Precipitation Estimate Using NEXRAD Ground-Based Radar Images: Validation, Calibration and Spatial Analysis

Book · Mon Dec 17 04:00:00 EST 2012

OSTI ID:1081422

Zhang, Xuesong

Precipitation is an important input variable for hydrologic and ecological modeling and analysis. Next Generation Radar (NEXRAD) can provide precipitation products that cover most of the continental United States with a high resolution display of approximately 4 × 4 km2. Two major issues concerning the applications of NEXRAD data are (1) lack of a NEXRAD geo-processing and geo-referencing program and (2) bias correction of NEXRAD estimates. In this chapter, a geographic information system (GIS) based software that can automatically support processing of NEXRAD data for hydrologic and ecological models is presented. Some geostatistical approaches to calibrating NEXRAD data using rain gauge data are introduced, and two case studies on evaluating accuracy of NEXRAD Multisensor Precipitation Estimator (MPE) and calibrating MPE with rain-gauge data are presented. The first case study examines the performance of MPE in mountainous region versus south plains and cold season versus warm season, as well as the effect of sub-grid variability and temporal scale on NEXRAD performance. From the results of the first case study, performance of MPE was found to be influenced by complex terrain, frozen precipitation, sub-grid variability, and temporal scale. Overall, the assessment of MPE indicates the importance of removing bias of the MPE precipitation product before its application, especially in the complex mountainous region. The second case study examines the performance of three MPE calibration methods using rain gauge observations in the Little River Experimental Watershed in Georgia. The comparison results show that no one method can perform better than the others in terms of all evaluation coefficients and for all time steps. For practical estimation of precipitation distribution, implementation of multiple methods to predict spatial precipitation is suggested.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1081422
• Report Number(s): PNNL-SA-78987; KP1703010
• Country of Publication: United States
• Language: English