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