gcc-weather-forecast-transformer

🌦️ GCC Weather Forecast Transformer

A specialized Transformer-based model designed for short-horizon meteorological forecasting in the Gulf Cooperation Council (GCC) region. Trained on NVIDIA DGX Spark infrastructure with Grace Blackwell GPU, this model leverages high-resolution historical data to predict immediate future weather conditions for Dubai and Riyadh.

🎮 Live Demo

👉 Try the model here: GCC Weather Forecast Transformer Space

• Real-time Inference: Generates forecasts using live data fetched on-demand.
• Supported Cities:
  • Dubai, United Arab Emirates 🇦🇪
  • Riyadh, Saudi Arabia 🇸🇦

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⚡ Technical Summary

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🚀 Quick Start (Python)

You can download and use this model directly in your Python environment.

from huggingface_hub import hf_hub_download
import torch
import pickle
import sys

# 1. Download Model Artifacts
repo_id = "assix-research/gcc-weather-forecast-transformer"
model_path = hf_hub_download(repo_id=repo_id, filename="weather_transformer.pt")
scaler_path = hf_hub_download(repo_id=repo_id, filename="feature_scaler.pkl")
code_path = hf_hub_download(repo_id=repo_id, filename="model.py")

# 2. Load Architecture Dynamically
import importlib.util
spec = importlib.util.spec_from_file_location("weather_model", code_path)
weather_model = importlib.util.module_from_spec(spec)
spec.loader.exec_module(weather_model)

# 3. Initialize & Load Weights
model = weather_model.WeatherTransformer(input_dim=4, seq_len=72)
model.load_state_dict(torch.load(model_path))
model.eval()

print("✅ Model loaded successfully!")

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🧠 Model Architecture

The core is a compact Transformer Encoder optimized for structured time-series data. Unlike LSTM or RNN baselines, the Transformer mechanism allows the model to attend to the entire 72-hour history simultaneously, capturing long-range dependencies (e.g., diurnal cycles and pressure fronts). Hyperparameters

Embedding Dimension (dmodel​): 256

Attention Heads: 8 (Multi-Head Self-Attention)

Layers: 4 Encoder Blocks

Feedforward Network: 512 dimensions

Dropout: 0.1

Positional Encoding: Sinusoidal injection to preserve temporal order.

Inputs & Outputs

Input X: Tensor of shape (Batch, 72, 4).

    Features: temperature_2m, relative_humidity_2m, pressure_msl, wind_speed_10m.

Output y: Tensor of shape (Batch, 4).

    Predicted values for the very next hour.

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🛠️ Data Pipeline

The system operates on a rigorous pipeline ensuring data consistency between training and inference.

Ingestion:

    Fetches hourly historical data from the Open-Meteo API.

    Locations: Dubai (25.2°N, 55.2°E) and Riyadh (24.7°N, 46.6°E).

Preprocessing:

    Time Alignment: All timestamps standardized to UTC.

    Normalization: Scikit-Learn StandardScaler (Z-score normalization) fit on the training corpus.

Inference:

    The model accepts normalized tensors.

    Outputs are inverse-transformed back to physical units (°C, %, hPa, km/h) for display.

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👨‍💻 Attribution & Credits

• Developed by: Assix Research (2026)
• Compute: Trained on NVIDIA DGX Spark infrastructure.
• Data Provider: Open-Meteo