Tracking the error sources of spatiotemporal differences in TRMM accuracy using error decomposition method

Tropical Rainfall Measuring Mission (TRMM) products are widely utilized, but the causes of the differences in their spatiotemporal accuracy require further investigation to improve satellite precipitation estimation. In this study, the spatiotemporal accuracy of TRMM 3B42 V7 data was systematically evaluated using the rain gauge data of the densely gauged Xiangjiang River basin, a humid region in South China. The effects of the precipitation intensity and elevation on different error components derived from the error decomposition method were analysed to reveal the causes of spatiotemporal differences of the data errors. The results showed that: (1) TRMM performs better in the wet season than in the dry season, and it underestimates precipitation in winter and in high-elevation areas. (2) Precipitation intensity directly influences the occurrence and magnitude of error components. Most of the missed precipitation (precipitation detected only by rain-gauged data) and false precipitation (precipitation detected only by TRMM data) occur in low-intensity precipitation events. Hit events (precipitation detected by both TRMM and rain-gauged data) tend to overestimate low-intensity precipitation and underestimate high-intensity precipitation. Elevation has no direct relation with daily bias, but affects the distribution of occurrence and intensity of precipitation events. (3) Missed precipitation is the main contributing source of error in winter. The negative error increases in high-elevation areas, which is contributed by the larger proportion of high intensity hit precipitation and the missed events. This study is not only beneficial for understanding the effect of topography and climate factors on the accuracy of TRMM precipitation data but also provides a reference for the application and error improvement of satellite precipitation products.