Upload ConvGRU-Ensemble model

README.md

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+---
+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

config.json

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+{
+  "input_channels": 1,
+  "forecast_steps": 12,
+  "num_blocks": 5,
+  "ensemble_size": 10,
+  "noisy_decoder": true,
+  "loss_class": "crps",
+  "loss_params": {
+    "temporal_lambda": 0.01
+  },
+  "masked_loss": false,
+  "optimizer_class": "<class 'torch.optim.adam.Adam'>",
+  "optimizer_params": {
+    "lr": 0.0001,
+    "fused": true
+  },
+  "lr_scheduler_class": "<class 'torch.optim.lr_scheduler.ReduceLROnPlateau'>",
+  "lr_scheduler_params": {
+    "mode": "min",
+    "factor": 0.5,
+    "patience": 10
+  }
+}

model.ckpt

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+size 770192441