Vitart et al. (2025) Subseasonal-to-seasonal prediction of weather extremes

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-11-14
• Authors: Frédéric Vitart, Christopher Cunningham, Michael J. DeFlorio, Daniela I.V. Domeisen, Laura Ferranti, Brian Golding, Debra Hudson, Charles Jones, Emanuel Dutra, Christophe Lavaysse, Joanne Robbins, Michael K. Tippett
• DOI: 10.1016/b978-0-443-31538-1.00017-8

Research Groups

• European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom
• National Centre for Monitoring and Early Warning of Natural Disasters (CEMADEN), São José dos Campos, SP, Brazil
• Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, United States
• Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
• Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
• Met Office, Exeter, United Kingdom
• Bureau of Meteorology, Melbourne, VIC, Australia
• University of California, Santa Barbara (UCSB), Santa Barbara, CA, United States
• Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
• Institut des Géosciences de l’Environnement, CNRS-UGA-INRAE-IRD-Grenoble INP, Grenoble, France
• Columbia University, New York, NY, United States

Short Summary

This chapter introduces the critical importance of subseasonal-to-seasonal (S2S) prediction for mitigating the impacts of extreme weather events, highlighting the significant economic and social damages caused by such phenomena. It positions S2S prediction as a primary duty for developing early warning systems and improving preparedness against weather extremes.

Objective

• To underscore the necessity and role of subseasonal-to-seasonal (S2S) prediction in forecasting and mitigating the impacts of extreme weather events.

Study Configuration

• Spatial Scale: Regional to continental (e.g., "regional storms," "across Germany, Belgium, Luxembourg, and the Netherlands").
• Temporal Scale: Subseasonal-to-seasonal (typically 2 weeks to 2 months).

Methodology and Data

• Models used: Not detailed in this introductory chapter snippet.
• Data sources: Not detailed in this introductory chapter snippet (NOAA and Munich Re cited for damage statistics, not predictive data).

Main Results

• As this is an introductory chapter snippet, specific main results of the full chapter are not presented. The text primarily establishes the problem and the importance of S2S prediction.

Contributions

• This introductory chapter establishes the context and urgency for advancing subseasonal-to-seasonal prediction of weather extremes, emphasizing its value for early warning systems and disaster preparedness.

Funding

• Not detailed in this introductory chapter snippet.

Citation

@article{Vitart2025Subseasonaltoseasonal,
  author = {Vitart, Frédéric and Cunningham, Christopher and DeFlorio, Michael J. and Domeisen, Daniela I.V. and Ferranti, Laura and Golding, Brian and Hudson, Debra and Jones, Charles and Dutra, Emanuel and Lavaysse, Christophe and Robbins, Joanne and Tippett, Michael K.},
  title = {Subseasonal-to-seasonal prediction of weather extremes},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-31538-1.00017-8},
  url = {https://doi.org/10.1016/b978-0-443-31538-1.00017-8}
}