metadata

license: cc-by-4.0
tags:
  - croissant
  - weather-forecasting
  - extreme-weather
  - deep-learning
  - high-resolution

HR-Extreme Dataset

Overview

HR-Extreme is a high-resolution dataset designed to evaluate the performance of state-of-the-art models in predicting extreme weather events. The dataset contains 17 types of extreme weather events from 2020, based on High-Resolution Rapid Refresh (HRRR) data. It is intended for researchers in weather forecasting, encompassing both physical and deep learning methods. [Github Link](github_link: https://github.com/HuskyNian/HR-Extreme)

Dataset Structure

The dataset is divided into two main folders:

202001_202006: Contains data from January 2020 to June 2020.
202007_202012: Contains data from July 2020 to December 2020.

Each folder stores the dataset in the WebDataset format, following Hugging Face's recommendations. Every 10 .npz files are aggregated into a single .tar file, named sequentially as i.tar (e.g., 0001.tar).

Usage

To construct the dataset, use the provided scripts in the GitHub repository. The main script, make_datasetall.py, generates an index file for the dataset:

python make_datasetall.py 20200101 20200630