AI-Based Forecast Bust Detection for Medium-Range Weather Forecasts
“Weather models often fail during extreme events like cyclones or heatwaves, leading to wrong predictions. This project asks you to build an AI system that flags when a weather forecast is likely to be wrong so that officials can be extra cautious.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
A predictive analytics dashboard that monitors Numerical Weather Prediction (NWP) outputs and assigns a 'confidence score' to forecast regions, highlighting potential 'bust' zones.
WHO IS IT FOR?
Primary: NCMRWF meteorologists and disaster management authorities. Beneficiaries: Emergency response teams, farmers, and the general public in high-risk zones.
WHY DOES IT EXIST?
Current weather models are deterministic and don't always communicate their own uncertainty. When a model fails during a critical event, it leads to poor decision-making, loss of life, and economic damage.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Historical NWP forecast data, observed weather station data (IMD/NCMRWF), satellite imagery (INSAT/Kalpana), and reanalysis datasets (ERA5).
Anomaly detection between forecast and observation, time-series forecasting of error patterns, and SHAP/LIME-based explainability to identify meteorological drivers of the error.
A heat-mapped confidence dashboard, region-specific bust probability alerts, and an 'Explainability Module' showing why the model flagged a specific area.
CONSTRAINTS
- Must handle massive multi-dimensional spatio-temporal datasets
- Explainability is mandatory (black-box models are not acceptable)
- Must align with standard meteorological grid formats (NetCDF/GRIB)
- Low latency for operational decision-making
JURY METRICS
- Brier Score for probability calibration
- F1-Score for bust detection accuracy
- Mean Absolute Error (MAE) reduction
- Explainability interpretability score
- Dashboard UI/UX responsiveness
CORE BOTTLENECK
The 'Data Mismatch' problem: Aligning gridded NWP forecast data with sparse, point-based ground station observations while maintaining spatial consistency.
Why You Should Choose This PS (Pros)
- High national priority for MoES
- Clear, quantifiable success metrics
- Strong potential for real-world deployment in government operations
- Excellent opportunity to showcase advanced AI/ML and data visualization skills
Challenges & Risks to Watch (Cons)
- High domain knowledge requirement (Meteorology)
- Complexity of handling large-scale scientific data formats like NetCDF
- Difficulty in obtaining high-quality historical 'bust' labels
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
Expected Outcome