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A Machine Learning‐Based Global Atmospheric Forecast Model
Author(s) -
Arcomano Troy,
Szunyogh Istvan,
Pathak Jaideep,
Wikner Alexander,
Hunt Brian R.,
Ott Edward
Publication year - 2020
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl087776
Subject(s) - parameterized complexity , numerical weather prediction , global forecast system , supercomputer , atmospheric model , forecast skill , computer science , meteorology , massively parallel , climatology , algorithm , geology , geography , parallel computing
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.