Exploring machine-learning forecasts of extreme weather
Exploration des prévisions de phénomènes météorologiques extrêmes par l'apprentissage automatique
Magnusson, Linus
Over the last few years, developments in data-driven numerical weather prediction (NWP) based on machine learning have been very fast. A common setup is to use ECMWF's ERA5 reanalysis to train global models for medium-range forecasting. These work in a similar way to a conventional model, in the sense that they are initialised from an analysis and step forward in time using a model. Two of these models have been made public, namely Huawei's Pangu-Weather (PGW hereafter) and NVIDIA's FourCastNet. In the last few months, ECMWF staff have built infrastructure to run these models. They can now be run from our archived data as initial conditions, with the output saved in standardised formats and connected to our verification tools. For evaluation purposes, ECMWF has run 10-day forecasts initialised from our operational analysis, with output every six hours. For upper-air verification scores, such as 500 hPa geopotential height, PGW shows very competitive results compared to high-resolution forecasts (9 km horizontal resolution - HRES) of ECMWF's Integrated Forecasting System (IFS) in Cycle 47r3, in other words before the recent upgrade to Cycle 48r1. In this article, we will revisit two extreme cases from the past year to examine the ability of the PGW data-driven model to produce forecast extremes.</p>
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