Delhi weather forecasting is a useful machine-learning project—but it is not primarily an NLP problem. BERT, GPT, and T5 are designed for language tasks; they should not be the default choice for predicting temperature, rainfall, humidity, or air pressure from hourly observations. A stronger approach is to use Hugging Face’s time-series ecosystem, combine it with numerical weather data, and treat official forecasts as an important baseline.
This guide explains how to build a practical Delhi weather prediction system in 2026, from data collection and feature design to evaluation and deployment.
Define the forecasting task first
Start with a precise target. “Predict Delhi weather” is too broad for a useful model. Specify:
- Target variable: temperature, rainfall probability, wind speed, humidity, or a weather category.
- Forecast horizon: the next hour, 6 hours, 24 hours, or 7 days.
- Update frequency: hourly or daily.
- Geographic scope: a single station, Delhi NCR, or multiple neighbourhoods.
- Output type: a numeric forecast, prediction interval, or alert such as heavy rain.
For a first build, predict next-day maximum temperature and rainfall occurrence using hourly observations. Later, expand to multivariate forecasting or probabilistic predictions. Keep separate models for materially different targets: a rainfall classifier and a temperature regressor are usually easier to validate than one model that tries to generate a complete weather report.
Build a Delhi-specific dataset
Use several years of time-stamped observations from reliable sources. Possible inputs include India Meteorological Department data where available, airport and automatic weather station records, reanalysis products, and a reputable weather API. Record the source, station coordinates, units, licence, and retrieval time. Do not silently merge measurements from stations with different elevations or exposure conditions.
Useful fields include:
- Temperature, dew point, relative humidity, and apparent temperature
- Rainfall amount and precipitation occurrence
- Wind speed, direction, and gusts
- Surface pressure, cloud cover, and solar radiation
- Visibility and, where relevant, particulate matter
- Calendar features such as hour, weekday, month, and holiday periods
Delhi’s strong seasonality makes context essential. Include monsoon indicators, winter fog proxies, heat-wave flags, and lagged observations. If air quality is included, treat it as an auxiliary signal rather than assuming pollution causes every short-term weather change.
Avoid leakage. A feature is valid only if it would be available at prediction time. For example, using the final daily rainfall total to predict rainfall earlier that day will produce an impressive but unusable score.
Choose a time-series model, not an arbitrary language model
Hugging Face supports models and tooling for forecasting, but model selection should follow the data and horizon. Candidate architectures include transformer-based time-series models such as Time Series Transformer, PatchTST, and other encoder architectures available through the Transformers ecosystem. Also establish baselines with persistence, seasonal averages, linear regression, gradient-boosted trees, or a dedicated statistical model.
A persistence baseline—using the latest observation as the next forecast—can be surprisingly difficult to beat for short horizons. Compare every new model against it and against an operational forecast from a weather provider. A model that outperforms a weak baseline but not an available forecast is not ready for production.
Text models still have a role. A Hindi or English language model can summarise forecast outputs, classify weather bulletins, or extract structured observations from reports. It should not be asked to infer physical weather dynamics from a string of numbers unless the experiment is carefully designed and benchmarked. For language components, explore open-source small language models for Hindi when building citizen-facing summaries or alerts.
Prepare features and splits correctly
Resample observations to a consistent interval and convert all timestamps to a clearly documented timezone. For Delhi applications, store source timestamps in UTC and expose local time as Asia/Kolkata. Handle missingness explicitly: short gaps may be interpolated for some variables, while long gaps should be masked or removed.
Useful transformations include:
- Rolling means, minimums, maximums, and standard deviations
- Lagged values for 1, 3, 6, 12, and 24 hours
- Sine and cosine encodings for hour and annual seasonality
- Wind direction converted into sine and cosine components
- Rainfall accumulation over recent windows
- Station, elevation, and location identifiers for multi-station training
Use chronological splits: train on earlier periods, validate on a later period, and reserve the most recent period for testing. Add rolling-origin backtesting so the result is not dominated by one unusually mild or extreme season. In Delhi, report performance separately for summer heat, monsoon rainfall, winter fog, and ordinary days.
Fine-tune and evaluate
For numeric forecasts, use MAE and RMSE, but do not stop there. MAE is easy to interpret in degrees Celsius or millimetres of rain; RMSE exposes large misses. For rainfall occurrence, report precision, recall, F1, and a precision-recall curve because rainy events are often imbalanced. For probabilistic forecasts, use calibration plots and proper scoring rules such as CRPS or Brier score.
A minimal training configuration should include a fixed random seed, logged hyperparameters, early stopping, and a reproducible data snapshot. Transformer training can be resource-intensive, so begin with a small context window and a compact model. Use a GPU only after the baseline pipeline works. For broader deployment options, compare the operating cost with how to deploy ML models on AWS Lambda in India, especially if inference is infrequent and the model package is small.
Evaluate operational usefulness, not just average accuracy. A temperature error of 1°C may be acceptable in one application but not for heat-health alerts. Rainfall misses during a severe event deserve separate analysis. Always retain prediction intervals or confidence scores where possible, and communicate uncertainty instead of presenting a single number as fact.
Package the forecast as a reliable service
A production pipeline normally has five components:
1. Ingestion: retrieve new observations and validate units, timestamps, and ranges.
2. Feature generation: reproduce training-time transformations exactly.
3. Inference: load the model once and generate forecasts on schedule.
4. Storage and monitoring: retain inputs, predictions, actual outcomes, and model versions.
5. Delivery: expose results through an API, dashboard, SMS workflow, or alerting system.
FastAPI is sufficient for a lightweight service. Add input validation, timeouts, retries, authentication, and a fallback forecast if the model or data provider fails. If the model must run on constrained infrastructure, review techniques for deploying large language models locally, while remembering that time-series models may have different memory and runtime requirements.
Track data drift, missingness, forecast error by horizon, and alert frequency. Retrain on a schedule only after checking whether new data improves backtested performance. Version the model, feature code, training data, and configuration together. For a larger multi-service system, the deployment principles in how to deploy deep learning models on GKE provide a useful starting point.
Common failure modes
- Using BERT or GPT as a default forecaster: choose a time-series architecture for numeric sequences.
- Random train-test splitting: this leaks future patterns into training.
- Ignoring station changes: sensor relocation can look like climate variation.
- Optimising only average error: rare heat, rain, and fog events need separate metrics.
- Overclaiming precision: weather is chaotic; publish uncertainty and forecast horizons.
- Treating APIs as ground truth: compare multiple sources and document provenance.
A practical project roadmap
Build a persistence and seasonal baseline first. Then add lagged features and a tree-based model before fine-tuning a transformer. Once a transformer consistently improves rolling backtests, package it behind an API and monitor it through one complete monsoon or summer cycle. Add a language model only for explanation, translation, or alert composition—and keep the numeric forecast independently auditable.
For builders working across modalities, the workflow resembles other applied AI projects: define the task, establish a baseline, prevent leakage, evaluate on representative Indian data, and deploy with monitoring. Related guidance on building computer vision models on GitHub is useful for the same reproducibility and repository-structure principles, even though the data modality differs.
FAQ
Can Hugging Face models predict Delhi weather directly?
Yes, Hugging Face time-series models can be fine-tuned for Delhi observations. They require appropriately formatted numerical sequences and should be compared with simple and operational baselines.
Should I use BERT or GPT for temperature prediction?
Not as the primary forecasting model. Use a time-series model for numerical prediction; use language models to summarise or translate forecast results.
How much historical data is needed?
Several years covering multiple summers, monsoons, and winters is preferable. The exact requirement depends on the sampling frequency, forecast horizon, number of stations, and data quality.
How can I make the forecast trustworthy?
Use chronological backtesting, event-specific metrics, uncertainty estimates, documented data provenance, drift monitoring, and a visible fallback when inputs are unavailable.