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--- |
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license: cc-by-4.0 |
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tags: |
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- weather-forecasting |
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- climate |
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language: |
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- en |
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pretty_name: ECMWF's ERA5, HRES, (and fake) data, formatted for DeepMind GraphCast |
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configs: |
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- config_name: source-era5_date-2022-01-01_res-0.25_levels-13_steps-01 |
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data_files: "dataset/source-era5_date-2022-01-01_res-0.25_levels-13_steps-01.nc" |
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- config_name: source-era5_date-2022-01-01_res-0.25_levels-13_steps-04 |
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data_files: "dataset/source-era5_date-2022-01-01_res-0.25_levels-13_steps-04.nc" |
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- config_name: source-era5_date-2022-01-01_res-0.25_levels-13_steps-12 |
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data_files: "dataset/source-era5_date-2022-01-01_res-0.25_levels-13_steps-12.nc" |
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- config_name: source-era5_date-2022-01-01_res-0.25_levels-13_steps-12 |
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data_files: "dataset/source-era5_date-2022-01-01_res-0.25_levels-13_steps-12.nc" |
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- config_name: source-era5_date-2022-01-01_res-0.25_levels-37_steps-01 |
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data_files: "dataset/source-era5_date-2022-01-01_res-0.25_levels-37_steps-01.nc" |
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- config_name: source-era5_date-2022-01-01_res-0.25_levels-37_steps-04 |
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data_files: "dataset/source-era5_date-2022-01-01_res-0.25_levels-37_steps-04.nc" |
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- config_name: source-era5_date-2022-01-01_res-0.25_levels-37_steps-12 |
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data_files: "dataset/source-era5_date-2022-01-01_res-0.25_levels-37_steps-12.nc" |
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- config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-01 |
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data_files: "dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-01.nc" |
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- config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-04 |
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data_files: "dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-04.nc" |
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- config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-12 |
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data_files: "dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-12.nc" |
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- config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-20 |
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data_files: "dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-20.nc" |
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- config_name: source-era5_date-2022-01-01_res-1.0_levels-13_steps-40 |
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data_files: "dataset/source-era5_date-2022-01-01_res-1.0_levels-13_steps-40.nc" |
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- config_name: source-era5_date-2022-01-01_res-1.0_levels-37_steps-01 |
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data_files: "dataset/source-era5_date-2022-01-01_res-1.0_levels-37_steps-01.nc" |
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- config_name: source-era5_date-2022-01-01_res-1.0_levels-37_steps-04 |
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data_files: "dataset/source-era5_date-2022-01-01_res-1.0_levels-37_steps-04.nc" |
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- config_name: source-era5_date-2022-01-01_res-1.0_levels-37_steps-12 |
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data_files: "dataset/source-era5_date-2022-01-01_res-1.0_levels-37_steps-12.nc" |
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- config_name: source-era5_date-2022-01-01_res-1.0_levels-37_steps-20 |
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data_files: "dataset/source-era5_date-2022-01-01_res-1.0_levels-37_steps-20.nc" |
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--- |
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# ECMWF's ERA5, HRES, (and fake) data, formatted for DeepMind GraphCast |
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Original files are from this Google Cloud Bucket: https://console.cloud.google.com/storage/browser/dm_graphcast |
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This repo contains both the `dataset` and `stats` files needed for GraphCast inference. |
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## License and Attribution |
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ECMWF data products are subject to the following terms: |
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1. Copyright statement: Copyright "© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)". |
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2. Source www.ecmwf.int |
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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/ |
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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. |
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## Usage |
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Use the Huggingface Hub file system to load files. The `datasets` library doesn't support netCDF files yet. |
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```python |
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from huggingface_hub import HfFileSystem, hf_hub_download |
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import xarray |
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fs = HfFileSystem() |
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files = [ |
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file.rsplit("/", 1)[1] for file in fs.ls("datasets/shermansiu/dm_graphcast_datasets/dataset", detail=False) |
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] |
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local_file: str = hf_hub_download(repo_id="shermansiu/dm_graphcast_datasets", filename=f"dataset/{files[0]}", repo_type="dataset") |
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with open(local_file, "rb") as f: |
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example_batch = xarray.load_dataset(f).compute() |
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``` |
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## Citation |
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- Paper: https://www.science.org/doi/10.1126/science.adi2336 |
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- Preprint: https://arxiv.org/abs/2212.12794 |
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``` |
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@article{ |
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doi:10.1126/science.adi2336, |
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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 }, |
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title = {Learning skillful medium-range global weather forecasting}, |
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journal = {Science}, |
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volume = {382}, |
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number = {6677}, |
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pages = {1416-1421}, |
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year = {2023}, |
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doi = {10.1126/science.adi2336}, |
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URL = {https://www.science.org/doi/abs/10.1126/science.adi2336}, |
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eprint = {https://www.science.org/doi/pdf/10.1126/science.adi2336}, |
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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.}} |
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``` |
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