| --- |
| license: bsd-2-clause |
| language: en |
| tags: |
| - weather |
| - nowcasting |
| - radar |
| - precipitation |
| - ensemble-forecasting |
| - convgru |
| - earth-observation |
| library_name: pytorch |
| pipeline_tag: image-to-image |
| --- |
| |
| # IRENE — Italian Radar Ensemble Nowcasting Experiment |
|
|
| **IRENE** is a ConvGRU encoder-decoder model for short-term precipitation forecasting (nowcasting) from radar data. The model generates probabilistic ensemble forecasts, producing multiple plausible future scenarios from a single input sequence. |
|
|
| ## Model Description |
|
|
| - **Architecture**: ConvGRU encoder-decoder with PixelShuffle/PixelUnshuffle for spatial scaling |
| - **Input**: Sequence of past radar rain rate fields (T, H, W) in mm/h |
| - **Output**: Ensemble of future rain rate forecasts (E, T, H, W) in mm/h |
| - **Temporal resolution**: 5 minutes per timestep |
| - **Training loss**: Continuous Ranked Probability Score (CRPS) with temporal consistency regularization |
|
|
| The model encodes past radar observations into multi-scale hidden states using stacked ConvGRU blocks with PixelUnshuffle downsampling. The decoder generates forecasts by unrolling with different random noise inputs, producing diverse ensemble members that capture forecast uncertainty. |
|
|
| ## Intended Uses |
|
|
| - Short-term precipitation forecasting (0-60 min ahead) from radar reflectivity data |
| - Probabilistic nowcasting with uncertainty quantification via ensemble spread |
| - Research on deep learning for weather prediction |
| - Fine-tuning on regional radar datasets |
|
|
| ## How to Use |
|
|
| ```python |
| from convgru_ensemble import RadarLightningModel |
| |
| # Load from HuggingFace Hub |
| model = RadarLightningModel.from_pretrained("it4lia/irene") |
| |
| # Run inference on past radar data (rain rate in mm/h) |
| import numpy as np |
| past = np.random.rand(6, 256, 256).astype(np.float32) # 6 past timesteps |
| forecasts = model.predict(past, forecast_steps=12, ensemble_size=10) |
| # forecasts.shape = (10, 12, 256, 256) — 10 ensemble members, 12 future steps |
| ``` |
|
|
| ## Training Data |
|
|
| Trained on the Italian DPC (Dipartimento della Protezione Civile) radar mosaic surface rain intensity (SRI) dataset, covering the Italian territory at ~1 km resolution with 5-minute temporal resolution. |
|
|
| ## Training Procedure |
|
|
| - **Optimizer**: Adam (lr=1e-4) |
| - **Loss**: CRPS with temporal consistency penalty (lambda=0.01) |
| - **Batch size**: 16 |
| - **Ensemble size during training**: 2 members |
| - **Input window**: 6 past timesteps (30 min) |
| - **Forecast horizon**: 12 future timesteps (60 min) |
| - **Data augmentation**: Random rotations and flips |
| - **NaN handling**: Masked loss for missing radar data |
|
|
| ## Limitations |
|
|
| - Trained on Italian radar data; performance may degrade on other domains without fine-tuning |
| - 5-minute temporal resolution only |
| - Best suited for convective and stratiform precipitation; extreme events may be underrepresented |
| - Ensemble spread is generated via noisy decoder inputs, not a full Bayesian approach |
|
|
| ## Acknowledgements |
|
|
| This model was developed as part of the **Italian AI-Factory** (IT4LIA), an EU-funded initiative supporting the adoption of AI across SMEs, academia, and public/private sectors. The AI-Factory provides free HPC compute, consultancy, and AI-ready datasets. This work showcases capabilities in the **Earth (weather and climate) vertical domain**. |
|
|
| Developed at **Fondazione Bruno Kessler (FBK)**, Trento, Italy. |
|
|
| ## License |
|
|
| BSD 2-Clause License |
|
|
