Abstract

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, some of the background on these developments will be given. A new generation of machine learning model will then be introduced 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. It will be shown that the system outperforms an operational NWP baseline for multiple variables and lead times for gridded and station forecasts. Finally, the talk will end by discussing how these ideas might develop over the next few years, including their application to multiple parts of the Earth system on multiple time-scales, and their potential impact on climate modelling