Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement

Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement

Abstract The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or “tuning” the many free...

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Bibliographic Details
Main Authors: Fleur Couvreux, Frédéric Hourdin, Daniel Williamson, Romain Roehrig, Victoria Volodina, Najda Villefranque, Catherine Rio, Olivier Audouin, James Salter, Eric Bazile, Florent Brient, Florence Favot, Rachel Honnert, Marie‐Pierre Lefebvre, Jean‐Baptiste Madeleine, Quentin Rodier, Wenzhe Xu
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2021-03-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
calibration
large‐eddy simulations
physical parameterizations
process‐oriented model tuning
single‐column models
Online Access:https://doi.org/10.1029/2020MS002217

Internet

https://doi.org/10.1029/2020MS002217

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