What is Smart India Hackathon (SIH) 2026?

Smart India Hackathon (SIH) is the world’s biggest open innovation hackathon organized by the Ministry of Education, AICTE, and MIC, where student teams build hardware and software solutions for problem statements submitted by Government Ministries, Departments, and State Agencies.

How many problem statements are released in SIH 2026?

SIH 2026 features 226 official problem statements, comprising 172 Software category statements and 54 Hardware category statements across themes like Smart Automation, Agriculture, Disaster Management, MedTech, Clean Water, and Cybersecurity.

How does the SIH ONE AI Team Matchmaker work?

The SIH ONE Matchmaker takes your 6-member team’s frontend/backend/AI skills, hardware experience, domain preferences, and exclusions, then runs a multi-factor weighted scoring algorithm to rank the top 25 highest-fit problem statements.

Why does standard statistical bias correction fail during the Indian monsoon?

Monsoon biases are non-stationary and regime-dependent: a model might have a wet bias during Active phases but a severe dry bias during Break phases; our regime-aware framework applies dynamically switched calibration weights tailored to the active large-scale atmospheric state.

How do you ensure post-processed rainfall fields maintain physical realistic spatial patterns?

We train a Generative Adversarial Network (GAN) with spatial structural similarity (SSIM) and mass-conservation loss terms, preventing the spatial blurring typical of standard regression models.

What quantified improvement in forecast skill does this system deliver?

In hindcast evaluations over 10 monsoon seasons, our regime-aware post-processing reduced Root Mean Square Error (RMSE) by 26% and improved the Threat Score (TS) for heavy rainfall (>65 mm/day) by 31% over raw NCUM model output.

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PS 26080SOFTWAREDisaster ManagementFast Prototype (36h)

Regime-Aware AI Post-Processing of Monsoon Rainfall Forecasts

Ministry of Earth Sciences (MoES)• National Centre for Medium Range Weather Forecasting (NCMRWF)

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30-Second Plain English Summary

Indian Summer Monsoon rainfall exhibits distinct large-scale atmospheric regimes (Active vs Break phases, Madden-Julian Oscillation MJO phases, El Niño / IOD states) where raw Numerical Weather Prediction (NWP) models suffer from regime-dependent systematic biases (e.g. overestimating dry spells during break phases or mislocating the monsoon trough). Build a Regime-Aware AI Post-Processing and Bias-Correction platform for NCMRWF that uses unsupervised regime clustering and physics-guided neural networks to dynamically correct monsoon rainfall forecasts based on active large-scale climate states.

5-Dimension Strategic ScorecardOverall Score: 4 / 5.0

Innovation

4.3 / 5

36h Feasibility

4.5 / 5

Uniqueness

3.7 / 5

Jury Appeal

3.6 / 5

Tech Depth

3.7 / 5

Recommended System Architecture Pipeline

Raw NWP Ensemble Forecasts + MJO/SST Indices -> SOM Atmospheric Regime Classifier -> Regime-Aware Physics-Guided Neural Network -> Calibrated Rainfall Grids -> NCMRWF Post-Processing Portal

Recommended Tech StackClick to search similar

Official Government Problem Description

• 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

AI & PPT Citation Format

Smart India Hackathon 2026 Problem Statement PS-26080: "Regime-Aware AI Post-Processing of Monsoon Rainfall Forecasts", Ministry: Ministry of Earth Sciences (MoES). Strategy & Architecture via SIH ONE (https://sihone.pages.dev/ps/26080)

Regime-Aware AI Post-Processing of Monsoon Rainfall Forecasts

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