What is it about?

Recent work has begun to explore building global weather prediction models using only machine learning techniques trained on large amounts of atmospheric data. We develop a vastly improved machine learning algorithm capable of operating like traditional weather models and predicting several fundamental atmospheric variables, including near‐surface temperature. While our model does not yet compete with the state‐of‐the‐art in numerical weather prediction, it computes realistic forecasts that perform well and execute extremely quickly, offering a potential avenue for future developments in probabilistic weather forecasting.

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Why is it important?

This is an important milestone on the path to potentially replacing current global numerical weather prediction models with models that use artificial intelligence to capture the evolution of the atmosphere.

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This page is a summary of: Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere, Journal of Advances in Modeling Earth Systems, September 2020, American Geophysical Union (AGU),
DOI: 10.1029/2020ms002109.
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