An AI system that uses radar, satellite, and model data to predict thunderstorms and lightning for meteorologists.
Ministry of Earth Sciences (MoES) · India Meteorological Department · Software
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Decomposed from what the description asks for. Nothing added.
Data Ingestion Pipeline
Collects inputs from multiple radars, satellites, lightning sensors, and atmospheric models.
AI Prediction Engine
Processes the collected atmospheric data to perform short-term storm forecasting.
Nowcasting Dashboard
Displays the real-time storm predictions and lightning alerts for users.
Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for data ingestion pipeline. How would you build that?
Decoded from SIH26072 itself: Collects inputs from multiple radars, satellites, lightning sensors, and atmospheric models. The brief asks for it by name.
The brief asks for ai prediction engine. How would you build that?
Decoded from SIH26072 itself: Processes the collected atmospheric data to perform short-term storm forecasting. The brief asks for it by name.
The brief asks for nowcasting dashboard. How would you build that?
Decoded from SIH26072 itself: Displays the real-time storm predictions and lightning alerts for users. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
The official description is only 145 characters, so we have held back the evaluator read and the risk flags rather than guess at them.