EGU24-5611, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Advances and Prospects of Deep Learning for Medium-Range Extreme Weather Forecasting

Leonardo Olivetti1 and Gabriele Messori1,2
Leonardo Olivetti and Gabriele Messori
  • 1Department of Earth Sciences and Centre of Natural Hazards and Disaster Science, Uppsala University, 75236 Uppsala, Sweden
  • 2Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden

In recent years, deep learning models have rapidly emerged as a standalone alternative to physics-based numerical models for medium-range weather forecasting. Several independent research groups claim to have developed deep learning weather forecasts which outperform those from state-of-the-art physics-basics models, and operational implementation of data-driven forecasts appears to be drawing near. Yet, questions remain about the capabilities of deep learning models to provide robust forecasts of extreme weather.

Our current work aims to provide an overview of recent developments in the field of deep learning weather forecasting, and highlight the challenges that extreme weather events pose to leading deep learning models. Specifically, we problematise the fact that predictions generated by many deep learning models appear to be oversmooth, tending to underestimate the magnitude of wind and temperature extremes. To address these challenges, we argue for the need to tailor data-driven models to forecast extreme events, and develop models aiming to maximise the skill in the tails rather than in the mean of the distribution. Lastly, we propose a foundational workflow to develop robust models for extreme weather, which may function as a blueprint for future research on the topic.

How to cite: Olivetti, L. and Messori, G.: Advances and Prospects of Deep Learning for Medium-Range Extreme Weather Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5611,, 2024.