Implications for Validation of IMERG Satellite Precipitation in a Complex Mountainous Region

Abstract

Satellite-based precipitation retrievals such as the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), provide alternative data in mountainous regions. In this study, we evaluated IMERG in the Yarlung Tsangbo Grand Canyon (YGC) using ground observations. It was found that IMERG underestimated the total rainfall primarily due to under-detection of rainfall events, with misses being more prevalent than false alarms. We analyzed the relationships between the probability of detection (POD), false alarm ratio (FAR), bias in detection (BID), and Heidke skill score (HSS) and terrain factors. It was found that the POD decreased with elevation, leading to increased underestimation of rainfall events at higher elevations, and the FAR was higher in valley sites. In terms of the hit events, IMERG overestimated the light rainfall events and underestimated the heavy rainfall events and the negative bias in the hit events decreased with elevation. IMERG could capture the early morning peak precipitation in the YGC region but underestimated the amplitude of the diurnal variation. This bias was inherent at the sensor level, and the Global Precipitation Climatology Center (GPCC) calibration partially improved the underestimation. However, this improvement was not sufficient for the YGC region. This study fills the gap in IMERG validation in a complex mountainous region and has implications for users and developers.

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

“…However, challenges still exist, including the estimation of precipitation over complex topographic regions such as snow/ice-covered regions at high latitudes, coast lines and lakes (Mroz et al, 2021;Panegrossi et al, 2022), and the estimation heavy precipitation from convective weather systems (Foufoula-Georgiou et al, 2020). The evaluation results show that the retrieved precipitation product still contains substantial errors when compared with regional gauge observations (Li et al, 2023;Wang et al, 2017;Yuan et al, 2021). To improve the reliability and accuracy of satellite-based research, improving the accuracy of retrieval is the focus of retrieval algorithm research.…”