SoftwareDisaster ManagementSIH26080
Regime-Aware AI Post-Processing of Monsoon Rainfall Forecasts
Ministry of Earth Sciences (MoES)
- Organisation
- Ministry of Earth Sciences (MoES)
- Department
- National Centre for Medium Range Weather Forecasting (NCMRWF)
- Category
- Software
- Theme
- Disaster Management
- Submission deadline
- 20 September 20262026-09-20
- Ideas submitted
- 0/500
- Serial number
- 80
- Data captured on
- 2026-08-23
- Dataset link
- No dataset link published
- Contact info
- No contact published
- Youtube link
- No video published
Problem brief
- 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