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Weather forecasts have become much more accurate: https://ourworldindata.org/weather-forecasts

Nature paper: https://www.nature.com/articles/s41586-023-06185-3 Bi K, Xie L, Zhang H, Chen X, Gu X, Tian Q. Accurate medium-range global weather forecasting with 3D neural networks. Nature. 2023 Jul;619(7970):533-538. doi: 10.1038/s41586-023-06185-3. Epub 2023 Jul 5. Erratum in: Nature. 2023 Sep;621(7980):E45. PMID: 37407823; PMCID: PMC10356604.

https://github.com/pvigier/perlin-numpy

The code base of Pangu-Weather was established on PyTorch, a Python-based library for deep learning. In building and optimizing the backbones, Swin transformer was used: https://github.com/microsoft/Swin-Transformer.

The computation of the CRPS metric relied on the xskillscore package, https://github.com/xarray-contrib/xskillscore/.

The implementation of Perlin noise was inherited from a GitHub repository, https://github.com/pvigier/perlin-numpy.

We also used other Python libraries, such as NumPy and Matplotlib, in the research project. We released the trained models, inference code and the pseudocode of details to the public at a GitHub repository: https://github.com/198808xc/Pangu-Weather (https://doi.org/10.5281/zenodo.7678849). The trained models allow the researchers to explore Pangu-Weather’s ability on either ERA5 initial fields or ECMWF initial fields, where the latter is more practical as it can be used as an API for almost real-time weather forecasting.

DATA For training and testing Pangu-Weather, a subset of the ERA5 dataset (around 60 TB) was used: https://cds.climate.copernicus.eu/, the official website of Copernicus Climate Data (CDS).

For comparison with operational IFS, he forecast data and tropical cyclone tracking results of ECMWF: https://confluence.ecmwf.int/display/TIGGE, the official website of the TIGGE archive. Truth routes of tropical cyclones from the International Best Track Archive for Climate Stewardship (IBTrACS) project, https://www.ncei.noaa.gov/products/international-best-track-archive.

Weather raw data: https://weather.cod.edu/forecast/