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README.md

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+---
+license: cc-by-4.0
+tags:
+  - pytorch
+  - tornado-detection
+  - weather
+  - radar
+  - nexrad
+  - 3d-cnn
+  - video-classification
+  - severe-weather
+  - dual-pol
+datasets:
+  - deepguess/tornet-temporal
+pipeline_tag: video-classification
+---
+
+# ResNet3D-18 for Tornado Detection
+
+A 3D convolutional neural network trained on temporal dual-polarimetric NEXRAD radar sequences for tornado detection and prediction.
+
+## Model Description
+
+This model uses a **ResNet3D-18** backbone (3D adaptation of ResNet-18) with a **dual-head architecture**:
+
+- **Detection head**: Classifies whether a tornado is currently occurring in the radar sequence
+- **Prediction head**: Classifies whether a tornado will occur, using only pre-tornado frames
+
+The model processes **8 consecutive radar volume scans** (~40 minutes of data) with **24 dual-polarimetric channels** across a **128x128 km storm-centered grid**.
+
+### Architecture Details
+
+| Parameter | Value |
+|-----------|-------|
+| Backbone | ResNet3D-18 (BasicBlock, layers=[2,2,2,2]) |
+| Parameters | 33.3M |
+| Input shape | (B, 24, 8, 128, 128) -- channels, time, height, width |
+| Output shape | (B, 4) -- [det_class0, det_class1, pred_class0, pred_class1] |
+| Channels | 24 (6 dual-pol products x 4 elevation angles) |
+| Temporal frames | 8 |
+| Spatial resolution | 1 km/pixel, 128x128 grid |
+
+### Channel Layout
+
+| Channels | Product | Description |
+|----------|---------|-------------|
+| 0-3 | REF | Reflectivity at 0.5, 0.9, 1.3, 1.8 deg |
+| 4-7 | VEL | Radial velocity |
+| 8-11 | SW | Spectrum width |
+| 12-15 | ZDR | Differential reflectivity |
+| 16-19 | CC | Correlation coefficient |
+| 20-23 | KDP | Specific differential phase |
+
+## Performance
+
+### Validation Set (2022, 2,117 events)
+
+| Head | AUC | CSI | F1 |
+|------|-----|-----|-----|
+| Detection | 0.927 | 0.652 | 0.789 |
+| Prediction | 0.993 | 0.883 | 0.938 |
+| **Combined** | **0.960** | -- | -- |
+
+### Test Set (3,685 events)
+
+| Head | AUC | CSI | F1 | Precision | Recall |
+|------|-----|-----|-----|-----------|--------|
+| Detection | 0.896 | 0.540 | 0.702 | 0.602 | 0.841 |
+| Prediction | 0.988 | 0.856 | 0.922 | 0.958 | 0.889 |
+| **Combined** | **0.942** | -- | -- | -- | -- |
+
+Per-category prediction accuracy: TOR 88.9%, WRN 96.8%, NUL 99.1%
+
+### Comparison with Literature
+
+| Model | Year | Frames | Channels | AUC |
+|-------|------|--------|----------|-----|
+| TorNet (MIT Lincoln Lab) | 2024 | 1 | 12 | ~0.88 |
+| **This model** | **2026** | **8** | **24** | **0.942** |
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Dataset | [tornet-temporal](https://huggingface.co/datasets/deepguess/tornet-temporal) (24,862 events) |
+| Train split | 2013-2021 (~19,061 events) |
+| Optimizer | AdamW (lr=1e-3, weight_decay=0.01) |
+| Scheduler | Cosine annealing with 3-epoch linear warmup |
+| Batch size | 256 |
+| Epochs | 20 |
+| Mixed precision | FP16 (AMP) with FP32 classification heads |
+| GPU | NVIDIA H100 NVL (96GB) |
+| Training time | ~4 hours |
+
+## Usage
+
+```python
+import torch
+import numpy as np
+from model_resnet3d import DualHeadResNet3D  # from this repo
+
+# Load model
+model = DualHeadResNet3D(in_channels=24, arch="resnet18")
+state = torch.load("best.pt", map_location="cpu")
+model.load_state_dict(state["model_state_dict"])
+model.eval()
+
+# Load a radar sequence
+event = np.load("tornet_EVENTID_TOR/sequence.npz")
+data = event["data"][:8]  # first 8 frames, shape (8, 24, 128, 128)
+x = torch.from_numpy(data).float().unsqueeze(0)  # (1, 8, 24, 128, 128)
+x = x.permute(0, 2, 1, 3, 4)  # (1, 24, 8, 128, 128) -- channels first
+
+with torch.no_grad():
+    out = model(x)  # (1, 4)
+    det_prob = torch.softmax(out[:, :2], dim=1)[:, 1]  # detection probability
+    pred_prob = torch.softmax(out[:, 2:], dim=1)[:, 1]  # prediction probability
+
+print(f"Detection probability: {det_prob.item():.3f}")
+print(f"Prediction probability: {pred_prob.item():.3f}")
+```
+
+## Real-World Deployment
+
+### How to use this in an operational setting
+
+1. **Data ingestion**: Ingest real-time NEXRAD Level-II data from the [Unidata IDD](https://www.unidata.ucar.edu/projects/idd/) or [AWS NEXRAD archive](https://registry.opendata.aws/noaa-nexrad/). Extract the 6 dual-pol products (REF, VEL, SW, ZDR, CC, KDP) at 4 elevation angles (0.5, 0.9, 1.3, 1.8 deg).
+
+2. **Storm tracking**: Use an existing storm tracker (e.g., [TINT](https://github.com/openradar/TINT), SCIT, or a simple reflectivity centroid tracker) to identify storm cells and extract 128x128 km storm-centered patches.
+
+3. **Temporal buffering**: Maintain a rolling buffer of the last 8 radar volume scans (~40 minutes) for each tracked storm cell. New scans arrive every ~4-5 minutes.
+
+4. **Inference**: Run the model on each storm cell's 8-frame sequence. The **prediction head** output is most useful operationally -- it answers "will this storm produce a tornado?"
+
+5. **Thresholding**: Use a probability threshold of ~0.66 (optimized for CSI on the test set) to generate tornado warnings. At this threshold:
+   - **Precision 95.8%**: When the model warns, it's almost always right
+   - **Recall 88.9%**: Catches ~89% of actual tornadoes
+   - **Lead time**: The prediction head uses pre-tornado frames, providing inherent lead time
+
+6. **Integration**: Feed predictions into NWS warning decision support systems or automated alerting pipelines. The model runs in <50ms on a modern GPU, well within real-time requirements.
+
+### Limitations
+
+- Trained on CONUS NEXRAD data only (2013-2022). May not generalize to other radar networks or non-US storm environments.
+- Requires all 24 dual-pol channels. Single-pol radars are not supported (see 12-channel ablation for degraded single-pol performance).
+- Storm-centered input assumes a working storm tracker upstream.
+- The detection head (AUC 0.896) is weaker than the prediction head (AUC 0.988). Active tornado signatures may be more variable than pre-tornado mesocyclone patterns.
+- NUL (null) training samples are drawn from tornado-day radar scans only. The model has not been tested on truly quiescent weather.
+
+## Citation
+
+```bibtex
+@model{resnet3d-18-tornet,
+  title={ResNet3D-18 for Temporal Radar Tornado Detection},
+  author={DeepGuess},
+  year={2026},
+  url={https://huggingface.co/deepguess/resnet3d-18-tornet},
+}
+```