资源环境科技发展态势分析平台

With the end of the Tropical Rainfall Measuring Mission (TRMM) era, its successor of Global Precipitation Measurement (GPM) aims to provide a new generation of satellite precipitation estimates (SPE) with higher spatiotemporal resolution and more spatial coverage. As the representative precipitation retrieval algorithms for GPM and TRMM eras, it is necessary to compare the Integrated Multi-satellite Retrievals for GPM (IMERG) with the TRMM Multi-satellite Precipitation Analysis (TMPA) to investigate the improvements of SPEs from TRMM to GPM. This study analyzes post/near-post SPEs from TMPA and IMERG over Mainland China from April 2014 to March 2018 at daily scale, using precipitation observations from 510 meteorological stations as reference. A very comprehensive evaluation system, which contains four indicators of continuous metrics, categorical metrics, error components, and systematic and random errors is adopted in this study. Besides, four patterns with spatial distribution, temporal pattern, precipitation intensity distribution, and overall metrics are applied to illustrate each indicator's performance. The results show that IMERG products have lower RB and RMSE and higher CC than TMPA and greatly reduce the three error components (hit bias, missed precipitation, and false precipitation). Furthermore, IMERG products present a higher capability to capture the precipitation events correctly and generally have lower systematic and random errors than TMPA products. However, there are still rooms for IMERG products to improve, such as higher false alarm ratio (FAR) and more false precipitation of IMERG products at relatively low rain rates, the 25% higher systematic error of IMERG-E (IMERG “Early Run” near-real-time SPE) compared to TMPA-RT (TMPA near-real-time SPE). In addition, we find the gauge adjustment from GPCC is not a very effective way to improve the detecting capability and reduce the systematic errors for near-real-time SPEs over Mainland China. This study provides some valuable information on the transition from TMPA to IMERG, which may advance the algorithm's development and the hydrometeorological applications.