Evaluation of GPM IMERG Product Over the Yellow River Basin Using an Improved Error-Component Procedure

Abstract

In the Global Precipitation Measurement (GPM) era, the Integrated Multi-satellite Retrievals for GPM (IMERG) stands as a pivotal precipitation algorithm. This study aims to evaluate the accuracy of IMERG in capturing precipitation frequency and volume in the Yellow River Basin, China. Four satellite-based precipitation estimates (SPEs) from three algorithmic versions of IMERG were analyzed on an hourly scale using gauge observations. An improved error component procedure was employed to identify error sources. Results showed that all four IMERG products effectively captured the spatial distribution patterns and seasonal changes of precipitation, IMERG_F demonstrated the best overall performance, followed by IMERG_L, while IMERG_E performed the worst. However, they tended to overestimate precipitation. IMERG_F_Cal performed the best for precipitation frequency detection, with the highest probability of detection (POD = 52.7%) and the lowest missed events (MIS = 47.3%). Error component analysis highlights False Bias as the main source of error, followed by Missed Bias. In winter, Missed Bias was the primary error source. Notably, a significant overestimation of precipitation was observed along the Yellow River. In detail, False Bias dominated IMERG_E, IMERG_L, and IMERG_F_UnCal below 800m in spring, summer, and autumn. However, in winter, Missed Bias became the primary error source for these three products at elevations above 200m and for IMERG_F_Cal above 500m. IMERG_F_Cal exhibited False Bias as the primary error source in all seasons. Suggested algorithm developers focus on improving IMERG SPEs' identification capabilities for light precipitation events and rainstorms. Findings can provide a reference for improving the IMERG product algorithms and enhancing users' understanding of the error characteristics and sources of IMERG products.