The Rann of Kutch is a difficult forecasting environment: sparse observation points, intense heat, salt flats, seasonal inundation, coastal influence, dust, and rapidly changing wind conditions all interact across a large area. A useful short-term system must do more than produce a single temperature number. It should forecast variables such as rainfall, wind, humidity, visibility, and heat stress at specific locations and lead times—and communicate how confident each prediction is.
Transformers are a strong candidate for this task because attention mechanisms can connect observations separated by many time steps and combine data from multiple stations, satellite pixels, and numerical weather forecasts. They are not automatically better than physical models or simpler machine-learning baselines, however. Their value comes from careful data design, realistic validation, and deployment around local operational needs.
Define the forecasting task first
Start with a precise use case rather than training a generic “weather model.” Specify:
- Forecast horizon: for example, 1, 3, 6, 12, 24, or 72 hours ahead.
- Update cycle: hourly, three-hourly, or whenever new observations arrive.
- Target variables: rainfall occurrence and intensity, maximum temperature, wind speed and direction, relative humidity, visibility, or a heat index.
- Spatial unit: a weather station, village, road segment, industrial site, tourist route, or grid cell.
- Decision threshold: such as rainfall above 10 mm, wind above 40 km/h, or visibility below a safety limit.
For many projects, separate models or output heads for continuous and event forecasts work better than one undifferentiated target. A district administration may need a probability of heavy rain, while a renewable-energy operator may need wind-speed quantiles for the next six hours.
Assemble a local, time-aligned dataset
The model is only as reliable as the time and location alignment of its inputs. Build a historical table with consistent timestamps, units, station identifiers, and quality flags. Potential sources include:
- IMD observations and warnings, subject to access conditions and licensing.
- Automatic weather stations operated by government departments, universities, ports, utilities, or research programmes.
- Satellite products for cloud, land-surface temperature, rainfall estimates, and atmospheric moisture.
- Radar or lightning observations where coverage is available.
- Reanalysis and numerical weather prediction outputs for broader atmospheric context.
- Elevation, distance to the coast, land cover, salt-pan extent, and seasonal water masks.
Use UTC internally or store an explicit timezone offset; do not mix local time and UTC silently. Resample measurements to a documented interval, preserve missingness indicators, and remove impossible values without deleting legitimate extremes. A missing sensor reading is information about data quality, not a zero reading.
The spatial problem is especially important in Kutch. A station near the coast, a salt flat, and an inland settlement may experience materially different winds and humidity. Add station coordinates and static geographic embeddings, or train a regional model that explicitly represents station-to-station relationships. For satellite inputs, aggregate pixels carefully and retain the original acquisition time.
Projects that use satellite data for agricultural decisions can also learn from a satellite-based yield prediction workflow for Indian insurance providers, particularly its approach to spatial features and leakage control.
Prepare inputs for a transformer
A practical first version can use a rolling context window of the previous 24–168 hours. Each time step may contain:
- Temperature, dew point, pressure, humidity, rainfall, wind components, and visibility.
- Satellite or radar summaries.
- Numerical weather prediction variables.
- Calendar and seasonal signals encoded as sine and cosine values.
- Missingness flags and the age of the latest valid observation.
Represent wind as eastward and northward components rather than raw direction, because 359° and 1° are close physically but far apart numerically. Scale continuous variables using statistics from the training period only. Do not let future observations enter interpolation, normalization, feature selection, or target construction.
For sparse or irregular data, consider a time-aware transformer that receives elapsed-time embeddings and masks. For multiple stations, add location embeddings and use attention across both time and space. A compact encoder-only model is usually a sensible starting point for direct multi-horizon prediction; an encoder-decoder architecture is useful when generating a sequence of forecasts, but it introduces more opportunities for error accumulation.
Train against realistic baselines
Before tuning a large model, establish benchmarks:
- Persistence: the latest observation remains unchanged.
- Seasonal or hourly climatology.
- Linear regression or gradient-boosted trees using lagged features.
- A recurrent or temporal convolutional model.
- Numerical weather prediction guidance, where available.
Use chronological splits, such as earlier years for training, a later period for validation, and the most recent monsoon and dry-season periods for testing. Randomly shuffling time steps can produce impressive but unusable scores through leakage. Hold out entire stations or unusual weather episodes as an additional generalisation test.
Use MAE and RMSE for continuous variables, but do not stop there. For rainfall and hazards, measure precision, recall, F1, threat score, Brier score, calibration, and lead-time performance. A probabilistic model should be rewarded for calibrated intervals or quantiles, not only for a low average error. Quantile loss can produce forecasts such as the 10th, 50th, and 90th percentile wind speed.
Handle extremes and uncertainty
Rare heavy rainfall, dust events, and extreme heat are precisely the cases that matter most and the cases a model may underrepresent. Use event-weighted losses, balanced sampling, or a two-stage event-plus-intensity design—but report results on the original distribution as well. Never manufacture extreme examples without validating that the synthetic data preserve local physics.
Pair every operational forecast with uncertainty. Use ensembles, Monte Carlo dropout, quantile heads, or conformal prediction calibrated on a recent validation window. Monitor whether a 90% prediction interval actually contains the observed value roughly 90% of the time. During sensor outages, novel weather regimes, or shifts in satellite coverage, the system should lower confidence and escalate to human review rather than present false precision.
Deploy for field use
A useful deployment may run on a local server, cloud instance, or edge device near a station. Export a compact model, batch incoming observations, and define a fallback forecast when inputs are delayed. Store the exact model version, feature schema, input timestamp, and output probabilities for every prediction.
Create dashboards and alerts around decisions, not model internals. For example, show the probability of heavy rain over the next six hours, expected wind ranges, data freshness, and the reason a forecast was downgraded. Establish thresholds with district officials, emergency teams, farmers, transport operators, and local communities. Warnings should be available in relevant local languages and should clearly distinguish a model estimate from an official warning.
Monitor data drift, sensor failure, calibration, seasonal performance, and subgroup performance by station and geography. Retrain on a schedule only after checking whether new data are correctly labelled and quality-controlled. A model that performs well at Bhuj may not transfer to remote salt-flat stations without local adaptation.
For an implementation pattern involving pretrained models, compare the local pipeline with weather prediction using Hugging Face models in Bhubaneswar and weather prediction in Guwahati. The climates differ, but the lessons on reproducible inference, model packaging, and evaluation transfer well. If inference must run on low-power hardware, optimising vision transformers for edge deployment offers relevant compression and latency principles, even when the final model is multimodal.
A sensible 2026 project plan
Begin with one target—such as six-hour rainfall probability—at a small set of stations. Build a clean data catalogue, benchmark persistence and boosted trees, then train a compact transformer with strict chronological evaluation. Add satellite and NWP features only after the station-only baseline is understood. Conduct a monsoon and extreme-event review with domain experts before expanding to more variables or locations.
The strongest system is not necessarily the largest one. In the Rann of Kutch, trustworthy timestamps, local calibration, uncertainty estimates, resilient operations, and clear warnings will matter as much as attention layers. Teams developing such applied systems can also explore AI Grants India for support, partnerships, and funding pathways.