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A Machine-Learning-Based Global Atmospheric Forecast Model
  • +3
  • Istvan Szunyogh,
  • Troy Arcomano,
  • Jaideep Pathak,
  • Alexander Wikner,
  • Brian Hunt,
  • Edward Ott
Istvan Szunyogh
Texas A&M University, Texas A&M University

Corresponding Author:[email protected]

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Troy Arcomano
Texas A&M University, Texas A&M University
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Jaideep Pathak
University of Maryland, University of Maryland
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Alexander Wikner
University of Maryland, University of Maryland
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Brian Hunt
University of Maryland, University of Maryland
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Edward Ott
University of Maryland, University of Maryland
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Abstract

The paper investigates the applicability of machine learning (ML) to weather prediction by building a reservoir-computing-based, low-resolution, global prediction model. The model is designed to take advantage of the massively parallel architecture of a modern supercomputer. The forecast performance of the model is assessed by comparing it to that of daily climatology, persistence, and a numerical (physics-based) model of identical prognostic state variables and resolution. Hourly resolution 20-day forecasts with the model predict realistic values of the atmospheric state variables at all forecast times for the entire globe. The ML model outperforms both climatology and persistence for the first three forecast days in the midlatitudes, but not in the tropics. Compared to the numerical model, the ML model performs best for the state variables most affected by parameterized processes in the numerical model.