Enhancing accuracy of IMERG-E satellite rainfall products for Mahanadi River Basin using bias-correction methods

Satellite precipitation products, such as early runs of the Integrated Multi-satellitE Retrievals for GPM (IMERG-E), provide comprehensive spatial and temporal coverage, offering significant improvements in rainfall monitoring in remote regions. These products are essential for enhancing the accuracy of flood forecasting. However, compared to the ground-based observations, IMERG data not free from biases. In this study, we apply Cumulative Distribution Function (CDF) matching combined with Support Vector Machines (SVM) to correct biases over the Mahanadi River Basin in eastern India. The Kernel-based SVM is used to capture the nonlinear relationships between the 0.1º× 0.5h IMERG-E and 0.25º×1-day IMD gridded observations. Subsequently, this method is compared with the traditional CDF bias-correction techniques, aiming to improve the IMERG-E precipitation estimates. The corrected IMERG estimates can contribute to more reliable flood forecasting, supporting informed decision-making processes in flood risk management.