Assessment of Satellite-based Precipitation Products in Monthly, Seasonal, and Annual Time-Scale over Iran

Understanding the spatial and temporal distribution of precipitation globally is advantageous for advancing climate knowledge and improving weather and climate forecasting models. Despite the complexity of determining precipitation distribution, numerous satellite-based precipitation products (SPPs) have been developed in recent decades to estimate precipitation with sufficient coverage and accuracy. This study evaluates the performance of four SPPs, namely Integrated Multi-satellite Retrievals for GPM (IMERG-FRV6), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Tropical Rainfall Measuring Mission (TRMM-3B43V7), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record (PERSIANN-CDR) on monthly, seasonal, and annual scales in Iran, and aimed to enhance the accuracy of the evaluation by extending the statistical period and selecting evaluation indicators based on error, efficiency, and correlation. Measured rainfall data from 81 synoptic stations across Iran from 2008 to 2019 were used for this evaluation. To accurately assess the selected SPPs, several statistical indices including Correlation Coefficient (CC), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), and Bias were calculated and analyzed at all synoptic stations. The results demonstrate that MSWEP has a significant advantage over other products at all time scales. The performance of all four products in areas with high monthly rainfall is associated with more errors. PERSIANN-CDR exhibited the highest monthly RMSE, while TRMM-3B43V7 performed better in drier regions with low to moderate precipitation. MSWEP showed the closest average precipitation to observational data in spring, summer, and winter, while IMERG-FRV6 overestimated precipitation in all seasons.

Graphical Abstract