• Problem Statement Rainfall forecast errors over India vary with weather regimes such as active monsoon, break monsoon, monsoon lows/depressions, orographic rainfall, coastal rainfall and western disturbances. A single bias-correction method may not work equally well in all situations.The challenge is to build an AI/ML-based rainfall post-processing system that first identifies the prevailing weather regime and then applies suitable correction to the raw NWP rainfall forecast.The aim is to improve district/grid-level rainfall forecasts, especially for heavy and very heavy rainfall events. • Expected Outcome Expected Outcome - Description:
Weather regime classifier - Classification of active, break, depression,coastal/orographic rainfall regimes Bias-corrected rainfall forecast - Improved rainfall forecast compared to raw NWP output Heavy rainfall probability - Probability of rainfall exceeding operational thresholds District-level rainfall product - User-friendly rainfall forecast table/map Verification report - Skill comparison using RMSE, ETS, CSI, POD, FAR and FSS