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