Integrating Observations from MFG and GPM for Near Real Time Heavy Precipitation Monitoring

Abstract

This study deals with merging of high accurate precipitation estimates from Global Precipitation Measurement (GPM) with sampling gap free satellite observations from Meteosat 7 of Meteosat First Generation (MFG) to develop a regional rainfall monitoring algorithm for monitoring precipitation over India and nearby oceanic regions. For this purpose, we derived rainfall signature from Meteosat observations to co-locate it against rainfall from GPM. A relationship is then established between rainfall and rainfall signature using observations from various rainy seasons. Relationship thus derived can be used to monitor precipitation over India and nearby oceanic regions. 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 developed algorithm for monitoring heavy rainfall during flood event of Tamil Nadu in 2015.