Machine Learning Gravity Wave Parameterization Generalizes to Capture the QBO and Response to Increased CO <sub>2</sub>-Reference-Cited by-同舟云学术

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Machine Learning Gravity Wave Parameterization Generalizes to Capture the QBO and Response to Increased CO ₂

Published:2022-04-16 Issue:8 Volume:49 Page:
ISSN:0094-8276
Container-title:Geophysical Research Letters
language:en
Short-container-title:Geophysical Research Letters

Author:

Espinosa Zachary I.¹^ORCID,Sheshadri Aditi¹^ORCID,Cain Gerald R.²,Gerber Edwin P.³^ORCID,DallaSanta Kevin J.⁴⁵^ORCID

Affiliation:

1. Department of Earth System Science Stanford University Stanford CA USA

2. Department of Computer Science Stanford University Stanford CA USA

3. Courant Institute of Mathematical Sciences New York University New York NY USA

4. NASA Goddard Institute for Space Studies New York NY USA

5. Universities Space Research Association Columbia MD USA

Funder

National Science Foundation

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022GL098174

Reference43 articles.

1. A Spectral Parameterization of Mean-Flow Forcing due to Breaking Gravity Waves

2. A quantitative study on the role of gravity waves in driving the tropical Stratospheric Semiannual Oscillation

3. Prognostic Validation of a Neural Network Unified Physics Parameterization

4. Momentum transport by gravity waves

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1. Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System;Scientific Data;2024-08-21

2. Interpretable Multiscale Machine Learning‐Based Parameterizations of Convection for ICON;Journal of Advances in Modeling Earth Systems;2024-08

3. Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM;Journal of Advances in Modeling Earth Systems;2024-07

4. Regression Forest Approaches to Gravity Wave Parameterization for Climate Projection;Journal of Advances in Modeling Earth Systems;2024-07

5. Uncertainty Quantification of a Machine Learning Subgrid‐Scale Parameterization for Atmospheric Gravity Waves;Journal of Advances in Modeling Earth Systems;2024-07

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