Remote Sensing of Near-Real-Time Heavy Precipitation Using Observations from GPM and MFG over India and Nearby Oceanic Regions

Abstract

This study deals with the integration of merging highly accurate precipitation estimates from the Global Precipitation Measurement (GPM) with sampling gap-free satellite observations from Meteosat 7 of the Meteosat First Generation (MFG) to develop a regional rainfall monitoring algorithm to monitor precipitation over India and nearby oceanic regions. For this purpose, we derived precipitation signatures from Meteosat observations to co-locate them against precipitation from GPM. A relationship was then established between rainfall and rainfall signature using observations from various rainy seasons. The relationship thus derived can be used to monitor precipitation over India and nearby oceanic regions. The performance of this technique was tested against rain gauges and global precipitation products including the Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Centre MORPHing (CMORPH), Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) and Integrated Multi-satellitE Retrievals for GPM (IMERG). A case study is presented here to examine the performance of the developed algorithm in monitoring heavy rainfall during the flood event in Tamil Nadu in 2015. This is the first attempt to use near-real-time observations from GPM and MFG to monitor heavy precipitation over the Indian region. Due to its finer resolution and near-real-time availability, this technique can be used to monitor near-real-time flash floods.