanding the impact of historic land use change is important for management of future resources. Effects of land use change on the water and energy cycle of three Great Lakes states: Minnesota, Wisconsin, and Michigan, are analyzed using the Variable Infiltration Capacity (VIC) model. Land Data Assimilation System (LDAS) meteorological and soil data as well as pre-settlement and modern vegetation data taken from the USGS Land Use History of North American (LUHNA) were used as model input. Default vegetation input parameters were adjusted for the region based on a review of published studies. Results from a single grid cell vegetation sensitivity test show that on an average annual basis, forests transpire more than cropland and cropland more than grassland due to seasonal variations in Leaf Area Index (LAI) and stomatal resistances of vegetations. The hydrologic impact of region wide land use change was then analyzed by comparing simulations using both pre-settlement and current vegetation cover but the same meteorological forcings. Simulated changes resulting from land cover change vary with season and vegetation types. Reduction in forest cover increases water yield by decreasing evapotranspiration. Conversion between forest types resulted only in small differences in evaporation and water fluxes response. The most significant hydrologic changes were located in the southern part of the region where land use change has been primarily forest converted to cropland.

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.

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.

Daily River Flow Forecasting with Hybrid Support Vector Machine – Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Zaini, N.; Malek, M. A.; Yusoff, M.; Mardi, N. H.; Norhisham, S.

2018-04-01

The application of artificial intelligence techniques for river flow forecasting can further improve the management of water resources and flood prevention. This study concerns the development of support vector machine (SVM) based model and its hybridization with particle swarm optimization (PSO) to forecast short term daily river flow at Upper Bertam Catchment located in Cameron Highland, Malaysia. Ten years duration of historical rainfall, antecedent river flow data and various meteorology parameters data from 2003 to 2012 are used in this study. Four SVM based models are proposed which are SVM1, SVM2, SVM-PSO1 and SVM-PSO2 to forecast 1 to 7 day ahead of river flow. SVM1 and SVM-PSO1 are the models with historical rainfall and antecedent river flow as its input, while SVM2 and SVM-PSO2 are the models with historical rainfall, antecedent river flow data and additional meteorological parameters as input. The performances of the proposed model are measured in term of RMSE and R2 . It is found that, SVM2 outperformed SVM1 and SVM-PSO2 outperformed SVM-PSO1 which meant the additional meteorology parameters used as input to the proposed models significantly affect the model performances. Hybrid models SVM-PSO1 and SVM-PSO2 yield higher performances as compared to SVM1 and SVM2. It is found that hybrid models are more effective in forecasting river flow at 1 to 7 day ahead at the study area.

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.

A program and data base for evaluating SMMR algorithms

NASA Technical Reports Server (NTRS)

1979-01-01

A program (PARAM) is described which enables a user to compare the values of meteorological parameters derived from data obtained by the scanning multichannel microwave radiometer (SMMR) instrument on NIMBUS 7 with surface observations made over the ocean. The input to this program is a data base, also described, which contains the surface observations and coincident SMMR data. The evaluation of meteorological parameters using SMMR data is done by a user supplied subroutine. Instruments are given for executing the program and writing the subroutine.

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.

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.

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.

Tropical Montane Cloud Forests: Hydrometeorological variability in three neighbouring catchments with different forest cover

NASA Astrophysics Data System (ADS)

Ramírez, Beatriz H.; Teuling, Adriaan J.; Ganzeveld, Laurens; Hegger, Zita; Leemans, Rik

2017-09-01

Mountain areas are characterized by a large heterogeneity in hydrological and meteorological conditions. This heterogeneity is currently poorly represented by gauging networks and by the coarse scale of global and regional climate and hydrological models. Tropical Montane Cloud Forests (TMCFs) are found in a narrow elevation range and are characterized by persistent fog. Their water balance depends on local and upwind temperatures and moisture, therefore, changes in these parameters will alter TMCF hydrology. Until recently the hydrological functioning of TMCFs was mainly studied in coastal regions, while continental TMCFs were largely ignored. This study contributes to fill this gap by focusing on a TMCF which is located on the northern eastern Andes at an elevation of 1550-2300 m asl, in the Orinoco river basin highlands. In this study, we describe the spatial and seasonal meteorological variability, analyse the corresponding catchment hydrological response to different land cover, and perform a sensitivity analysis on uncertainties related to rainfall interpolation, catchment area estimation and streamflow measurements. Hydro-meteorological measurements, including hourly solar radiation, temperature, relative humidity, wind speed, precipitation, soil moisture and streamflow, were collected from June 2013 to May 2014 at three gauged neighbouring catchments with contrasting TMCF/grassland cover and less than 250 m elevation difference. We found wetter and less seasonally contrasting conditions at higher elevations, indicating a positive relation between elevation and fog or rainfall persistence. This pattern is similar to that of other eastern Andean TMCFs, however, the study site had higher wet season rainfall and lower dry season rainfall suggesting that upwind contrasts in land cover and moisture can influence the meteorological conditions at eastern Andean TMCFs. Contrasting streamflow dynamics between the studied catchments reflect the overall system response as a function of the catchments' elevation and land cover. The forested catchment, located at the higher elevations, had the highest seasonal streamflows. During the wet season, different land covers at the lower elevations were important in defining the streamflow responses between the deforested catchment and the catchment with intermediate forest cover. Streamflows were higher and the rainfall-runoff responses were faster in the deforested catchment than in the intermediate forest cover catchment. During the dry season, the catchments' elevation defined streamflows due to higher water inputs and lower evaporative demand at the higher elevations.

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.

Using Terrain Analysis and Remote Sensing to Improve Snow Mass Balance and Runoff Prediction

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Coleman, A. M.; Wigmosta, M. S.

2010-12-01

Approximately 70-80% of the water in the international Columbia River basin is sourced from snowmelt. The demand for this water has competing needs, as it is used for agricultural irrigation, municipal, hydro and nuclear power generation, and environmental in-stream flow requirements. Accurate forecasting of water supply is essential for planning current needs and prediction of future demands due to growth and climate change. A significant limitation on current forecasting is spatial and temporal uncertainty in snowpack characteristics, particularly snow water equivalent. Currently, point measurements of snow mass balance are provided by the NRCS SNOTEL network. Each site consists of a snow mass sensor and meteorology station that monitors snow water equivalent, snow depth, precipitation, and temperature. There are currently 152 sites in the mountains of Oregon and Washington. An important step in improving forecasts is determining how representative each SNOTEL site is of the total mass balance of the watershed through a full accounting of the spatiotemporal variability in snowpack processes. This variation is driven by the interaction between meteorological processes, land cover, and landform. Statistical and geostatistical spatial models relate the state of the snowpack (characterized through SNOTEL, snow course measurements, and multispectral remote sensing) to terrain attributes derived from digital elevation models (elevation, aspect, slope, compound topographic index, topographic shading, etc.) and land cover. Time steps representing the progression of the snow season for several meteorologically distinct water years are investigated to identify and quantify dominant physical processes. The spatially distributed snow balance data can be used directly as model inputs to improve short- and long-range hydrologic forecasts.

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.

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.

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.

Copula Multivariate analysis of Gross primary production and its hydro-environmental driver; A BIOME-BGC model applied to the Antisana páramos

NASA Astrophysics Data System (ADS)

Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur

2014-05-01

Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos

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

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.

Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area

NASA Astrophysics Data System (ADS)

Kustas, William P.; Alfieri, Joseph G.; Anderson, Martha C.; Colaizzi, Paul D.; Prueger, John H.; Evett, Steven R.; Neale, Christopher M. U.; French, Andrew N.; Hipps, Lawrence E.; Chávez, José L.; Copeland, Karen S.; Howell, Terry A.

2012-12-01

Application and validation of many thermal remote sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal thermal remote sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) [1], and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) [2]. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the remote sensing observations with the local flux measurement footprint.

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...

Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

NASA Astrophysics Data System (ADS)

Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

2017-09-01

In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

Spatial regression test for ensuring temperature data quality in southern Spain

NASA Astrophysics Data System (ADS)

Estévez, J.; Gavilán, P.; García-Marín, A. P.

2018-01-01

Quality assurance of meteorological data is crucial for ensuring the reliability of applications and models that use such data as input variables, especially in the field of environmental sciences. Spatial validation of meteorological data is based on the application of quality control procedures using data from neighbouring stations to assess the validity of data from a candidate station (the station of interest). These kinds of tests, which are referred to in the literature as spatial consistency tests, take data from neighbouring stations in order to estimate the corresponding measurement at the candidate station. These estimations can be made by weighting values according to the distance between the stations or to the coefficient of correlation, among other methods. The test applied in this study relies on statistical decision-making and uses a weighting based on the standard error of the estimate. This paper summarizes the results of the application of this test to maximum, minimum and mean temperature data from the Agroclimatic Information Network of Andalusia (southern Spain). This quality control procedure includes a decision based on a factor f, the fraction of potential outliers for each station across the region. Using GIS techniques, the geographic distribution of the errors detected has been also analysed. Finally, the performance of the test was assessed by evaluating its effectiveness in detecting known errors.

Application of Terrestrial Microwave Remote Sensing to Agricultural Drought Monitoring

NASA Astrophysics Data System (ADS)

Crow, W. T.; Bolten, J. D.

2014-12-01

Root-zone soil moisture information is a valuable diagnostic for detecting the onset and severity of agricultural drought. Current attempts to globally monitor root-zone soil moisture are generally based on the application of soil water balance models driven by observed meteorological variables. Such systems, however, are prone to random error associated with: incorrect process model physics, poor parameter choices and noisy meteorological inputs. The presentation will describe attempts to remediate these sources of error via the assimilation of remotely-sensed surface soil moisture retrievals from satellite-based passive microwave sensors into a global soil water balance model. Results demonstrate the ability of satellite-based soil moisture retrieval products to significantly improve the global characterization of root-zone soil moisture - particularly in data-poor regions lacking adequate ground-based rain gage instrumentation. This success has lead to an on-going effort to implement an operational land data assimilation system at the United States Department of Agriculture's Foreign Agricultural Service (USDA FAS) to globally monitor variations in root-zone soil moisture availability via the integration of satellite-based precipitation and soil moisture information. Prospects for improving the performance of the USDA FAS system via the simultaneous assimilation of both passive and active-based soil moisture retrievals derived from the upcoming NASA Soil Moisture Active/Passive mission will also be discussed.

Likelihood of achieving air quality targets under model uncertainties.

PubMed

Digar, Antara; Cohan, Daniel S; Cox, Dennis D; Kim, Byeong-Uk; Boylan, James W

2011-01-01

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Future directions of meteorology related to air-quality research.

PubMed

Seaman, Nelson L

2003-06-01

Meteorology is one of the major factors contributing to air-pollution episodes. More accurate representation of meteorological fields has been possible in recent years through the use of remote sensing systems, high-speed computers and fine-mesh meteorological models. Over the next 5-20 years, better meteorological inputs for air quality studies will depend on making better use of a wealth of new remotely sensed observations in more advanced data assimilation systems. However, for fine mesh models to be successful, parameterizations used to represent physical processes must be redesigned to be more precise and better adapted for the scales at which they will be applied. Candidates for significant overhaul include schemes to represent turbulence, deep convection, shallow clouds, and land-surface processes. Improvements in the meteorological observing systems, data assimilation and modeling, coupled with advancements in air-chemistry modeling, will soon lead to operational forecasting of air quality in the US. Predictive capabilities can be expected to grow rapidly over the next decade. This will open the way for a number of valuable new services and strategies, including better warnings of unhealthy atmospheric conditions, event-dependent emissions restrictions, and now casting support for homeland security in the event of toxic releases into the atmosphere.

Meteorological data fields 'in perspective'

NASA Technical Reports Server (NTRS)

Hasler, A. F.; Pierce, H.; Morris, K. R.; Dodge, J.

1985-01-01

Perspective display techniques can be applied to meteorological data sets to aid in their interpretation. Examples of a perspective display procedure applied to satellite and aircraft visible and infrared image pairs and to stereo cloud-top height analyses are presented. The procedure uses a sophisticated shading algorithm that produces perspective images with greatly improved comprehensibility when compared with the wire-frame perspective displays that have been used in the past. By changing the 'eye-point' and 'view-point' inputs to the program in a systematic way, movie loops that give the impression of flying over or through the data field have been made. This paper gives examples that show how several kinds of meteorological data fields are more effectively illustrated using the perspective technique.

Presentation of the EURODELTA III intercomparison exercise - evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with meteorology

NASA Astrophysics Data System (ADS)

Bessagnet, Bertrand; Pirovano, Guido; Mircea, Mihaela; Cuvelier, Cornelius; Aulinger, Armin; Calori, Giuseppe; Ciarelli, Giancarlo; Manders, Astrid; Stern, Rainer; Tsyro, Svetlana; García Vivanco, Marta; Thunis, Philippe; Pay, Maria-Teresa; Colette, Augustin; Couvidat, Florian; Meleux, Frédérik; Rouïl, Laurence; Ung, Anthony; Aksoyoglu, Sebnem; María Baldasano, José; Bieser, Johannes; Briganti, Gino; Cappelletti, Andrea; D'Isidoro, Massimo; Finardi, Sandro; Kranenburg, Richard; Silibello, Camillo; Carnevale, Claudio; Aas, Wenche; Dupont, Jean-Charles; Fagerli, Hilde; Gonzalez, Lucia; Menut, Laurent; Prévôt, André S. H.; Roberts, Pete; White, Les

2016-10-01

The EURODELTA III exercise has facilitated a comprehensive intercomparison and evaluation of chemistry transport model performances. Participating models performed calculations for four 1-month periods in different seasons in the years 2006 to 2009, allowing the influence of different meteorological conditions on model performances to be evaluated. The exercise was performed with strict requirements for the input data, with few exceptions. As a consequence, most of differences in the outputs will be attributed to the differences in model formulations of chemical and physical processes. The models were evaluated mainly for background rural stations in Europe. The performance was assessed in terms of bias, root mean square error and correlation with respect to the concentrations of air pollutants (NO2, O3, SO2, PM10 and PM2.5), as well as key meteorological variables. Though most of meteorological parameters were prescribed, some variables like the planetary boundary layer (PBL) height and the vertical diffusion coefficient were derived in the model preprocessors and can partly explain the spread in model results. In general, the daytime PBL height is underestimated by all models. The largest variability of predicted PBL is observed over the ocean and seas. For ozone, this study shows the importance of proper boundary conditions for accurate model calculations and then on the regime of the gas and particle chemistry. The models show similar and quite good performance for nitrogen dioxide, whereas they struggle to accurately reproduce measured sulfur dioxide concentrations (for which the agreement with observations is the poorest). In general, the models provide a close-to-observations map of particulate matter (PM2.5 and PM10) concentrations over Europe rather with correlations in the range 0.4-0.7 and a systematic underestimation reaching -10 µg m-3 for PM10. The highest concentrations are much more underestimated, particularly in wintertime. Further evaluation of the mean diurnal cycles of PM reveals a general model tendency to overestimate the effect of the PBL height rise on PM levels in the morning, while the intensity of afternoon chemistry leads formation of secondary species to be underestimated. This results in larger modelled PM diurnal variations than the observations for all seasons. The models tend to be too sensitive to the daily variation of the PBL. All in all, in most cases model performances are more influenced by the model setup than the season. The good representation of temporal evolution of wind speed is the most responsible for models' skillfulness in reproducing the daily variability of pollutant concentrations (e.g. the development of peak episodes), while the reconstruction of the PBL diurnal cycle seems to play a larger role in driving the corresponding pollutant diurnal cycle and hence determines the presence of systematic positive and negative biases detectable on daily basis.

Dengue: recent past and future threats

PubMed Central

Rogers, David J.

2015-01-01

This article explores four key questions about statistical models developed to describe the recent past and future of vector-borne diseases, with special emphasis on dengue: (1) How many variables should be used to make predictions about the future of vector-borne diseases?(2) Is the spatial resolution of a climate dataset an important determinant of model accuracy?(3) Does inclusion of the future distributions of vectors affect predictions of the futures of the diseases they transmit?(4) Which are the key predictor variables involved in determining the distributions of vector-borne diseases in the present and future?Examples are given of dengue models using one, five or 10 meteorological variables and at spatial resolutions of from one-sixth to two degrees. Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data. Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s. The Garthwaite–Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions. PMID:25688021

The effect of clouds on photolysis rates and ozone formation in the unpolluted troposphere

NASA Technical Reports Server (NTRS)

Thompson, A. M.

1984-01-01

The photochemistry of the lower atmosphere is sensitive to short- and long-term meteorological effects; accurate modeling therefore requires photolysis rates for trace gases which reflect this variability. As an example, the influence of clouds on the production of tropospheric ozone has been investigated, using a modification of Luther's two-stream radiation scheme to calculate cloud-perturbed photolysis rates in a one-dimensional photochemical transport model. In the unpolluted troposphere, where stratospheric inputs of odd nitrogen appear to represent the photochemical source of O3, strong cloud reflectance increases the concentration of NO in the upper troposphere, leading to greatly enhanced rates of ozone formation. Although the rate of these processes is too slow to verify by observation, the calculation is useful in distinguishing some features of the chemistry of regions of differing mean cloudiness.

Observation and simulation of net primary productivity in Qilian Mountain, western China.

PubMed

Zhou, Y; Zhu, Q; Chen, J M; Wang, Y Q; Liu, J; Sun, R; Tang, S

2007-11-01

We modeled net primary productivity (NPP) at high spatial resolution using an advanced spaceborne thermal emission and reflection radiometer (ASTER) image of a Qilian Mountain study area using the boreal ecosystem productivity simulator (BEPS). Two key driving variables of the model, leaf area index (LAI) and land cover type, were derived from ASTER and moderate resolution imaging spectroradiometer (MODIS) data. Other spatially explicit inputs included daily meteorological data (radiation, precipitation, temperature, humidity), available soil water holding capacity (AWC), and forest biomass. NPP was estimated for coniferous forests and other land cover types in the study area. The result showed that NPP of coniferous forests in the study area was about 4.4 tCha(-1)y(-1). The correlation coefficient between the modeled NPP and ground measurements was 0.84, with a mean relative error of about 13.9%.

Department of Defense meteorological and environmental inputs to aviation systems

NASA Technical Reports Server (NTRS)

Try, P. D.

1983-01-01

Recommendations based on need, cost, and achievement of flight safety are offered, and the re-evaluation of weather parameters needed for safe landing operations that lead to reliable and consistent automated observation capabilities are considered.

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.

Spoilt for choice - A comparison of downscaling approaches for hydrological impact studies

NASA Astrophysics Data System (ADS)

Rössler, Ole; Fischer, Andreas; Kotlarski, Sven; Keller, Denise; Liniger, Mark; Weingartner, Rolf

2017-04-01

With the increasing number of available climate downscaling approaches, users are often faced with the luxury problem of which downscaling method to apply in a climate change impact assessment study. In Switzerland, for instance, the new generation of local scale climate scenarios CH2018 will be based on quantile mapping (QM), replacing the previous delta change (DC) method. Parallel to those two methods, a multi-site weather generator (WG) was developed to meet specific user needs. The question poses which downscaling method is the most suitable for a given application. Here, we analyze the differences of the three approaches in terms of hydro-meteorological responses in the Swiss pre-Alps in terms of mean values as well as indices of extremes. The comparison of the three different approaches was carried out in the frame of a hydrological impact assessment study that focused on different runoff characteristics and their related meteorological indices in the meso-scale catchment of the river Thur ( 1700 km2), Switzerland. For this purpose, we set up the hydrological model WaSiM-ETH under present (1980-2009) and under future conditions (2070-2099), assuming the SRES A1B emission scenario. Input to the three downscaling approaches were 10 GCM-RCM simulations of the ENSEMBLES project, while eight meteorological station observations served as the reference. All station data, observed and downscaled, were interpolated to obtain meteorological fields of temperature and precipitation required by the hydrological model. For the present-day reference period we evaluated the ability of each downscaling method to reproduce today's hydro-meteorological patterns. In the scenario runs, we focused on the comparison of change signals for each hydro-meteorological parameter generated by the three downscaling techniques. The evaluation exercise reveals that QM and WG perform equally well in representing present day average conditions, but that QM outperforms WG in reproducing indices related to extreme conditions like the number of drought events or multi-day rain sums. In terms of mean monthly discharge changes, the three downscaling methods reveal notable differences: DC shows the strongest (in summer) and less pronounced (in winter) change signal. Regarding some extreme features of runoff like frequency of droughts and the low flow level, DC shows similar change signals compared to QM and WG. This was unexpected as DC is commonly reported to fail in terms of projecting extreme changes. In contrast, QM mostly shows the strongest change signals for the 10 different extreme related indices, due to its ability to pick up more features of the climate change signals from the RCM. This indicates that DC and also WG miss some aspects, especially for flood related indices. Hence, depending on the target variable of interest, DC and QM typically provide the full range of change signals, while WG mostly lies in between both method. However, it offers the great advantage of multiple realizations combined with inter-variable consistency.

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

Crop evapotranspiration estimation using remote sensing and the existing network of meteorological stations in Cyprus

NASA Astrophysics Data System (ADS)

Papadavid, G.; Hadjimitsis, D.; Michaelides, S.; Nisantzi, A.

2011-05-01

Cyprus is frequently confronted with severe droughts and the need for accurate and systematic data on crop evapotranspiration (ETc) is essential for decision making, regarding water irrigation management and scheduling. The aim of this paper is to highlight how data from meteorological stations in Cyprus can be used for monitoring and determining the country's irrigation demands. This paper shows how daily ETc can be estimated using FAO Penman-Monteith method adapted to satellite data and auxiliary meteorological parameters. This method is widely used in many countries for estimating crop evapotranspiration using auxiliary meteorological data (maximum and minimum temperatures, relative humidity, wind speed) as inputs. Two case studies were selected in order to determine evapotranspiration using meteorological and low resolution satellite data (MODIS - TERRA) and to compare it with the results of the reference method (FAO-56) which estimates the reference evapotranspiration (ETo) by using only meteorological data. The first approach corresponds to the FAO Penman-Monteith method adapted for using both meteorological and remotely sensed data. Furthermore, main automatic meteorological stations in Cyprus were mapped using Geographical Information System (GIS). All the agricultural areas of the island were categorized according to the nearest meteorological station which is considered as "representative" of the area. Thiessen polygons methodology was used for this purpose. The intended goal was to illustrate what can happen to a crop, in terms of water requirements, if meteorological data are retrieved from other than the representative stations. The use of inaccurate data can result in low yields or excessive irrigation which both lead to profit reduction. The results have shown that if inappropriate meteorological data are utilized, then deviations from correct ETc might be obtained, leading to water losses or crop water stress.

Calculating distributed glacier mass balance for the Swiss Alps from RCM output: Development and testing of downscaling and validation methods

NASA Astrophysics Data System (ADS)

Machguth, H.; Paul, F.; Kotlarski, S.; Hoelzle, M.

2009-04-01

Climate model output has been applied in several studies on glacier mass balance calculation. Hereby, computation of mass balance has mostly been performed at the native resolution of the climate model output or data from individual cells were selected and statistically downscaled. Little attention has been given to the issue of downscaling entire fields of climate model output to a resolution fine enough to compute glacier mass balance in rugged high-mountain terrain. In this study we explore the use of gridded output from a regional climate model (RCM) to drive a distributed mass balance model for the perimeter of the Swiss Alps and the time frame 1979-2003. Our focus lies on the development and testing of downscaling and validation methods. The mass balance model runs at daily steps and 100 m spatial resolution while the RCM REMO provides daily grids (approx. 18 km resolution) of dynamically downscaled re-analysis data. Interpolation techniques and sub-grid parametrizations are combined to bridge the gap in spatial resolution and to obtain daily input fields of air temperature, global radiation and precipitation. The meteorological input fields are compared to measurements at 14 high-elevation weather stations. Computed mass balances are compared to various sets of direct measurements, including stake readings and mass balances for entire glaciers. The validation procedure is performed separately for annual, winter and summer balances. Time series of mass balances for entire glaciers obtained from the model run agree well with observed time series. On the one hand, summer melt measured at stakes on several glaciers is well reproduced by the model, on the other hand, observed accumulation is either over- or underestimated. It is shown that these shifts are systematic and correlated to regional biases in the meteorological input fields. We conclude that the gap in spatial resolution is not a large drawback, while biases in RCM output are a major limitation to model performance. The development and testing of methods to reduce regionally variable biases in entire fields of RCM output should be a focus of pursuing studies.

Modelling two-way interactions between atmospheric pollution and weather using high-resolution GEM-MACH

NASA Astrophysics Data System (ADS)

Makar, Paul; Gong, Wanmin; Pabla, Balbir; Cheung, Philip; Milbrandt, Jason; Gravel, Sylvie; Moran, Michael; Gilbert, Samuel; Zhang, Junhua; Zheng, Qiong

2013-04-01

The Global Environmental Multiscale (GEM) model is the source of the Canadian government's operational numerical weather forecast guidance, and GEM-MACH is the Canadian operational air-quality forecast model. GEM-MACH comprises GEM and the 'Modelling Air-quality and Chemistry' module, a gas-phase, aqueous-phase and aerosol chemistry and microphysics subroutine package called from within GEM's physics module. The present operational GEM-MACH model is "on-line" (both chemistry and meteorology are part of the same modelling structure) but is not fully coupled (weather variables are provided as inputs to the chemistry, but the chemical variables are not used to modify the weather). In this work, we describe modifications made to GEM-MACH as part of the 2nd phase of the Air Quality Model Evaluation International Initiative, in order to bring the model to a fully coupled status and present the results of initial tests comparing uncoupled and coupled versions of the model to observations for a high-resolution forecasting system. Changes to GEM's cloud microphysics and radiative transfer packages were carried out to allow two-way coupling. The cloud microphysics package used here is the Milbrandt-Yau 2-moment (MY2) bulk microphysics scheme, which solves prognostic equations for the total droplet number concentration and the mass mixing ratios of six hydrometeor categories. Here, we have replaced the original cloud condensation nucleation parameterization of MY2 (empirically relating supersaturation and CCN number) with the aerosol activation scheme of Abdul-Razzak and Ghan (2002). The latter scheme makes use of the particle size and speciation distribution of GEM-MACH's chemistry code as well as meteorological inputs to predict the number of aerosol particles activated to form cloud droplets, which is then used in the MY2 microphysics. The radiative transfer routines of GEM assume a default constant concentration aerosol profile between the surface and 1500m, and a single set of optical properties for extinction, single scattering albedo, and asymmetry factor. Ozone in GEM is taken from a default 2D (latitude-height) monthly climatology. We have replaced the ozone below the model top with the ozone calculated from GEM-MACH's chemistry, and the default optical parameters associated with particulate matter have been replaced by those calculated with a Mie scattering algorithm. These changes were found to have a significant local impact on both weather and air-quality predictions for short-term test runs of 24 hours duration. In that particular case, the maximum number concentration of cloud droplets decreased by an order of magnitude, while the number of raindrops increased by an order of magnitude and changed in spatial distribution, but surface rainfall was found to decrease. The differences in meteorology had a profound effect on local pollutant plume concentrations at specific locations and times. We compare results over a longer time period, using two parallel forecast systems, one with feedbacks between meteorology and chemistry, one without. Both nest GEM-MACH from a North American domain (10 km horizontal grid spacing) to a 1535 x 1360 km, 2.5 km domain. These systems will be evaluated against monitoring networks within the high resolution domain.

Homogeneous and heterogeneous chemistry along air parcel trajectories

NASA Technical Reports Server (NTRS)

Jones, R. L.; Mckenna, D. L.; Poole, L. R.; Solomon, S.

1990-01-01

The study of coupled heterogeneous and homogeneous chemistry due to polar stratospheric clouds (PSC's) using Lagrangian parcel trajectories for interpretation of the Airborne Arctic Stratosphere Experiment (AASE) is discussed. This approach represents an attempt to quantitatively model the physical and chemical perturbation to stratospheric composition due to formation of PSC's using the fullest possible representation of the relevant processes. Further, the meteorological fields from the United Kingdom Meteorological office global model were used to deduce potential vorticity and inferred regions of PSC's as an input to flight planning during AASE.

Testing two temporal upscaling schemes for the estimation of the time variability of the actual evapotranspiration

NASA Astrophysics Data System (ADS)

Maltese, A.; Capodici, F.; Ciraolo, G.; La Loggia, G.

2015-10-01

Temporal availability of grapes actual evapotranspiration is an emerging issue since vineyards farms are more and more converted from rainfed to irrigated agricultural systems. The manuscript aims to verify the accuracy of the actual evapotranspiration retrieval coupling a single source energy balance approach and two different temporal upscaling schemes. The first scheme tests the temporal upscaling of the main input variables, namely the NDVI, albedo and LST; the second scheme tests the temporal upscaling of the energy balance output, the actual evapotranspiration. The temporal upscaling schemes were implemented on: i) airborne remote sensing data acquired monthly during a whole irrigation season over a Sicilian vineyard; ii) low resolution MODIS products released daily or weekly; iii) meteorological data acquired by standard gauge stations. Daily MODIS LST products (MOD11A1) were disaggregated using the DisTrad model, 8-days black and white sky albedo products (MCD43A) allowed modeling the total albedo, and 8-days NDVI products (MOD13Q1) were modeled using the Fisher approach. Results were validated both in time and space. The temporal validation was carried out using the actual evapotranspiration measured in situ using data collected by a flux tower through the eddy covariance technique. The spatial validation involved airborne images acquired at different times from June to September 2008. Results aim to test whether the upscaling of the energy balance input or output data performed better.

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.

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.

Coastal Fog Sustains Summer Baseflow in Northern Californian Watershed

NASA Astrophysics Data System (ADS)

Chung, M.; Dufour, A.; Leonardson, R.; Thompson, S. E.; Dawson, T. E.

2015-12-01

The Mediterranean climate of Northern California imposes significant water stress on ecosystems and water resources during the dry summer months. During summer, frequently the only water inputs occur as occult precipitation, in the form of fog and dew. In this study, we characterized the role of coastal fog, a dominant feature of Northern Californian coastal ecosystems and a widespread phenomenon associated with deep marine upwelling in west coast, arid, and Mediterranean climates worldwide. We monitored fog occurrence and intensity, throughfall following canopy interception of fog, soil moisture, streamflow, and meteorological variables, and made visual observations of the spatial extent of fog using time-lapse imagery in Upper Pilarcitos Creek Watershed (managed by San Francisco Public Utilities Commission as part of the San Francisco area water supply). We adopted a stratified sampling design that captured the watershed's elevation gradient, forest-edge versus interior locations, and different vegetation cover. The point-scale observations of throughfall inputs and transpiration suppression, estimated from the Penman equation, were upscaled using such watershed features and the observed fog "footprint" identified from the time-lapse images. When throughfall input and fog-induced transpiration suppression were incorporated into the operational watershed model, they improved estimates of summer baseflow, which remained persistently higher than could be explained without the fog effects. Fog, although providing relatively small volumetric inputs to the water balance, appears to offer significant relief of water stress throughout the terrestrial and aquatic components of the coastal Californian ecosystem and thus should be accounted for when assessing water stress availability in dry ecosystems.

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.

Consequences of varied soil hydraulic and meteorological complexity on unsaturated zone time lag estimates.

PubMed

Vero, S E; Ibrahim, T G; Creamer, R E; Grant, J; Healy, M G; Henry, T; Kramers, G; Richards, K G; Fenton, O

2014-12-01

The true efficacy of a programme of agricultural mitigation measures within a catchment to improve water quality can be determined only after a certain hydrologic time lag period (subsequent to implementation) has elapsed. As the biophysical response to policy is not synchronous, accurate estimates of total time lag (unsaturated and saturated) become critical to manage the expectations of policy makers. The estimation of the vertical unsaturated zone component of time lag is vital as it indicates early trends (initial breakthrough), bulk (centre of mass) and total (Exit) travel times. Typically, estimation of time lag through the unsaturated zone is poor, due to the lack of site specific soil physical data, or by assuming saturated conditions. Numerical models (e.g. Hydrus 1D) enable estimates of time lag with varied levels of input data. The current study examines the consequences of varied soil hydraulic and meteorological complexity on unsaturated zone time lag estimates using simulated and actual soil profiles. Results indicated that: greater temporal resolution (from daily to hourly) of meteorological data was more critical as the saturated hydraulic conductivity of the soil decreased; high clay content soils failed to converge reflecting prevalence of lateral component as a contaminant pathway; elucidation of soil hydraulic properties was influenced by the complexity of soil physical data employed (textural menu, ROSETTA, full and partial soil water characteristic curves), which consequently affected time lag ranges; as the importance of the unsaturated zone increases with respect to total travel times the requirements for high complexity/resolution input data become greater. The methodology presented herein demonstrates that decisions made regarding input data and landscape position will have consequences for the estimated range of vertical travel times. Insufficiencies or inaccuracies regarding such input data can therefore mislead policy makers regarding the achievability of water quality targets. Copyright © 2014 Elsevier B.V. All rights reserved.

Global Surface Solar Energy Anomalies Including El Nino and La Nina Years

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Brown, D. E.; Chandler, W. S.; DiPasquale, R. C.; Ritchey, Nancy A.; Gupta, Shashi K.; Wilber, Anne C.; Kratz, David P.; Stackhouse, Paul W.

2001-01-01

This paper synthesizes past events in an attempt to define the general magnitude, duration, and location of large surface solar anomalies over the globe. Surface solar energy values are mostly a function of solar zenith angle, cloud conditions, column atmospheric water vapor, aerosols, and surface albedo. For this study, solar and meteorological parameters for the 10-yr period July 1983 through June 1993 are used. These data were generated as part of the Release 3 Surface meteorology and Solar Energy (SSE) activity under the NASA Earth Science Enterprise (ESE) effort. Release 3 SSE uses upgraded input data and methods relative to previous releases. Cloud conditions are based on recent NASA Version-D International Satellite Cloud Climatology Project (ISCCP) global satellite radiation and cloud data. Meteorological inputs are from Version-I Goddard Earth Observing System (GEOS) reanalysis data that uses both weather station and satellite information. Aerosol transmission for different regions and seasons are for an 'average' year based on historic solar energy data from over 1000 ground sites courtesy of Natural Resources Canada (NRCan). These data are input to a new Langley Parameterized Shortwave Algorithm (LPSA) that calculates surface albedo and surface solar energy. That algorithm is an upgraded version of the 'Staylor' algorithm. Calculations are performed for a 280X280 km equal-area grid system over the globe based on 3-hourly input data. A bi-linear interpolation process is used to estimate data output values on a 1 X 1 degree grid system over the globe. Maximum anomalies are examined relative to El Nino and La Nina events in the tropical Pacific Ocean. Maximum year-to-year anomalies over the globe are provided for a 10-year period. The data may assist in the design of systems with increased reliability. It may also allow for better planning for emergency assistance during some atypical events.

Assessing uncertainty in radar measurements on simplified meteorological scenarios

NASA Astrophysics Data System (ADS)

Molini, L.; Parodi, A.; Rebora, N.; Siccardi, F.

2006-02-01

A three-dimensional radar simulator model (RSM) developed by Haase (1998) is coupled with the nonhydrostatic mesoscale weather forecast model Lokal-Modell (LM). The radar simulator is able to model reflectivity measurements by using the following meteorological fields, generated by Lokal Modell, as inputs: temperature, pressure, water vapour content, cloud water content, cloud ice content, rain sedimentation flux and snow sedimentation flux. This work focuses on the assessment of some uncertainty sources associated with radar measurements: absorption by the atmospheric gases, e.g., molecular oxygen, water vapour, and nitrogen; attenuation due to the presence of a highly reflecting structure between the radar and a "target structure". RSM results for a simplified meteorological scenario, consisting of a humid updraft on a flat surface and four cells placed around it, are presented.

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.

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.

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.

Apollo: AN Automatic Procedure to Forecast Transport and Deposition of Tephra

NASA Astrophysics Data System (ADS)

Folch, A.; Costa, A.; Macedonio, G.

2007-05-01

Volcanic ash fallout represents a serious threat to communities around active volcanoes. Reliable short term predictions constitute a valuable support for to mitigate the effects of fallout on the surrounding area during an episode of crisis. We present a platform-independent automatic procedure aimed to daily forecast volcanic ash dispersal. The procedure builds on a series of programs and interfaces that allow an automatic data/results flow. Firstly the procedure downloads mesoscale meteorological forecasts for the region and period of interest, filters and converts data from its native format (typically GRIB format files), and sets up the CALMET diagnostic meteorological model to obtain hourly wind field and micro-meteorological variables on a finer mesh. Secondly a 1-D version of the buoyant plume equations assesses the distribution of mass along the eruptive column depending on the obtained wind field and on the conditions at the vent (granulometry, mass flow rate, etc.). All these data are used as input for the ash dispersion model(s). Any model able to face physical complexity and coupling processes with adequate solving times can be plugged into the system by means of an interface. Currently, the procedure contains the models HAZMAP, TEPHRA and FALL3D, the latter in both serial and parallel versions. Parallelization of FALL3D is done at two levels one for particle classes and one for spatial domain. The last step is to post-processes the model(s) outcomes to end up with homogeneous maps written on portable format files. Maps plot relevant quantities such as predicted ground load, expected deposit thickness or visual and flight safety concentration thresholds. Several applications are shown as examples.

The impact of the resolution of meteorological datasets on catchment-scale drought studies

NASA Astrophysics Data System (ADS)

Hellwig, Jost; Stahl, Kerstin

2017-04-01

Gridded meteorological datasets provide the basis to study drought at a range of scales, including catchment scale drought studies in hydrology. They are readily available to study past weather conditions and often serve real time monitoring as well. As these datasets differ in spatial/temporal coverage and spatial/temporal resolution, for most studies there is a tradeoff between these features. Our investigation examines whether biases occur when studying drought on catchment scale with low resolution input data. For that, a comparison among the datasets HYRAS (covering Central Europe, 1x1 km grid, daily data, 1951 - 2005), E-OBS (Europe, 0.25° grid, daily data, 1950-2015) and GPCC (whole world, 0.5° grid, monthly data, 1901 - 2013) is carried out. Generally, biases in precipitation increase with decreasing resolution. Most important variations are found during summer. In low mountain range of Central Europe the datasets of sparse resolution (E-OBS, GPCC) overestimate dry days and underestimate total precipitation since they are not able to describe high spatial variability. However, relative measures like the correlation coefficient reveal good consistencies of dry and wet periods, both for absolute precipitation values and standardized indices like the Standardized Precipitation Index (SPI) or Standardized Precipitation Evaporation Index (SPEI). Particularly the most severe droughts derived from the different datasets match very well. These results indicate that absolute values of sparse resolution datasets applied to catchment scale might be critical to use for an assessment of the hydrological drought at catchment scale, whereas relative measures for determining periods of drought are more trustworthy. Therefore, studies on drought, that downscale meteorological data, should carefully consider their data needs and focus on relative measures for dry periods if sufficient for the task.

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.

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

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 ...

MOM: A meteorological data checking expert system in CLIPS

NASA Technical Reports Server (NTRS)

Odonnell, Richard

1990-01-01

Meteorologists have long faced the problem of verifying the data they use. Experience shows that there is a sizable number of errors in the data reported by meteorological observers. This is unacceptable for computer forecast models, which depend on accurate data for accurate results. Most errors that occur in meteorological data are obvious to the meteorologist, but time constraints prevent hand-checking. For this reason, it is necessary to have a 'front end' to the computer model to ensure the accuracy of input. Various approaches to automatic data quality control have been developed by several groups. MOM is a rule-based system implemented in CLIPS and utilizing 'consistency checks' and 'range checks'. The system is generic in the sense that it knows some meteorological principles, regardless of specific station characteristics. Specific constraints kept as CLIPS facts in a separate file provide for system flexibility. Preliminary results show that the expert system has detected some inconsistencies not noticed by a local expert.

The design of 1-wire net meteorological observatory for 2.4 m telescope

NASA Astrophysics Data System (ADS)

Zhu, Gao-Feng; Wei, Ka-Ning; Fan, Yu-Feng; Xu, Jun; Qin, Wei

2005-03-01

The weather is an important factor to affect astronomical observations. The 2.4 m telescope can not work in Robotic Mode without the weather data input. Therefore it is necessary to build a meteorological observatory near the 2.4 m telescope. In this article, the design of the 1-wire net meteorological observatory, which includes hardware and software systems, is introduced. The hardware system is made up of some kinds of sensors and ADC. A suited power station system is also designed. The software system is based on Windows XP operating system and MySQL data management system, and a prototype system of browse/server model is developed by JAVA and JSP. After being tested, the meteorological observatory can register the immediate data of weather, such as raining, snowing, and wind speed. At last, the data will be stored for feature use. The product and the design can work well for the 2.4 m telescope.

A noise assessment and prediction system

NASA Technical Reports Server (NTRS)

Olsen, Robert O.; Noble, John M.

1990-01-01

A system has been designed to provide an assessment of noise levels that result from testing activities at Aberdeen Proving Ground, Md. The system receives meteorological data from surface stations and an upper air sounding system. The data from these systems are sent to a meteorological model, which provides forecasting conditions for up to three hours from the test time. The meteorological data are then used as input into an acoustic ray trace model which projects sound level contours onto a two-dimensional display of the surrounding area. This information is sent to the meteorological office for verification, as well as the range control office, and the environmental office. To evaluate the noise level predictions, a series of microphones are located off the reservation to receive the sound and transmit this information back to the central display unit. The computer models are modular allowing for a variety of models to be utilized and tested to achieve the best agreement with data. This technique of prediction and model validation will be used to improve the noise assessment system.

NASA Prediction of Worldwide Energy Resource High Resolution Meteorology Data For Sustainable Building Design

NASA Technical Reports Server (NTRS)

Chandler, William S.; Hoell, James M.; Westberg, David; Zhang, Taiping; Stackhouse, Paul W., Jr.

2013-01-01

A primary objective of NASA's Prediction of Worldwide Energy Resource (POWER) project is to adapt and infuse NASA's solar and meteorological data into the energy, agricultural, and architectural industries. Improvements are continuously incorporated when higher resolution and longer-term data inputs become available. Climatological data previously provided via POWER web applications were three-hourly and 1x1 degree latitude/longitude. The NASA Modern Era Retrospective-analysis for Research and Applications (MERRA) data set provides higher resolution data products (hourly and 1/2x1/2 degree) covering the entire globe. Currently POWER solar and meteorological data are available for more than 30 years on hourly (meteorological only), daily, monthly and annual time scales. These data may be useful to several renewable energy sectors: solar and wind power generation, agricultural crop modeling, and sustainable buildings. A recent focus has been working with ASHRAE to assess complementing weather station data with MERRA data. ASHRAE building design parameters being investigated include heating/cooling degree days and climate zones.

Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Ebtehaj, Isa; Bonakdari, Hossein; Deo, Ravinesh C.; Danandeh Mehr, Ali; Mohtar, Wan Hanna Melini Wan; Diop, Lamine; El-shafie, Ahmed; Singh, Vijay P.

2017-11-01

The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a novel combination of the ANFIS model with the firefly algorithm as an optimizer tool to construct a hybrid ANFIS-FFA model. The results of the ANFIS-FFA model is compared with the classical ANFIS model, which utilizes the fuzzy c-means (FCM) clustering method in the Fuzzy Inference Systems (FIS) generation. The historical monthly streamflow data for Pahang River, which is a major river system in Malaysia that characterized by highly stochastic hydrological patterns, is used in the study. Sixteen different input combinations with one to five time-lagged input variables are incorporated into the ANFIS-FFA and ANFIS models to consider the antecedent seasonal variations in historical streamflow data. The mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (r) are used to evaluate the forecasting performance of ANFIS-FFA model. In conjunction with these metrics, the refined Willmott's Index (Drefined), Nash-Sutcliffe coefficient (ENS) and Legates and McCabes Index (ELM) are also utilized as the normalized goodness-of-fit metrics. Comparison of the results reveals that the FFA is able to improve the forecasting accuracy of the hybrid ANFIS-FFA model (r = 1; RMSE = 0.984; MAE = 0.364; ENS = 1; ELM = 0.988; Drefined = 0.994) applied for the monthly streamflow forecasting in comparison with the traditional ANFIS model (r = 0.998; RMSE = 3.276; MAE = 1.553; ENS = 0.995; ELM = 0.950; Drefined = 0.975). The results also show that the ANFIS-FFA is not only superior to the ANFIS model but also exhibits a parsimonious modelling framework for streamflow forecasting by incorporating a smaller number of input variables required to yield the comparatively better performance. It is construed that the FFA optimizer can thus surpass the accuracy of the traditional ANFIS model in general, and is able to remove the false (inaccurately) forecasted data in the ANFIS model for extremely low flows. The present results have wider implications not only for streamflow forecasting purposes, but also for other hydro-meteorological forecasting variables requiring only the historical data input data, and attaining a greater level of predictive accuracy with the incorporation of the FFA algorithm as an optimization tool in an ANFIS model.

Automatic design of basin-specific drought indexes for highly regulated water systems

NASA Astrophysics Data System (ADS)

Zaniolo, Marta; Giuliani, Matteo; Castelletti, Andrea Francesco; Pulido-Velazquez, Manuel

2018-04-01

Socio-economic costs of drought are progressively increasing worldwide due to undergoing alterations of hydro-meteorological regimes induced by climate change. Although drought management is largely studied in the literature, traditional drought indexes often fail at detecting critical events in highly regulated systems, where natural water availability is conditioned by the operation of water infrastructures such as dams, diversions, and pumping wells. Here, ad hoc index formulations are usually adopted based on empirical combinations of several, supposed-to-be significant, hydro-meteorological variables. These customized formulations, however, while effective in the design basin, can hardly be generalized and transferred to different contexts. In this study, we contribute FRIDA (FRamework for Index-based Drought Analysis), a novel framework for the automatic design of basin-customized drought indexes. In contrast to ad hoc empirical approaches, FRIDA is fully automated, generalizable, and portable across different basins. FRIDA builds an index representing a surrogate of the drought conditions of the basin, computed by combining all the relevant available information about the water circulating in the system identified by means of a feature extraction algorithm. We used the Wrapper for Quasi-Equally Informative Subset Selection (W-QEISS), which features a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables, and optimizing relevance and redundancy of the subset. The preferred variable subset is selected among the efficient solutions and used to formulate the final index according to alternative model structures. We apply FRIDA to the case study of the Jucar river basin (Spain), a drought-prone and highly regulated Mediterranean water resource system, where an advanced drought management plan relying on the formulation of an ad hoc state index is used for triggering drought management measures. The state index was constructed empirically with a trial-and-error process begun in the 1980s and finalized in 2007, guided by the experts from the Confederación Hidrográfica del Júcar (CHJ). Our results show that the automated variable selection outcomes align with CHJ's 25-year-long empirical refinement. In addition, the resultant FRIDA index outperforms the official State Index in terms of accuracy in reproducing the target variable and cardinality of the selected inputs set.

Atmosphere-ionosphere coupling from convectively generated gravity waves

NASA Astrophysics Data System (ADS)

Azeem, Irfan; Barlage, Michael

2018-04-01

Ionospheric variability impacts operational performances of a variety of technological systems, such as HF communication, Global Positioning System (GPS) navigation, and radar surveillance. The ionosphere is not only perturbed by geomagnetic inputs but is also influenced by atmospheric tides and other wave disturbances propagating from the troposphere to high altitudes. Atmospheric Gravity Waves (AGWs) excited by meteorological sources are one of the largest sources of mesoscale variability in the ionosphere. In this paper, Total Electron Content (TEC) data from networks of GPS receivers in the United States are analyzed to investigate AGWs in the ionosphere generated by convective thunderstorms. Two case studies of convectively generated gravity waves are presented. On April 4, 2014 two distinct large convective systems in Texas and Arkansas generated two sets of concentric AGWs that were observed in the ionosphere as Traveling Ionospheric Disturbances (TIDs). The period of the observed TIDs was 20.8 min, the horizontal wavelength was 182.4 km, and the horizontal phase speed was 146.4 m/s. The second case study shows TIDs generated from an extended squall line on December 23, 2015 stretching from the Gulf of Mexico to the Great Lakes in North America. Unlike the concentric wave features seen in the first case study, the extended squall line generated TIDs, which exhibited almost plane-parallel phase fronts. The TID period was 20.1 min, its horizontal wavelength was 209.6 km, and the horizontal phase speed was 180.1 m/s. The AGWs generated by both of these meteorological events have large vertical wavelength (>100 km), which are larger than the F2 layer thickness, thus allowing them to be discernible in the TEC dataset.

Estimating inter-annual diversity of seasonal agricultural area using multi-temporal resourcesat data.

PubMed

Sreenivas, K; Sekhar, N Seshadri; Saxena, Manoj; Paliwal, R; Pathak, S; Porwal, M C; Fyzee, M A; Rao, S V C Kameswara; Wadodkar, M; Anasuya, T; Murthy, M S R; Ravisankar, T; Dadhwal, V K

2015-09-15

The present study aims at analysis of spatial and temporal variability in agricultural land cover during 2005-6 and 2011-12 from an ongoing program of annual land use mapping using multidate Advanced Wide Field Sensor (AWiFS) data aboard Resourcesat-1 and 2. About 640-690 multi-temporal AWiFS quadrant data products per year (depending on cloud cover) were co-registered and radiometrically normalized to prepare state (administrative unit) mosaics. An 18-fold classification was adopted in this project. Rule-based techniques along with maximum-likelihood algorithm were employed to deriving land cover information as well as changes within agricultural land cover classes. The agricultural land cover classes include - kharif (June-October), rabi (November-April), zaid (April-June), area sown more than once, fallow lands and plantation crops. Mean kappa accuracy of these estimates varied from 0.87 to 0.96 for various classes. Standard error of estimate has been computed for each class annually and the area estimates were corrected using standard error of estimate. The corrected estimates range between 99 and 116 Mha for kharif and 77-91 Mha for rabi. The kharif, rabi and net sown area were aggregated at 10 km × 10 km grid on annual basis for entire India and CV was computed at each grid cell using temporal spatially-aggregated area as input. This spatial variability of agricultural land cover classes was analyzed across meteorological zones, irrigated command areas and administrative boundaries. The results indicate that out of various states/meteorological zones, Punjab was consistently cropped during kharif as well as rabi seasons. Out of all irrigated commands, Tawa irrigated command was consistently cropped during rabi season. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

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

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.

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

Long-term solar UV radiation reconstructed by Artificial Neural Networks (ANN)

NASA Astrophysics Data System (ADS)

Feister, U.; Junk, J.; Woldt, M.

2008-01-01

Artificial Neural Networks (ANN) are efficient tools to derive solar UV radiation from measured meteorological parameters such as global radiation, aerosol optical depths and atmospheric column ozone. The ANN model has been tested with different combinations of data from the two sites Potsdam and Lindenberg, and used to reconstruct solar UV radiation at eight European sites by more than 100 years into the past. Annual totals of UV radiation derived from reconstructed daily UV values reflect interannual variations and long-term patterns that are compatible with variabilities and changes of measured input data, in particular global dimming by about 1980-1990, subsequent global brightening, volcanic eruption effects such as that of Mt. Pinatubo, and the long-term ozone decline since the 1970s. Patterns of annual erythemal UV radiation are very similar at sites located at latitudes close to each other, but different patterns occur between UV radiation at sites in different latitude regions.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Stochastic Watershed Models for Risk Based Decision Making

NASA Astrophysics Data System (ADS)

Vogel, R. M.

2017-12-01

Over half a century ago, the Harvard Water Program introduced the field of operational or synthetic hydrology providing stochastic streamflow models (SSMs), which could generate ensembles of synthetic streamflow traces useful for hydrologic risk management. The application of SSMs, based on streamflow observations alone, revolutionized water resources planning activities, yet has fallen out of favor due, in part, to their inability to account for the now nearly ubiquitous anthropogenic influences on streamflow. This commentary advances the modern equivalent of SSMs, termed `stochastic watershed models' (SWMs) useful as input to nearly all modern risk based water resource decision making approaches. SWMs are deterministic watershed models implemented using stochastic meteorological series, model parameters and model errors, to generate ensembles of streamflow traces that represent the variability in possible future streamflows. SWMs combine deterministic watershed models, which are ideally suited to accounting for anthropogenic influences, with recent developments in uncertainty analysis and principles of stochastic simulation

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.

Phenology Atlas of Czechia in preparation - aim & content

NASA Astrophysics Data System (ADS)

Hajkova, L.; Nekovar, J.; Novak, M.; Richterova, D.

2009-09-01

The main task is to create Phenology Atlas of Czechia for the period 1991 - 2010 by using geographic information systems. The general outputs will be maps (average phenophase onset at different altitudes), graphs (evaluation of phenophase onset in time) and tables (statistical results) with text, picture and botanical specification. The publication will be divided into 6 main chapters (Introduction, Phenology in Czechia & Europe, Methodology of observation, Field crops & Fruit trees & Wild plants, Phenology regionalisation, Temporal and Spatial variability). The essantial emphasis will be enforced on wild plants especially allergology important plants and phenophases. CHMI phenological and meteorological data will be used as an input data. This publication will be allocated for general public, supposed size B4, 270 - 300 pages. The research project is proposed for 3 years (2009 - 2011). In the presentation will be given several examples of Atlas content (Norway Spruce and Birch phenophases from Transaction of CHMI Nr.50, 2007).

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.

A blueprint for using climate change predictions in an eco-hydrological study

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.

2009-12-01

There is a growing interest to extend climate change predictions to smaller, catchment-size scales and identify their implications on hydrological and ecological processes. Small scale processes are, in fact, expected to mediate climate changes, producing local effects and feedbacks that can interact with the principal consequences of the change. This is particularly applicable, when a complex interaction, such as the inter-relationship between the hydrological cycle and vegetation dynamics, is considered. This study presents a blueprint methodology for studying climate change impacts, as inferred from climate models, on eco-hydrological dynamics at the catchment scale. Climate conditions, present or future, are imposed through input hydrometeorological variables for hydrological and eco-hydrological models. These variables are simulated with an hourly weather generator as an outcome of a stochastic downscaling technique. The generator is parameterized to reproduce the climate of southwestern Arizona for present (1961-2000) and future (2081-2100) conditions. The methodology provides the capability to generate ensemble realizations for the future that take into account the heterogeneous nature of climate predictions from different models. The generated time series of meteorological variables for the two scenarios corresponding to the current and mean expected future serve as input to a coupled hydrological and vegetation dynamics model, “Tethys-Chloris”. The hydrological model reproduces essential components of the land-surface hydrological cycle, solving the mass and energy budget equations. The vegetation model parsimoniously parameterizes essential plant life-cycle processes, including photosynthesis, phenology, carbon allocation, and tissue turnover. The results for the two mean scenarios are compared and discussed in terms of changes in the hydrological balance components, energy fluxes, and indices of vegetation productivity The need to account for uncertainties in projections of future climate is discussed and a methodology for propagating these uncertainties into the probability density functions of changes in eco-hydrological variables is presented.

An experimental detrending approach to attributing change of pan evaporation in comparison with the traditional partial differential method

NASA Astrophysics Data System (ADS)

Wang, Tingting; Sun, Fubao; Xia, Jun; Liu, Wenbin; Sang, Yanfang

2017-04-01

In predicting how droughts and hydrological cycles would change in a warming climate, change of atmospheric evaporative demand measured by pan evaporation (Epan) is one crucial element to be understood. Over the last decade, the derived partial differential (PD) form of the PenPan equation is a prevailing attribution approach to attributing changes to Epan worldwide. However, the independency among climatic variables required by the PD approach cannot be met using long term observations. Here we designed a series of numerical experiments to attribute changes of Epan over China by detrending each climatic variable, i.e., an experimental detrending approach, to address the inter-correlation among climate variables, and made comparison with the traditional PD method. The results show that the detrending approach is superior not only to a complicate system with multi-variables and mixing algorithm like aerodynamic component (Ep,A) and Epan, but also to a simple case like radiative component (Ep,R), when compared with traditional PD method. The major reason for this is the strong and significant inter-correlation of input meteorological forcing. Very similar and fine attributing results have been achieved based on detrending approach and PD method after eliminating the inter-correlation of input through a randomize approach. The contribution of Rh and Ta in net radiation and thus Ep,R, which has been overlooked based on the PD method but successfully detected by detrending approach, provides some explanation to the comparing results. We adopted the control run from the detrending approach and applied it to made adjustment of PD method. Much improvement has been made and thus proven this adjustment an effective way in attributing changes to Epan. Hence, the detrending approach and the adjusted PD method are well recommended in attributing changes in hydrological models to better understand and predict water and energy cycle.

Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

NASA Astrophysics Data System (ADS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-03-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.

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.

Monitoring Building Energy Systems at NASA Centers Using NASA Earth Science data, CMIP5 climate data products and RETScreen Expert Clean Energy Tool

NASA Astrophysics Data System (ADS)

Stackhouse, P. W., Jr.; Ganoe, R. E.; Westberg, D. J.; Leng, G. J.; Teets, E.; Hughes, J. M.; De Young, R.; Carroll, M.; Liou, L. C.; Iraci, L. T.; Podolske, J. R.; Stefanov, W. L.; Chandler, W.

2016-12-01

The NASA Climate Adaptation Science Investigator team is devoted to building linkages between NASA Earth Science and those within NASA responsible for infrastructure assessment, upgrades and planning. One of the focus areas is assessing NASA center infrastructure for energy efficiency, planning to meet new energy portfolio standards, and assessing future energy needs. These topics intersect at the provision of current and predicted future weather and climate data. This presentation provides an overview of the multi-center effort to access current building energy usage using Earth science observations, including those from in situ measurements, satellite measurement analysis, and global model data products as inputs to the RETScreen Expert, a clean energy decision support tool. RETScreen® Expert, sponsored by Natural Resources Canada (NRCan), is a tool dedicated to developing and providing clean energy project analysis software for the feasibility design and assessment of a wide range of building projects that incorporate renewable energy technologies. RETScreen Expert requires daily average meteorological and solar parameters that are available within less than a month of real-time. A special temporal collection of meteorological parameters was compiled from near-by surface in situ measurements. These together with NASA data from the NASA CERES (Clouds and Earth's Radiance Energy System)/FLASHFlux (Fast Longwave and SHortwave radiative Fluxes) provides solar fluxes and the NASA GMAO (Global Modeling and Assimilation Office) GEOS (Goddard Earth Observing System) operational meteorological analysis are directly used for meteorological input parameters. Examples of energy analysis for a few select buildings at various NASA centers are presented in terms of the energy usage relationship that these buildings have with changes in their meteorological environment. The energy requirements of potential future climates are then surveyed for a range of changes using the most recent CMIP5 global climate model data output.

Code Description for Generation of Meteorological Height and Pressure Level and Layer Profiles

DTIC Science & Technology

2016-06-01

defined by user input height or pressure levels. It can process input profiles from sensing systems such as radiosonde, lidar, or wind profiling radar...nearly the same way, but the split between wind and temperature/humidity (TH) special levels leads to some changes to one other routine. If changes are...top of the sounding, sometimes the moisture, the thermal, both thermal and moisture, and/or the wind data are missing. Missing data items in the

Modeling groundwater nitrate concentrations in private wells in Iowa

USGS Publications Warehouse

Wheeler, David C.; Nolan, Bernard T.; Flory, Abigail R.; DellaValle, Curt T.; Ward, Mary H.

2015-01-01

Contamination of drinking water by nitrate is a growing problem in many agricultural areas of the country. Ingested nitrate can lead to the endogenous formation of N-nitroso compounds, potent carcinogens. We developed a predictive model for nitrate concentrations in private wells in Iowa. Using 34,084 measurements of nitrate in private wells, we trained and tested random forest models to predict log nitrate levels by systematically assessing the predictive performance of 179 variables in 36 thematic groups (well depth, distance to sinkholes, location, land use, soil characteristics, nitrogen inputs, meteorology, and other factors). The final model contained 66 variables in 17 groups. Some of the most important variables were well depth, slope length within 1 km of the well, year of sample, and distance to nearest animal feeding operation. The correlation between observed and estimated nitrate concentrations was excellent in the training set (r-square = 0.77) and was acceptable in the testing set (r-square = 0.38). The random forest model had substantially better predictive performance than a traditional linear regression model or a regression tree. Our model will be used to investigate the association between nitrate levels in drinking water and cancer risk in the Iowa participants of the Agricultural Health Study cohort.

Modeling groundwater nitrate concentrations in private wells in Iowa.

PubMed

Wheeler, David C; Nolan, Bernard T; Flory, Abigail R; DellaValle, Curt T; Ward, Mary H

2015-12-01

Contamination of drinking water by nitrate is a growing problem in many agricultural areas of the country. Ingested nitrate can lead to the endogenous formation of N-nitroso compounds, potent carcinogens. We developed a predictive model for nitrate concentrations in private wells in Iowa. Using 34,084 measurements of nitrate in private wells, we trained and tested random forest models to predict log nitrate levels by systematically assessing the predictive performance of 179 variables in 36 thematic groups (well depth, distance to sinkholes, location, land use, soil characteristics, nitrogen inputs, meteorology, and other factors). The final model contained 66 variables in 17 groups. Some of the most important variables were well depth, slope length within 1 km of the well, year of sample, and distance to nearest animal feeding operation. The correlation between observed and estimated nitrate concentrations was excellent in the training set (r-square=0.77) and was acceptable in the testing set (r-square=0.38). The random forest model had substantially better predictive performance than a traditional linear regression model or a regression tree. Our model will be used to investigate the association between nitrate levels in drinking water and cancer risk in the Iowa participants of the Agricultural Health Study cohort. Copyright © 2015 Elsevier B.V. All rights reserved.

Dispersion modeling of accidental releases of toxic gases - utility for the fire brigades.

NASA Astrophysics Data System (ADS)

Stenzel, S.; Baumann-Stanzer, K.

2009-09-01

Several air dispersion models are available for prediction and simulation of the hazard areas associated with accidental releases of toxic gases. The most model packages (commercial or free of charge) include a chemical database, an intuitive graphical user interface (GUI) and automated graphical output for effective presentation of results. The models are designed especially for analyzing different accidental toxic release scenarios ("worst-case scenarios”), preparing emergency response plans and optimal countermeasures as well as for real-time risk assessment and management. The research project RETOMOD (reference scenarios calculations for toxic gas releases - model systems and their utility for the fire brigade) was conducted by the Central Institute for Meteorology and Geodynamics (ZAMG) in cooperation with the Viennese fire brigade, OMV Refining & Marketing GmbH and Synex Ries & Greßlehner GmbH. RETOMOD was funded by the KIRAS safety research program of the Austrian Ministry of Transport, Innovation and Technology (www.kiras.at). The main tasks of this project were 1. Sensitivity study and optimization of the meteorological input for modeling of the hazard areas (human exposure) during the accidental toxic releases. 2. Comparison of several model packages (based on reference scenarios) in order to estimate the utility for the fire brigades. For the purpose of our study the following models were tested and compared: ALOHA (Areal Location of Hazardous atmosphere, EPA), MEMPLEX (Keudel av-Technik GmbH), Trace (Safer System), Breeze (Trinity Consulting), SAM (Engineering office Lohmeyer). A set of reference scenarios for Chlorine, Ammoniac, Butane and Petrol were proceed, with the models above, in order to predict and estimate the human exposure during the event. Furthermore, the application of the observation-based analysis and forecasting system INCA, developed in the Central Institute for Meteorology and Geodynamics (ZAMG) in case of toxic release was investigated. INCA (Integrated Nowcasting through Comprehensive Analysis) data are calculated operationally with 1 km horizontal resolution and based on the weather forecast model ALADIN. The meteorological field's analysis with INCA include: Temperature, Humidity, Wind, Precipitation, Cloudiness and Global Radiation. In the frame of the project INCA data were compared with measurements from the meteorological observational network, conducted at traffic-near sites in Vienna. INCA analysis and very short term forecast fields (up to 6 hours) are found to be an advanced possibility to provide on-line meteorological input for the model package used by the fire brigade. Since the input requirements differ from model to model, and the outputs are based on unequal criteria for toxic area and exposure, a high degree of caution in the interpretation of the model results is required - especially in the case of slow wind speeds, stable atmospheric condition, and flow deflection by buildings in the urban area or by complex topography.

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...

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 measurements. Chapter 3

Treesearch

David Y. Hollinger

2008-01-01

Environmental measurements are useful for detecting climatic trends, understanding how the environment influences biological processes, and as input to ecosystem models. Landscape-scale monitoring requires a suite of environmental measures for all of these purposes, including air and soil temperature, humidity, wind speed, precipitation and soil moisture, and different...

A Sensitivity Study of the Aircraft Vortex Spacing System (AVOSS) Wake Predictor Algorithm to the Resolution of Input Meteorological Profiles

NASA Technical Reports Server (NTRS)

Rutishauser, David K.; Butler, Patrick; Riggins, Jamie

2004-01-01

The AVOSS project demonstrated the feasibility of applying aircraft wake vortex sensing and prediction technologies to safe aircraft spacing for single runway arrivals. On average, AVOSS provided spacing recommendations that were less than the current FAA prescribed spacing rules, resulting in a potential airport efficiency gain. Subsequent efforts have included quantifying the operational specifications for future Wake Vortex Advisory Systems (WakeVAS). In support of these efforts, each of the candidate subsystems for a WakeVAS must be specified. The specifications represent a consensus between the high-level requirements and the capabilities of the candidate technologies. This report documents the beginnings of an effort to quantify the capabilities of the AVOSS Prediction Algorithm (APA). Specifically, the APA horizontal position and circulation strength output sensitivity to the resolution of its wind and turbulence inputs is examined. The results of this analysis have implications for the requirements of the meteorological sensing and prediction systems comprising a WakeVAS implementation.

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.

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.

Wave-driven sediment mobilization on a storm-controlled continental shelf (Northwest Iberia)

USGS Publications Warehouse

Oberle, Ferdinand; Storlazzi, Curt D.; Hanebuth, Till

2014-01-01

Seafloor sediment mobilization on the inner Northwest Iberian continental shelf is caused largely by ocean surface waves. The temporal and spatial variability in the wave height, wave period, and wave direction has a profound effect on local sediment mobilization, leading to distinct sediment mobilization scenarios. Six grain-size specific sediment mobilization scenarios, representing seasonal average and storm conditions, were simulated with a physics-based numerical model. Model inputs included meteorological and oceanographic data in conjunction with seafloor grain-size and the shelf bathymetric data. The results show distinct seasonal variations, most importantly in wave height, leading to sediment mobilization, specifically on the inner shelf shallower than 30 m water depth where up to 49% of the shelf area is mobilized. Medium to severe storm events are modeled to mobilize up to 89% of the shelf area above 150 m water depth. The frequency of each of these seasonal and storm-related sediment mobilization scenarios is addressed using a decade of meteorological and oceanographic data. The temporal and spatial patterns of the modeled sediment mobilization scenarios are discussed in the context of existing geological and environmental processes and conditions to assist scientific, industrial and environmental efforts that are directly affected by sediment mobilization. Examples, where sediment mobilization plays a vital role, include seafloor nutrient advection, recurrent arrival of oil from oil-spill-laden seafloor sediment, and bottom trawling impacts.

Synthetic Vision Systems in GA Cockpit-Evaluation of Basic Maneuvers Performed by Low Time GA Pilots During Transition from VMC to IMC

NASA Technical Reports Server (NTRS)

Takallu, M. A.; Wong, D. T.; Uenking, M. D.

2002-01-01

An experimental investigation was conducted to study the effectiveness of modern flight displays in general aviation cockpits for mitigating Low Visibility Loss of Control and the Controlled Flight Into Terrain accidents. A total of 18 General Aviation (GA) pilots with private pilot, single engine land rating, with no additional instrument training beyond private pilot license requirements, were recruited to evaluate three different display concepts in a fixed-based flight simulator at the NASA Langley Research Center's General Aviation Work Station. Evaluation pilots were asked to continue flight from Visual Meteorological Conditions (VMC) into Instrument Meteorological Conditions (IMC) while performing a series of 4 basic precision maneuvers. During the experiment, relevant pilot/vehicle performance variables, pilot control inputs and physiological data were recorded. Human factors questionnaires and interviews were administered after each scenario. Qualitative and quantitative data have been analyzed and the results are presented here. Pilot performance deviations from the established target values (errors) were computed and compared with the FAA Practical Test Standards. Results of the quantitative data indicate that evaluation pilots committed substantially fewer errors when using the Synthetic Vision Systems (SVS) displays than when they were using conventional instruments. Results of the qualitative data indicate that evaluation pilots perceived themselves to have a much higher level of situation awareness while using the SVS display concept.

Neural network simulation of soil NO3 dynamic under potato crop system

NASA Astrophysics Data System (ADS)

Goulet-Fortin, Jérôme; Morais, Anne; Anctil, François; Parent, Léon-Étienne; Bolinder, Martin

2013-04-01

Nitrate leaching is a major issue in sandy soils intensively cropped to potato. Modelling could test and improve management practices, particularly as regard to the optimal N application rates. Lack of input data is an important barrier for the application of classical process-based models to predict soil NO3 content (SNOC) and NO3 leaching (NOL). Alternatively, data driven models such as neural networks (NN) could better take into account indicators of spatial soil heterogeneity and plant growth pattern such as the leaf area index (LAI), hence reducing the amount of soil information required. The first objective of this study was to evaluate NN and hybrid models to simulate SNOC in the 0-40 cm soil layer considering inter-annual variations, spatial soil heterogeneity and differential N application rates. The second objective was to evaluate the same methodology to simulate seasonal NOL dynamic at 1 m deep. To this aim, multilayer perceptrons with different combinations of driving meteorological variables, functions of the LAI and state variables of external deterministic models have been trained and evaluated. The state variables from external models were: drainage estimated by the CLASS model and the soil temperature estimated by an ICBM subroutine. Results of SNOC simulations were compared to field data collected between 2004 and 2011 at several experimental plots under potato cropping systems in Québec, Eastern Canada. Results of NOL simulation were compared to data obtained in 2012 from 11 suction lysimeters installed in 2 experimental plots under potato cropping systems in the same region. The most performing model for SNOC simulation was obtained using a 4-input hybrid model composed of 1) cumulative LAI, 2) cumulative drainage, 3) soil temperature and 4) day of year. The most performing model for NOL simulation was obtained using a 5-input NN model composed of 1) N fertilization rate at spring, 2) LAI, 3) cumulative rainfall, 4) the day of year and 5) the percentage of clay content. The MAE was 22% for SNOC simulation and 23% for NOL simulation. High sensitivity to LAI suggests that the model may take into account field and sub-field spatial variability and support N management. Further studies are needed to fully validate the method, particularly in the case of NOL simulation.

On using TRMM data and rainfall forecasts from meteorological models in data-scarce transboundary catchments - an example of Bangladesh

NASA Astrophysics Data System (ADS)

Bhattacharya, Biswa; Tohidul Islam, Md.

2014-05-01

This research focuses on the flood risk of the Haor region in the north-eastern part of Bangladesh. The prediction of the hydrological variables at different spatial and temporal scales in the Haor region is dependent on the influence of several upstream rivers in the Meghalaya catchment in India. Limitation in hydro-meteorological data collection and data sharing issues between the two countries dominate the feasibility of hydrological studies, particularly for near-realtime predictions. One of the possible solutions seems to be in making use of the variety of satellite based and meteorological model products for rainfall. The abundance of a variety of rainfall products provides a good basis of hydrological modelling of a part of the Ganges and Brahmaputra basin. In this research the TRMM data and rainfall forecasts from ECMWF have been compared with the scarce rain gauge data from the upstream Meghalaya catchment. Subsequently, the TRMM data and rainfall forecasts from ECMWF have been used as the meteorological input to a rainfall-runoff model of the Meghalaya catchment. The rainfall-runoff model of Meghalaya has been developed using the DEM data from SRTM. The generated runoff at the outlet of Meghalaya has been used as the upstream boundary condition in the existing rainfall-runoff model of the Haor region. The simulation results have been compared with the existing results based on simulations without any information of the rainfall-runoff in the upstream Meghalaya catchment. The comparison showed that the forecasting lead time has been substantially increased. As per the existing results the forecasting lead time at a number of locations in the catchment was about 6 to 8 hours. With the new results the forecasting lead time has gone up, with different levels of accuracy, to about 24 hours. This additional lead time will be highly beneficial in managing flood risk of the Haor region of Bangladesh. The research shows that satellite based rainfall products and rainfall forecasts from meteorological models can be very useful in flood risk management, particularly for data scarce regions and/or transboundary regions with data sharing issues. Keywords: flood risk management, TRMM, ECMWF, flood forecasting, Haor, Bangladesh. Abbreviations: TRMM: Tropical Rainfall Measuring Mission ECMWF: European Centre for Medium-Range Weather Forecasts DEM: Digital Elevation Model SRTM: Shuttle Radar Topography Mission

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.

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.

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.

Synergistically combining Optical and Thermal radiative transfer modelswithin the EO-LDAS data assimilation framework to estimate land surfaceand component temperatures from MODIS and Sentinel-3

NASA Astrophysics Data System (ADS)

Timmermans, J.; Gomez-Dans, J. L.; Verhoef, W.; Tol, C. V. D.; Lewis, P.

2017-12-01

Evapotranspiration (ET) cannot be directly measured from space. Instead it relies on modelling approaches that use several land surface parameters (LSP), LAI and LST, in conjunction with meteorological parameters. Such a modelling approach presents two caveats: the validity of the model, and the consistency between the different input parameters. Often this second step is not considered, ignoring that without good inputs no decent output can provided. When LSP- dynamics contradict each other, the output of the model cannot be representative of reality. At present however, the LSPs used in large scale ET estimations originate from different single-sensor retrieval-approaches and even from different satellite sensors. In response, the Earth Observation Land Data Assimilation System (EOLDAS) was developed. EOLDAS uses a multi-sensor approach to couple different satellite observations/types to radiative transfer models (RTM), consistently. It is therefore capable of synergistically estimating a variety of LSPs. Considering that ET is most sensitive to the temperatures of the land surface (components), the goal of this research is to expand EOLDAS to the thermal domain. This research not only focuses on estimating LST, but also on retrieving (soil/vegetation, Sunlit/shaded) component temperatures, to facilitate dual/quad-source ET models. To achieve this, The Soil Canopy Observations of Photosynthesis and Energy (SCOPE) model was integrated into EOLDAS. SCOPE couples key-parameters to key-processes, such as photosynthesis, ET and optical/thermal RT. In this research SCOPE was also coupled to MODTRAN RTM, in order to estimate BOA component temperatures directly from TOA observations. This paper presents the main modelling steps of integrating these complex models into an operational platform. In addition it highlights the actual retrieval using different satellite observations, such as MODIS and Sentinel-3, and meteorological variables from the ERA-Interim.

A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

PubMed

Zhou, Qingping; Jiang, Haiyan; Wang, Jianzhou; Zhou, Jianling

2014-10-15

Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems. Copyright © 2014 Elsevier B.V. All rights reserved.

SENSITIVITY OF THE NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION MULTILAYER MODEL TO INSTRUMENT ERROR AND PARAMETERIZATION UNCERTAINTY

EPA Science Inventory

The response of the National Oceanic and Atmospheric Administration multilayer inferential dry deposition velocity model (NOAA-MLM) to error in meteorological inputs and model parameterization is reported. Monte Carlo simulations were performed to assess the uncertainty in NOA...

INDIRECT ESTIMATION OF CONVECTIVE BOUNDARY LAYER STRUCTURE FOR USE IN ROUTINE DISPERSION MODELS

EPA Science Inventory

Dispersion models of the convectively driven atmospheric boundary layer (ABL) often require as input meteorological parameters that are not routinely measured. These parameters usually include (but are not limited to) the surface heat and momentum fluxes, the height of the cappin...

[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.

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.

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.

Computer programs for producing single-event aircraft noise data for specific engine power and meteorological conditions for use with USAF (United States Air Force) community noise model (NOISEMAP)

NASA Astrophysics Data System (ADS)

Mohlman, H. T.

1983-04-01

The Air Force community noise prediction model (NOISEMAP) is used to describe the aircraft noise exposure around airbases and thereby aid airbase planners to minimize exposure and prevent community encroachment which could limit mission effectiveness of the installation. This report documents two computer programs (OMEGA 10 and OMEGA 11) which were developed to prepare aircraft flight and ground runup noise data for input to NOISEMAP. OMEGA 10 is for flight operations and OMEGA 11 is for aircraft ground runups. All routines in each program are documented at a level useful to a programmer working with the code or a reader interested in a general overview of what happens within a specific subroutine. Both programs input normalized, reference aircraft noise data; i.e., data at a standard reference distance from the aircraft, for several fixed engine power settings, a reference airspeed and standard day meteorological conditions. Both programs operate on these normalized, reference data in accordance with user-defined, non-reference conditions to derive single-event noise data for 22 distances (200 to 25,000 feet) in a variety of physical and psycho-acoustic metrics. These outputs are in formats ready for input to NOISEMAP.

The European 2015 drought from a groundwater perspective

NASA Astrophysics Data System (ADS)

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

2017-04-01

In 2015 central and eastern Europe were affected by severe drought. Impacts of the drought were felt across many sectors, incl. agriculture, drinking water supply, electricity production, navigation, fisheries, and recreation. This drought event has recently been studied from meteorological and streamflow perspective, but no analysis of the groundwater drought has been performed. This is not surprising because real-time groundwater level observations often are not available. In this study we use previously established spatially-explicit relationships between meteorological drought and groundwater drought to quantify the 2015 groundwater drought over two regions in southern Germany and eastern Netherlands. We also tested the applicability of the Gravity Recovery Climate Experiment (GRACE) Terrestrial Water Storage (TWS) and GRACE-based groundwater anomalies to capture the spatial variability of the 2003 and 2015 drought events. We use the monthly groundwater observations from 2040 wells to establish the spatially varying optimal accumulation period between the Standardized Groundwater Index (SGI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at a 0.250 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 these optimal accumulation periods, we found that in Germany a uniform severe groundwater drought persisted for several months (i.e. SGI below the drought threshold of 20th percentile for almost all grid cells in August, September and October 2015), whereas the Netherlands appeared to have relatively high groundwater levels (never below the drought threshold of 20th percentile). The differences between this event and the European 2003 benchmark drought are striking. The 2003 groundwater drought was less uniformly pronounced, both in the Netherlands and Germany, with the regional averaged SGI above the 50th percentile. This is because slowly responding wells still were above average from the wet year of 2002-2003, which experienced severe flooding in central Europe. GRACE-TWS does show that both 2003 and 2015 were relatively dry, but the difference between Germany and the Netherlands in 2015 and the spatially-variable groundwater drought pattern in 2003 were not captured. This could be associated to 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. These are therefore not suitable for use in real-time groundwater drought monitoring in our case study regions. Our study shows that the relationship between meteorological drought and groundwater drought can be used to quantify groundwater drought and that the 2015 groundwater drought in southern Germany was more severe than the 2003 drought, because of preconditions in slowly responding groundwater wells. For sustainable groundwater drought management strategies the use of groundwater level monitoring is needed to study the spatial variability of local groundwater drought, which mostly coincides with drought impacts.

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.

Data Driven Ionospheric Modeling in Relation to Space Weather: Percent Cloud Coverage

NASA Astrophysics Data System (ADS)

Tulunay, Y.; Senalp, E. T.; Tulunay, E.

2009-04-01

Since 1990, a small group at METU has been developing data driven models in order to forecast some critical system parameters related with the near-Earth space processes. The background on the subject supports new achievements, which contributed the COST 724 activities, which will contribute to the new ES0803 activities. This work mentions one of the outstanding contributions, namely forecasting of meteorological parameters by considering the probable influence of cosmic rays (CR) and sunspot numbers (SSN). The data-driven method is generic and applicable to many Near-Earth Space processes including ionospheric/plasmaspheric interactions. It is believed that the EURIPOS initiative would be useful in supplying wide range reliable data to the models developed. Quantification of physical mechanisms, which causally link Space Weather to the Earth's Weather, has been a challenging task. In this basis, the percent cloud coverage (%CC) and cloud top temperatures (CTT) were forecast one month ahead of time between geographic coordinates of (22.5˚N; 57.5˚N); and (7.5˚W; 47.5˚E) at 96 grid locations and covering the years of 1983 to 2000 using the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M) [Tulunay, 2008]. The Near Earth Space variability at several different time scales arises from a number of separate factors and the physics of the variations cannot be modeled due to the lack of current information about the parameters of several natural processes. CR are shielded by the magnetosphere to a certain extent, but they can modulate the low level cloud cover. METU-FNN-M was developed, trained and applied for forecasting the %CC and CTT, by considering the history of those meteorological variables; Cloud Optical Depth (COD); the Ionization (I) value that is formulized and computed by using CR data and CTT; SSN; temporal variables; and defuzified cloudiness. The temporal and spatial variables and the cut off rigidity are used to compute the defuzified cloudiness. The forecast %CC and CTT values at uniformly spaced grids over the region of interest are used for mapping by Bezier surfaces. The major advantage of the fuzzy model is that it uses its inputs and the expert knowledge in coordination. Long-term cloud analysis was performed on a region having differences in terms of atmospheric activity, in order to show the generalization capability. Global and local parameters of the process were considered. Both CR Flux and SSN reflect the influence of Space Weather on general planetary situation; but other parameters in the inputs of the model reflect local situation. Error and correlation analysis on the forecast and observed parameters were performed. The correlations between the forecast and observed parameters are very promising. The model contributes to the dependence of the cloud formation process on CR Fluxes. The one-month in advance forecast values of the model can also be used as inputs to other models, which forecast some other local or global parameters in order to further test the hypothesis on possible link(s) between Space Weather and the Earth's Weather. The model based, theoretical and numerical works mentioned are promising and have potential for future research and developments. References Tulunay Y., E.T. Şenalp, Ş. Öz, L.I. Dorman, E. Tulunay, S.S. Menteş and M.E. Akcan (2008), A Fuzzy Neural Network Model to Forecast the Percent Cloud Coverage and Cloud Top Temperature Maps, Ann. Geophys., 26(12), 3945-3954, 2008.

Identification and simulation of space-time variability of past hydrological drought events in the Limpopo river basin, Southern Africa

NASA Astrophysics Data System (ADS)

Trambauer, P.; Maskey, S.; Werner, M.; Pappenberger, F.; van Beek, L. P. H.; Uhlenbrook, S.

2014-03-01

Droughts are widespread natural hazards and in many regions their frequency seems to be increasing. A finer resolution version (0.05° x 0.05°) of the continental scale hydrological model PCR-GLOBWB was set up for the Limpopo river basin, one of the most water stressed basins on the African continent. An irrigation module was included to account for large irrigated areas of the basin. The finer resolution model was used to analyse droughts in the Limpopo river basin in the period 1979-2010 with a view to identifying severe droughts that have occurred in the basin. Evaporation, soil moisture, groundwater storage and runoff estimates from the model were derived at a spatial resolution of 0.05° (approximately 5 km) on a daily time scale for the entire basin. PCR-GLOBWB was forced with daily precipitation, temperature and other meteorological variables obtained from the ERA-Interim global atmospheric reanalysis product from the European Centre for Medium-Range Weather Forecasts. Two agricultural drought indicators were computed: the Evapotranspiration Deficit Index (ETDI) and the Root Stress Anomaly Index (RSAI). Hydrological drought was characterised using the Standardized Runoff Index (SRI) and the Groundwater Resource Index (GRI), which make use of the streamflow and groundwater storage resulting from the model. Other more widely used drought indicators, such as the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evaporation Index (SPEI) were also computed for different aggregation periods. Results show that a carefully set up process-based model that makes use of the best available input data can successfully identify hydrological droughts even if the model is largely uncalibrated. The indicators considered are able to represent the most severe droughts in the basin and to some extent identify the spatial variability of droughts. Moreover, results show the importance of computing indicators that can be related to hydrological droughts, and how these add value to the identification of droughts/floods and the temporal evolution of events that would otherwise not have been apparent when considering only meteorological indicators. In some cases, meteorological indicators alone fail to capture the severity of the drought. Therefore, a combination of some of these indicators (e.g. SPEI-3, SRI-6, SPI-12) is found to be a useful measure for identifying hydrological droughts in the Limpopo river basin. Additionally, it is possible to make a characterisation of the drought severity, indicated by its duration and intensity.

Spatio-temporal Variability of Stemflow Volume in a Beech-Yellow Poplar Forest in Relation to Tree Species and Size

NASA Astrophysics Data System (ADS)

Levia, D. F.; van Stan, J. T.; Mage, S.; Hauske, P. W.

2009-05-01

Stemflow is a localized point input at the base of trees that can account for more than 10% of the incident gross precipitation in deciduous forests. Despite the fact that stemflow has been documented to be of hydropedological importance, affecting soil moisture patterns, soil erosion, soil chemistry, and the distribution of understory vegetation, our current understanding of the temporal variability of stemflow yield is poor. The aim of the present study, conducted in a beech-yellow poplar forest in northeastern Maryland (39°42'N, 75°50'W), was to better understand the temporal and variability of stemflow production from Fagus grandifolia Ehrh. (American beech) and Liriodendron tulipifera L. (yellow poplar) in relation to meteorological conditions and season in order to better assess its importance to canopy-soil interactions. The experimental plot had a stand density of 225 trees/ha, a stand basal area of 36.8 sq. m/ha, a mean dbh of 40.8 cm, and a mean tree height of 27.8 m. The stand leaf area index (LAI) is 5.3. Yellow poplar and beech constitute three- quarters of the stand basal area. Using a high resolution (5 min) sequential stemflow sampling network, consisting of tipping-bucket gauges interfaced with a Campbell CR1000 datalogger, the temporal variability of stemflow yield was examined. Beech produced significantly larger stemflow amounts than yellow poplar. The amount of stemflow produced by individual beech trees in 5 minute intervals reached three liters. Stemflow yield and funneling ratios decreased with increasing rain intensity. Temporal variability of stemflow inputs were affected by the nature of incident gross rainfall, season, tree species, tree size, and bark water storage capacity. Stemflow was greater during the leafless period than full leaf period. Stemflow yield was greater for larger beech trees and smaller yellow poplar trees, owing to differences in bark water storage capacity. The findings of this study indicate that stemflow has a detectable affect on soil moisture patterning and the hydraulic conductivity of forest soils.

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

Evaluation of an atmospheric model with surface and ABL meteorological data for energy applications in structured areas

NASA Astrophysics Data System (ADS)

Triantafyllou, A. G.; Kalogiros, J.; Krestou, A.; Leivaditou, E.; Zoumakis, N.; Bouris, D.; Garas, S.; Konstantinidis, E.; Wang, Q.

2018-03-01

This paper provides the performance evaluation of the meteorological component of The Air Pollution Model (TAPM), a nestable prognostic model, in predicting meteorological variables in urban areas, for both its surface layer and atmospheric boundary layer (ABL) turbulence parameterizations. The model was modified by incorporating four urban land surface types, replacing the existing single urban surface. Control runs were carried out over the wider area of Kozani, an urban area in NW Greece. The model was evaluated for both surface and ABL meteorological variables by using measurements of near-surface and vertical profiles of wind and temperature. The data were collected by using monitoring surface stations in selected sites as well as an acoustic sounder (SOnic Detection And Ranging (SODAR), up to 300 m above ground) and a radiometer profiler (up to 600 m above ground). The results showed the model demonstrated good performance in predicting the near-surface meteorology in the Kozani region for both a winter and a summer month. In the ABL, the comparison showed that the model's forecasts generally performed well with respect to the thermal structure (temperature profiles and ABL height) but overestimated wind speed at the heights of comparison (mostly below 200 m) up to 3-4 ms-1.

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.

Impact of meteorology on air quality modeling over the Po valley in northern Italy

NASA Astrophysics Data System (ADS)

Pernigotti, D.; Georgieva, E.; Thunis, P.; Bessagnet, B.

2012-05-01

A series of sensitivity tests has been performed using both a mesoscale meteorological model (MM5) and a chemical transport model (CHIMERE) to better understand the reasons why all models underestimate particulate matter concentrations in the Po valley in winter. Different options are explored to nudge meteorological observations from regulatory networks into MM5 in order to improve model performances, especially during the low wind speed regimes frequently present in this area. The sensitivity of the CHIMERE modeled particulate matter concentrations to these different meteorological inputs are then evaluated for the January 2005 time period. A further analysis of the CHIMERE model results revealed the need of improving the parametrization of the in-cloud scavenging and vertical diffusivity schemes; such modifications are relevant especially when the model is applied under mist, fog and low stratus conditions, which frequently occur in the Po valley during winter. The sensitivity of modeled particulate matter concentrations to turbulence parameters, wind, temperature and cloud liquid water content in one of the most polluted and complex areas in Europe is finally discussed.

Introduction of the Mobile Platform for the Meteorological Observations in Seoul Metropolitan City of Korea

NASA Astrophysics Data System (ADS)

Baek, K. T.; Lee, S.; Kang, M.; Lee, G.

2016-12-01

Traffic accidents due to adverse weather such as fog, heavy rainfall, flooding and road surface freezing have been increasing in Korea. To reduce damages caused by the severe weather on the road, a forecast service of combined real-time road-wise weather and the traffic situation is required. Conventional stationary meteorological observations in sparse location system are limited to observe the detailed road environment. For this reason, a mobile meteorological observation platform has been coupled in Weather Information Service Engine (WISE) which is the prototype of urban-scale high resolution weather prediction system in Seoul metropolitan area of Korea in early August 2016. The instruments onboard are designed to measure 15 meteorological parameters; pressure, temperature, relative humidity, precipitation, up/down net radiation, up/down longwave radiation, up/down shortwave radiation, road surface condition, friction coefficient, water depth, wind direction and speed. The observations from mobile platform show a distinctive advantage of data collection in need for road conditions and inputs for the numerical forecast model. In this study, we introduce and examine the feasibility of mobile observations in urban weather prediction and applications.

Implications of the 2015 European drought on groundwater storage

NASA Astrophysics Data System (ADS)

Rangecroft, S.; Van Loon, A.; Kumar, R.; Mishra, V.

2016-12-01

In 2015 central and eastern Europe were affected by severe drought. Impacts of the drought were felt across many sectors, incl. agriculture, drinking water supply, electricity production, navigation, fisheries, and recreation. This drought event has recently been studied from meteorological and streamflow perspective, but no analysis of the groundwater (GW) drought has been performed. This is not surprising because real-time GW level observations often are not available. In this study we use previously established spatially-explicit relationships between meteorological drought and GW drought to quantify the 2015 GW drought over two regions in southern Germany and eastern Netherlands. We use the monthly GW observations from 2040 wells to establish the spatially varying optimal accumulation period between the Standardized Groundwater Index (SGI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at a 0.250 gridded scale. The resulting optimal accumulation periods range between 1 and more than 24 months, indicating strong spatial differences in GW response time to meteorological input over the region. Based on these optimal accumulation periods, we found that in Germany a uniform severe GW drought persisted for several months (i.e. SGI below the drought threshold of 20th percentile for almost all grid cells in August, September and October 2015), whereas the Netherlands appeared to had relatively high GW levels (never below the drought threshold of 20th percentile). The differences between this event and the European 2003 benchmark drought are striking. The 2003 GW drought was less uniformly pronounced, both in the Netherlands and Germany, with the regional averaged SGI above the 50th percentile. This is because slowly responding wells still were above average from the wet year of 2002-2003, which experienced severe flooding in central Europe. Our study shows that the relationship between meteorological drought and GW drought can be used to quantify GW drought and that the 2015 GW drought in southern Germany was more severe than the 2003 drought, because of preconditions in slowly responding GW wells. For sustainable GW drought management strategies the use of GW level monitoring is needed to study the spatial variability of local GW drought, which mostly coincides with drought impacts.

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.

Human thermal perception related to Föhn winds due to Saharan dust outbreaks in Crete Island, Greece

NASA Astrophysics Data System (ADS)

Nastos, P. T.; Bleta, A. G.; Matsangouras, I. T.

2017-05-01

Crete Island is located in the southmost border of East Mediterranean basin, facing exacerbating atmospheric conditions (mainly concentrations of particulates) due to Saharan dust outbreaks. It is worth to note that these episodes are more frequent during spring and autumn, when mild biometeorological conditions become intolerable due to the synergy of the so called Föhn winds. Cretan mountains, especially Psiloritis Mt. (summit at 2456 m), are orientated perpendicularly to the southwest air mass flow, generating the Föhn winds. Propagating from the leeward of the mountains, these dry, hot winds have an effect on prevailing biometeorological conditions. While descending to the lowlands on the leeward side of the range, the wind becomes strong, gusty, and desiccating. This wind often lasts less than an hour to several days, with gradual weakening after the first or the second day. Sometimes, it stops very abruptly. In this work, the authors examined and analyzed the abrupt changes of human thermal perception within specific case studies during which Föhn winds appeared in Heraklion city at the leeward of Psiloritis Mt, associated with extreme Saharan dust episodes, observed within the period 2006-2010. In order to verify the development of Föhn winds, Meteorological Terminal Aviation Routine Weather Reports (METARs, meteorological observations every half hour), were acquired from the Heraklion meteorological station installed by the Hellenic National Meteorological Service (HNMS). The biometeorological conditions analyzed are based on human thermal bioclimatic indices such as the Physiologically equivalent temperature (PET) and the Universal Thermal Climate Index (UTCI). METAR recordings of meteorological variables, such as air temperature, vapor pressure, wind speed, and cloudiness, were used as input variables in modeling the aforementioned thermal indices, so that to interpret the grade of the thermo-physiological stress. The PET and UTCI analysis was performed by the use of the radiation and bioclimate model, "RayMan," which is well-suited to calculate radiation fluxes and human biometeorological indices. The results of the performed analysis showed even an increase of air temperature from 20 to 30 °C within 5 h, associated with a decrease in the vapor pressure from 11.5 to 9.3 hPa. In addition, the wind speed at 10 m increased from 5.1 to 20.1 m/s, 3.7 to 14.3 m/s with respect to 1.1 m height, during the events of Föhn winds. The biometeorological analysis has given evidence that slight/moderate heat stress classes of the examined thermal indices appear during Saharan dust episodes. Such conditions are uncommon at the beginning of spring season, indicating that Saharan dust episodes are not only responsible for acute health impacts but also for adverse biometeorological conditions, due to the very likely development of Föhn winds towards the wider area of Heraklion, a coastal city in the eastern Mediterranean.

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.

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...

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.

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.

Meteorological and air pollution modeling for an urban airport

NASA Technical Reports Server (NTRS)

Swan, P. R.; Lee, I. Y.

1980-01-01

Results are presented of numerical experiments modeling meteorology, multiple pollutant sources, and nonlinear photochemical reactions for the case of an airport in a large urban area with complex terrain. A planetary boundary-layer model which predicts the mixing depth and generates wind, moisture, and temperature fields was used; it utilizes only surface and synoptic boundary conditions as input data. A version of the Hecht-Seinfeld-Dodge chemical kinetics model is integrated with a new, rapid numerical technique; both the San Francisco Bay Area Air Quality Management District source inventory and the San Jose Airport aircraft inventory are utilized. The air quality model results are presented in contour plots; the combined results illustrate that the highly nonlinear interactions which are present require that the chemistry and meteorology be considered simultaneously to make a valid assessment of the effects of individual sources on regional air quality.

Evaluation of satellite-based, modeled-derived daily solar radiation data for the continental U.S.

USDA-ARS?s Scientific Manuscript database

Many applications of simulation models and related decision support tools for agriculture and natural resource management require daily meteorological data as inputs. Availability and quality of such data, however, often constrain research and decision support activities that require use of these to...

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.

When Can Information from Ordinal Scale Variables Be Integrated?

ERIC Educational Resources Information Center

Kemp, Simon; Grace, Randolph C.

2010-01-01

Many theoretical constructs of interest to psychologists are multidimensional and derive from the integration of several input variables. We show that input variables that are measured on ordinal scales cannot be combined to produce a stable weakly ordered output variable that allows trading off the input variables. Instead a partial order is…

Probabilistic Meteorological Characterization for Turbine Loads

NASA Astrophysics Data System (ADS)

Kelly, M.; Larsen, G.; Dimitrov, N. K.; Natarajan, A.

2014-06-01

Beyond the existing, limited IEC prescription to describe fatigue loads on wind turbines, we look towards probabilistic characterization of the loads via analogous characterization of the atmospheric flow, particularly for today's "taller" turbines with rotors well above the atmospheric surface layer. Based on both data from multiple sites as well as theoretical bases from boundary-layer meteorology and atmospheric turbulence, we offer probabilistic descriptions of shear and turbulence intensity, elucidating the connection of each to the other as well as to atmospheric stability and terrain. These are used as input to loads calculation, and with a statistical loads output description, they allow for improved design and loads calculations.

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.

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.

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.

Predicting cloud-to-ground lightning with neural networks

NASA Technical Reports Server (NTRS)

Barnes, Arnold A., Jr.; Frankel, Donald; Draper, James Stark

1991-01-01

A neural network is being trained to predict lightning at Cape Canaveral for periods up to two hours in advance. Inputs consist of ground based field mill data, meteorological tower data, lightning location data, and radiosonde data. High values of the field mill data and rapid changes in the field mill data, offset in time, provide the forecasts or desired output values used to train the neural network through backpropagation. Examples of input data are shown and an example of data compression using a hidden layer in the neural network is discussed.

Rapid Debris Analysis Project Task 3 Final Report - Sensitivity of Fallout to Source Parameters, Near-Detonation Environment Material Properties, Topography, and Meteorology

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

Goldstein, Peter

2014-01-24

This report describes the sensitivity of predicted nuclear fallout to a variety of model input parameters, including yield, height of burst, particle and activity size distribution parameters, wind speed, wind direction, topography, and precipitation. We investigate sensitivity over a wide but plausible range of model input parameters. In addition, we investigate a specific example with a relatively narrow range to illustrate the potential for evaluating uncertainties in predictions when there are more precise constraints on model parameters.

[Application of artificial neural networks in forecasting the number of circulatory system diseases death toll].

PubMed

Zhang, Ying; Shao, Yi; Shang, Kezheng; Wang, Shigong; Wang, Jinyan

2014-09-01

Set up the model of forecasting the number of circulatorys death toll based on back-propagation (BP) artificial neural networks discuss the relationship between the circulatory system diseases death toll meteorological factors and ambient air pollution. The data of tem deaths, meteorological factors, and ambient air pollution within the m 2004 to 2009 in Nanjing were collected. On the basis of analyzing the ficient between CSDDT meteorological factors and ambient air pollution, leutral network model of CSDDT was built for 2004 - 2008 based on factors and ambient air pollution within the same time, and the data of 2009 est the predictive power of the model. There was a closely system diseases relationship between meteorological factors, ambient air pollution and the circulatory system diseases death toll. The ANN model structure was 17 -16 -1, 17 input notes, 16 hidden notes and 1 output note. The training precision was 0. 005 and the final error was 0. 004 999 42 after 487 training steps. The results of forecast show that predict accuracy over 78. 62%. This method is easy to be finished with smaller error, and higher ability on circulatory system death toll on independent prediction, which can provide a new method for forecasting medical-meteorological forecast and have the value of further research.

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.

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.

Sensitivity of Crop Gross Primary Production Simulations to In-situ and Reanalysis Meteorological Data

NASA Astrophysics Data System (ADS)

Jin, C.; Xiao, X.; Wagle, P.

2014-12-01

Accurate estimation of crop Gross Primary Production (GPP) is important for food securityand terrestrial carbon cycle. Numerous publications have reported the potential of the satellite-based Production Efficiency Models (PEMs) to estimate GPP driven by in-situ climate data. Simulations of the PEMs often require surface reanalysis climate data as inputs, for example, the North America Regional Reanalysis datasets (NARR). These reanalysis datasets showed certain biases from the in-situ climate datasets. Thus, sensitivity analysis of the PEMs to the climate inputs is needed before their application at the regional scale. This study used the satellite-based Vegetation Photosynthesis Model (VPM), which is driven by solar radiation (R), air temperature (T), and the satellite-based vegetation indices, to quantify the causes and degree of uncertainties in crop GPP estimates due to different meteorological inputs at the 8-day interval (in-situ AmeriFlux data and NARR surface reanalysis data). The NARR radiation (RNARR) explained over 95% of the variability in in-situ RAF and TAF measured from AmeriFlux. The bais of TNARR was relatively small. However, RNARR had a systematical positive bias of ~3.5 MJ m-2day-1 from RAF. A simple adjustment based on the spatial statistic between RNARR and RAF produced relatively accurate radiation data for all crop site-years by reducing RMSE from 4 to 1.7 MJ m-2day-1. The VPM-based GPP estimates with three climate datasets (i.e., in-situ, and NARR before and after adjustment, GPPVPM,AF, GPPVPM,NARR, and GPPVPM,adjNARR) showed good agreements with the seasonal dynamics of crop GPP derived from the flux towers (GPPAF). The GPPVPM,AF differed from GPPAF by 2% for maize, and -8% to -12% for soybean on the 8-day interval. The positive bias of RNARR resulted in an overestimation of GPPVPM,NARR at both maize and soybean systems. However, GPPVPM,adjNARR significantly reduced the uncertainties of the maize GPP from 25% to 2%. The results from this study revealed that the errors of the NARR surface reanalysis data introduced significant uncertainties of the PEMs-based GPP estimates. Therefore, it is important to develop more accurate radiation datasets at the regional and global scales to estimate gross and net primary production of terrestrial ecosystems at the regional and global scales.

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.

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.

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.

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.

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.

Mesoscale landscape model of gypsy moth phenology

Treesearch

Joseph M. Russo; John G. W. Kelley; Andrew M. Liebhold

1991-01-01

A recently-developed high resolution climatological temperature data base was input into a gypsy moth phenology model. The high resolution data were created from a coupling of 30-year averages of station observations and digital elevation data. The resultant maximum and minimum temperatures have about a 1 km resolution which represents meteorologically the mesoscale....

TESTING PHYSICS AND CHEMISTRY SENSITIVITIES IN THE U.S. EPA COMMUNITY MULTISCALE AIR QUALITY MODELING SYSTEM (CMAQ)

EPA Science Inventory

Uncertainties in key elements of emissions and meteorology inputs to air quality models (AQMs) can range from 50 to 100% with some areas of emissions uncertainty even higher (Russell and Dennis, 2000). Uncertainties in the chemical mechanisms are thought to be smaller (Russell an...

[Airports and air quality: a critical synthesis of the literature].

PubMed

Cattani, Giorgio; Di Menno di Bucchianico, Alessandro; Gaeta, Alessandra; Romani, Daniela; Fontana, Luca; Iavicoli, Ivo

2014-01-01

This work reviewed existing literature on airport related activities that could worsen surrounding air quality; its aim is to underline the progress coming from recent-year studies, the knowledge emerging from new approaches, the development of semi-empiric analytical methods as well as the questions still needing to be clarified. To estimate pollution levels, spatial and temporal variability, and the sources relative contributions integrated assessment, using both fixed point measurement and model outputs, are needed. The general picture emerging from the studies was a non-negligible and highly spatially variable (within 2-3 km from the fence line) airport contribution; even if it is often not dominant compared to other concomitant pollution sources. Results were highly airport-specific. Traffic volumes, landscape and meteorology were the key variables that drove the impacts. Results were thus hardly exportable to other contexts. Airport related pollutant sources were found to be characterized by unusual emission patterns (particularly ultrafine particles, black carbon and nitrogen oxides during take-off); high time-resolution measurements allow to depict the rapidly changing take-off effect on air quality that could not be adequately observed otherwise. Few studies used high time resolution data in a successful way as statistical models inputs to estimate the aircraft take-off contribution to the observed average levels. These findings should not be neglected when exposure of people living near airports is to be assessed.

Ozone indices based on simple meteorological parameters: potentials and limitations of regression and neural network models

NASA Astrophysics Data System (ADS)

Soja, G.; Soja, A.-M.

This study tested the usefulness of extremely simple meteorological models for the prediction of ozone indices. The models were developed with the input parameters of daily maximum temperature and sunshine duration and are based on a data collection period of three years. For a rural environment in eastern Austria, the meteorological and ozone data of three summer periods have been used to develop functions to describe three ozone exposure indices (daily maximum, 7 h mean 9.00-16.00 h, accumulated ozone dose AOT40). Data sets for other years or stations not included in the development of the models were used as test data to validate the performance of the models. Generally, optimized regression models performed better than simplest linear models, especially in the case of AOT40. For the description of the summer period from May to September, the mean absolute daily differences between observed and calculated indices were 8±6 ppb for the maximum half hour mean value, 6±5 ppb for the 7 h mean and 41±40 ppb h for the AOT40. When the parameters were further optimized to describe individual months separately, the mean absolute residuals decreased by ⩽10%. Neural network models did not always perform better than the regression models. This is attributed to the low number of inputs in this comparison and to the simple architecture of these models (2-2-1). Further factorial analyses of those days when the residuals were higher than the mean plus one standard deviation should reveal possible reasons why the models did not perform well on certain days. It was observed that overestimations by the models mainly occurred on days with partly overcast, hazy or very windy conditions. Underestimations more frequently occurred on weekdays than on weekends. It is suggested that the application of this kind of meteorological model will be more successful in topographically homogeneous regions and in rural environments with relatively constant rates of emission and long-range transport of ozone precursors. Under conditions too demanding for advanced physico/chemical models, the presented models may offer useful alternatives to derive ecologically relevant ozone indices directly from meteorological parameters.

Climate Change and Hydrological Extreme Events - Risks and Perspectives for Water Management in Bavaria and Québec

NASA Astrophysics Data System (ADS)

Ludwig, R.

2017-12-01

There is as yet no confirmed knowledge whether and how climate change contributes to the magnitude and frequency of hydrological extreme events and how regional water management could adapt to the corresponding risks. The ClimEx project (2015-2019) investigates the effects of climate change on the meteorological and hydrological extreme events and their implications for water management in Bavaria and Québec. High Performance Computing is employed to enable the complex simulations in a hydro-climatological model processing chain, resulting in a unique high-resolution and transient (1950-2100) dataset of climatological and meteorological forcing and hydrological response: (1) The climate module has developed a large ensemble of high resolution data (12km) of the CRCM5 RCM for Central Europe and North-Eastern North America, downscaled from 50 members of the CanESM2 GCM. The dataset is complemented by all available data from the Euro-CORDEX project to account for the assessment of both natural climate variability and climate change. The large ensemble with several thousand model years provides the potential to catch rare extreme events and thus improves the process understanding of extreme events with return periods of 1000+ years. (2) The hydrology module comprises process-based and spatially explicit model setups (e.g. WaSiM) for all major catchments in Bavaria and Southern Québec in high temporal (3h) and spatial (500m) resolution. The simulations form the basis for in depth analysis of hydrological extreme events based on the inputs from the large climate model dataset. The specific data situation enables to establish a new method for `virtual perfect prediction', which assesses climate change impacts on flood risk and water resources management by identifying patterns in the data which reveal preferential triggers of hydrological extreme events. The presentation will highlight first results from the analysis of the large scale ClimEx model ensemble, showing the current and future ratio of natural variability and climate change impacts on meteorological extreme events. Selected data from the ensemble is used to drive a hydrological model experiment to illustrate the capacity to better determine the recurrence periods of hydrological extreme events under conditions of climate change.

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.

Availability of selected meteorological data in computer-based files of the U.S. Geological Survey, Montana, North Dakota, South Dakota, and Wyoming

USGS Publications Warehouse

Link, Brenda L.; Cary, L.E.

1986-01-01

Meteorological data were located, acquired, and stored from selected stations in Montana and North Dakota coal regions and adjacent areas including South Dakota and Wyoming. Data that were acquired have potential use in small watershed modeling studies. Emphasis was placed on acquiring data that was collected during the period 1970 to the present (1984). A map shows the location and type of stations selected. A narration summarizing conventions used in acquiring and storing the meteorological data is provided along with the various retrieval options available. Individual station descriptions are followed by tables listing the meteorological variables collected, period of obtained record, percentage of data recovery, and the instruments used and their description. (USGS)

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

NASA Astrophysics Data System (ADS)

Citakoglu, Hatice

2017-10-01

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient ( R 2 )) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R 2 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R 2 0.93, 0.91) in estimating soil temperature in Turkey.

A Study on Regional Frequency Analysis using Artificial Neural Network - the Sumjin River Basin

NASA Astrophysics Data System (ADS)

Jeong, C.; Ahn, J.; Ahn, H.; Heo, J. H.

2017-12-01

Regional frequency analysis means to make up for shortcomings in the at-site frequency analysis which is about a lack of sample size through the regional concept. Regional rainfall quantile depends on the identification of hydrologically homogeneous regions, hence the regional classification based on hydrological homogeneous assumption is very important. For regional clustering about rainfall, multidimensional variables and factors related geographical features and meteorological figure are considered such as mean annual precipitation, number of days with precipitation in a year and average maximum daily precipitation in a month. Self-Organizing Feature Map method which is one of the artificial neural network algorithm in the unsupervised learning techniques solves N-dimensional and nonlinear problems and be shown results simply as a data visualization technique. In this study, for the Sumjin river basin in South Korea, cluster analysis was performed based on SOM method using high-dimensional geographical features and meteorological factor as input data. then, for the results, in order to evaluate the homogeneity of regions, the L-moment based discordancy and heterogeneity measures were used. Rainfall quantiles were estimated as the index flood method which is one of regional rainfall frequency analysis. Clustering analysis using SOM method and the consequential variation in rainfall quantile were analyzed. This research was supported by a grant(2017-MPSS31-001) from Supporting Technology Development Program for Disaster Management funded by Ministry of Public Safety and Security(MPSS) of the Korean government.

Simultaneous calibration of ensemble river flow predictions over an entire range of lead times

NASA Astrophysics Data System (ADS)

Hemri, S.; Fundel, F.; Zappa, M.

2013-10-01

Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical postprocessing is required. In this work Bayesian model averaging (BMA) is applied to statistically postprocess ensemble runoff raw forecasts for a catchment in Switzerland, at lead times ranging from 1 to 240 h. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the postprocessed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.

On the usage of classical nucleation theory in quantification of the impact of bacterial INP on weather and climate

NASA Astrophysics Data System (ADS)

Sahyoun, Maher; Wex, Heike; Gosewinkel, Ulrich; Šantl-Temkiv, Tina; Nielsen, Niels W.; Finster, Kai; Sørensen, Jens H.; Stratmann, Frank; Korsholm, Ulrik S.

2016-08-01

Bacterial ice-nucleating particles (INP) are present in the atmosphere and efficient in heterogeneous ice-nucleation at temperatures up to -2 °C in mixed-phase clouds. However, due to their low emission rates, their climatic impact was considered insignificant in previous modeling studies. In view of uncertainties about the actual atmospheric emission rates and concentrations of bacterial INP, it is important to re-investigate the threshold fraction of cloud droplets containing bacterial INP for a pronounced effect on ice-nucleation, by using a suitable parameterization that describes the ice-nucleation process by bacterial INP properly. Therefore, we compared two heterogeneous ice-nucleation rate parameterizations, denoted CH08 and HOO10 herein, both of which are based on classical-nucleation-theory and measurements, and use similar equations, but different parameters, to an empirical parameterization, denoted HAR13 herein, which considers implicitly the number of bacterial INP. All parameterizations were used to calculate the ice-nucleation probability offline. HAR13 and HOO10 were implemented and tested in a one-dimensional version of a weather-forecast-model in two meteorological cases. Ice-nucleation-probabilities based on HAR13 and CH08 were similar, in spite of their different derivation, and were higher than those based on HOO10. This study shows the importance of the method of parameterization and of the input variable, number of bacterial INP, for accurately assessing their role in meteorological and climatic processes.

A Large-Scale, High-Resolution Hydrological Model Parameter Data Set for Climate Change Impact Assessment for the Conterminous US

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

Oubeidillah, Abdoul A; Kao, Shih-Chieh; Ashfaq, Moetasim

2014-01-01

To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computation- and data-intensive effort was conducted to organize a comprehensive hydrologic dataset with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation including meteorologic forcings, soil, land class, vegetation, and elevation were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous United States at refined 1/24 (~4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter dataset was prepared for the macro-scale Variable Infiltration Capacity (VIC) hydrologic model. The VICmore » simulation was driven by DAYMET daily meteorological forcing and was calibrated against USGS WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter dataset may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous United States. We anticipate that through this hydrologic parameter dataset, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter dataset will be provided to interested parties to support further hydro-climate impact assessment.« less

Wave-driven spatial and temporal variability in sea-floor sediment mobility in the Monterey Bay, Cordell Bank, and Gulf of the Farallones National Marine Sanctuaries

USGS Publications Warehouse

Storlazzi, Curt D.; Reid, Jane A.; Golden, Nadine E.

2007-01-01

Wind and wave patterns affect many aspects of continental shelves and shorelines geomorphic evolution. Although our understanding of the processes controlling sediment suspension on continental shelves has improved over the past decade, our ability to predict sediment mobility over large spatial and temporal scales remains limited. The deployment of robust operational buoys along the U.S. West Coast in the early 1980s provides large quantities of high-resolution oceanographic and meteorologic data. By 2006, these data sets were long enough to clearly identify long-term trends and compute statistically significant probability estimates of wave and wind behavior during annual and interannual climatic cycles (that is, El Niño and La Niña). Wave-induced sediment mobility on the shelf and upper slope off central California was modeled using synthesized oceanographic and meteorologic data as boundary input for the Delft SWAN model, sea-floor grain-size data provided by the usSEABED database, and regional bathymetry. Differences in waves (heights, periods, and directions) and winds (speeds and directions) between El Niño and La Niña months cause temporal and spatial variations in peak wave-induced bed shear stresses. These variations, in conjunction with spatially heterogeneous unconsolidated sea-floor sedimentary cover, result in predicted sediment mobility widely varying in both time and space. These findings indicate that these factors have significant consequences for both geological and biological processes.

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.

Evaluating the Credibility of Transport Processes in the Global Modeling Initiative 3D Model Simulations of Ozone Recovery

NASA Technical Reports Server (NTRS)

Strahan, Susan E.; Douglass, Anne R.

2003-01-01

The Global Modeling Initiative has integrated two 35-year simulations of an ozone recovery scenario with an offline chemistry and transport model using two different meteorological inputs. Physically based diagnostics, derived from satellite and aircraft data sets, are described and then used to evaluate the realism of temperature and transport processes in the simulations. Processes evaluated include barrier formation in the subtropics and polar regions, and extratropical wave-driven transport. Some diagnostics are especially relevant to simulation of lower stratospheric ozone, but most are applicable to any stratospheric simulation. The temperature evaluation, which is relevant to gas phase chemical reactions, showed that both sets of meteorological fields have near climatological values at all latitudes and seasons at 30 hPa and below. Both simulations showed weakness in upper stratospheric wave driving. The simulation using input from a general circulation model (GMI(sub GCM)) showed a very good residual circulation in the tropics and northern hemisphere. The simulation with input from a data assimilation system (GMI(sub DAS)) performed better in the midlatitudes than at high latitudes. Neither simulation forms a realistic barrier at the vortex edge, leading to uncertainty in the fate of ozone-depleted vortex air. Overall, tracer transport in the offline GMI(sub GCM) has greater fidelity throughout the stratosphere than the GMI(sub DAS).

Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

NASA Astrophysics Data System (ADS)

Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

2008-07-01

SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

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.

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.

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.

Comparison of short-term associations with meteorological variables between COPD and pneumonia hospitalization among the elderly in Hong Kong—a time-series study

NASA Astrophysics Data System (ADS)

Lam, Holly Ching-yu; Chan, Emily Ying-yang; Goggins, William Bernard

2018-05-01

Pneumonia and chronic obstructive pulmonary diseases (COPD) are the commonest causes of respiratory hospitalization among older adults. Both diseases have been reported to be associated with ambient temperature, but the associations have not been compared between the diseases. Their associations with other meteorological variables have also not been well studied. This study aimed to evaluate the associations between meteorological variables, pneumonia, and COPD hospitalization among adults over 60 and to compare these associations between the diseases. Daily cause-specific hospitalization counts in Hong Kong during 2004-2011 were regressed on daily meteorological variables using distributed lag nonlinear models. Associations were compared between diseases by ratio of relative risks. Analyses were stratified by season and age group (60-74 vs. ≥ 75). In hot season, high temperature (> 28 °C) and high relative humidity (> 82%) were statistically significantly associated with more pneumonia in lagged 0-2 and lagged 0-10 days, respectively. Pneumonia hospitalizations among the elderly (≥ 75) also increased with high solar radiation and high wind speed. During the cold season, consistent hockey-stick associations with temperature and relative humidity were found for both admissions and both age groups. The minimum morbidity temperature and relative humidity were at about 21-22 °C and 82%. The lagged effects of low temperature were comparable for both diseases (lagged 0-20 days). The low-temperature-admissions associations with COPD were stronger and were strongest among the elderly. This study found elevated pneumonia and COPD admissions risks among adults ≥ 60 during periods of extreme weather conditions, and the associations varied by season and age group. Vulnerable groups should be advised to avoid exposures, such as staying indoor and maintaining satisfactory indoor conditions, to minimize risks.

Regional meteorological drivers and long term trends of winter-spring nitrate dynamics across watersheds in northeastern North America

Treesearch

Jill Crossman; M. Catherine Eimers; Nora J. Casson; Douglas A. Burns; John L. Campbell; Gene E. Likens; Myron J. Mitchell; Sarah J. Nelson; James B. Shanley; Shaun A. Watmough; Kara L. Webster

2016-01-01

This study evaluated the contribution of winter rain-on-snow (ROS) events to annual and seasonal nitrate (N-NO3) export and identified the regional meteorological drivers of inter-annual variability in ROS N-NO3 export (ROS-N) at 9 headwater streams located across Ontario, Canada and the northeastern United States. Although...

Estimation of clear-sky insolation using satellite and ground meteorological data

NASA Technical Reports Server (NTRS)

Staylor, W. F.; Darnell, W. L.; Gupta, S. K.

1983-01-01

Ground based pyranometer measurements were combined with meteorological data from the Tiros N satellite in order to estimate clear-sky insolations at five U.S. sites for five weeks during the spring of 1979. The estimates were used to develop a semi-empirical model of clear-sky insolation for the interpretation of input data from the Tiros Operational Vertical Sounder (TOVS). Using only satellite data, the estimated standard errors in the model were about 2 percent. The introduction of ground based data reduced errors to around 1 percent. It is shown that although the errors in the model were reduced by only 1 percent, TOVS data products are still adequate for estimating clear-sky insolation.

Crowd-sourcing Meteorological Data for Student Field Projects

NASA Astrophysics Data System (ADS)

Bullard, J. E.

2016-12-01

This paper explains how students can rapidly collect large datasets to characterise wind speed and direction under different meteorological conditions. The tools used include a mobile device (tablet or phone), low cost wind speed/direction meters that are plugged in to the mobile device, and an app with online web support for uploading, collating and georeferencing data. Electronic customised data input forms downloaded to the mobile device are used to ensure students collect data using specified protocols which streamlines data management and reduces the likelihood of data entry errors. A key benefit is the rapid collection and quality control of field data that can be promptly disseminated to students for subsequent analysis.

Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale.

PubMed

Mauree, Dasaraden; Coccolo, Silvia; Kaempf, Jérôme; Scartezzini, Jean-Louis

2017-01-01

A new methodology is proposed to couple a meteorological model with a building energy use model. The aim of such a coupling is to improve the boundary conditions of both models with no significant increase in computational time. In the present case, the Canopy Interface Model (CIM) is coupled with CitySim. CitySim provides the geometrical characteristics to CIM, which then calculates a high resolution profile of the meteorological variables. These are in turn used by CitySim to calculate the energy flows in an urban district. We have conducted a series of experiments on the EPFL campus in Lausanne, Switzerland, to show the effectiveness of the coupling strategy. First, measured data from the campus for the year 2015 are used to force CIM and to evaluate its aptitude to reproduce high resolution vertical profiles. Second, we compare the use of local climatic data and data from a meteorological station located outside the urban area, in an evaluation of energy use. In both experiments, we demonstrate the importance of using in building energy software, meteorological variables that account for the urban microclimate. Furthermore, we also show that some building and urban forms are more sensitive to the local environment.

Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale

PubMed Central

Coccolo, Silvia; Kaempf, Jérôme; Scartezzini, Jean-Louis

2017-01-01

A new methodology is proposed to couple a meteorological model with a building energy use model. The aim of such a coupling is to improve the boundary conditions of both models with no significant increase in computational time. In the present case, the Canopy Interface Model (CIM) is coupled with CitySim. CitySim provides the geometrical characteristics to CIM, which then calculates a high resolution profile of the meteorological variables. These are in turn used by CitySim to calculate the energy flows in an urban district. We have conducted a series of experiments on the EPFL campus in Lausanne, Switzerland, to show the effectiveness of the coupling strategy. First, measured data from the campus for the year 2015 are used to force CIM and to evaluate its aptitude to reproduce high resolution vertical profiles. Second, we compare the use of local climatic data and data from a meteorological station located outside the urban area, in an evaluation of energy use. In both experiments, we demonstrate the importance of using in building energy software, meteorological variables that account for the urban microclimate. Furthermore, we also show that some building and urban forms are more sensitive to the local environment. PMID:28880883

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.

Modeling Vegetation Growth Impact on Groundwater Recharge

NASA Astrophysics Data System (ADS)

Anurag, H.; Ng, G. H. C.; Tipping, R.

2017-12-01

Vegetation growth is affected by variability in climate and land-cover / land-use over a range of temporal and spatial scales. Vegetation also modifies water budget through interception and evapotranspiration and thus has a significant impact on groundwater recharge. Most groundwater recharge assessments represent vegetation using specified, static parameter, such as for leaf-area-index, but this neglects the effect of vegetation dynamics on recharge estimates. Our study addresses this gap by including vegetation growth in model simulations of recharge. We use NCAR's Community Land Model v4.5 with its BGC module (BGC is the new CLM4.5 biogeochemistry). It integrates prognostic vegetation growth with land-surface and subsurface hydrological processes and can thus capture the effect of vegetation on groundwater. A challenge, however, is the need to resolve uncertainties in model inputs ranging from vegetation growth parameters all the way down to the water table. We have compiled diverse data spanning meteorological inputs to subsurface geology and use these to implement ensemble model simulations to evaluate the possible effects of dynamic vegetation growth (versus specified, static vegetation parameterizations) on estimating groundwater recharge. We present preliminary results for select data-intensive test locations throughout the state of Minnesota (USA), which has a sharp east-west precipitation gradient that makes it an apt testbed for examining ecohydrologic relationships across different temperate climatic settings and ecosystems. Using the ensemble simulations, we examine the effect of seasonal to interannual variability of vegetation growth on recharge and water table depths, which has implications for predicting the combined impact of climate, vegetation, and geology on groundwater resources. Future work will include distributed model simulations over the entire state, as well as conditioning uncertain vegetation and subsurface parameters on remote sensing data and statewide water table records using data assimilation.

An assessment of the feasibility of the use of satellite-only rainfall estimates for the hydrological monitoring in central Italy

NASA Astrophysics Data System (ADS)

Campo, Lorenzo; Caparrini, Francesca

2013-04-01

The need for accurate distributed hydrological modelling has constantly increased in last years for several purposes: agricultural applications, water resources management, hydrological balance at watershed scale, floods forecast. The main input for the hydrological numerical models is rainfall data that present, at the same time, a large availability of measures (in gauged regions, with respect to other micro-meteorological variables) and the most complex spatial patterns. While also in presence of densely gauged watersheds the spatial interpolation of the rainfall is a non-trivial problem, due to the spatial intermittence of the variable (especially at finer temporal scales), ungauged regions need an alternative source of rainfall data in order to perform the hydrological modelling. Such source can be constituted by the satellite-estimated rainfall fields, with reference to both geostationary and polar-orbit platforms. In this work the rainfall product obtained by the Aqua-AIRS sensor were used in order to assess the feasibility of the use of satellite-based rainfall as input for distributed hydrological modelling. The MOBIDIC (MOdello di BIlancio Distribuito e Continuo) model, developed at the Department of civil and Environmental Engineering of the University of Florence and operationally used by Tuscany Region and Umbria Region for flood prediction and management, was used for the experiments. In particular three experiments were carried on: a) hydrological simulation with the use of rain-gauges data, b) simulation with the use of satellite-only rainfall estimates, c) simulation with the combined use of the two sources of data in order to obtain an optimal estimate of the actual rainfall fields. The domain of the study was the central Italy. Several critical events occurred in the area were analyzed. A discussion of the results is provided.

A probabilistic method for streamflow projection and associated uncertainty analysis in a data sparse alpine region

NASA Astrophysics Data System (ADS)

Ren, Weiwei; Yang, Tao; Shi, Pengfei; Xu, Chong-yu; Zhang, Ke; Zhou, Xudong; Shao, Quanxi; Ciais, Philippe

2018-06-01

Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide.

Development of a distributed air pollutant dry deposition modeling framework.

PubMed

Hirabayashi, Satoshi; Kroll, Charles N; Nowak, David J

2012-12-01

A distributed air pollutant dry deposition modeling system was developed with a geographic information system (GIS) to enhance the functionality of i-Tree Eco (i-Tree, 2011). With the developed system, temperature, leaf area index (LAI) and air pollutant concentration in a spatially distributed form can be estimated, and based on these and other input variables, dry deposition of carbon monoxide (CO), nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), and particulate matter less than 10 microns (PM10) to trees can be spatially quantified. Employing nationally available road network, traffic volume, air pollutant emission/measurement and meteorological data, the developed system provides a framework for the U.S. city managers to identify spatial patterns of urban forest and locate potential areas for future urban forest planting and protection to improve air quality. To exhibit the usability of the framework, a case study was performed for July and August of 2005 in Baltimore, MD. Copyright © 2012 Elsevier Ltd. All rights reserved.

A flexible tool for diagnosing water, energy, and entropy budgets in climate models

NASA Astrophysics Data System (ADS)

Lembo, Valerio; Lucarini, Valerio

2017-04-01

We have developed a new flexible software for studying the global energy budget, the hydrological cycle, and the material entropy production of global climate models. The program receives as input radiative, latent and sensible energy fluxes, with the requirement that the variable names are in agreement with the Climate and Forecast (CF) conventions for the production of NetCDF datasets. Annual mean maps, meridional sections and time series are computed by means of Climate Data Operators (CDO) collection of command line operators developed at Max-Planck Institute for Meteorology (MPI-M). If a land-sea mask is provided, the program also computes the required quantities separately on the continents and oceans. Depending on the user's choice, the program also calls the MATLAB software to compute meridional heat transports and location and intensities of the peaks in the two hemispheres. We are currently planning to adapt the program in order to be included in the Earth System Model eValuation Tool (ESMValTool) community diagnostics.

Definition of Pluviometric Thresholds For A Real Time Flood Forecasting System In The Arno Watershed

NASA Astrophysics Data System (ADS)

Amadio, P.; Mancini, M.; Mazzetti, P.; Menduni, G.; Nativi, S.; Rabuffetti, D.; Ravazzani, G.; Rosso, R.

The pluviometric flood forecasting thresholds are an easy method that helps river flood emergency management collecting data from limited area meteorologic model or telemetric raingauges. The thresholds represent the cumulated rainfall depth which generate critic discharge for a particular section. The thresholds were calculated for different sections of Arno river and for different antecedent moisture condition using the flood event distributed hydrologic model FEST. The model inputs were syntethic hietographs with different shape and duration. The system realibility has been verified by generating 500 year syntethic rainfall for 3 important subwatersheds of the studied area. A new technique to consider spatial variability of rainfall and soil properties effects on hydrograph has been investigated. The "Geomorphologic Weights" were so calculated. The alarm system has been implemented in a dedicated software (MIMI) that gets measured and forecast rainfall data from Autorità di Bacino and defines the state of the alert of the river sections.

Effects of Meteorological Variability on the Thermosphere-Ionosphere System during the Moderate Geomagnetic Disturbed January 2013 Period As Simulated By Time-GCM

NASA Astrophysics Data System (ADS)

Maute, A. I.; Hagan, M. E.; Richmond, A. D.; Liu, H.; Yudin, V. A.

2014-12-01

The ionosphere-thermosphere system is affected by solar and magnetospheric processes and by meteorological variability. Ionospheric observations of total electron content during the current solar cycle have shown that variability associated with meteorological forcing is important during solar minimum, and can have significant ionospheric effects during solar medium to maximum conditions. Numerical models can be used to study the comparative importance of geomagnetic and meterological forcing.This study focuses on the January 2013 Stratospheric Sudden Warming (SSW) period, which is associated with a very disturbed middle atmosphere as well as with moderately disturbed solar geomagntic conditions. We employ the NCAR Thermosphere-Ionosphere-Mesosphere-Electrodynamics General Circulation Model (TIME-GCM) with a nudging scheme using Whole-Atmosphere-Community-Climate-Model-Extended (WACCM-X)/Goddard Earth Observing System Model, Version 5 (GEOS5) results to simulate the effects of the meteorological and solar wind forcing on the upper atmosphere. The model results are evaluated by comparing with observations e.g., TEC, NmF2, ion drifts. We study the effect of the SSW on the wave spectrum, and the associated changes in the low latitude vertical drifts. These changes are compared to the impact of the moderate geomagnetic forcing on the TI-system during the January 2013 time period by conducting numerical experiments. We will present select highlights from our study and elude to the comparative importance of the forcing from above and below as simulated by the TIME-GCM.

Human thermal bioclimatic conditions associated with acute cardiovascular syndromes in Crete Island, Greece

NASA Astrophysics Data System (ADS)

Bleta, Anastasia G.; Nastos, Panagiotis T.

2013-04-01

The aim of this study is to quantify the association between bioclimatic conditions and daily counts of admissions for non-fatal acute cardiovascular (acute coronary syndrome, arrhythmia, decompensation of heart failure) syndromes (ACS) registered by the two main hospitals in Heraklion, Crete Island, during a five-year period 2008-2012. The bioclimatic conditions analyzed are based on human thermal bioclimatic indices such as the Physiological Equivalent Temperature (PET) and the Universal Thermal Climate Index (UTCI). Mean daily meteorological parameters, such as air temperature, relative humidity, wind speed and cloudiness, were acquired from the meteorological station of Heraklion (Hellenic National Meteorological Service). These parameters were used as input variables in modeling the aforementioned thermal indices, in order to interpret the grade of the thermo-physiological stress. The PET and UTCI analysis was performed by the use of the radiation and bioclimate model, "RayMan", which is well-suited to calculate radiation fluxes and human biometeorological indices. Generalized linear models (GLM) were applied to time series of daily numbers of outpatients with ACS against bioclimatic variations, after controlling for possible confounders and adjustment for season and trends. The interpretation of the results of this analysis suggests a significant association between cold weather and increased coronary heart disease incidence, especially in the elderly and males. Additionally, heat stress plays an important role in the configuration of daily ACS outpatients, even in temperate climate, as that in Crete Island. In this point it is worth mentioning that Crete Island is frequently affected by Saharan outbreaks, which are associated in many cases with miscellaneous phenomena, such as Föhn winds - hot and dry winds - causing extreme bioclimatic conditions (strong heat stress). Taking into consideration the projected increased ambient temperature in the future, ACS exacerbation is very likely to happen during the warm period, against mitigation during the cold period of the year.

Long-term meteorologically independent trend analysis of ozone air quality at an urban site in the greater Houston area.

PubMed

Botlaguduru, Venkata S V; Kommalapati, Raghava R; Huque, Ziaul

2018-04-19

The Houston-Galveston-Brazoria (HGB) area of Texas has a history of ozone exceedances and is currently classified under moderate nonattainment status for the 2008 8-hr ozone standard of 75 ppb. The HGB area is characterized by intense solar radiation, high temperature, and humidity, which influence day-to-day variations in ozone concentrations. Long-term air quality trends independent of meteorological influence need to be constructed for ascertaining the effectiveness of air quality management in this area. The Kolmogorov-Zurbenko (KZ) filter technique used to separate different scales of motion in a time series, is applied in the current study for maximum daily 8-hr (MDA8) ozone concentrations at an urban site (EPA AQS Site ID: 48-201-0024, Aldine) in the HGB area. This site located within 10 miles of downtown Houston and the George Bush Intercontinental Airport, was selected for developing long-term meteorologically independent MDA8 ozone trends for the years 1990-2016. Results from this study indicate a consistent decrease in meteorologically independent MDA8 ozone between 2000-2016. This pattern could be partially attributed to a reduction in underlying NO X emissions, particularly that of lowering nitrogen dioxide (NO 2 ) levels, and a decrease in the release of highly reactive volatile organic compounds (HRVOC). Results also suggest solar radiation to be most strongly correlated to ozone, with temperature being the secondary meteorological control variable. Relative humidity and wind speed have tertiary influence at this site. This study observed that meteorological variability accounts for a high of 61% variability in baseline ozone (low-frequency component, sum of long-term and seasonal components), while 64% of the change in long-term MDA8 ozone post-2000 could be attributed to NO X emissions reduction. Long-term MDA8 ozone trend component was estimated to be decreasing at a linear rate of 0.412 ± 0.007 ppb/yr for the years 2000-2016, and 0.155 ± 0.005 ppb/yr for the overall period of 1990-2016. Implications Statement The effectiveness of air emission controls can be evaluated by developing long-term air quality trends independent of meteorological influences. KZ filter technique is a well-established method to separate an air quality time-series into: short-term, seasonal and long-term components. This paper applies the KZ filter technique to MDA8 ozone data between 1990-2016 at an urban site in the Greater Houston area and estimates the variance accounted for, by the primary meteorological control variables. Estimates for linear trends of MDA8 ozone are calculated and underlying causes are investigated to provide a guidance for further investigation into air quality management of the Greater Houston Area.

Quantification of water-level variability effect on plant species populations using paleoecological and hydrological time series data

USGS Publications Warehouse

Roehl, Edwin A.; Conrads, Paul; Bernhardt, Christopher

2012-01-01

Soil cores provide valuable data on historical changes in vegetation and hydrologic conditions. Empirical models were developed to quantify the effect of meteorological and hydrologic forcing on plant species distributions over a 110-year period in Water Conservation Area 1 (WCA1) in the Florida Everglades, also known as the Arthur R. Marshall Loxahatchee National Wildlife Refuge. Empirical models that predict plant species distributions at sites within WCA1 were developed by linking temporally sparse seed bank data from soil cores with continuous multi-decadal daily meteorological and hydrologic time series data. The meteorological data included rainfall and maximum daily temperatures that spanned the entire study period of 110 years. The hydrologic data included stage data from two gages in WCA1 established in 1954. These stage data were hindcasted to be concurrent with the meteorological data by using correlation models that fit measured stages as a function of the meteorological parameters. The historical plant species data came from seven peat cores from WCA1. Different depths from each core were carbon-dated and assayed for relative percentages of 83 plant species using pollen counts. The oldest dates were more than 1,000 years old; however, only core data that overlapped the study period were used, for a total of 67 assays among the seven cores. Twenty-three of the species had ratios of at least 5 percent for one or more of the 67 assays, hereafter referred to as the "top23". Using the assays as input vectors, the top23 were grouped using the k-means clustering into four plant classes that represented the extent to which the various species have historically appeared together. This reduced the modeling problem to one of predicting the relative ratios of the four plant classes from the hindcasted stage time-series data. A separate empirical model was developed for each class using a multi-layer perceptron artificial neural network, which provides multivariate, nonlinear curve fitting. The models predicted the relative ratios of the classes, and the sums of the predictions are near 1. The coefficient of determination (R2) of the models varied from 0.87 to 0.96, indicating that the relative ratios of the plant classes are predictable, and therefore controllable, from stage forcing. Similar soil cores are available for the Coastal Plain of North Carolina and are planned for the Congaree National Park in South Carolina.

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.

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.

Seven years (2008-2014) of meteorological observations plus a synthetic El Nino drought for BCI Panama.

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

Powell, Thomas; Kueppers, Lara; Paton, Steve

This dataset is a derivative product of raw meteorological data collected at Barro Colorado Island, Panama (see acknowledgements below). This dataset contains the following: 1) a seven-year record (2008-2014) of meteorological observations from BCI that is in a comma delimited text format, 2) an R-script that converts the observed meteorology into an hdf5 format that can be read by the ED2 model, 3) two decades of meteorological drivers in hdf5 format that are based on the 7-year record of observations and include a synthetic 2-yr El Nino drought, 4) a ReadMe.txt file that explains how the data in the hdf5more » meteorological drivers correspond to the observations. The raw meteorological data were further QC'd as part of the NGEE-Tropics project to derive item 1 above. The R-script makes the appropriate unit conversions for all observed meteorological variables to be compatible with the ED2 model. The R-script also converts RH into specific humidity, splits total shortwave radiation into its 4-stream parts, and calculates longwave radiation from air temperature and RH. The synthetic El Nino drought is based on selected months from the observed meteorology where in each, precipitation (only) of the selected months was modified to reflect the precipitation patterns of the 1982/83 El Nino observed at BCI.« less

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.

PREDICTING CLINICALLY DIAGNOSED DYSENTERY INCIDENCE OBTAINED FROM MONTHLY CASE REPORTING BASED ON METEOROLOGICAL VARIABLES IN DALIAN, LIAONING PROVINCE, CHINA, 2005-2011 USING A DEVELOPED MODEL.

PubMed

An, Qingyu; Yao, Wei; Wu, Jun

2015-03-01

This study describes our development of a model to predict the incidence of clinically diagnosed dysentery in Dalian, Liaoning Province, China, using time series analysis. The model was developed using the seasonal autoregressive integrated moving average (SARIMA). Spearman correlation analysis was conducted to explore the relationship between meteorological variables and the incidence of clinically diagnosed dysentery. The meteorological variables which significantly correlated with the incidence of clinically diagnosed dysentery were then used as covariables in the model, which incorporated the monthly incidence of clinically diagnosed dysentery from 2005 to 2010 in Dalian. After model development, a simulation was conducted for the year 2011 and the results of this prediction were compared with the real observed values. The model performed best when the temperature data for the preceding month was used to predict clinically diagnosed dysentery during the following month. The developed model was effective and reliable in predicting the incidence of clinically diagnosed dysentery for most but not all months, and may be a useful tool for dysentery disease control and prevention, but further studies are needed to fine tune the model.

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).

Airborne black carbon concentrations over an urban region in western India-temporal variability, effects of meteorology, and source regions.

PubMed

Bapna, Mukund; Sunder Raman, Ramya; Ramachandran, S; Rajesh, T A

2013-03-01

This study characterizes over 5 years of high time resolution (5 min), airborne black carbon (BC) concentrations (July 2003 to December 2008) measured over Ahmedabad, an urban region in western India. The data were used to obtain different time averages of BC concentrations, and these averages were then used to assess the diurnal, seasonal, and annual variability of BC over the study region. Assessment of diurnal variations revealed a strong association between BC concentrations and vehicular traffic. Peaks in BC concentration were co-incident with the morning (0730 to 0830, LST) and late evening (1930 to 2030, LST) rush hour traffic. Additionally, diurnal variability in BC concentrations during major festivals (Diwali and Dushera during the months of October/November) revealed an increase in BC concentrations due to fireworks displays. Maximum half hourly BC concentrations during the festival days were as high as 79.8 μg m(-3). However, the high concentrations rapidly decayed suggesting that local meteorology during the festive season was favorable for aerosol dispersion. A multiple linear regression (MLR) model with BC as the dependent variable and meteorological parameters as independent variables was fitted. The variability in temperature, humidity, wind speed, and wind direction accounted for about 49% of the variability in measured BC concentrations. Conditional probability function (CPF) analysis was used to identify the geographical location of local source regions contributing to the effective BC measured (at 880 nm) at the receptor site. The east north-east (ENE) direction to the receptor was identified as a major source region. National highway (NH8) and two coal-fired thermal power stations (at Gandhinagar and Sabarmati) were located in the identified direction, suggesting that local traffic and power plant emissions were likely contributors to the measured BC.

Harmonic analysis of the precipitation in Greece

NASA Astrophysics Data System (ADS)

Nastos, P. T.; Zerefos, C. S.

2009-04-01

Greece is a country with a big variety of climates due to its geographical position, to the many mountain ranges and also to the multifarious and long coastline. The mountainous volumes are of such orientation that influences the distribution of the precipitation, having as a result, Western Greece to present great differentiations from Central and Eastern Greece. The application of harmonic analysis to the annual variability of precipitation is the goal of this study, so that the components, which compose the annual variability, be elicited. For this purpose, the mean monthly precipitation data from 30 meteorological stations of National Meteorological Service were used for the time period 1950-2000. The initial target is to reduce the number of variables and to detect structure in the relationships between variables. The most commonly used technique for this purpose is the application of Factor Analysis to a table having as columns the meteorological stations-variables and rows the monthly mean precipitation, so that 2 main factors were calculated, which explain the 98% of total variability of precipitation in Greece. Factor 1, representing the so-called uniform field and interpreting the most of the total variance, refers in fact to the Mediterranean depressions, affecting mainly the West of Greece and also the East Aegean and the Asia Minor coasts. In the process, the Fourier Analysis was applied to the factor scores extracted from the Factor Analysis, so that 2 harmonic components are resulted, which explain above the 98% of the total variability of each main factor, and are due to different synoptic and thermodynamic processes associated with Greece's precipitation construction. Finally, the calculation of the time of occurrence of the maximum precipitation, for each harmonic component of each one of the two main factors, gives the spatial distribution of appearance of the maximum precipitation in the Hellenic region.

Inter-annual variability of carbon fluxes in temperate forest ecosystems: effects of biotic and abiotic factors

NASA Astrophysics Data System (ADS)

Chen, M.; Keenan, T. F.; Hufkens, K.; Munger, J. W.; Bohrer, G.; Brzostek, E. R.; Richardson, A. D.

2014-12-01

Carbon dynamics in terrestrial ecosystems are influenced by both abiotic and biotic factors. Abiotic factors, such as variation in meteorological conditions, directly drive biophysical and biogeochemical processes; biotic factors, referring to the inherent properties of the ecosystem components, reflect the internal regulating effects including temporal dynamics and memory. The magnitude of the effect of abiotic and biotic factors on forest ecosystem carbon exchange has been suggested to vary at different time scales. In this study, we design and conduct a model-data fusion experiment to investigate the role and relative importance of the biotic and abiotic factors for inter-annual variability of the net ecosystem CO2 exchange (NEE) of temperate deciduous forest ecosystems in the Northeastern US. A process-based model (FöBAAR) is parameterized at four eddy-covariance sites using all available flux and biometric measurements. We conducted a "transplant" modeling experiment, that is, cross- site and parameter simulations with different combinations of site meteorology and parameters. Using wavelet analysis and variance partitioning techniques, analysis of model predictions identifies both spatial variant and spatially invariant parameters. Variability of NEE was primarily modulated by gross primary productivity (GPP), with relative contributions varying from hourly to yearly time scales. The inter-annual variability of GPP and NEE is more regulated by meteorological forcing, but spatial variability in certain model parameters (biotic response) has more substantial effects on the inter-annual variability of ecosystem respiration (Reco) through the effects on carbon pools. Both the biotic and abiotic factors play significant roles in modulating the spatial and temporal variability in terrestrial carbon cycling in the region. Together, our study quantifies the relative importance of both, and calls for better understanding of them to better predict regional CO2 exchanges.

Urban and regional land use analysis: CARETS and census cities experiment package

NASA Technical Reports Server (NTRS)

Alexander, R. (Principal Investigator); Lins, H. F., Jr.; Gallagher, D. B.

1975-01-01

The author has identified the following significant results. Temperatures in degrees Celsius were derived from PCM counts using the Pease's modified gray window technique. The Outcalt simulator was setup on the USGS computer. The input data to the model are basically meteorological and geographical in nature. The output data is presented in three matrices.

Attributing uncertainty in streamflow simulations due to variable inputs via the Quantile Flow Deviation metric

NASA Astrophysics Data System (ADS)

Shoaib, Syed Abu; Marshall, Lucy; Sharma, Ashish

2018-06-01

Every model to characterise a real world process is affected by uncertainty. Selecting a suitable model is a vital aspect of engineering planning and design. Observation or input errors make the prediction of modelled responses more uncertain. By way of a recently developed attribution metric, this study is aimed at developing a method for analysing variability in model inputs together with model structure variability to quantify their relative contributions in typical hydrological modelling applications. The Quantile Flow Deviation (QFD) metric is used to assess these alternate sources of uncertainty. The Australian Water Availability Project (AWAP) precipitation data for four different Australian catchments is used to analyse the impact of spatial rainfall variability on simulated streamflow variability via the QFD. The QFD metric attributes the variability in flow ensembles to uncertainty associated with the selection of a model structure and input time series. For the case study catchments, the relative contribution of input uncertainty due to rainfall is higher than that due to potential evapotranspiration, and overall input uncertainty is significant compared to model structure and parameter uncertainty. Overall, this study investigates the propagation of input uncertainty in a daily streamflow modelling scenario and demonstrates how input errors manifest across different streamflow magnitudes.

Decadal Trends and Variability of Tropospheric Ozone over Oil and Gas Regions over 2005 - 2015

NASA Astrophysics Data System (ADS)

Zhou, Y.; Mao, H.; Sive, B. C.

2017-12-01

Tropospheric ozone (O3), which is produced largely by photochemical oxidation of nitrogen oxides (NOx) and volatile organic compounds, is a serious and ubiquitous air pollutant with strong negative health effects. Recent technological innovations such as horizontal drilling and hydraulic fracturing have accelerated oil and natural gas production in the U.S. since 2005. The additional input of O3 precursors from expanding natural gas production might prolong the effort to comply the current O3 standard (70 ppbv). The objective of this study is to investigate the impact of oil and gas extractions on variability and long term trends of O3 in the intermountain west under varying meteorological conditions. We investigated long-term O3 trends at 13 rural sites, which were within 100 km of the shale play in the U.S. intermountain west. Significant decreasing trends (-0.35 - -3.38 ppbv yr-1) were found in seasonal O3 design values at six sites in spring, summer, or fall, while no trends were found in wintertime O3 at any sites. Wintertime O3 at each site showed strong and consistent interannual variation over 2006 - 2015, and was negatively correlated with the Arctic Oscillation (AO) Index. The negative correlation was a result of multiple factors, such as in situ O3 photochemical production, stratospheric intrusion, and transport from the Arctic and California. In summer, wildfire emissions were the dominate driver to the interannual variations of high percentiles O3 at each site, while meteorological conditions (i.e., temperature and relative humidity) determined the interannual variations of low percentiles O3. Box model simulations indicated that O3 production rates were 31.51 ppbv h-1 over winters of 2012 - 2014 and 32.12 ppbv h-1 in summer 2014 around shale gas extraction regions.

Whole Atmosphere Modeling and Data Analysis: Success Stories, Challenges and Perspectives

NASA Astrophysics Data System (ADS)

Yudin, V. A.; Akmaev, R. A.; Goncharenko, L. P.; Fuller-Rowell, T. J.; Matsuo, T.; Ortland, D. A.; Maute, A. I.; Solomon, S. C.; Smith, A. K.; Liu, H.; Wu, Q.

2015-12-01

At the end of the 20-th century Raymond Roble suggested an ambitious target of developing an atmospheric general circulation model (GCM) that spans from the surface to the thermosphere for modeling the coupled atmosphere-ionosphere with drivers from terrestrial meteorology and solar-geomagnetic inputs. He pointed out several areas of research and applications that would benefit highly from the development and improvement of whole atmosphere modeling. At present several research groups using middle and whole atmosphere models have attempted to perform coupled ionosphere-thermosphere predictions to interpret the "unexpected" anomalies in the electron content, ions and plasma drifts observed during recent stratospheric warming events. The recent whole atmosphere inter-comparison case studies also displayed striking differences in simulations of prevailing flows, planetary waves and dominant tidal modes even when the lower atmosphere domain of those models were constrained by similar meteorological analyses. We will present the possible reasons of such differences between data-constrained whole atmosphere simulations when analyses with 6-hour time resolution are used and discuss the potential model-data and model-model differences above the stratopause. The possible shortcomings of the whole atmosphere simulations associated with model physics, dynamical cores and resolutions will be discussed. With the increased confidence in the space-borne temperature, winds and ozone observations and extensive collections of ground-based upper atmosphere observational facilities, the whole atmosphere modelers will be able to quantify annual and year-to-variability of the zonal mean flows, planetary wave and tides. We will demonstrate the value of tidal and planetary wave variability deduced from the space-borne data and ground-based systems for evaluation and tune-up of whole atmosphere simulations including corrections of systematic model errors. Several success stories on the middle and whole atmosphere simulations coupled with the ionosphere models will be highlighted, and future perspectives for links of the space and terrestrial weather predictions constrained by current and scheduled ionosphere-thermosphere-mesosphere satellite missions will be presented

Radon and radioactivity at a town overlying Uranium ores in northern Greece.

PubMed

Kourtidis, K; Georgoulias, A K; Vlahopoulou, M; Tsirliganis, N; Kastelis, N; Ouzounis, K; Kazakis, N

2015-12-01

Extensive measurements of (222)Rn in the town of Xanthi in N Greece show that the part of the town overlying granite deposits and the outcrop of a uranium ore has exceptionally high indoor radon levels, with monthly means up to 1500 Bq m(-3). A large number of houses (40%) in this part of the town exhibit radon levels above 200 Bq m(-3) while 11% of the houses had radon levels above 400 Bq m(-3). Substantial interannual variability as well as the highest in Europe winter/summer ratios (up to 12) were observed in this part of the town, which consist of traditional stone masonry buildings of the late 19th-early 20th century. Measurements of (238)U and (232)Th content of building materials from these houses as well as radionuclide measurements in different floors show that the high levels of indoor radon measured in these buildings are not due to high radon emanation rates from the building materials themselves but rather due to high radon flux from the soil because of the underlying geology, high radon penetration rates into the buildings from underground due to the lack of solid concrete foundations in these buildings, or a combination thereof. From the meteorological variables studied, highest correlation with indoor (222)Rn was found with temperature (r(2) = 0.65). An indoor radon prognostic regression model using temperature, pressure and precipitation as input was developed, that reproduced indoor radon with r(2) = 0.69. Hence, meteorology is the main driving factor of indoor radon, with temperature being the most important determinant. Preliminary flux measurements indicate that the soil-atmosphere (222)Rn flux should be in the range 150-250 Bq m(-2) h(-1), which is in the upper 10% of flux values for Europe. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal.

PubMed

Diouf, Ibrahima; Rodriguez-Fonseca, Belen; Deme, Abdoulaye; Caminade, Cyril; Morse, Andrew P; Cisse, Moustapha; Sy, Ibrahima; Dia, Ibrahima; Ermert, Volker; Ndione, Jacques-André; Gaye, Amadou Thierno

2017-09-25

The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal. The findings confirm that the risk of malaria transmission is mainly linked to climate variables such as rainfall and temperature as well as specific landscape characteristics. For the whole of Senegal, a lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November, corresponding to the second peak of temperature occurring in October. Observed malaria data from the Programme National de Lutte contre le Paludisme (PNLP, National Malaria control Programme in Senegal) and outputs from the meteorological data used in this study were compared. The malaria model outputs present some consistencies with observed malaria dynamics over Senegal, and further allow the exploration of simulations performed with reanalysis data sets over a longer time period. The simulated malaria risk significantly decreased during the 1970s and 1980s over Senegal. This result is consistent with the observed decrease of malaria vectors and malaria cases reported by field entomologists and clinicians in the literature. The main differences between model outputs and observations regard amplitude, but can be related not only to reanalysis deficiencies but also to other environmental and socio-economic factors that are not included in this mechanistic malaria model framework. The present study can be considered as a validation of the reliability of reanalysis to be used as inputs for the calculation of malaria parameters in the Sahel using dynamical malaria models.

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.

Long-term weather predictability: Ural case study

NASA Astrophysics Data System (ADS)

Kubyshen, Alexander; Shopin, Sergey

2016-04-01

The accuracy of the state-of-the-art long-term meteorological forecast (at the seasonal level) is still low. Here it is presented approach (RAMES method) realizing different forecasting methodology. It provides prediction horizon of up to 19-22 years under equal probabilities of determination of parameters in every analyzed period [1]. Basic statements of the method are the following. 1. Long-term forecast on the basis of numerical modeling of the global meteorological process is principally impossible. Extension of long-term prediction horizon could be obtained only by the revealing and using a periodicity of meteorological situations at one point of observation. 2. Conventional calendar is unsuitable for generalization of meteorological data and revealing of cyclicity of meteorological processes. RAMES method uses natural time intervals: one day, synodic month and one year. It was developed a set of special calendars using these natural periods and the Metonic cycle. 3. Long-term time series of meteorological data is not a uniform universal set, it is a sequence of 28 universal sets appropriately superseding each other in time. The specifics of the method are: 1. Usage of the original research toolkit consisting of - a set of calendars based on the Metonic cycle; - a set of charts (coordinate systems) for the construction of sequence diagrams (of daily variability of a meteorological parameter during the analyzed year; of daily variability of a meteorological parameter using long-term dynamical time series of periods-analogues; of monthly and yearly variability of accumulated value of meteorological parameter). 2. Identification and usage of new virtual meteorological objects having several degrees of generalization appropriately located in the used coordinate systems. 3. All calculations are integrated into the single technological scheme providing comparison and mutual verification of calculation results. During the prolonged testing in the Ural region, it was proved the efficiency of the method for forecasting the following meteorological parameters: ­- air temperature (minimum, maximum, daily mean, diurnal variation, last spring and first autumn freeze); - periods of winds with speeds of >5m/s and the maximal expected wind speed; - precipitation periods and amount of precipitations; -­ relative humidity; - atmospheric pressure. Atmospheric events (thunderstorms, fog) and hydrometeors also occupy the appropriate positions at the sequence diagrams that provides a possibility of long-term forecasting also for these events. Accuracy of forecasts was tested in 2006-2009 years. The difference between the forecasted monthly mean temperature and actual values was <0.5°C in 40.9% of cases, between 0.5°C and 1°C in 18.2% of cases, between 1°C and 1.5°C in 18.2% of cases, <2°C in 86% of cases. The RAMES method provides the toolkit to successfully forecast the weather conditions in advance of several years. 1. A.F. Kubyshen, "RAMES method: revealing the periodicity of meteorological processes and it usage for long-term forecast [Metodika «RAMES»: vyjavlenie periodichnosti meteorologicheskih processov i ee ispol'zovanie dlja dolgosrochnogo prognozirovanija]", in A.E. Fedorov (ed.), Sistema «Planeta Zemlja»: 200 let so dnja rozhdenija Izmaila Ivanovicha Sreznevskogo. 100 let so dnja izdanija ego slovarja drevnerusskogo jazyka. LENAND. Moscow. pp. 305-311. (In Russian)

Spatial and temporal trends of reference crop evapotranspiration and its influential variables in Yangtze River Delta, eastern China

NASA Astrophysics Data System (ADS)

Xu, Yu; Xu, Youpeng; Wang, Yuefeng; Wu, Lei; Li, Guang; Song, Song

2017-11-01

Reference crop evapotranspiration (ETo) is one of the most important links in hydrologic circulation and greatly affects regional agricultural production and water resource management. Its variation has drawn more and more attention in the context of global warming. We used the Penman-Monteith method of the Food and Agriculture Organization, based on meteorological factors such as air temperature, sunshine duration, wind speed, and relative humidity to calculate the ETo over 46 meteorological stations located in the Yangtze River Delta, eastern China, from 1957 to 2014. The spatial distributions and temporal trends in ETo were analyzed based on the modified Mann-Kendall trend test and linear regression method, while ArcGIS software was employed to produce the distribution maps. The multiple stepwise regression method was applied in the analysis of the meteorological variable time series to identify the causes of any observed trends in ETo. The results indicated that annual ETo showed an obvious spatial pattern of higher values in the north than in the south. Annual increasing trends were found at 34 meteorological stations (73.91 % of the total), which were mainly located in the southeast. Among them, 12 (26.09 % of the total) stations showed significant trends. We saw a dominance of increasing trends in the monthly ETo except for January, February, and August. The high value zone of monthly ETo appeared in the northwest from February to June, mid-south area from July to August, and southeast coastal area from September to January. The research period was divided into two stages—stage I (1957-1989) and stage II (1990-2014)—to investigate the long-term temporal ETo variation. In stage I, almost 85 % of the total stations experienced decreasing trends, while more than half of the meteorological stations showed significant increasing trends in annual ETo during stage II except in February and September. Relative humidity, wind speed, and sunshine duration were identified as the most dominant meteorological variables influencing annual ETo changes. The results are expected to assist water resource managers and policy makers in making better planning decisions in the research region.

Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

2017-04-01

Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the reliability diagram. This study covers 10 nordic watersheds. We show that forecast performance according to the CRPS varies with lead-time but also with the period of the year. The raw forecasts from the ECMWF System4 display important biases for both temperature and precipitation, which need to be corrected. The linear scaling method is used for this purpose and is found effective. Bias correction improves forecasts performance, especially during the summer when the precipitations are over-estimated. According to the CRPS, bias corrected forecasts from System4 show performances comparable to those of the ESP system. However, the Ignorance score, which penalizes the lack of calibration (under-dispersive forecasts in this case) more severely than the CRPS, provides a different outlook for the comparison of the two systems. In fact, according to the Ignorance score, the ESP system outperforms forecasts based on System4 in most cases. This illustrates that the joint use of several metrics is crucial to assess the quality of a forecasts system thoroughly. Globally, ESP provide reliable forecasts which can be over-dispersed whereas bias corrected ECMWF System4 forecasts are sharper but at the risk of missing events.

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.

Comparative analysis of meteorological and hydrological drought in the Pearl River basin during the period 1960-2012

NASA Astrophysics Data System (ADS)

Xu, K.; Wu, C.; Hu, B.; Niu, J.

2017-12-01

Drought is one of the major natural hazards that can have devastating impacts on the regional environment, agriculture, and water resources. Previous studies have conducted the assessment of historic changes in meteorological drought over various regional scales but rarely considered hydrological drought due to limited hydrological observations. Here, we use a long-term (1960-2012) gridded hydro-meteorological data to present a comparative analysis of meteorological and hydrological drought in the Pearl River basin in southern China using the standardized precipitation index (SPI) and the standardized runoff index (SRI). The variation in SPI and SRI at four different timescales (1-, 3-, 6-, and 12-month) is investigated using the Mann-Kendall (M-K) method and continuous wavelet transform (CWT). The results indicate that the correlation between SPI and SRI is strong over the Pearl River basin and tends to be stronger at the longer timescale. Meanwhile, the periodic oscillation pattern of SPI becomes more consistent with that of SRI with the increased timescale. The SPI can be used as a substitute for SRI to represent the hydrological drought at the long-term scale. Overall there is a noticeably wetting trend mainly in the eastern parts and a significant drying trend mainly in the western regions and the downstream area of the Pearl River basin. The variability of meteorological drought is significant mainly in the eastern and western regions, while the variability of hydrological drought tends to be larger mainly in the western region. CWT analysis indicates a period of 0.75-7 years in both meteorological and hydrological droughts during the period 1960-2012 in the study region.

Variability of winds and temperature in the Bergen area

NASA Astrophysics Data System (ADS)

Schönbein, Daniel; Ólafsson, Haraldur; Asle Olseth, Jan; Furevik, Birgitte

2017-04-01

In recent years, observations have been made by a dense network of automatic weather stations in the Bergen area in W-Norway (Bergen School of Meteorology). Here, cases are presented that feature large spatial variability in winds and temperature and the ability of a numerical model to reproduce this variability is assessed.

The evolution of biomass-burning aerosol size distributions due to coagulation: dependence on fire and meteorological details and parameterization

NASA Astrophysics Data System (ADS)

Sakamoto, Kimiko M.; Laing, James R.; Stevens, Robin G.; Jaffe, Daniel A.; Pierce, Jeffrey R.

2016-06-01

Biomass-burning aerosols have a significant effect on global and regional aerosol climate forcings. To model the magnitude of these effects accurately requires knowledge of the size distribution of the emitted and evolving aerosol particles. Current biomass-burning inventories do not include size distributions, and global and regional models generally assume a fixed size distribution from all biomass-burning emissions. However, biomass-burning size distributions evolve in the plume due to coagulation and net organic aerosol (OA) evaporation or formation, and the plume processes occur on spacial scales smaller than global/regional-model grid boxes. The extent of this size-distribution evolution is dependent on a variety of factors relating to the emission source and atmospheric conditions. Therefore, accurately accounting for biomass-burning aerosol size in global models requires an effective aerosol size distribution that accounts for this sub-grid evolution and can be derived from available emission-inventory and meteorological parameters. In this paper, we perform a detailed investigation of the effects of coagulation on the aerosol size distribution in biomass-burning plumes. We compare the effect of coagulation to that of OA evaporation and formation. We develop coagulation-only parameterizations for effective biomass-burning size distributions using the SAM-TOMAS large-eddy simulation plume model. For the most-sophisticated parameterization, we use the Gaussian Emulation Machine for Sensitivity Analysis (GEM-SA) to build a parameterization of the aged size distribution based on the SAM-TOMAS output and seven inputs: emission median dry diameter, emission distribution modal width, mass emissions flux, fire area, mean boundary-layer wind speed, plume mixing depth, and time/distance since emission. This parameterization was tested against an independent set of SAM-TOMAS simulations and yields R2 values of 0.83 and 0.89 for Dpm and modal width, respectively. The size distribution is particularly sensitive to the mass emissions flux, fire area, wind speed, and time, and we provide simplified fits of the aged size distribution to just these input variables. The simplified fits were tested against 11 aged biomass-burning size distributions observed at the Mt. Bachelor Observatory in August 2015. The simple fits captured over half of the variability in observed Dpm and modal width even though the freshly emitted Dpm and modal widths were unknown. These fits may be used in global and regional aerosol models. Finally, we show that coagulation generally leads to greater changes in the particle size distribution than OA evaporation/formation does, using estimates of OA production/loss from the literature.

Uncertainties in discharge projections in consequence of climate change

NASA Astrophysics Data System (ADS)

Liebert, J.; Düthmann, D.; Berg, P.; Feldmann, H.; Ihringer, J.; Kunstmann, H.; Merz, B.; Ott, I.; Schädler, G.; Wagner, S.

2012-04-01

The fourth assessment report of the IPCC summarizes possible effects of the global climate change. For Europe an increasing variability of temperature and precipitation is expected. While the increasing temperature is projected almost uniformly for Europe, for precipitation the models indicate partly heterogeneous tendencies. In order to maintain current safety-standards in the infrastructure of our various water management systems, the possible future floods discharges are very often a central question. In the planning and operation of water infrastructure systems uncertainties considerations have an important function. In times of climate change the analyses of measured historical gauge data (normally 30 - 80 years) are not sufficient enough, because even significant trends are only valid in the analyzed time period and extrapolations are exceedingly difficult. Therefore combined climate and hydrological modeling for scenario based projections become more and more popular. Regarding that adaptation measures in water infrastructure are in general very time-consuming and cost intensive qualified questions to the variability and uncertainty of model based results are important as well. The CEDIM-Project "Flood hazards in a changing climate" is focusing on both: future changes in flood discharge and assess the uncertainties that are involved in such model based future predictions. In detail the study bases on an ensemble of hydrological model (HM) simulations in 3 representative small to medium sized German river catchments (Ammer, Mulde and Ruhr). The meteorological Input bases on 2 high resolution (7 km) regional climate models (RCM) driven by 2 global climate models (GCM) for the near future (2021 - 2050) following the A1B emission scenario (SRES). Two of the catchments (Ruhr and Mulde) have sub-mountainous and one (Ammer) has alpine character. Besides analyzing the future changes in discharge in the catchments, the describing and potential quantification of the variability of the results, based on the different driving data, regionalization methods, spatial resolutions and model types, is one main goal of the study and should stay in the focus of the poster. The general result is a large variability in the discharge projection. The identified variabilities are in the annual regime mainly attributable to different causes in the used model chain (GCM-RCM-HM). In winter the global climate models (GCM) bring the main uncertainties in the future projection. In summer the main variability refers to the meteorological downscaling to the regional scale (RCM) in combination with the hydrological modeling (HM). But with an appropriate ensemble statistic are despite the large variabilities mean future tendencies detectable. The Ruhr catchment shows tendencies to future higher flood discharges and in the Ammer and Mulde catchments are no significant changes expected.

The role of climate and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria

NASA Astrophysics Data System (ADS)

Abdussalam, Auwal; Thornes, John; Leckebusch, Gregor

2015-04-01

Nigeria has a number of climate-sensitive infectious diseases; one of the most important of these diseases that remains a threat to public health is cholera. This study investigates the influences of both meteorological and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria. A stepwise multiple regression models are used to estimate the influence of the year-to-year variations of cholera cases and deaths for individual states in the country and as well for three groups of states that are classified based on annual rainfall amount. Specifically, seasonal mean maximum and minimum temperatures and annual rainfall totals were analysed with annual aggregate count of cholera cases and deaths, taking into account of the socioeconomic factors that are potentially enhancing vulnerability such as: absolute poverty, adult literacy, access to pipe borne water and population density. Result reveals that the most important explanatory meteorological and socioeconomic variables in explaining the spatiotemporal variability of the disease are rainfall totals, seasonal mean maximum temperature, absolute poverty, and accessibility to pipe borne water. The influences of socioeconomic factors appeared to be more pronounced in the northern part of the country, and vice-versa in the case of meteorological factors. Also, cross validated models output suggests a strong possibility of disease prediction, which will help authorities to put effective control measures in place which depend on prevention, and or efficient response.

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

PubMed

Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

2016-09-01

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

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.

Environmental and Physiographic Controls on Inter-Growing Season Variability of Carbon Dioxide and Water Vapour Fluxes in a Minerotrophic Fen

NASA Astrophysics Data System (ADS)

van der Kamp, G.; Sonnentag, O.; Chen, J. M.; Barr, A.; Hedstrom, N.; Granger, R.

2008-12-01

The interaction of fens with groundwater is spatially and temporally highly variable in response to meteorological conditions, resulting in frequent changes of groundwater fluxes in both vertical and lateral directions (flow reversals) across the mineral soil-peat boundary. However, despite the importance of the topographic and hydrogeological setting of fens, no study has been reported in the literature that explores a fen's atmospheric CO2 and energy flux densities under contrasting meteorological conditions in response to its physiographic setting. In our contribution we report four years of growing season eddy covariance and supporting measurements from the Canada Fluxnet-BERMS fen (formerly BOREAS southern peatland) in Saskatchewan, Canada. We first analyze hydrological data along two piezometer transects across the mineral soil-peat boundary with the objective of assessing changes in water table configuration and thus hydraulic gradients, indicating flow reversals, in response to dry and wet meteorological conditions. Next we quantify and compare growing season totals and diurnal and daily variations in evapotranspiration (ET) and net ecosystem exchange (NEE) and its component fluxes gross ecosystem productivity (GPP) and terrestrial ecosystem respiration (TER) to identify their controls with a major focus on water table depth. While ET growing season totals were similar (~ 310 mm) under dry and wet meteorological conditions, the CO2 sink- source strength of Sandhill fen varied substantially from carbon neutral (NEE = -2 [+-7] g C m-2 per growing season) under dry meteorological condition (2003) to a moderate CO2- sink with NEE ranging between 157 [+- 10] and 190 [+- 11] g C m-2 per growing season under wet meteorological conditions (2004, 2005, and 2006). Using a process-oriented ecosystem model, BEPS-TerrainLab, we investigate how different canopy components at Sandhill contribute to total ET and GPP, and thus water use efficiency, under dry and wet meteorological conditions.

The Quality Control Algorithms Used in the Creation of NASA Kennedy Space Center Lightning Protection System Towers Meteorological Database

NASA Technical Reports Server (NTRS)

Orcutt, John M.; Brenton, James C.

2016-01-01

An accurate database of meteorological data is essential for designing any aerospace vehicle and for preparing launch commit criteria. Meteorological instrumentation were recently placed on the three Lightning Protection System (LPS) towers at Kennedy Space Center (KSC) launch complex 39B (LC-39B), which provide a unique meteorological dataset existing at the launch complex over an extensive altitude range. Data records of temperature, dew point, relative humidity, wind speed, and wind direction are produced at 40, 78, 116, and 139 m at each tower. The Marshall Space Flight Center Natural Environments Branch (EV44) received an archive that consists of one-minute averaged measurements for the period of record of January 2011 - April 2015. However, before the received database could be used EV44 needed to remove any erroneous data from within the database through a comprehensive quality control (QC) process. The QC process applied to the LPS towers' meteorological data is similar to other QC processes developed by EV44, which were used in the creation of meteorological databases for other towers at KSC. The QC process utilized in this study has been modified specifically for use with the LPS tower database. The QC process first includes a check of each individual sensor. This check includes removing any unrealistic data and checking the temporal consistency of each variable. Next, data from all three sensors at each height are checked against each other, checked against climatology, and checked for sensors that erroneously report a constant value. Then, a vertical consistency check of each variable at each tower is completed. Last, the upwind sensor at each level is selected to minimize the influence of the towers and other structures at LC-39B on the measurements. The selection process for the upwind sensor implemented a study of tower-induced turbulence. This paper describes in detail the QC process, QC results, and the attributes of the LPS towers meteorological database.

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...

Meteorological variables and bacillary dysentery cases in Changsha City, China.

PubMed

Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Zhou, Maigeng; Li, Xiujun; Jiang, Baofa

2014-04-01

This study aimed to investigate the association between meteorological-related risk factors and bacillary dysentery in a subtropical inland Chinese area: Changsha City. The cross-correlation analysis and the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model were used to quantify the relationship between meteorological factors and the incidence of bacillary dysentery. Monthly mean temperature, mean relative humidity, mean air pressure, mean maximum temperature, and mean minimum temperature were significantly correlated with the number of bacillary dysentery cases with a 1-month lagged effect. The ARIMAX models suggested that a 1°C rise in mean temperature, mean maximum temperature, and mean minimum temperature might lead to 14.8%, 12.9%, and 15.5% increases in the incidence of bacillary dysentery disease, respectively. Temperature could be used as a forecast factor for the increase of bacillary dysentery in Changsha. More public health actions should be taken to prevent the increase of bacillary dysentery disease with consideration of local climate conditions, especially temperature.

Meteorological Variables and Bacillary Dysentery Cases in Changsha City, China

PubMed Central

Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Zhou, Maigeng; Li, Xiujun; Jiang, Baofa

2014-01-01

This study aimed to investigate the association between meteorological-related risk factors and bacillary dysentery in a subtropical inland Chinese area: Changsha City. The cross-correlation analysis and the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model were used to quantify the relationship between meteorological factors and the incidence of bacillary dysentery. Monthly mean temperature, mean relative humidity, mean air pressure, mean maximum temperature, and mean minimum temperature were significantly correlated with the number of bacillary dysentery cases with a 1-month lagged effect. The ARIMAX models suggested that a 1°C rise in mean temperature, mean maximum temperature, and mean minimum temperature might lead to 14.8%, 12.9%, and 15.5% increases in the incidence of bacillary dysentery disease, respectively. Temperature could be used as a forecast factor for the increase of bacillary dysentery in Changsha. More public health actions should be taken to prevent the increase of bacillary dysentery disease with consideration of local climate conditions, especially temperature. PMID:24591435

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.

The role of the global electric circuit in solar and internal forcing of clouds and climate

NASA Astrophysics Data System (ADS)

Tinsley, Brian A.; Burns, G. B.; Zhou, Limin

Reports of a variety of short-term meteorological responses to changes in the global electric circuit associated with a set of disparate inputs are analyzed. The meteorological responses consist of changes in cloud cover, atmospheric temperature, pressure, or dynamics. All of these are found to be responding to changes in a key linking agent, that of the downward current density, Jz, that flows from the ionosphere through the troposphere to the surface (ocean and land). As it flows through layer clouds, Jz generates space charge in conductivity gradients at the upper and lower boundaries, and this electrical charge is capable of affecting the microphysical interactions between droplets and both ice-forming nuclei and condensation nuclei. Four short-term inputs to the global circuit are due to solar activity and consist of (1) Forbush decreases of the galactic cosmic ray flux; (2) solar energetic particle events; (3) relativistic electron precipitation changes; and (4) polar cap ionospheric convection potential changes. One input that is internal to the global circuit consists of (5) global ionospheric potential changes due to changes in the current output of the highly electrified clouds (mainly deep convective clouds at low latitudes) that act as generators for the circuit. The observed short-term meteorological responses to these five inputs are of small amplitude but high statistical significance for repeated Jz changes of order 5% for low latitudes increasing to 25-30% at high latitudes. On the timescales of multidecadal solar minima, such as the Maunder minimum, changes in tropospheric dynamics and climate related to Jz are also larger at high latitudes, and correlate with the lower energy component (˜1 GeV) of the cosmic ray flux increasing by as much as a factor of two relative to present values. Also, there are comparable cosmic ray flux changes and climate responses on millennial timescales. The persistence of the longer-term Jz changes for many decades to many centuries would produce an integrated effect on climate that could dominate over short-term weather and climate variations, and explain the observed correlations. Thus, we propose that mechanisms responding to Jz are a candidate for explanations of sun-weather-climate correlations on multidecadal to millenial timescales, as well as on the day-to-day timescales analyzed here.

Evaluation of regression and neural network models for solar forecasting over different short-term horizons

DOE PAGES

Inanlouganji, Alireza; Reddy, T. Agami; Katipamula, Srinivas

2018-04-13

Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar photovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for different forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with different climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term fluctuations, different variants of themore » lagged moving average (LMX) model with cloud cover as the input variable are evaluated. Finally, the proposed LMX model is evaluated against an artificial neural network (ANN) model. How the one-hour and 24-hour models can be used in conjunction to predict different short-term rolling horizons is discussed, and this joint application is illustrated for a four-hour rolling horizon forecast scheme. Lastly, the effect of using predicted cloud cover values, instead of measured ones, on the accuracy of the models is assessed. Results show that LMX models do not degrade in forecast accuracy if models are trained with the forecast cloud cover data.« less

Bio-optical properties of coastal waters in the Eastern English Channel

NASA Astrophysics Data System (ADS)

Vantrepotte, Vincent; Brunet, Christophe; Mériaux, Xavier; Lécuyer, Eric; Vellucci, Vincenzo; Santer, Richard

2007-03-01

Strong tidal currents, shallow water and numerous freshwater inputs characterize the coastal waters of the eastern English Channel. These case 2 waters were investigated through an intensive sampling effort in 2000 aiming to study the distribution and variability of the Chromophoric Dissolved Organic Matter (CDOM), Non-Algal Particles (NAP) and phytoplankton absorption at the mesoscale. Four cruises were carried out in February, March, May and July and more than 80 stations each cruise were sampled for hydrographical, chemical and bio-optical analyses. Results showed two distinct situations, the winter period characterized by the strong dominance of CDOM absorption over the particulate matter, and the spring-summer period when phytoplankton and CDOM represented the same contribution. Meteorology was the main factor driving the bio-optical properties of the water column in winter whereas in spring-summer the biological activity seemed to be the more active driving force. The algal community composition in term of dominant cell size and, therefore pigment packaging, is the main factor driving the phytoplankton specific absorption in the water column. Photoprotective pigments did not significantly influence algal absorption, due to turbid and highly mixed water masses. This feature also explained the bio-optical homogeneity found along the water column. On the mesoscale, distinct bio-optical provinces were defined in relation with the observed bio-hydrographical variability.

Insights on the Optical Properties of Estuarine DOM - Hydrological and Biological Influences.

PubMed

Santos, Luísa; Pinto, António; Filipe, Olga; Cunha, Ângela; Santos, Eduarda B H; Almeida, Adelaide

2016-01-01

Dissolved organic matter (DOM) in estuaries derives from a diverse array of both allochthonous and autochthonous sources. In the estuarine system Ria de Aveiro (Portugal), the seasonality and the sources of the fraction of DOM that absorbs light (CDOM) were inferred using its optical and fluorescence properties. CDOM parameters known to be affected by aromaticity and molecular weight were correlated with physical, chemical and meteorological parameters. Two sites, representative of the marine and brackish water zones of the estuary, and with different hydrological characteristics, were regularly surveyed along two years, in order to determine the major influences on CDOM properties. Terrestrial-derived compounds are the predominant source of CDOM in the estuary during almost all the year and the two estuarine zones presented distinct amounts, as well as absorbance and fluorescence characteristics. Freshwater inputs have major influence on the dynamics of CDOM in the estuary, in particular at the brackish water zone, where accounted for approximately 60% of CDOM variability. With a lower magnitude, the biological productivity also impacted the optical properties of CDOM, explaining about 15% of its variability. Therefore, climate changes related to seasonal and inter-annual variations of the precipitation amounts might impact the dynamics of CDOM significantly, influencing its photochemistry and the microbiological activities in estuarine systems.

Evaluation of regression and neural network models for solar forecasting over different short-term horizons

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

Inanlouganji, Alireza; Reddy, T. Agami; Katipamula, Srinivas

Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar photovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for different forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with different climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term fluctuations, different variants of themore » lagged moving average (LMX) model with cloud cover as the input variable are evaluated. Finally, the proposed LMX model is evaluated against an artificial neural network (ANN) model. How the one-hour and 24-hour models can be used in conjunction to predict different short-term rolling horizons is discussed, and this joint application is illustrated for a four-hour rolling horizon forecast scheme. Lastly, the effect of using predicted cloud cover values, instead of measured ones, on the accuracy of the models is assessed. Results show that LMX models do not degrade in forecast accuracy if models are trained with the forecast cloud cover data.« less

Insights on the Optical Properties of Estuarine DOM – Hydrological and Biological Influences

PubMed Central

Santos, Luísa; Pinto, António; Filipe, Olga; Cunha, Ângela; Santos, Eduarda B. H.

2016-01-01

Dissolved organic matter (DOM) in estuaries derives from a diverse array of both allochthonous and autochthonous sources. In the estuarine system Ria de Aveiro (Portugal), the seasonality and the sources of the fraction of DOM that absorbs light (CDOM) were inferred using its optical and fluorescence properties. CDOM parameters known to be affected by aromaticity and molecular weight were correlated with physical, chemical and meteorological parameters. Two sites, representative of the marine and brackish water zones of the estuary, and with different hydrological characteristics, were regularly surveyed along two years, in order to determine the major influences on CDOM properties. Terrestrial-derived compounds are the predominant source of CDOM in the estuary during almost all the year and the two estuarine zones presented distinct amounts, as well as absorbance and fluorescence characteristics. Freshwater inputs have major influence on the dynamics of CDOM in the estuary, in particular at the brackish water zone, where accounted for approximately 60% of CDOM variability. With a lower magnitude, the biological productivity also impacted the optical properties of CDOM, explaining about 15% of its variability. Therefore, climate changes related to seasonal and inter-annual variations of the precipitation amounts might impact the dynamics of CDOM significantly, influencing its photochemistry and the microbiological activities in estuarine systems. PMID:27195702

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).

Does a more skilful meteorological input lead to a more skilful flood forecast at seasonal timescales?

NASA Astrophysics Data System (ADS)

Neumann, Jessica; Arnal, Louise; Magnusson, Linus; Cloke, Hannah

2017-04-01

Seasonal river flow forecasts are important for many aspects of the water sector including flood forecasting, water supply, hydropower generation and navigation. In addition to short term predictions, seasonal forecasts have the potential to realise higher benefits through more optimal and consistent decisions. Their operational use however, remains a challenge due to uncertainties posed by the initial hydrologic conditions (e.g. soil moisture, groundwater levels) and seasonal climate forcings (mainly forecasts of precipitation and temperature), leading to a decrease in skill with increasing lead times. Here we present a stakeholder-led case study for the Thames catchment (UK), currently being undertaken as part of the H2020 IMPREX project. The winter of 2013-14 was the wettest on record in the UK; driven by 12 major Atlantic depressions, the Thames catchment was subject to compound (concurrent) flooding from fluvial and groundwater sources. Focusing on the 2013-14 floods, this study aims to see whether increased skill in meteorological input translates through to more accurate forecasting of compound flood events at seasonal timescales in the Thames catchment. An earlier analysis of the ECMWF System 4 (S4) seasonal meteorological forecasts revealed that it did not skilfully forecast the extreme event of winter 2013-14. This motivated the implementation of an atmospheric experiment by the ECMWF to force the S4 to more accurately represent the low-pressure weather conditions prevailing in winter 2013-14 [1]. Here, we used both the standard and the "improved" S4 seasonal meteorological forecasts to force the EFAS (European Flood Awareness System) LISFLOOD hydrological model. Both hydrological forecasts were started on the 1st of November 2013 and run for 4 months of lead time to capture the peak of the 2013-14 flood event. Comparing the seasonal hydrological forecasts produced with both meteorological forcing data will enable us to assess how the improved meteorology translates into skill in the hydrological forecast for this extreme compound event. As primary stakeholders involved in the study, the Environment Agency and Flood Forecasting Centre are responsible for managing flood risk in the UK. For them, the detection of a potential flood signal weeks or months in advance could be of great value in terms of operational practice, decision-making and early warning. [1] Rodwell, M.J., Ferranti, L., Magnusson, L., Weisheimer, A., Rabier, F. & Richardson, D. (2015) Diagnosis of northern hemispheric regime behaviour during winter 2013/14. ECMWF Technical Memoranda 769.

Measurement of volatile organic chemicals at selected sites in California

NASA Technical Reports Server (NTRS)

Singh, Hanwant B.; Salas, L.; Viezee, W.; Sitton, B.; Ferek, R.

1992-01-01

Urban air concentrations of 24 selected volatile organic chemicals that may be potentially hazardous to human health and environment were measured during field experiments conducted at two California locations, at Houston, and at Denver. Chemicals measured included chlorofluorocarbons, halomethanes, haloethanes, halopropanes, chloroethylenes, and aromatic hydrocarbons. With emphasis on California sites, data from these studies are analyzed and interpreted with respect to variabilities in ambient air concentrations, diurnal changes, relation to prevailing meteorology, sources and trends. Except in a few instances, mean concentrations are typically between 0 and 5 ppb. Significant variabilities in atmospheric concentrations associated with intense sources and adverse meteorological conditions are shown to exist. In addition to short-term variability, there is evidence of systematic diurnal and seasonal trends. In some instances it is possible to detect declining trends resulting from the effectiveness of control strategies.

A neural circuit mechanism for regulating vocal variability during song learning in zebra finches.

PubMed

Garst-Orozco, Jonathan; Babadi, Baktash; Ölveczky, Bence P

2014-12-15

Motor skill learning is characterized by improved performance and reduced motor variability. The neural mechanisms that couple skill level and variability, however, are not known. The zebra finch, a songbird, presents a unique opportunity to address this question because production of learned song and induction of vocal variability are instantiated in distinct circuits that converge on a motor cortex analogue controlling vocal output. To probe the interplay between learning and variability, we made intracellular recordings from neurons in this area, characterizing how their inputs from the functionally distinct pathways change throughout song development. We found that inputs that drive stereotyped song-patterns are strengthened and pruned, while inputs that induce variability remain unchanged. A simple network model showed that strengthening and pruning of action-specific connections reduces the sensitivity of motor control circuits to variable input and neural 'noise'. This identifies a simple and general mechanism for learning-related regulation of motor variability.

Proceedings of the NASA Workshop on Flight Deck Centered Parallel Runway Approaches in Instrument Meteorological Conditions

NASA Technical Reports Server (NTRS)

Waller, Marvin C. (Editor); Scanlon, Charles H. (Editor)

1996-01-01

A Government and Industry workshop on Flight-Deck-Centered Parallel Runway Approaches in Instrument Meteorological Conditions (IMC) was conducted October 29, 1996 at the NASA Langley Research Center. This document contains the slides and records of the proceedings of the workshop. The purpose of the workshop was to disclose to the National airspace community the status of ongoing NASA R&D to address the closely spaced parallel runway problem in IMC and to seek advice and input on direction of future work to assure an optimized research approach. The workshop also included a description of a Paired Approach Concept which is being studied at United Airlines for application at the San Francisco International Airport.

Inherent uncertainties in meteorological parameters for wind turbine design

NASA Technical Reports Server (NTRS)

Doran, J. C.

1982-01-01

Major difficulties associated with meteorological measurments such as the inability to duplicate the experimental conditions from one day to the next are discussed. This lack of consistency is compounded by the stochastic nature of many of the meteorological variables of interest. Moreover, simple relationships derived in one location may be significantly altered by topographical or synoptic differences encountered at another. The effect of such factors is a degree of inherent uncertainty if an attempt is made to describe the atmosphere in terms of universal laws. Some of these uncertainties and their causes are examined, examples are presented and some implications for wind turbine design are suggested.

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.

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.

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

NASA Astrophysics Data System (ADS)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Apparent relationship between solar-sector boundaries and 300-mb vorticity: Possible explanation in terms of upward propagation of planetary-scale waves

NASA Technical Reports Server (NTRS)

Deland, R. J.

1974-01-01

The selection process for sector structure boundary crossings used in vorticity correlation studies is examined and the possible influence of ascending planetary scale waves is assessed. It is proposed that some of the observed correlations between geomagnetic and meteorological variations may be due to meteorological effects on the geometric variables, rather than due to common solar origin.

Correlation of spring spore concentrations and meteorological conditions in Tulsa, Oklahoma

NASA Astrophysics Data System (ADS)

Troutt, C.; Levetin, E.

Different spore types are abundant in the atmosphere depending on the weather conditions. Ascospores generally follow precipitation, while spore types such as Alternaria and Cladosporium are abundant in dry conditions. This project attempted to correlate fungal spore concentrations with meteorological data from Tulsa, Oklahoma during May 1998 and May 1999. Air samples were collected and analyzed by the 12-traverse method. The spore types included were Cladosporium, Alternaria, Epicoccum, Curvularia, Pithomyces, Drechslera, smut spores, ascospores, basidiospores, and other spores. Weather variables included precipitation levels, temperature, dew point, air pressure, wind speed, wind direction and wind gusts. There were over 242.57 mm of rainfall in May 1999 and only 64.01 mm in May 1998. The most abundant spore types during May 1998 and May 1999 were Cladosporium, ascospores, and basidiospores. Results showed that there were significant differences in the dry-air spora between May 1998 and May 1999. There were twice as many Cladosporium in May 1998 as in May 1999; both ascospores and basidiospores showed little change. Multiple regression analysis was used to determine which meteorological variables influenced spore concentrations. Results showed that there was no single model for all spore types. Different combinations of factors were predictors of concentration for the various fungi examined; however, temperature and dew point seemed to be the most important meteorological factors.

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.

Intercomparison of Downscaling Methods on Hydrological Impact for Earth System Model of NE United States

NASA Astrophysics Data System (ADS)

Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.

2012-12-01

Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be downscaled from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this downscaling procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several downscaling techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four downscaling approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial downscaling (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic Downscaling based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different downscaling methods against observational datasets was carried out for assessment of the downscaled climate model outputs. The effects of using the different approaches to downscale atmospheric variables (specifically air temperature and precipitation) for use as inputs to the Water Balance Model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) for simulation of daily discharge and monthly stream flow in the Northeast US for a 100-year period in the 21st century were also assessed. Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly stream flow simulation. However, Dynamic Downscaling will provide important complements to the statistical approaches tested.

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.

Acceptance criteria for urban dispersion model evaluation

NASA Astrophysics Data System (ADS)

Hanna, Steven; Chang, Joseph

2012-05-01

The authors suggested acceptance criteria for rural dispersion models' performance measures in this journal in 2004. The current paper suggests modified values of acceptance criteria for urban applications and tests them with tracer data from four urban field experiments. For the arc-maximum concentrations, the fractional bias should have a magnitude <0.67 (i.e., the relative mean bias is less than a factor of 2); the normalized mean-square error should be <6 (i.e., the random scatter is less than about 2.4 times the mean); and the fraction of predictions that are within a factor of two of the observations (FAC2) should be >0.3. For all data paired in space, for which a threshold concentration must always be defined, the normalized absolute difference should be <0.50, when the threshold is three times the instrument's limit of quantification (LOQ). An overall criterion is then applied that the total set of acceptance criteria should be satisfied in at least half of the field experiments. These acceptance criteria are applied to evaluations of the US Department of Defense's Joint Effects Model (JEM) with tracer data from US urban field experiments in Salt Lake City (U2000), Oklahoma City (JU2003), and Manhattan (MSG05 and MID05). JEM includes the SCIPUFF dispersion model with the urban canopy option and the urban dispersion model (UDM) option. In each set of evaluations, three or four likely options are tested for meteorological inputs (e.g., a local building top wind speed, the closest National Weather Service airport observations, or outputs from numerical weather prediction models). It is found that, due to large natural variability in the urban data, there is not a large difference between the performance measures for the two model options and the three or four meteorological input options. The more detailed UDM and the state-of-the-art numerical weather models do provide a slight improvement over the other options. The proposed urban dispersion model acceptance criteria are satisfied at over half of the field experiments.

Urban Landscape Characterization Using Remote Sensing Data For Input into Air Quality Modeling

NASA Technical Reports Server (NTRS)

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

2005-01-01

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.

Variability of Springtime Transpacific Pollution Transport During 2000-2006: The INTEX-5 Mission in the Context of Previous Years

NASA Technical Reports Server (NTRS)

Pfister, G. G.; Emmons, L. K.; Edwards, D. P.; Arellano, A.; Sachse, G.; Campos, T.

2010-01-01

We analyze the transport of pollution across the Pacific during the NASA INTEX-B (Intercontinental Chemical Transport Experiment Part 8) campaign in spring 2006 and examine how this year compares to the time period for 2000 through 2006. In addition to aircraft measurements of carbon monoxide (CO) collected during INTEX-B, we include in this study multi-year satellite retrievals of CO from the Measurements of Pollution in the Troposphere (MOPITT) instrument and simulations from the chemistry transport model MOZART-4. Model tracers are used to examine the contributions of different source regions and source types to pollution levels over the Pacific. Additional modeling studies are performed to separate the impacts of inter-annual variability in meteorology and .dynamics from changes in source strength. interannual variability in the tropospheric CO burden over the Pacific and the US as estimated from the MOPITT data range up to 7% and a somewhat smaller estimate (5%) is derived from the model. When keeping the emissions in the model constant between years, the year-to-year changes are reduced (2%), but show that in addition to changes in emissions, variable meteorological conditions also impact transpacific pollution transport. We estimate that about 113 of the variability in the tropospheric CO loading over the contiguous US is explained by changes in emissions and about 213 by changes in meteorology and transport. Biomass burning sources are found to be a larger driver for inter-annual variability in the CO loading compared to fossil and biofuel sources or photochemical CO production even though their absolute contributions are smaller. Source contribution analysis shows that the aircraft sampling during INTEX-B was fairly representative of the larger scale region, but with a slight bias towards higher influence from Asian contributions.

Transport of a Power Plant Tracer Plume over Grand Canyon National Park.

NASA Astrophysics Data System (ADS)

Chen, Jun; Bornstein, Robert; Lindsey, Charles G.

1999-08-01

Meteorological and air-quality data, as well as surface tracer concentration values, were collected during 1990 to assess the impacts of Navajo Generating Station (NGS) emissions on Grand Canyon National Park (GCNP) air quality. These data have been used in the present investigation to determine between direct and indirect transport routes taken by the NGS plume to produce measured high-tracer concentration events at GCNP.The meteorological data were used as input into a three-dimensional mass-consistent wind model, whose output was used as input into a horizontal forward-trajectory model. Calculated polluted air locations were compared with observed surface-tracer concentration values.Results show that complex-terrain features affect local wind-flow patterns during winter in the Grand Canyon area. Local channeling, decoupled canyon winds, and slope and valley flows dominate in the region when synoptic systems are weak. Direct NGS plume transport to GCNP occurs with northeasterly plume-height winds, while indirect transport to the park is caused by wind direction shifts associated with passing synoptic systems. Calculated polluted airmass positions along the modeled streak lines match measured surface-tracer observations in both space and time.

Engineering description of the ascent/descent bet product

NASA Technical Reports Server (NTRS)

Seacord, A. W., II

1986-01-01

The Ascent/Descent output product is produced in the OPIP routine from three files which constitute its input. One of these, OPIP.IN, contains mission specific parameters. Meteorological data, such as atmospheric wind velocities, temperatures, and density, are obtained from the second file, the Corrected Meteorological Data File (METDATA). The third file is the TRJATTDATA file which contains the time-tagged state vectors that combine trajectory information from the Best Estimate of Trajectory (BET) filter, LBRET5, and Best Estimate of Attitude (BEA) derived from IMU telemetry. Each term in the two output data files (BETDATA and the Navigation Block, or NAVBLK) are defined. The description of the BETDATA file includes an outline of the algorithm used to calculate each term. To facilitate describing the algorithms, a nomenclature is defined. The description of the nomenclature includes a definition of the coordinate systems used. The NAVBLK file contains navigation input parameters. Each term in NAVBLK is defined and its source is listed. The production of NAVBLK requires only two computational algorithms. These two algorithms, which compute the terms DELTA and RSUBO, are described. Finally, the distribution of data in the NAVBLK records is listed.

The Langley Parameterized Shortwave Algorithm (LPSA) for Surface Radiation Budget Studies. 1.0

NASA Technical Reports Server (NTRS)

Gupta, Shashi K.; Kratz, David P.; Stackhouse, Paul W., Jr.; Wilber, Anne C.

2001-01-01

An efficient algorithm was developed during the late 1980's and early 1990's by W. F. Staylor at NASA/LaRC for the purpose of deriving shortwave surface radiation budget parameters on a global scale. While the algorithm produced results in good agreement with observations, the lack of proper documentation resulted in a weak acceptance by the science community. The primary purpose of this report is to develop detailed documentation of the algorithm. In the process, the algorithm was modified whenever discrepancies were found between the algorithm and its referenced literature sources. In some instances, assumptions made in the algorithm could not be justified and were replaced with those that were justifiable. The algorithm uses satellite and operational meteorological data for inputs. Most of the original data sources have been replaced by more recent, higher quality data sources, and fluxes are now computed on a higher spatial resolution. Many more changes to the basic radiation scheme and meteorological inputs have been proposed to improve the algorithm and make the product more useful for new research projects. Because of the many changes already in place and more planned for the future, the algorithm has been renamed the Langley Parameterized Shortwave Algorithm (LPSA).

Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

NASA Astrophysics Data System (ADS)

Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

2012-04-01

The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the phenomenon which shows the relationship between the input and output parameters. This study provided new alternatives for solar radiation estimation based on temperatures.

Seasonal groundwater contribution to crop-water use assessed with lysimeter observations and model simulations

USGS Publications Warehouse

Luo, Y.; Sophocleous, M.

2010-01-01

Groundwater evaporation can play an important role in crop-water use where the water table is shallow. Lysimeters are often used to quantify the groundwater evaporation contribution influenced by a broad range of environmental factors. However, it is difficult for such field facilities, which are operated under limited conditions within limited time, to capture the whole spectrum of capillary upflow with regard to the inter-seasonal variability of climate, especially rainfall. Therefore, in this work, the method of combining lysimeter and numerical experiments was implemented to investigate seasonal groundwater contribution to crop-water use. Groundwater evaporation experiments were conducted through a weighing lysimeter at an agricultural experiment station located within an irrigation district in the lower Yellow River Basin for two winter wheat growth seasons. A HYDRUS-1D model was first calibrated and validated with weighing lysimeter data, and then was employed to perform scenario simulations of groundwater evaporation under different depths to water table (DTW) and water input (rainfall plus irrigation) driven by long term meteorological data. The scenario simulations revealed that the seasonally averaged groundwater evaporation amount was linearly correlated to water input for different values of DTW. The linear regression could explain more than 70% of the variability. The seasonally averaged ratio of the groundwater contribution to crop-water use varied with the seasonal water input and DTW. The ratio reached as high as 75% in the case of DTW=1.0. m and no irrigation, and as low as 3% in the case of DTW=3.0. m and three irrigation applications. The results also revealed that the ratio of seasonal groundwater evaporation to potential evapotranspiration could be fitted to an exponential function of the DTW that may be applied to estimate seasonal groundwater evaporation. In this case study of multilayered soil profile, the depth at which groundwater may evaporate at potential rate was 0.60-0.65. m, and the extinction depth of groundwater evaporation was approximately 3.8. m. ?? 2010 Elsevier B.V.

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-07-01

Soil temperature (T s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T s. For this aim, the monthly mean data of the T s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T min, T max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R s, R n, R a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u 2); and water vapor pressure (Vp) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R 2) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T max, T min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R 2 = 0.995) and for RH, V p, P, and u 2 inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R 2 = 0.996), respectively.

Bottom-up and Top-down Input Augment the Variability of Cortical Neurons

PubMed Central

Nassi, Jonathan J.; Kreiman, Gabriel; Born, Richard T.

2016-01-01

SUMMARY Neurons in the cerebral cortex respond inconsistently to a repeated sensory stimulus, yet they underlie our stable sensory experiences. Although the nature of this variability is unknown, its ubiquity has encouraged the general view that each cell produces random spike patterns that noisily represent its response rate. In contrast, here we show that reversibly inactivating distant sources of either bottom-up or top-down input to cortical visual areas in the alert primate reduces both the spike train irregularity and the trial-to-trial variability of single neurons. A simple model in which a fraction of the pre-synaptic input is silenced can reproduce this reduction in variability, provided that there exist temporal correlations primarily within, but not between, excitatory and inhibitory input pools. A large component of the variability of cortical neurons may therefore arise from synchronous input produced by signals arriving from multiple sources. PMID:27427459

Comparison of meteorological forcing (WFDEI, AGRI4CAST) to in-situ observations in a semi arid catchment. The case of Merguellil in Tunisia.

NASA Astrophysics Data System (ADS)

Le Page, Michel; Gosset, Cindy; Oueslati, Ines; Calvez, Roger; Zribi, Mehrez; Lilli Chabaane, Zohra

2015-04-01

Meteorological forcing is essential to hydrological and hydro-geological modeling. In the case of the semi-arid catchment of Merguellil in Tunisia, long term time series are only available in the plain for a SYNOP station. Other meteorological stations have been installed since 2010. Therefore, this study aims at qualifying the reliability of the meteorological forcing necessary for an integrated model conception. We compare the meteorological data from 7 stations (sources: WMO and our own station), inside and around the Merguellil catchment, with daily gridded data at 25*25 km from AGRI4CAST and 50*50km from WFDEI. AGRI4CAST (Biaveti et al, 2008) is an interpolated dataset based on actual weather stations produced by the Joint Research Centre (JRC) for the Monitoring Agricultural Resources Unit (MARS). The WFDEI second version dataset (Weedon et al, 2014) has been generated using the same methodology as the widely used WATCH Forcing Data (WFD) by making use of the ERA-Interim reanalysis data. The studied meteorological variables are Rs, Tmoy, U2, P, RH and ET0, with the scores RMSE, bias and R pearson. Regarding the AGRI4CAST dataset, the scores are established over different periods according to variables based on stepping between the observed and interpolated data. The scores show good correlations between the observed temperatures, but with a spatial variability bound to the stations elevations. The moderate and interpolated radiations also show a good concordance indicating a good reliability. The R pearson score obtained for the values of relative humidity show a good correlation between the observations and the interpolations, however, the short periods of comparisons do not allow obtaining significant information and the RMSE and bias are important. Wind speed has an important negative bias for a majority of stations (positively for only one). Only one station shows concordances between the data. The study of the data indicates that we shall have to adjust the wind speeds and the relative humidity of the air for the implementation of a model. Finally the reference evapotranspiration seems relatively coherent, in spite of the dispersal observed during the meteorological measures, but with biases rather high and RMSE also rather high (> 1.3 mm). After revised the parameter U2 and RH, AGRI4CAST can possibly be corrected by ancillary ground stations. The analysis of the WFDEI dataset is currently under evaluation. (1) Biavetti, I., Karetsos, S., Ceglar, A., Toreti, A., Panagos P. (2014), European meteorological data: contribution to research, development and policy support, Proc. of SPIE Vol. 9229 922907-1 (2) Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best, and P. Viterbo (2014), The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505-7514, doi:10.1002/ 2014WR015638.

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Reconstruction of daily solar UV irradiation from 1893 to 2002 in Potsdam, Germany

NASA Astrophysics Data System (ADS)

Junk, Jürgen; Feister, Uwe; Helbig, Alfred

2007-08-01

Long-term records of solar UV radiation reaching the Earth’s surface are scarce. Radiative transfer calculations and statistical models are two options used to reconstruct decadal changes in solar UV radiation from long-term records of measured atmospheric parameters that contain information on the effect of clouds, atmospheric aerosols and ground albedo on UV radiation. Based on earlier studies, where the long-term variation of daily solar UV irradiation was derived from measured global and diffuse irradiation as well as atmospheric ozone by a non-linear regression method [Feister et al. (2002) Photochem Photobiol 76:281 293], we present another approach for the reconstruction of time series of solar UV radiation. An artificial neural network (ANN) was trained with measurements of solar UV irradiation taken at the Meteorological Observatory in Potsdam, Germany, as well as measured parameters with long-term records such as global and diffuse radiation, sunshine duration, horizontal visibility and column ozone. This study is focussed on the reconstruction of daily broad-band UV-B (280 315 nm), UV-A (315 400 nm) and erythemal UV irradiation (ER). Due to the rapid changes in cloudiness at mid-latitude sites, solar UV irradiance exhibits appreciable short-term variability. One of the main advantages of the statistical method is that it uses doses of highly variable input parameters calculated from individual spot measurements taken at short time intervals, which thus do represent the short-term variability of solar irradiance.

“ How Reliable is the Couple of WRF & VIC Models”

EPA Science Inventory

The ability of the fully coupling of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological and climate variables was evaluated. First, the VIC model was run by using observed meteorological data and calibrated in the Upp...

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.

Usage of NASA's Near Real-Time Solar and Meteorological Data for Monitoring Building Energy Systems Using RETScreen International's Performance Analysis Module

NASA Technical Reports Server (NTRS)

Stackhouse, Paul W., Jr.; Charles, Robert W.; Chandler, William S.; Hoell, James M.; Westberg, David; Zhang, Taiping; Ziegler, Urban; Leng, Gregory J.; Meloche, Nathalie; Bourque, Kevin

2012-01-01

This paper describes building energy system production and usage monitoring using examples from the new RETScreen Performance Analysis Module, called RETScreen Plus. The module uses daily meteorological (i.e., temperature, humidity, wind and solar, etc.) over a period of time to derive a building system function that is used to monitor building performance. The new module can also be used to target building systems with enhanced technologies. If daily ambient meteorological and solar information are not available, these are obtained over the internet from NASA's near-term data products that provide global meteorological and solar information within 3-6 days of real-time. The accuracy of the NASA data are shown to be excellent for this purpose enabling RETScreen Plus to easily detect changes in the system function and efficiency. This is shown by several examples, one of which is a new building at the NASA Langley Research Center that uses solar panels to provide electrical energy for building energy and excess energy for other uses. The system shows steady performance within the uncertainties of the input data. The other example involves assessing the reduction in energy usage by an apartment building in Sweden before and after an energy efficiency upgrade. In this case, savings up to 16% are shown.

Hydrological modelling of the Mabengnong catchment in the southeast Tibet with support of short term intensive precipitation observation

NASA Astrophysics Data System (ADS)

Wang, L.; Zhang, F.; Zhang, H.; Scott, C. A.; Zeng, C.; SHI, X.

2017-12-01

Precipitation is one of the crucial inputs for models used to better understand hydrological processes. In high mountain areas, it is a difficult task to obtain a reliable precipitation data set describing the spatial and temporal characteristic due to the limited meteorological observations and high variability of precipitation. This study carries out intensive observation of precipitation in a high mountain catchment in the southeast of the Tibet during July to August 2013. According to the rain gauges set up at different altitudes, it is found that precipitation is greatly influenced by altitude. The observed precipitation is used to depict the precipitation gradient (PG) and hourly distribution (HD), showing that the average duration is around 0.1, 0.8 and 6.0 hours and the average PG is 0.10, 0.28 and 0.26 mm/d/100m for trace, light and moderate rain, respectively. Based on the gridded precipitation derived from the PG and HD and the nearby Linzhi meteorological station at lower altitude, a distributed biosphere hydrological model based on water and energy budgets (WEB-DHM) is applied to simulate the hydrological processes. Beside the observed runoff, MODIS/Terra snow cover area (SCA) data, and MODIS/Terra land surface temperature (LST) data are also used for model calibration and validation. The resulting runoff, SCA and LST simulations are all reasonable. Sensitivity analyses indicate that runoff is greatly underestimated without considering PG, illustrating that short-term intensive precipitation observation contributes to improving hydrological modelling of poorly gauged high mountain catchments.