Published December 14, 2024 | Version 1.0

Data release: Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events

  • 1. ROR icon University of Geneva
  • 2. ROR icon Helmholtz Centre for Environmental Research
  • 3. ROR icon TU Dresden

Description

This dataset contains the data created and analysed in "Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events" by Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang, Jakob Zscheischler, and Sebastian Engelke (DOI: 10.1175/AIES-D-24-0033.1). 

It contains ground-truth data, numerical forecasts (HRES) and deep learning forecasts (GraphCast, Pangu-Weather, FourCastNet) of the weather during the three extreme events studied in the paper.

The corresponding analysis code is available at https://github.com/jonathanwider/DLWP-eval-extremes.

 

Methods

The preprocessing mainly consists of two steps:

  • restricting data to a region relevant to the respective case study & unifying metadata, variable names, dimensions, etc between the different data sets
  • merging HRES forecasts. After downloading, the HRES data sets for initialization times 00:00 / 12:00 UTC and 06:00 / 18:00 UTC differ in their length. For some analyses, we extend the 06:00 / 18:00 UTC forecasts. The details are described in section "2b - Initialization Times" in the paper.

Table of contents

For each case study, we have two files <case-study-name>_gt.nc and <case-study-name>_fc.nc in which we combine the ground truth and forecast data sets respectively.

For the Pacific Northwest heatwave case study, we additionally include a climatology for ERA5, which we adapted from WeatherBench 2, and two files for each of the two years 2020 and 2022, which we used as baselines in some analyses.

2021_NA_winter_storm_college_station_ts.csv contains time-series of wind speed, temperatures, and wind chill during the 2021 North American Winter storm in College Station, Texas. For details, see the paper.

Technical info

Data sources

The inputs to the dataset preprocessing were obtained from the following sources:

  • Deep learning forecasts were produced by re-running the deep-learning weather models ourselves. The code to produce forecasts is available on the respective model repositories(GraphCast, Pangu-Weather, and FourCastNet).
  • The ERA5 and HRES-fc0 ground truth data sets as well as the HRES forecasts were retrieved from the following sources:

Technical info

This dataset contains modified versions of ECMWF archive datasets (source: www.ecmwf.int) and ERA5, which can be downloaded through the Copernicus Climate Data Store

ECMWF archive data sets

The ECMWF archive data is published under a Creative Commons Attribution Non-Commercial 4.0 International (CC BY NC 4.0). https://creativecommons.org/licenses/by-nc/4.0/legalcode. License notes are also summarized here.

Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.


ERA5

We modified ERA5 data which is a product of the Copernicus Climate Change Service. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

 

Files

2021_NA_winter_storm_college_station_ts.csv

Files (21.1 GB)

Name Size
md5:71f59d06de3d30b7ac0fe7426c9e8ea5
7.1 MB Preview Download
md5:596a8c6d5592750575e254180fb1fc13
3.1 GB Download
md5:f5c7485bf9dc01ea5390f222f1947f7f
18.8 MB Download
md5:6935fdb47a4934e4894b9f4d07ab91ce
400.4 MB Download
md5:a3a911938bbc142d93a8dab31ca9bb56
8.6 MB Download
md5:4851d803eaddce0613fc129d8a2441f5
1.1 GB Download
md5:2a8071f049f3bea88b3a958d2bd49062
8.6 MB Download
md5:c52a1d6f8a1945a4a516973c8c6182b8
4.3 MB Download
md5:461d18b5e62864513012e6ab6bb8d535
1.1 GB Download
md5:6ee927008bb3048309400e8d21648020
8.6 MB Download
md5:f0faa0349aba2e775341a81e9ccf0baf
15.3 GB Download
md5:0db55f2df01ff98ce32c20643dcd0d53
108.5 MB Download

Additional details

Related works

Is described by
Publication: 10.1175/AIES-D-24-0033.1 (DOI)
Preprint: arXiv:2404.17652 (arXiv)

Funding

Swiss National Science Foundation
Graph structures, sparsity and high-dimensional inference for extremes 186858

Software