Evaluation of bias correction methods for multi-satellite rainfall estimation products

Abstract

Abstract Rainfall data from satellites provides hydrological studies with special temporal and spatial advantages. However, recognising the biases in satellite data is critical, so robust validation and correction methods using ground-based observational data are necessary. This research seeks to correct and validate multi-satellite rainfall data (TRMM, GPM-IMERG, and GSMaP) in order to enable hydrological applications. The corrective methods include linear scaling (LS), empirical quantile mapping (EQM), and local intensity scaling (LOCI). In validation, three statistical metrics are employed: Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Relative Bias (RB). Assessing ten years of monthly data from the Kuranji watershed, LS and EQM emerged as optimal bias correction algorithms for all satellites, with LOCI outperforming TRMM and GSMaP. Corrected monthly rainfall patterns using LS and EQM closely correlate with observed data, substantially reducing discrepancies between field records and satellite-derived rainfall data. This enhances the usability of satellite data for in-depth hydrological studies.