How to Use CNNs to Predict Weather at HPCA Stadium, Dharamshala

What a CNN can—and cannot—predict

A convolutional neural network (CNN) is useful when weather information has a spatial structure: satellite frames, radar maps, numerical-weather-model grids, or camera imagery. For HPCA Stadium in Dharamshala, the goal should be a short-horizon nowcasting system, not a replacement for official forecasts. A well-designed model can estimate the probability of rain, heavy rain, lightning, low visibility, or unsafe wind over the next 15 minutes to six hours.

Dharamshala presents a demanding test case. The stadium sits in hilly terrain where elevation, orographic lifting, monsoon convection, and rapidly changing cloud cover can produce conditions that differ from a broad district-level forecast. A point sensor at the venue, nearby observations, and gridded atmospheric data should therefore be combined rather than treated as interchangeable.

For broader model-engineering guidance, review how to create custom neural networks in Python and implementing scalable ML pipelines for predictive analytics.

Define the operational target first

Do not begin with “predict the weather.” Define one decision and one forecast horizon. Useful targets for a cricket venue include:

Rain occurrence: Will measurable rain occur at the stadium in the next 30, 60, or 180 minutes?
Rain intensity: Will rainfall exceed a threshold that affects the pitch or spectator areas?
Lightning risk: Is there a high probability of thunderstorm activity nearby?
Visibility and wind: Will conditions cross a safety or broadcast threshold?
Match interruption: Is play likely to be suspended during a defined interval?

Use labels that operations teams can act on. For example, “rain in the next 60 minutes” is more useful than a vague class such as “cloudy.” Store both the event label and continuous measurements, such as millimetres of rain, so the model can support classification and regression.

Assemble a Dharamshala-specific dataset

A credible model needs aligned observations with timestamps and locations. Potential inputs include:

On-site weather station data: Rain gauge, temperature, humidity, pressure, wind, and visibility at or near HPCA Stadium.
Nearby automatic weather stations: These help identify whether a storm cell is approaching or confined to the venue.
Satellite imagery: Use geostationary imagery to track cloud-top temperature, cloud growth, and motion.
Radar or precipitation products: Where coverage and licensing permit, radar-derived rainfall is particularly valuable for short-range rain nowcasting.
Numerical weather prediction grids: Temperature, humidity, wind, precipitation, and geopotential-height fields add atmospheric context.
Terrain layers: Elevation, slope, aspect, and land-cover information help the model represent local effects.
Venue cameras: Sky-facing cameras can provide an additional visual signal, but require careful privacy controls and stable exposure settings.

India-focused deployment should account for data access, licensing, outages, and latency. Log the source, retrieval time, spatial resolution, missing-value rate, and units for every feature. If you use satellite-based modelling beyond weather, the same data-governance principles apply to satellite-based yield prediction for insurance providers in India.

Prepare the data without leaking the future

Resample all sources to a common interval, such as five or ten minutes, and map every input to a fixed geographic window around the stadium. A typical training example contains the previous 60–120 minutes of satellite or gridded frames plus recent station readings, with the label defined over the following forecast window.

Key preparation steps include:

Convert timestamps to a single standard, preferably UTC internally, while retaining Indian Standard Time for dashboards.
Check sensor calibration, impossible values, duplicated records, and gaps caused by connectivity failures.
Normalise each physical variable using statistics from the training period only.
Add missingness indicators instead of silently filling every gap.
Use cloud masks and quality flags for satellite products.
Avoid random train-test splits when records are time series. Train on earlier periods, validate on later periods, and reserve a full monsoon or event season for testing.

Do not use augmentation such as arbitrary image flips if geographic orientation matters. A west-to-east cloud movement is not equivalent to its mirrored version, particularly in mountainous terrain.

Choose a model that matches the data

A practical baseline is a CNN that processes a sequence of raster frames. For each timestamp, stack channels such as infrared brightness temperature, visible reflectance, precipitation, humidity, and elevation. A 2D CNN can extract features from each frame, followed by a temporal layer such as a gated recurrent unit or temporal convolution. A 3D CNN can learn spatial and temporal patterns jointly, but typically needs more data and compute.

For a first production prototype:

1. Build a persistence baseline: assume the next interval resembles the current one.
2. Train a tabular model using recent station variables and forecast data.
3. Add a CNN for satellite or radar frames.
4. Compare the combined system against each component.

A compact Keras sketch might look like this:

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(12, 64, 64, 6)),
    tf.keras.layers.Conv3D(32, (3, 3, 3), activation="relu"),
    tf.keras.layers.MaxPool3D((1, 2, 2)),
    tf.keras.layers.Conv3D(64, (3, 3, 3), activation="relu"),
    tf.keras.layers.GlobalAveragePooling3D(),
    tf.keras.layers.Dense(64, activation="relu"),
    tf.keras.layers.Dense(1, activation="sigmoid")
])
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["AUC"])

This is a starting point, not a validated forecast system. Beginners can compare architectural options using customizable neural network architectures for beginners, while teams running scientific experiments may benefit from open-source neural network libraries for physics simulations.

Validate for match-day decisions

Accuracy alone can hide dangerous failures. Measure precision, recall, F1 score, area under the precision-recall curve, and calibration. If the model says there is a 70% chance of rain, that probability should correspond to rain roughly 70% of the time across comparable cases.

Report performance separately for:

Monsoon and non-monsoon periods.
Day and night conditions.
Light rain, intense rain, and dry intervals.
Short lead times and longer lead times.
Missing-sensor and normal-data conditions.

Use blocked time-based testing and, where possible, independent observations from a later season. Compare against the India Meteorological Department forecast, a persistence baseline, and a simple numerical-weather-model baseline. Include confidence intervals, because a small number of major storms can make a result appear better or worse than it is.

Deploy a human-centred alerting system

The model should produce probabilities, lead time, data freshness, and an explanation of the strongest signals—not just a label. A venue dashboard could show a six-hour timeline with rain probability, observed rainfall, radar or satellite movement, and a “data stale” warning.

Set thresholds with venue staff. For example, a lower threshold may trigger pitch-cover preparation, while a higher threshold may trigger public safety procedures. Every alert should include an escalation path and a manual override. Weather forecasts must support, not replace, official meteorological guidance and the stadium’s safety protocols.

Run inference at the edge or on a modest cloud instance if connectivity is unreliable. Cache the latest model and inputs, queue observations during outages, and record every prediction, alert, operator action, and eventual outcome. These logs are essential for retraining and auditability.

Common failure modes

Too little local data: Pretraining on broader Indian imagery can help, but fine-tune with Dharamshala observations.
Class imbalance: Heavy rain and lightning are rare. Use weighted losses, focal loss, or careful sampling without duplicating entire weather events.
Spatial mismatch: A satellite pixel may cover a much larger area than the venue. Combine it with local gauges and nearby stations.
Data leakage: Future observations or revised weather products can accidentally enter training features.
Overconfident predictions: Apply calibration and display uncertainty.
Concept drift: Sensor replacements, satellite changes, and shifting seasonal patterns can degrade performance.

Treat the system as a maintained product. Schedule data-quality checks, monitor calibration and missed-event rates, retrain after meaningful drift, and keep a versioned model card. Teams familiar with operational AI can also apply lessons from building predictive maintenance systems with AI, particularly around monitoring, alerts, and failure handling.

A realistic 2026 implementation plan

Start with a four-week baseline: collect data, define labels, and establish persistence and official-forecast comparisons. Next, build a CNN prototype for one target, such as rain in the next 60 minutes, and validate it on a held-out seasonal period. Then run it in shadow mode during matches without influencing decisions. Only after measuring false alarms, missed events, latency, and operator usability should you introduce live alerts.

The strongest outcome is not a flashy accuracy number. It is a dependable local decision-support tool that tells HPCA Stadium staff what may happen, how soon, how certain the system is, and what action is appropriate.

Apply for AI Grants India