Convective environments in AI-models – What have Panguweather, Graphcast and Fourcastnet learned about atmospheric profiles?

Feldmann, Monika; Poulain-Auzéau, Louis; Gomez, Milton; Beucler, Tom; Martius, Olivia (1 March 2024). Convective environments in AI-models – What have Panguweather, Graphcast and Fourcastnet learned about atmospheric profiles? (Unpublished). In: 4th European Hail Workshop. Karslruhe. 05-07.03.2024.

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The recently released suite of AI-based medium-range forecast models can produce multi-day
forecasts within seconds, with a skill on par with the IFS model of ECMWF. Traditional model
evaluation predominantly targets global scores on single levels. Specific prediction tasks, such
as severe convective environments, require much more precision on a local scale and with
the correct vertical gradients in between levels. With a focus on the North American and
European convective season of 2020, we assess the performance of Panguweather, Graphcast
and Fourcastnet for instability and bulk shear at lead times of up to 5 days. By advancing the
assessment of large AI-models towards process-based evaluations we lay the foundation for
hazard-driven applications of AI-weather-forecasts.
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