Regime-Aware AI Post-Processing of Monsoon Rainfall Forecasts
Ministry of Earth Sciences (MoES) · Disaster Management · Software
Free data, published regime definitions and verification metrics named by the sponsor make this objectively assessable in a way almost nothing else here is — just watch the sample size once you split by regime, because that is how this method fails.
What it actually is
Rainfall forecasts over India are wrong in different ways depending on what the monsoon is doing — a correction that works during an active spell fails during a break or when a depression is crossing. The ask is a system that first works out which regime the atmosphere is in and then applies the correction that suits it, especially for the heavy rainfall days that matter most.
What to build
A two-stage post-processor: a classifier assigning each forecast day to one of the regimes the statement names — active monsoon, break, monsoon low or depression, orographic, coastal, or western disturbance — using published circulation and rainfall criteria rather than an invented taxonomy; and a regime-conditional correction stage applying a separately fitted bias correction per regime to the raw model rainfall, producing a corrected grid-and-district forecast together with a probability of exceeding operational heavy and very heavy thresholds, all evaluated with the exact verification suite the statement names so improvement over the raw forecast is reported in the metrics an operational centre actually uses rather than in a metric you chose.
Smallest thing that wins the room
Show a verification table for a monsoon season where your corrected forecast beats the raw model on the heavy rainfall categorical scores, then break it out by regime to show the correction helping most during depressions where the raw model is worst.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 41% of the 226 · #133 of 226 by expected field
A normal-sized field. Your idea has to be good, not miraculous.
Why: central ministry statements sat below the average.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
4/5Free real data, published regime criteria, named verification metrics and a measurable improvement over an honest baseline make this one of the most rigorously assessable statements in the block, and rainfall post-processing is unglamorous enough that the field will be thin.
Feasibility
4/5Everything needed is free and real — global forecast archives provide the raw rainfall, gridded observed rainfall over India provides verification, and the monsoon regime definitions have published objective criteria you can implement rather than invent.
Innovation scope
4/5The statement identifies the idea — that correction should be conditioned on regime — and prescribes nothing about how you classify regimes, how you correct within them, or how you handle the transition days that belong to neither, which is where the interesting problem lives.
Clarity
4/5Concise but properly specified: it names the regimes, the five deliverables and, unusually, the exact verification metrics including equitable threat score, critical success index, hit and false alarm rates and the fractions skill score, so success is defined externally rather than by you.
Effort
HeavyAssembling matched forecast and observation archives across seasons, implementing objective regime classification, fitting and validating per-regime corrections and building a proper verification harness across six metrics is four workstreams with the archive assembly the slowest.
Demo-ability
MediumThe verification table is the demo and it is genuinely convincing to a meteorologist because it uses their own metrics, but it is numbers rather than a visual and a general judge will need the regime breakdown explained before the result lands.
In its favour
- Green flag: The statement names the verification metrics itself, which means your evaluation protocol is fixed by the sponsor and your improvement claim is directly comparable to operational practice
- Green flag: Active and break monsoon phases have published objective definitions, so your regime classifier can be validated against an accepted criterion rather than defended as a design choice
- Green flag: Gridded observed rainfall over India is freely available and is exactly the verification data an operational centre uses, so your skill numbers mean the same thing to the panel as their own
- Green flag: Beating raw model output on heavy rainfall categorical scores is a hard, honest and unambiguous claim — very few statements let you say something that concrete
Against it
- Red flag: Heavy rainfall events are rare, so the categorical scores that matter most are computed on small samples and will be noisy — report confidence intervals or a sceptical judge will rightly discount a single season's improvement
- Red flag: Splitting the data by regime shrinks the sample available to fit each correction, which is exactly how regime-aware methods overfit and end up worse than a single global correction
- Red flag: Transition days belong to no regime cleanly and are often when forecasts are worst, so a hard classifier will mishandle precisely the cases the system exists for — consider soft weighting across regimes
- Red flag: The product is a verification table, which is dry, so plan how to make a regime-stratified skill improvement legible to a panel that is not made entirely of meteorologists
What you will be writing
- objective active-break monsoon regime classification
- quantile mapping and distributional bias correction
- IMD gridded rainfall verification
- ETS, CSI, POD, FAR and FSS verification harness
- threshold exceedance probability calibration
- regime-conditional model fitting
- Monsoon meteorology
- Forecast post-processing
- Statistical verification
Prior art to read before you start
regime-conditional bias correction · heavy rainfall threshold probability forecasting · categorical skill verification against raw NWP
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.