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What this project should deliver

Moradabad weather prediction using Hugging Face models is best treated as a data and forecasting engineering project—not as a matter of selecting a popular transformer and expecting accurate local results. A useful system should define its forecast horizon, target variables, update frequency, uncertainty, and fallback behaviour before training begins.

For Moradabad, a practical first version could predict the next 1–24 hours of temperature, relative humidity, wind speed, and rainfall probability. A second model can produce daily forecasts for the next 3–7 days. These are different tasks: short-horizon forecasts benefit from recent station observations, while longer horizons depend more heavily on regional weather patterns and numerical weather prediction inputs.

The goal is not to replace official forecasts. It is to create a local post-processing and decision-support layer for applications such as crop planning, outdoor operations, heat-risk alerts, logistics, and neighbourhood-level dashboards.

Understand Moradabad’s forecasting problem

Moradabad lies in Uttar Pradesh’s western Gangetic Plain. Its forecast errors are shaped by seasonality, monsoon rainfall, winter fog, heatwaves, convective storms, and rapid changes in humidity and wind. A model trained on a generic global dataset may capture broad patterns but still perform poorly during local extremes.

Design the dataset around the decisions the forecast will support:

• Temperature: minimum, maximum, and hourly temperature in °C.
• Rainfall: accumulated rainfall and probability of measurable rain.
• Humidity: relative humidity or dew-point temperature.
• Wind: speed, direction, and gusts where available.
• Pressure and visibility: useful for fog, storms, and changing systems.
• Calendar features: hour, day, month, monsoon season, and public-event or operational periods when relevant.
• Spatial context: nearby stations, gridded reanalysis, satellite features, or numerical forecast fields.

Use station observations where possible, but record station relocation, sensor changes, missing intervals, and time-zone conventions. Store timestamps in UTC internally and convert to Indian Standard Time for presentation. Never randomly shuffle a time series: that would allow future information to leak into training.

Select data sources and establish a baseline

A credible system should combine at least one local observation source with a broader atmospheric dataset. Depending on access and licensing, useful inputs may include India Meteorological Department observations, airport or automatic weather station data, ERA5-Land or other reanalysis products, satellite precipitation estimates, and operational numerical weather prediction outputs.

Before using a transformer, build simple baselines:

• Persistence: the next value equals the latest observation.
• Seasonal average: forecast from the typical hour and month.
• Moving average or exponential smoothing.
• A tree-based model such as LightGBM or XGBoost with lagged features.

These baselines answer an important question: does the Hugging Face model add value? Report performance separately for summer, monsoon, winter, heavy-rain events, and heat episodes. A sophisticated model that loses to persistence during the target operating window is not ready for deployment.

Choose a Hugging Face forecasting model carefully

Hugging Face provides model repositories, datasets, configuration tools, and the Transformers ecosystem. However, BERT is not a natural starting point for numerical weather forecasting. It was designed for language, and adapting it to weather data adds unnecessary complexity unless there is a specific research reason.

Look for time-series architectures that support numerical sequences and multi-step prediction, such as transformer-based forecasting models available through the Hugging Face ecosystem. Depending on the data shape and task, candidates may include encoder-decoder forecasting models, patch-based temporal models, or models designed for probabilistic prediction. Check each model card for expected input layout, frequency, context length, missing-value handling, scaling, and licensing.

Selection criteria should include:

• Forecast horizon: one hour, one day, or several days.
• Input coverage: univariate observations versus multiple weather variables and stations.
• Compute budget: CPU inference, a single GPU, or managed infrastructure.
• Probabilistic output: prediction intervals are more useful than a single number for rain and extreme events.
• Fine-tuning support: confirm that the model can be adapted to your frequency and variables.
• Reproducibility: pin package versions, model revisions, and preprocessing code.

If the project later needs image or satellite inputs, keep that work separate from the numerical time-series pipeline. Teams building broader multimodal systems can review approaches in open-source vision-language models for Indian languages, but a language-vision model is not automatically suitable for weather forecasting.

Build the training pipeline

Start with a clean, reproducible table or tensor. For every forecast origin, create a context window containing the previous observations and known future features. Add lag values, rolling means, rolling rainfall totals, and differences in temperature and pressure. Encode wind direction as sine and cosine rather than treating degrees as a linear number.

Handle missingness explicitly. A short sensor gap may be interpolated for selected variables, but rainfall should not be silently filled with a smooth average. Add missingness indicators so the model knows when an input was unavailable. Scale continuous variables using statistics from the training period only.

Use chronological splits, for example:

• Training: the earliest historical period.
• Validation: the next block of time for model and hyperparameter choices.
• Test: the most recent untouched block.
• Stress test: selected monsoon, fog, heatwave, and heavy-rain episodes.

Fine-tune with early stopping and monitor validation loss by variable. For rainfall, consider a two-stage design: classify whether measurable rain will occur, then estimate accumulation conditional on rain. This often handles the many zero-rain observations better than a single regression loss.

Evaluate accuracy and reliability

Mean absolute error (MAE) is easy to interpret: an MAE of 1.8°C means the average absolute temperature error is 1.8°C. Also report RMSE, which penalises large misses, and classification metrics such as precision, recall, F1, and area under the precision-recall curve for rain events.

Evaluation should answer operational questions:

• How often does the model miss heavy rain?
• Does it systematically underpredict afternoon heat?
• Are intervals calibrated—for example, does a stated 80% interval contain the outcome about 80% of the time?
• How quickly does accuracy degrade from one hour ahead to 24 hours ahead?
• Does performance change across stations or neighbourhoods?

Compare every result with the baseline and publish sample counts. Averages can hide dangerous failures. For public alerts, prefer calibrated uncertainty and conservative thresholds over false precision.

Deploy responsibly in India

A lightweight inference service can run on a scheduled VM, container, or serverless endpoint. If you need low-cost event-driven deployment, the principles in how to deploy ML models on AWS Lambda in India are relevant, although model size, cold starts, and regional data handling must be tested for the specific workload. For larger models or batch inference, container orchestration may be more appropriate; see how to deploy deep learning models on GKE.

A production workflow should:

• Ingest and validate fresh observations.
• Run the same versioned preprocessing used during training.
• Generate point forecasts and prediction intervals.
• Store inputs, outputs, model version, and timestamps for auditability.
• Fall back to a baseline when data is stale or outside expected ranges.
• Monitor drift, missingness, latency, and error by season.
• Retrain only after comparing the new model with the locked test set.

Show users the forecast time, source data age, horizon, and uncertainty. Do not present a model output as an official warning. Link to relevant government advisories where safety decisions are involved.

Common mistakes and a practical roadmap

Avoid claiming “real-time” accuracy without measuring data latency. Do not train on a global dataset and call the result Moradabad-specific without local validation. Do not use random splits, leak future weather variables, or report one overall metric for all seasons. Finally, do not deploy a large model when a smaller calibrated model meets the requirement.

A sensible 2026 roadmap is:

1. Build a 12-month clean hourly dataset and benchmark persistence, seasonal, and tree-based models.
2. Add a Hugging Face time-series model and evaluate it on untouched seasonal and extreme-event periods.
3. Introduce neighbouring stations, reanalysis, or numerical forecast inputs if local observations are insufficient.
4. Add probabilistic outputs, drift monitoring, and a documented fallback.
5. Pilot with one user group—such as farmers, municipal teams, or logistics operators—and measure decisions improved, not just model loss.

For teams seeking funding for this kind of India-focused applied AI work, AI Grants India can help connect a clearly scoped prototype with the right grant narrative, evaluation plan, and deployment milestones.