Machine Learning Emulation of Subgrid‐Scale Orographic Gravity Wave Drag in a General Circulation Model With Middle Atmosphere Extension

Machine Learning Emulation of Subgrid‐Scale Orographic Gravity Wave Drag in a General Circulation Model With Middle Atmosphere Extension

Abstract Gravity wave parameterizations contribute to uncertainties in middle atmosphere modeling. To investigate the potential for using machine learning to represent atmospheric gravity waves and the impact of implementing such schemes in a general circulation model (GCM), we train a random forest...

Full description

Bibliographic Details
Main Authors: Yixiong Lu, Xin Xu, Lin Wang, Yiming Liu, Tongwen Wu, Weihua Jie, Jian Sun
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2024-03-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
middle atmosphere modeling
gravity wave parameterization
machine learning
stratospheric variability
Online Access:https://doi.org/10.1029/2023MS003611

Internet

https://doi.org/10.1029/2023MS003611

Similar Items