How Accurately Satellite Precipitation Products Capture the Tropical Cyclone Rainfall?

The landfall tropical cyclones (TCs) produce heavy rainfall, which has a large socio-economic impact. The TC rainfall causes significant flooding and is a major threat to life and property of people on the coastal areas. The accurate representation of TC rainfall is a challenging issue due to its high temporal and spatial variability. In the present study, the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) Late (hereafter NRT) and Final (hereafter FNL), Global Satellite Mapping of Precipitation (GSMaP) near real time (NRT) and GSMaP-gauge, Indian National Satellite System (INSAT3D)-Hydro Estimator method (HEM) and a merged satellite gauge (NMSG) products are evaluated in terms of mean spatial patterns and rainfall intensities during Bay of Bengal (BoB) TCs. For the evaluation, twelve TCs formed in the BoB during 2016–2020 are considered. The statistical indices like, mean, mean bias, pattern root-mean-square error (here after ‘RMSE’) and pattern correlation coefficient (‘r’; here after’CC’) and categorical metrics like, Probability of Detection (POD), Frequency Bias Index (FBI), False Alarm Ratio (FAR) and Critical Success Index (CSI) are used to evaluate the rainfall estimation capability of satellite precipitation products (SPPs). Over the oceanic regions, NMSG, IMERG (NRT and FNL), GSMAP (NRT and gauge) show similar spatial structures and errors, as all the products use Global Precipitation Measurement Mission (GPM) estimation. Noticing the cross validation of mean bias, INSAT3D-HEM tends to overestimate, while IMERG-FNL tends to underestimate the rainfall with respect to other products. Over land areas, the gauge products NMSG, IMERG-FNL and GSMaP-gauge exhibits an overall better estimation than IMERG-NRT, GSMaP-NRT and INSAT3D. However, the GSMaP-NRT, IMERG-NRT exhibit better capability (high POD and FBI ~ 1) in calibrated heavy rainfall events than GSMaP-gauge and IMERG-FNL. This study provides a valuable feedback for algorithm developers to improve the precipitation retrievals especially for TCs, which will be helpful to understand the errors associated with the satellite rainfall retrievals in the application of storm monitoring and hydrological modelling.