Making open-source AI weather forecasting models easy to run

Hugging Face and Earthmover present tools and tutorials intended to simplify running open-weight AI weather forecasting models, which can generate atmospheric forecasts much faster than physics-based systems but often face hardware, storage, and data-transfer barriers. The article explains that models such as Microsoft Aurora, ECMWF AIFS, and others predict successive global atmospheric states autoregressively, and provides a demo space for launching forecasts, accessing initialization data, and comparing outputs with ERA5 reanalysis data. Its practical walkthrough shows how to use ERA5 data from Earthmover’s Marketplace to initialize Aurora, generate a 24-hour or longer forecast in six-hour increments, visualize predicted temperatures, and assess accuracy through spatial bias and global RMSE against ERA5. Earthmover’s cloud platform uses Zarr and Icechunk to stream selected portions of large scientific datasets rather than requiring complete downloads, while Hugging Face Jobs offers remote GPU execution for users without compatible local hardware. The collaboration also highlights plans to integrate more weather models, including Google’s WeatherNext 2, into the Transformers library to reduce dependencies and support easier inference and fine-tuning.

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