Weather forecasting for Rajkot is not a generic time-series exercise. The city’s semi-arid climate, intense summer heat, monsoon bursts, dust, coastal influence from Gujarat, and rapidly changing urban conditions create forecasting problems that require local data and careful validation. Hugging Face can provide reusable time-series architectures and tooling, but it does not make a forecast accurate by itself.
A useful system combines reliable observations, numerical weather prediction (NWP) inputs, a model suited to the forecast horizon, and operational monitoring. The goal should be a calibrated forecast that communicates uncertainty—not a single number that appears precise but fails during extreme events.
What Hugging Face contributes
Hugging Face is best understood as a model and deployment ecosystem rather than a single weather-prediction product. Its Hub, Transformers ecosystem, datasets, evaluation tools, and inference options can help teams prototype and ship forecasting models.
For Rajkot weather prediction using Hugging Face models, consider three model families:
- Transformer time-series models: Models such as Temporal Fusion Transformer-style implementations, PatchTST, Informer variants, and other encoder architectures can learn relationships across lagged weather variables and forecast horizons.
- Foundation forecasting models: Pre-trained models such as Chronos-style forecasters can offer a starting point when local labelled data is limited. They still require regional testing and calibration.
- Custom PyTorch models: A compact transformer, LSTM, or temporal convolutional network may outperform a large pre-trained model when the dataset is small, noisy, or narrowly focused on one station.
Hugging Face is particularly useful for reproducible experiments, model versioning, and sharing checkpoints. Teams already working on deploying deep learning models on GKE can also use the platform as part of a broader training and serving pipeline.
Define the forecast before collecting data
Start by specifying the decision the forecast will support. A 6-hour rainfall alert has different data and evaluation requirements from a 7-day temperature outlook.
Useful targets for Rajkot include:
- Temperature at 1, 6, 24, and 72 hours ahead
- Relative humidity and wind speed
- Rainfall accumulation over 1, 3, 6, and 24 hours
- Probability of rain above thresholds such as 1 mm, 10 mm, or 25 mm
- Heat-stress indicators derived from temperature and humidity
- Extreme-event flags for heavy rain, high wind, or dangerous heat
Use a multi-horizon, multi-variable design where possible. Predicting temperature, humidity, wind, and precipitation together can help the model learn physical relationships, although rainfall remains harder to forecast than smooth variables such as temperature.
Build a Rajkot-focused dataset
A robust dataset should combine station observations with broader atmospheric context. Potential sources include:
- India Meteorological Department observations and gridded products, subject to access and licensing conditions
- Automatic weather stations near Rajkot and nearby districts
- Satellite-derived cloud, land-surface, and rainfall products
- Reanalysis datasets for historical atmospheric variables
- NWP forecasts as model inputs or a baseline to improve through post-processing
- Elevation, land cover, urbanisation, and distance-to-coast features
Store timestamps in UTC internally, then expose forecasts in India Standard Time (IST). Retain station identifiers, sensor metadata, quality flags, and the original source for every value. Do not silently merge readings from stations with different exposure or instruments.
Important features may include hourly temperature, dew point, pressure, humidity, wind components, rainfall, cloud cover, solar radiation, day-of-year, hour-of-day, and lagged precipitation. Cyclical encodings for hour and season are usually more useful than treating time categories as arbitrary integers.
Preprocess without leaking the future
Weather datasets often contain gaps, sensor resets, duplicated timestamps, unit changes, and suspicious rainfall spikes. Create a data-quality pipeline before model training:
- Convert all measurements to consistent units.
- Flag impossible values rather than automatically deleting them.
- Impute short gaps only when the method is defensible; preserve a missingness indicator.
- Separate observed values from forecast or reanalysis values.
- Fit scalers and imputers on the training period only.
- Resample carefully so rainfall totals and instantaneous measurements are not treated the same way.
The most common forecasting error is temporal leakage. A random train-test split allows future weather regimes to appear in training and produces unrealistic scores. Split chronologically, for example into training, validation, and test periods. Reserve at least one monsoon season and one extreme-heat period for testing. Rolling-origin backtesting is stronger than a single split because it shows whether performance remains stable over time.
Select and fine-tune the model
Establish simple baselines before using a transformer:
- Persistence: the next value equals the latest observation
- Seasonal persistence: the value resembles the same hour or day in a recent cycle
- Linear regression or gradient-boosted trees
- A calibrated NWP forecast
A sophisticated model is valuable only if it beats these baselines consistently. For fine-tuning a Hugging Face time-series model, define the input context length, forecast horizon, feature schema, missing-value policy, and loss function explicitly. Quantile loss is useful when the output must represent uncertainty; classification losses can be added for rainfall or extreme-event probabilities.
Use a smaller model first. Rajkot-specific station data may not justify a large architecture, and a compact model is cheaper to retrain and easier to serve. If using a foundation model, test zero-shot or frozen-feature performance before fine-tuning. Compare it with a locally trained model rather than assuming pre-training transfers well across climates.
Evaluate accuracy and usefulness
Report metrics by variable, horizon, season, and event intensity. Recommended measures include:
- MAE and RMSE for temperature, humidity, and wind
- MAE in millimetres for rainfall totals
- Precision, recall, F1, and threat score for rain-event alerts
- Brier score and reliability diagrams for probabilistic predictions
- Continuous ranked probability score (CRPS) for distributional forecasts
- Calibration and coverage of prediction intervals
A model with a lower average error may still be worse for public safety if it misses heavy-rain events. Publish confusion matrices and performance during monsoon bursts, heatwaves, and sensor outages. Compare against IMD or NWP baselines where legally and technically appropriate.
Deploy an operational forecast service
A practical architecture can run a scheduled pipeline that ingests observations, validates them, generates features, runs inference, and stores forecasts with model version and timestamp. Expose results through an API and a dashboard that shows both the forecast and confidence range.
For cost-sensitive deployments, export a compact model to an optimised runtime and batch forecasts for multiple horizons. Teams considering serverless infrastructure can review guidance on deploying ML models on AWS Lambda in India, while latency-sensitive systems may need a persistent CPU or GPU service.
Monitor:
- Missing and delayed station data
- Feature drift and sensor distribution changes
- Forecast error by horizon and season
- Calibration after major weather regimes
- Inference latency and failed jobs
- Model performance compared with persistence and NWP baselines
Use a fallback forecast when inputs are stale or incomplete. Never present a model output as an official warning unless it has passed the relevant institutional review and communication process.
Common mistakes to avoid
- Training on a single station and claiming city-wide accuracy
- Using random splits for a time-series problem
- Treating rainfall as ordinary continuous regression only
- Ignoring station relocation and instrument changes
- Reporting one aggregate score without seasonal breakdowns
- Deploying without uncertainty estimates or fallback logic
- Assuming a model trained in another climate will transfer directly to Saurashtra
For broader model engineering, the same principles apply to fine-tuning large language models for Sanskrit translation: define the task precisely, control data quality, track experiments, and evaluate on realistic held-out cases. The domain differs, but leakage, drift, and reproducibility remain central concerns.
A practical 2026 build plan
Start with one Rajkot station, hourly observations, and a 24-hour horizon. Build persistence and gradient-boosted baselines, then test a compact Hugging Face time-series model. Add NWP features only after the observation pipeline is stable. Backtest across summer and monsoon periods, quantify uncertainty, and deploy a limited pilot for researchers, agribusinesses, or municipal operations.
If the project needs grant support for data collection, compute, or field validation, AI Grants India can help Indian AI builders identify relevant funding opportunities.