A software system that uses graph neural networks and diffusion models to track extreme weather anomalies from global forecast data and generate high-resolution, local 5km warnings for meteorologists.
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.
Spatio-Temporal GNN Module
Processes global forecast streams on an icosahedral mesh to isolate anomalies and output 4D bounding boxes.
Generative Diffusion Module
Takes the macroscale anomaly slice and downscales it from a 12 km grid to a 5 km grid while preserving extreme amplitudes.
Physics-Informed Loss Function
Embeds fluid dynamics and thermodynamic conservation laws to penalize physically impossible weather states.
Visualization Dashboard
Translates mathematical centroid arrays into clean geographic visual layers for weather data.
Alerting REST API
Programmatically drops pinpoint coordinates and triggers categorized spatial alerts across a 5 km radius.
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 GNN correctly handles the spherical mesh without geographic distortions and if your diffusion model successfully preserves high-amplitude peaks during downscaling without smoothing them out. They will test your custom physics-guided loss functions and your ability to process multigigabyte NetCDF or GRIB2 data arrays.
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.
Exact computational resource limits for the 36-hour final demo?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific cloud deployment infrastructure requirements?
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.
The brief asks for spatio-temporal gnn module. How would you build that?
Decoded from SIH26078 itself — Processes global forecast streams on an icosahedral mesh to isolate anomalies and output 4D bounding boxes. The brief asks for it by name.
Each one is quoted from a gap in the brief, not a guess about your team.
Needs data you may not get
The description requires historical NCUM and NEPS-G global ensemble datasets and ERA5 reanalysis data, which are massive and difficult for students to easily obtain.
No measurable target
The text lists expected deliverables but gives no quantitative accuracy percentage or precise latency benchmark that the models must hit to pass.
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 generative diffusion module. How would you build that?
Decoded from SIH26078 itself — Takes the macroscale anomaly slice and downscales it from a 12 km grid to a 5 km grid while preserving extreme amplitudes. The brief asks for it by name.
The brief asks for physics-informed loss function. How would you build that?
Decoded from SIH26078 itself — Embeds fluid dynamics and thermodynamic conservation laws to penalize physically impossible weather states. The brief asks for it by name.
The brief asks for visualization dashboard. How would you build that?
Decoded from SIH26078 itself — Translates mathematical centroid arrays into clean geographic visual layers for weather data. The brief asks for it by name.
The brief asks for alerting rest api. How would you build that?
Decoded from SIH26078 itself — Programmatically drops pinpoint coordinates and triggers categorized spatial alerts across a 5 km radius. 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 GNN correctly handles the spherical mesh without geographic distortions and if your diffusion model successfully preserves high-amplitude peaks during downscaling without smoothing them out.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Show me this working: they will test your custom physics-guided loss functions and your ability to process multigigabyte NetCDF or GRIB2 data arrays.
This is the evaluator read for your problem statement, decoded from the brief's own wording.