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Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
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  • Troy Arcomano,
  • Istvan Szunyogh,
  • Alexander Wikner,
  • Jaideep Pathak,
  • Brian R Hunt,
  • Edward Ott
Troy Arcomano
Texas A&M University, Texas A&M University, Texas A&M University

Corresponding Author:[email protected]

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Istvan Szunyogh
Texas A&M University, Texas A&M University, Texas A&M University
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Alexander Wikner
University of Maryland, University of Maryland, University of Maryland
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Jaideep Pathak
Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory
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Brian R Hunt
University of Maryland, University of Maryland, University of Maryland
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Edward Ott
University of Maryland, University of Maryland, University of Maryland
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Abstract

This paper describes an implementation of the Combined Hybrid-Parallel Prediction (CHyPP) approach of Wikner et al. (2020) on a low-resolution atmospheric global circulation model (AGCM). The CHyPP approach combines a physics-based numerical model of a dynamical system (e.g., the atmosphere) with a computationally efficient type of machine learning (ML) called reservoir computing (RC) to construct a hybrid model. This hybrid atmospheric model produces more accurate forecasts of most atmospheric state variables than the host AGCM for the first 7-8 forecast days, and for even longer times for the temperature and humidity near the earth’s surface. It also produces more accurate forecasts than a model based only on ML, or a model that combines linear regression, rather than ML, with the AGCM. The potential of the approach for climate research is demonstrated by a 10-year long hybrid model simulation of the atmospheric general circulation, which shows that the hybrid model can simulate the general circulation with substantially smaller systematic errors and more realistic variability than the host AGCM.
Mar 2022Published in Journal of Advances in Modeling Earth Systems volume 14 issue 3. 10.1029/2021MS002712