Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model-Reference-Cited by-同舟云学术

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Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

Published:2020-03 Issue:3 Volume:12 Page:
ISSN:1942-2466
Container-title:Journal of Advances in Modeling Earth Systems
language:en
Short-container-title:J. Adv. Model. Earth Syst.

Author:

Gagne David John¹^ORCID,Christensen Hannah M.¹²^ORCID,Subramanian Aneesh C.³^ORCID,Monahan Adam H.⁴^ORCID

Affiliation:

1. National Center for Atmospheric Research Boulder CO USA

2. Atmospheric, Oceanic and Planetary Physics University of Oxford Oxford UK

3. Department of Atmospheric and Oceanic Sciences University of Colorado Boulder CO USA

4. School of Earth and Ocean Sciences University of Victoria Victoria British Columbia Canada

Funder

National Centre for Earth Observation

National Science Foundation

Publisher

American Geophysical Union (AGU)

Subject

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

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019MS001896

Reference93 articles.

1. Realistic initiation and dynamics of the Madden-Julian Oscillation in a coarse resolution aquaplanet GCM

2. Stochastic parametrizations and model uncertainty in the Lorenz ’96 system

3. A Model Framework for Stochastic Representation of Uncertainties Associated with Physical Processes in NOAA’s Next Generation Global Prediction System (NGGPS)

4. Stochastic Parameterization: Toward a New View of Weather and Climate Models

5. Systematic Model Error: The Impact of Increased Horizontal Resolution versus Improved Stochastic and Deterministic Parameterizations

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