A dashboard and script system that combines multiple weather models dynamically, letting forecasters view blended weather predictions and model reliability weights.
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.
Data Ingestion Pipeline
Collects forecasts from physical NWP models, ensembles, and AI models.
Adaptive Weight Engine
Calculates and assigns dynamic weights based on region, season, lead time, and history.
Forecast Blending Module
Combines multiple sources into a single optimized prediction for rainfall, temperature, and wind.
Extreme Weather Indicator
Generates specific alerts for heavy rainfall, heat waves, and high winds.
Operational Dashboard
Provides an automated workflow interface and visualizes model weight maps for users.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The jury will check if your system can successfully blend multiple forecast sources using adaptive weights and output reliable extreme weather indicators.
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.
What specific data formats the input models use?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What historical skill datasets are available for training?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What quantitative improvement target counts as success?
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.
No measurable target
The description asks for improved forecast skill without defining any specific percentage or numerical target.
Needs data you may not get
Building a real hybrid NWP blending system requires access to professional meteorological model outputs and historical weather data.
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 data ingestion pipeline. How would you build that?
Decoded from SIH26081 itself — Collects forecasts from physical NWP models, ensembles, and AI models. The brief asks for it by name.
The brief asks for adaptive weight engine. How would you build that?
Decoded from SIH26081 itself — Calculates and assigns dynamic weights based on region, season, lead time, and history. The brief asks for it by name.
The brief asks for forecast blending module. How would you build that?
Decoded from SIH26081 itself — Combines multiple sources into a single optimized prediction for rainfall, temperature, and wind. The brief asks for it by name.
The brief asks for extreme weather indicator. How would you build that?
Decoded from SIH26081 itself — Generates specific alerts for heavy rainfall, heat waves, and high winds. The brief asks for it by name.
The brief asks for operational dashboard. How would you build that?
Decoded from SIH26081 itself — Provides an automated workflow interface and visualizes model weight maps 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.
Show me this working: the jury will check if your system can successfully blend multiple forecast sources using adaptive weights and output reliable extreme weather indicators.
This is the evaluator read for your problem statement, decoded from the brief's own wording.