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The quiet AI revolution in weather forecasting

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Abstract: Over the last 18 months a quiet AI revolution has begun in the field of numerical weather prediction. Medium-term weather prediction involves forecasting several days to a couple of weeks in the future and these forecasts are critical for making many social and economic decisions. The standard approach to this problem is to run detailed global simulations of the earth’s atmosphere using a supercomputer, so-called numerical weather prediction (NWP). As little as one year ago, researchers in this field had thought it unlikely that machine learning approaches would be competitive with numerical weather prediction any time soon. However, over the last year, the same advances that underpin large language models, like ChatGPT, have been applied to weather prediction. Surprisingly, these models achieve a performance which is already competitive with standard NWP , but with a computational cost that is 1000s of times cheaper. The deep learning based forecasts have also been shown to be surprisingly robust, performing reasonably even when faced with rare or extreme events. Consequently, weather prediction centres like the World Meteorological Organisation and the European Centre for Medium-Range Weather Forecasts (ECMWF) are now racing to build machine learning teams and publicly testing AI forecasts. This talk will describe this quieter AI revolution and it will end with a discussion of the opportunities for AI and machine learning in weather and climate, and speak a little more widely about the balancing act that must be struck between regulation and adoption of AI technology.

This talk is part of the Cambridge Philosophical Society series.

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