NeuralGCM harnesses AI to better simulate long-range global precipitation
Precipitation remains one of the trickiest tasks for global-scale weather and climate models. That’s because exactly where, when and how much precipitation will fall depends on a series of events happening at scales that are typically below the model resolution. Simulating precipitation is especially challenging for extreme events and over long periods of time. Whether it’s farmers knowing which day to plant seeds to optimize their harvest, or city planners knowing how to prepare for a 100-year storm, precipitation forecasts are some of the most relevant for humans.
Last year, we introduced our open-sourced hybrid atmospheric model NeuralGCM, which combines machine learning (ML) and physics to run fast, efficient and accurate global atmospheric simulations. In the 2024 paper, NeuralGCM generated more accurate 2–15 day weather forecasts and reproduced historical temperatures over four decades with greater precision than traditional atmospheric models, marking a significant step towards developing more accessible climate models.
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