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--- |
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license: cc-by-4.0 |
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tags: |
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- 3D |
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- Radar |
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- Prediction |
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--- |
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# Dataset Card for 3D-NEXRAD |
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<!-- Provide a quick summary of the dataset. --> |
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3D gridded radar reflectivity data collected from the U.S.NEXRAD WSR-88D radar network. |
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## Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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The **3D-NEXRDA** dataset comprises 3D radar observations of severe storm events across the United States, with each event captured at different geographic locations. |
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The dataset provides high-resolution insights into storm dynamics with high temporal and spatial resolution. |
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- **Temporal Coverage:** |
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- **Time Span:** 2020.01.01 - 2022.12.31 |
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- **Interval and Event Duration:** 25 frames for each sequence spanning a period of 125 minutes per event |
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**we are actively working to expand this dataset to cover the period from 2020 to 2022** |
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- **Spatial Dimensions:** |
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- **Horizontal Resolution:** 512 × 512 for Each observation frame |
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- **Vertical Resolution:** 28 levels, from 0.5 km to 7 km with 0.5 km intervals, and from 7 km to 22 km with 1 km intervals |
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- **Available Variables:** 7 radar variables: |
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- Radar Reflectivity |
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- Velocity Spectrum Width |
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- Azimuthal Shear of the Radial Velocity |
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- Radial Divergence of the Radial Velocity |
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- Differential Radar Reflectivity |
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- Specific Differential Phase |
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- Copolar Correlation Coefficient |
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- **Data Source:** |
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This dataset was re-collected, collated and pre-processed from: |
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GridRad-Severe - Three-Dimensional Gridded NEXRAD WSR-88D Radar Data for Severe Events. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Dataset. https://doi.org/10.5065/2B46-1A97. Accessed† 26 Nov 2024. |
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- **License:** cc-by-4.0 |
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## Uses |
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#### Download and extract |
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``` |
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cat nexrad-[YYYY].tar.gz.* | tar -zxv -C [your_dataset_dir]/ |
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``` |
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## Citation |
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``` |
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@inproceedings{ |
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wang2025highdynamic, |
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title={High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation}, |
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author={Ziye Wang and Yiran Qin and Lin Zeng and Ruimao Zhang}, |
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booktitle={The Thirteenth International Conference on Learning Representations}, |
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year={2025}, |
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url={https://openreview.net/forum?id=Cjz9Xhm7sI} |
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} |
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``` |
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``` |
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@misc{wang2025highdynamicradarsequenceprediction, |
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title={High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation}, |
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author={Ziye Wang and Yiran Qin and Lin Zeng and Ruimao Zhang}, |
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year={2025}, |
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eprint={2502.14895}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2502.14895}, |
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} |
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``` |
