Aardvark weather: end-to-end data-driven weather forecasting

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• Richard Turner; University of Cambridge
• Tuesday 19 November 2024, 13:00-14:00
• CBL Seminar room BE4-38.

If you have a question about this talk, please contact Dr H Ge.

Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Over the last two years, machine learning models have shown that they have the potential to transform the complex weather prediction pipeline, but current approaches still rely on numerical weather prediction (NWP) systems, limiting forecast speed and accuracy. In this talk, I will give some of the background on these developments. I will then introduce a machine learning model which can replace the entire operational NWP pipeline. Aardvark Weather, an end-to-end data-driven weather prediction system, ingests raw observations and outputs global gridded forecasts and local station forecasts. Further, it can be optimised end-to-end to maximise performance over quantities of interest. I will show that the system outperforms an operational NWP baseline for multiple variables and lead times for gridded and station forecasts. These forecasts are produced with a remarkably simple neural process model using just 8% of the input data and three orders of magnitude less compute than existing NWP and hybrid AI-NWP methods. We anticipate that Aardvark Weather will be the starting point for a new generation of end-to-end machine learning models for medium-range forecasting.

This talk is part of the Div-F Faculty Talks series.

This talk is included in these lists:

• Cambridge University Engineering Department, CBL Seminar room BE4-38. For directions see http://learning.eng.cam.ac.uk/Public/Directions
• CBL Seminar room BE4-38

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