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Comparison of Spatial and Temporal Rainfall Characteristics in WRF-Simulated Precipitation to Gauge and Radar Observations
Citation:
Price, K., S. Purucker, T. Anderson, T. Otte, C. Knightes, AND E. Cooter. Comparison of Spatial and Temporal Rainfall Characteristics in WRF-Simulated Precipitation to Gauge and Radar Observations. Presented at AGU Fall Meeting, San Francisco, CA, December 03 - 07, 2012.
Impact/Purpose:
see description
Description:
Weather Research and Forecasting (WRF) meteorological data are used for USEPA multimedia air and water quality modeling applications, within the CMAQ modeling system to estimate wet deposition and to evaluate future climate and land-use scenarios. While it is not expected that hindcasting applications of WRF simulations match observed rainfall on a day-to-day or individual event basis, it is important that the overall spatio-temporal structure of precipitation events represents reality. It has been shown that contaminant fate-and-transport is strongly event-dependent, and the temporal structure of precipitation (and subsequent streamflows) is a major driver of instream flows relating to habitat suitability, contaminant fluxes, dilution, water supply, etc. The spatial and temporal variability of WRF-simulations in the North Carolina Piedmont and Coastal Plain was compared to two observed precipitation datasets – National Climate Data Center (NCDC) gauge and Multisensor Precipitation Estimate (MPE) radar data. Variographic properties were used to compare spatial structure of rainfall across daily, monthly, seasonal, and annual rainfall totals during the five-year study period (2002, 2006-2009). We extracted the first three lags of the autocorrelation function to construct probability distributions of the temporal autocorrelation for each data source. Results showed that WRF realistically characterizes the magnitude of precipitation variability in this region, but there are several discrepancies in the spatial and temporal structure of WRF rainfall that could present problems in watershed modeling.
