CoSy seminar: "Recent advances in deep learning for medium-range (extreme) weather forecasting"

Everyone is welcome and the first 40 people to register will be treated to a free lunch sandwich. If you do not want lunch, you are still welcome to join.

Register for a lunch sandwich (deadline: Sunday 18 February)

Abstract: 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-based 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.

The talk is based on the following preprint: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2490/.

This is a lecture in the seminar series held by CIM (Centre for Interdisciplinary Mathematics).