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...
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 |
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Format: | Article |
Language: | English |
Published: |
American Geophysical Union (AGU)
2021-03-01
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Series: | Journal of Advances in Modeling Earth Systems |
Subjects: | |
Online Access: | https://doi.org/10.1029/2020MS002217 |
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