Advances and prospects of deep learning for medium-range extreme weather forecasting

Leonardo Olivetti, Gabriele Messori

Geoscientific Model Development · 21 March 2024

In one sentence. Reviews deep learning medium-range forecasts and shows leading models struggle with extremes, proposing a workflow for extreme-event forecasting models.

Abstract

In recent years, deep learning models have rapidly emerged as a stand-alone alternative to physics-based numerical models for medium-range weather forecasting. Several independent research groups claim to have developed deep learning weather forecasts that outperform those from state-of-the-art physics-based models, and operational implementation of data-driven forecasts appears to be drawing near. However, questions remain about the capabilities of deep learning models with respect to providing robust forecasts of extreme weather. This paper provides an overview of recent developments in the field of deep learning weather forecasts and scrutinises the challenges that extreme weather events pose to leading deep learning models. Lastly, it argues for the need to tailor data-driven models to forecast extreme events and proposes a foundational workflow to develop such models.

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