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---
license: cc-by-4.0
tags:
- 3D
- Radar
- Prediction
---
# Dataset Card for 3D-NEXRAD
<!-- Provide a quick summary of the dataset. -->
3D gridded radar reflectivity data collected from the U.S.NEXRAD WSR-88D radar network.
## Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
The **3D-NEXRDA** dataset comprises 3D radar observations of severe storm events across the United States, with each event captured at different geographic locations.
The dataset provides high-resolution insights into storm dynamics with high temporal and spatial resolution.
- **Temporal Coverage:**
- **Time Span:** 2022.01.01 - 2022.12.31
- **Interval and Event Duration:** 25 frames for each sequence spanning a period of 125 minutes per event
**we are actively working to expand this dataset to cover the period from 2020 to 2022**
- **Spatial Dimensions:**
- **Horizontal Resolution:** 512 × 512 for Each observation frame
- **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
- **Available Variables:** 7 radar variables:
- Radar Reflectivity
- Velocity Spectrum Width
- Azimuthal Shear of the Radial Velocity
- Radial Divergence of the Radial Velocity
- Differential Radar Reflectivity
- Specific Differential Phase
- Copolar Correlation Coefficient
- **Data Source:**
This dataset was re-collected, collated and pre-processed from:
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.
- **License:** cc-by-4.0
## Uses
#### Download and extract
```
cat nexrad-[YYYY].tar.gz.* | tar -zxv - -C [your_dataset_dir]/
```
## Citation
```
@inproceedings{
anonymous2024highdynamic,
title={High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Cjz9Xhm7sI},
note={under review}
}
``` |