Bias Adjustment of Satellite Precipitation Estimation Using Ground-Based Measurement: A Case Study Evaluation over the Southwestern United States

• DOI: 10.1175/2009jhm1099.1
• Publication Date: 2009-04-27T22:05:40Z
• Authors: Farid Ishak Boushaki; Kuo-Lin Hsu; Soroosh Sorooshian; Gi-Hyeon Park; Shayesteh Mahani; Wei Shi
• Keywords: Global Precipitation Measurement

Abstract Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation able to provide spatial and temporal distribution of precipitation, the measurements tend show systematic bias. This paper introduces grid-based merging procedure which satellite estimates from Precipitation Estimation Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on Climate Prediction Center (CPC) daily rain gauge analysis. To remove bias, hourly CCS were spatially temporally accumulated 1° × scale, resolution CPC The bias was then downscaled scale correct estimates. corrected called (CCSA) product. With adjustment measurement, CCSA data have been generated more reliable high temporal/spatial-resolution In case study, proposed approach compared against ground-based high-density networks located southwestern United States.