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UNITED STATES METEOROLOGICAL DATA - DAILY AND HOURLY FILES TO SUPPORT PREDICTIVE EXPOSURE MODELING
Citation:
BURNS, L. A., L. A. SUAREZ, AND L. M. PRIETO. UNITED STATES METEOROLOGICAL DATA - DAILY AND HOURLY FILES TO SUPPORT PREDICTIVE EXPOSURE MODELING. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-07/053 (NTIS PB2007-110161), 2007.
Impact/Purpose:
Extend existing model technologies to accommodate the full range of transport, fate and food chain contamination pathways, and their biogeographical variants, present in agricultural landscapes and watersheds. Assemble the range of datasets needed to execute risk assessments with appropriate geographic specificity in support of pesticide safety evaluations. Develop software integration technologies, user interfaces, and reporting capabilities for direct application to the EPA risk assessment paradigm in a statistical and probabilistic decision framework.
Description:
ORD numerical models for pesticide exposure include a model of spray drift (AgDisp), a cropland pesticide persistence model (PRZM), a surface water exposure model (EXAMS), and a model of fish bioaccumulation (BASS). A unified climatological database for these models has been assembled from several National Weather Service (NWS) datasets, including Solar and Meteorological Surface Observation Network (SAMSON) data for 1961-1990 (versions 1.0 and 1.1), combined with NWS precipitation and evaporation data. Together these NWS products provide coordinated access to solar radiation, sky cover, temperature, relative humidity, station atmospheric pressure, wind direction and speed, and precipitation. The resulting hourly and daily weather parameters provide a unified dataset for use in coordinated exposure modeling. The data files, which include some derived data of use to exposure modeling (e.g., short-grass crop standard evapotranspiration ET0) are publicly available (gratis) on EPA's Center for Exposure Assessment Modeling (CEAM) web site at http://www.epa.gov/ceampubl/tools/metdata/index.htm. By using observational data for models, trace-matching Monte Carlo simulation studies can transmit the effects of environmental variability directly to exposure metrics, by-passing issues of correlation (covariance) among external driving forces.