Improving PERSIANN-CCS Using Passive Microwave Rainfall Estimation

Abstract

Re-calibrated PERSIANN-CCS is one of the algorithms used in “Integrated Multi-satellitE Retrievals for GPM” (IMERG) to provide high-resolution precipitation estimations from the NASA Global Precipitation Measurement (GPM) program and retrospective data generation for the period covered by the Tropical Rainfall Measurement Mission (TRMM). This study presents the development of a re-calibrated PERSIANN-CCS algorithm for the next-generation GPM multi-sensor precipitation retrieval algorithm (IMERG). The activities include implementing the probability matching method to update PERSIANN-CCS using passive microwave (PMW) rainfall estimation from low earth orbit (LEO) satellites and validation of precipitation estimation using radar rainfall measurement. Further improvement by the addition of warm rain estimation to the PERSIANN-CCS algorithm using warmer temperature thresholds for cloud image segmentation is also presented. Additionally, developments using multispectral image analysis and machine learning approaches are discussed and proposed for future studies.