A Machine Learning‐Based Global Atmospheric Forecast Model. Issue 9 (9th May 2020)

• Record Type: Journal Article
• Authors: Arcomano, Troy; Szunyogh, Istvan; Pathak, Jaideep; Wikner, Alexander; Hunt, Brian R.; Ott, Edward
• Is Part Of: Geophysical research letters. Volume 47:Issue 9(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-09
• Subjects: Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2020GL087776
• Languages: English
• ISSNs: 0094-8276

Abstract

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. Key Points: A low‐resolution, global, reservoir computing‐based machine learning (ML) model can forecast the atmospheric state The training of the ML model is computationally efficient on a massively parallel computer Compared to a numerical (physics‐based) model, the ML model performs best for the state variables most affected by parameterized processes