Convective scale nowcasting for Thunderstorms, Hail & Cloudbursts (06 hr)
“Sudden extreme weather events like thunderstorms, hail, and cloudbursts develop rapidly within minutes and cause widespread devastation in India. Traditional weather computers are too slow to predict these hyper-localized events in real-time. You need to build a real-time tracking and early-warning system that predicts these storms 0 to 6 hours in advance at a high resolution.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
A real-time Multi-Source Data Fusion and Nowcasting Software Platform featuring an ingestion pipeline for radars/satellites and an interactive GIS dashboard.
WHO IS IT FOR?
Ministry of Earth Sciences (MoES), National Centre for Medium Range Weather Forecasting (NCMRWF), disaster response agencies (NDRF/SDRF), district administrations, aviation authorities, and farmers.
WHY DOES IT EXIST?
Conventional Numerical Weather Prediction (NWP) models use coarse grid resolutions and heavy physics calculations, making them computationally too slow to capture rapid, micro-scale convective initiation (like cloudbursts forming over specific valleys).
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Doppler Weather Radar (DWR) reflectivity/velocity fields, INSAT-3D/3DR geostationary satellite thermal/infrared bands, and ground-based lightning network feeds.
Real-time data ingestion, spatio-temporal data fusion, ML-based convective initiation detection, and optical flow or deep learning-based extrapolation for 0-6 hour tracking.
An interactive GIS-mapped hazard dashboard with 1-3 km resolution grid zones, severity indices (hail/lightning probability), storm arrival countdown clocks, and automated alert APIs for SMS/push notifications.
CONSTRAINTS
- Real-time processing latency must be under a few minutes despite handling heavy spatial raster datasets.
- Must handle missing or asynchronous data streams from radars and satellites gracefully.
- Must operate efficiently on limited cloud resources during the 36-hour hackathon implementation.
JURY METRICS
- Prediction accuracy (Probability of Detection vs. False Alarm Rate)
- Inference latency for generating the 0-6 hour nowcast
- UI/UX clarity of the GIS dashboard and countdown timers for emergency operators
- Robustness of multi-source data fusion logic
CORE BOTTLENECK
Synchronizing and fusing multi-modal, asynchronous geo-spatial data streams (radar raster data with satellite infrared bands and point-source lightning data) into a unified grid in real-time.
Why You Should Choose This PS (Pros)
- Extremely high national social impact addressing loss of life and property from cloudbursts and lightning.
- Clear deliverables: An ingestion pipeline, a processing/prediction backend, and a real-time GIS visualization dashboard.
- Allows strong integration of modern AI/ML (computer vision optical flow, spatiotemporal deep learning) combined with GIS visualization.
Challenges & Risks to Watch (Cons)
- Raw radar and satellite datasets are massive (NetCDF/GRIB formats) and hard to process locally during a short hackathon.
- Crowded domain if teams rely purely on basic weather APIs instead of actual data fusion mechanisms.
- Complex physics and meteorology domain knowledge required to make predictions look scientifically plausible.
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
Convective storms, such as severe thunderstorms, hail, downburst winds, and cloudbursts are among India’s deadliest natural hazards, especially during the pre-monsoon and monsoon seasons.Despite advancements in Numerical Weather Prediction (NWP) models, traditional systems often fail to accurately capture these mesoscale extreme weather events.The primary limitation stems from spatial and temporal constraints: these violent storms develop rapidly within a window of minutes and occur at localized scales that slip through coarse grid resolutions. Current early warning infrastructures struggle to provide high-resolution, short-term forecasts (0–6 hours), leaving local administrations, aviation sectors, and rural farming communities vulnerable to sudden, devastating impacts.
The challenge is to build a real-time, convective-scale Nowcasting System (0–6 hour lead time) operating at a hyper-local 1–3 km spatial resolution.
Because traditional physics-based models are too computationally slow to simulate these rapid developments in real-time, participants must design a system rooted in Multi-Source Data Fusion architectures. The core objective is to ingest high-frequency, heterogeneous meteorological streams, automatically detect early convective initiation, and dynamically forecast severe storm parameters (including lightning strike density, hail probability, downburst velocity, and cloudburst thresholds).Design a robust, real-time ingestion engine that fuses data streams from multiple sources: Doppler Weather Radars (DWR