Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations-Reference-Cited by-同舟云学术

Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations

Published:2022-02 Issue:2 Volume:14 Page:
ISSN:1942-2466
Container-title:Journal of Advances in Modeling Earth Systems
language:en
Short-container-title:J Adv Model Earth Syst

Author:

Bretherton Christopher S.¹^ORCID,Henn Brian¹^ORCID,Kwa Anna¹^ORCID,Brenowitz Noah D.¹^ORCID,Watt‐Meyer Oliver¹^ORCID,McGibbon Jeremy¹^ORCID,Perkins W. Andre¹^ORCID,Clark Spencer K.¹²^ORCID,Harris Lucas²^ORCID

Affiliation:

1. Vulcan Inc. and Allen Institute for Artificial Intelligence Seattle WA USA

2. Geophysical Fluid Dynamics Laboratory NOAA Princeton NJ USA

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021MS002794

Reference21 articles.

1. Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems

2. Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐Graining

3. Assessing Biases and Climate Implications of the Diurnal Precipitation Cycle in Climate Models

4. Normal mode initialization

5. Empirical Correction of a Coupled Land–Atmosphere Model

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