Preprints
https://doi.org/10.5194/egusphere-2023-2490
https://doi.org/10.5194/egusphere-2023-2490
24 Nov 2023
 | 24 Nov 2023

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

Leonardo Olivetti and Gabriele Messori

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.

Leonardo Olivetti and Gabriele Messori

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2490', Anonymous Referee #1, 31 Jan 2024
    • AC1: 'Reply on RC1', Leonardo Olivetti, 06 Feb 2024
  • RC2: 'Comment on egusphere-2023-2490', Anonymous Referee #2, 04 Feb 2024
    • AC2: 'Reply on RC2', Leonardo Olivetti, 06 Feb 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2490', Anonymous Referee #1, 31 Jan 2024
    • AC1: 'Reply on RC1', Leonardo Olivetti, 06 Feb 2024
  • RC2: 'Comment on egusphere-2023-2490', Anonymous Referee #2, 04 Feb 2024
    • AC2: 'Reply on RC2', Leonardo Olivetti, 06 Feb 2024
Leonardo Olivetti and Gabriele Messori
Leonardo Olivetti and Gabriele Messori

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This article provides a concise and well-written review of the current state of numerical weather prediction using machine learning models. Given how quickly this field is evolving, it's difficult for the traditional peer review process to capture all developments in this space, but this manuscript provides an excellent snapshot of the current state of the art.
Short summary
In recent years, deep learning models have emerged as a data-driven alternative to physics-based models for medium-range weather forecasting. This article provides an overview of recent developments in the field, and explores the challenges that deep learning models face when considering extreme weather events. It argues for the need to complement current approaches with models specifically designed to handle extreme events, and proposes a foundational framework to develop such models.