Spatiotemporal inter-comparison of satellite precipitation products over India

Region Satellite Precipitation Products (SPPs) were statistically evaluated for their usefulness at different spatial, temporal, seasonal, altitude, and rainfall intensities scales over India. Study Focus The study evaluates the performance of World Meteorological Organization (WMO) Space-based Weather and Climate Extremes Monitoring Demonstration Project (SEMDP) Global Satellite Mapping of Precipitation (GSMaP) Gauge Adjusted Near Real Time Product (GNRT), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center morphing method (CMORPH) for three years (2015-2017) using daily gridded gauge data from Indian Meteorological Department (IMD). The statistics used are visual interpretation (histograms, CDF plots), yes/no dichotomous [(Bias score (B), probability of detection (POD), false alarm ratio (FAR), probability of false alarm detection (POFD), accuracy (ACC), threat score (TS) and skill score (SS)] and continuous variable verification statistics [correlation coefficient (CC), relative bias (RB), mean absolute difference (MAD) and root mean square error (RMSE)]. New Meteorological Insights for the Region The study found that GNRT is better than other SPPs at different temporal, spatial, altitudinal and rainfall frequency bands. The spatial variability of the metrics are similar but maximum CC >0.8 and least RMSE <10mm was recorded for GNRT over Central India. Additionally, all SPPs underestimate the precipitation amount in the Northeast and overestimate the same in Southern India. The precipitation detection score is good except in the Himalayan foothills and the Southern coast. All the SPPs are able to represent well the CDF for the precipitation intensities. Among all SPPs, the elevation has the least impact on GNRT, however significant elevation error has been found for CMROPH. At first glance, the gauge adjusted GNRT and IMERG_F perform better than the other SPPs. The results of the study can be utilized for the disaster risk and hydrological modelling and irrigation system analysis in the rain gauge data poor regions of the country.