A web dashboard that classifies weather regimes and corrects raw rainfall forecasts to provide improved district and grid-level predictions.
Ministry of Earth Sciences (MoES) · National Centre for Medium Range Weather Forecasting (NCMRWF) · Software
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Decomposed from what the description asks for. Nothing added.
Weather Regime Classifier
An AI model that classifies prevailing weather regimes such as active monsoon, break monsoon, and depressions.
Bias Correction Engine
A post-processing module that applies suitable corrections to raw rainfall forecasts based on the identified regime.
Heavy Rainfall Probability
A component that calculates the probability of rainfall exceeding operational thresholds.
District-Level Product
A user-friendly table or map displaying the final corrected rainfall forecasts.
Verification Module
A reporting tool that compares forecast skill using metrics like RMSE, ETS, CSI, POD, FAR, and FSS.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The jury will check whether your system correctly identifies different weather regimes, improves raw rainfall forecasts, computes heavy rainfall probabilities, and provides a verification report using specified skill scores like RMSE, ETS, CSI, POD, FAR, and FSS.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.
Raw NWP forecast datasets required for training and testing?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact operational rainfall thresholds for heavy and very heavy events?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Geographic boundaries or grid resolution specifications?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
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.
Each one is quoted from a gap in the brief, not a guess about your team.
Needs data you may not get
The problem requires working with raw NWP rainfall forecasts, which are typically proprietary meteorological datasets not freely accessible to students.
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.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
The brief asks for weather regime classifier. How would you build that?
Decoded from SIH26080 itself — An AI model that classifies prevailing weather regimes such as active monsoon, break monsoon, and depressions. The brief asks for it by name.
The brief asks for bias correction engine. How would you build that?
Decoded from SIH26080 itself — A post-processing module that applies suitable corrections to raw rainfall forecasts based on the identified regime. The brief asks for it by name.
The brief asks for heavy rainfall probability. How would you build that?
Decoded from SIH26080 itself — A component that calculates the probability of rainfall exceeding operational thresholds. The brief asks for it by name.
The brief asks for district-level product. How would you build that?
Decoded from SIH26080 itself — A user-friendly table or map displaying the final corrected rainfall forecasts. The brief asks for it by name.
The brief asks for verification module. How would you build that?
Decoded from SIH26080 itself — A reporting tool that compares forecast skill using metrics like RMSE, ETS, CSI, POD, FAR, and FSS. 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.
Show me this working: the jury will check whether your system correctly identifies different weather regimes, improves raw rainfall forecasts, computes heavy rainfall probabilities, and provides a verification report using specified skill scores like RMSE, ETS, CSI, POD, FAR, and FSS.
This is the evaluator read for your problem statement, decoded from the brief's own wording.