ABSTRACT
Precipitation, an important input for land surface processes such as the hydrologic cycle and vegetation growth, is characterized by high spatial and temporal variability. Traditionally, precipitation measurements are available at rain gauge points, which are usually too sparsely distributed to capture spatial variability; therefore, these point data need to be interpolated to estimate the spatial distribution of precipitation. Development of methods to interpolate precipitation data from sparse networks of rain gauge stations has been a focus of past research (e.g., Phillips et al. 1992; Hasenauer et al. 2003). In recent years, the U.S. National Weather Service (NWS) installed a network of (approximately 160) Weather Surveillance Radar—1988 Dopplers (WSR-88Ds) radar stations as part of a Next Generation Radar (NEXRAD) program that began implementation in 1991 (Young et al. 2000; Hardegree et al. 2008). The NEXRAD products, located in the Contiguous United States (CONUS) at approximately 4 × 4 km2 resolution, provide nominal coverage of 96% of the country (Crum et al. 1998). The ability of NEXRAD to provide spatially distributed precipitation estimates makes it one important source of precipitation information for hydrologists and natural resources managers. The NEXRAD precipitation products have been used for multiple purposes in hydrologic modeling and agricultural and rangeland management (e.g., Diak et al. 1998; Krajewski and Smith 2002; Zhang et al. 2004; Hardegree et al. 2008).
