---
license: cc-by-4.0
tags:
- 3D
- Radar
- Prediction
---

# Dataset Card for 3D-NEXRAD

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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}
}
```