Exploring the source of GPM IMERG and GSMaP errors based on error component method over the Yellow River basin

Abstract

Precise precipitation data is essential for effective hydrologic modeling, water resource management, and climate research. IMERG and GSMaP, which are high-resolution and wide-coverage satellite products, have great potential in monitoring precipitation. However, further research is required to understand the error characteristics of precipitation. This study focuses on the Yellow River Basin, results demonstrate that: (1) GPM IMERG and GSMaP display strong spatial patterns, with more precise detection in the east than the west. Hit bias dominates in the total bias, and each product overestimates the error. The GSMaP_Gauge product performs better in reducing hit bias and improving detection accuracy. (2) The Yellow River Basin is generally biased towards low-intensity precipitation events, with the GSMaP_Gauge product performing the best, followed by the IMERG_Final product with the smallest missed precipitation. (3) Altitude has a significant impact on the total bias and error components, mainly showing a negative correlation. As altitude increases, the total bias and error components gradually decrease. This study can facilitate the creation of quantitative bias models and the implementation of techniques to reduce bias in GPM IMERG and GSMaP by examining the structure and attributes of error components.