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dm_graphcast_datasets

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metadata
license: cc-by-4.0
tags:
  - weather-forecasting
  - climate
language:
  - en
pretty_name: ECMWF's ERA5, HRES, (and fake) data, formatted for DeepMind GraphCast
configs:
  - config_name: source-era5_date-2022-01-01_res-0.25_levels-13_steps-01
    data_files: dataset/source-era5_date-2022-01-01_res-0.25_levels-13_steps-01.nc
  - config_name: source-era5_date-2022-01-01_res-0.25_levels-13_steps-04
    data_files: dataset/source-era5_date-2022-01-01_res-0.25_levels-13_steps-04.nc
  - config_name: source-era5_date-2022-01-01_res-0.25_levels-13_steps-12
    data_files: dataset/source-era5_date-2022-01-01_res-0.25_levels-13_steps-12.nc
  - config_name: source-era5_date-2022-01-01_res-0.25_levels-13_steps-12
    data_files: dataset/source-era5_date-2022-01-01_res-0.25_levels-13_steps-12.nc
  - config_name: source-era5_date-2022-01-01_res-0.25_levels-37_steps-01
    data_files: dataset/source-era5_date-2022-01-01_res-0.25_levels-37_steps-01.nc
  - config_name: source-era5_date-2022-01-01_res-0.25_levels-37_steps-04
    data_files: dataset/source-era5_date-2022-01-01_res-0.25_levels-37_steps-04.nc
  - config_name: source-era5_date-2022-01-01_res-0.25_levels-37_steps-12
    data_files: dataset/source-era5_date-2022-01-01_res-0.25_levels-37_steps-12.nc
  - config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-01
    data_files: dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-01.nc
  - config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-04
    data_files: dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-04.nc
  - config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-12
    data_files: dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-12.nc
  - config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-20
    data_files: dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-20.nc
  - config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-40
    data_files: dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-40.nc
  - config_name: source-era5_date-2022-01-01_res-1.0_levels-37_steps-01
    data_files: dataset/source-era5_date-2022-01-01_res-1.0_levels-37_steps-01.nc
  - config_name: source-era5_date-2022-01-01_res-1.0_levels-37_steps-04
    data_files: dataset/source-era5_date-2022-01-01_res-1.0_levels-37_steps-04.nc
  - config_name: source-era5_date-2022-01-01_res-1.0_levels-37_steps-12
    data_files: dataset/source-era5_date-2022-01-01_res-1.0_levels-37_steps-12.nc
  - config_name: source-era5_date-2022-01-01_res-1.0_levels-37_steps-20
    data_files: dataset/source-era5_date-2022-01-01_res-1.0_levels-37_steps-20.nc

ECMWF's ERA5, HRES, (and fake) data, formatted for DeepMind GraphCast

Original files are from this Google Cloud Bucket: https://console.cloud.google.com/storage/browser/dm_graphcast

This repo contains both the dataset and stats files needed for GraphCast inference.

License and Attribution

ECMWF data products are subject to the following terms:

  1. Copyright statement: Copyright "© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)".
  2. Source www.ecmwf.int
  3. Licence Statement: ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/
  4. 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.

Usage

Use the Huggingface Hub file system to load files. The datasets library doesn't support netCDF files yet.

from huggingface_hub import HfFileSystem, hf_hub_download
import xarray

fs = HfFileSystem()

files = [
    file.rsplit("/", 1)[1] for file in fs.ls("datasets/shermansiu/dm_graphcast_datasets/dataset", detail=False)
]
local_file: str = hf_hub_download(repo_id="shermansiu/dm_graphcast_datasets", filename=f"dataset/{files[0]}", repo_type="dataset")

with open(local_file, "rb") as f:
    example_batch = xarray.load_dataset(f).compute()

Citation 

@article{
doi:10.1126/science.adi2336,
author = {Remi Lam  and Alvaro Sanchez-Gonzalez  and Matthew Willson  and Peter Wirnsberger  and Meire Fortunato  and Ferran Alet  and Suman Ravuri  and Timo Ewalds  and Zach Eaton-Rosen  and Weihua Hu  and Alexander Merose  and Stephan Hoyer  and George Holland  and Oriol Vinyals  and Jacklynn Stott  and Alexander Pritzel  and Shakir Mohamed  and Peter Battaglia },
title = {Learning skillful medium-range global weather forecasting},
journal = {Science},
volume = {382},
number = {6677},
pages = {1416-1421},
year = {2023},
doi = {10.1126/science.adi2336},
URL = {https://www.science.org/doi/abs/10.1126/science.adi2336},
eprint = {https://www.science.org/doi/pdf/10.1126/science.adi2336},
abstract = {Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy but does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning–based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute. GraphCast significantly outperforms the most accurate operational deterministic systems on 90\% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting and helps realize the promise of machine learning for modeling complex dynamical systems. The numerical models used to predict weather are large, complex, and computationally demanding and do not learn from past weather patterns. Lam et al. introduced a machine learning–based method that has been trained directly from reanalysis data of past atmospheric conditions. In this way, the authors were able to quickly predict hundreds of weather variables globally up to 10 days in advance and at high resolution. Their predictions were more accurate than those of traditional weather models in 90\% of tested cases and displayed better severe event prediction for tropical cyclones, atmospheric rivers, and extreme temperatures. —H. Jesse Smith Machine learning leads to better, faster, and cheaper weather forecasting.}}