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Prognostic Validation of a Neural Network Unified Physics Parameterization
Author(s) -
Brenowitz N. D.,
Bretherton C. S.
Publication year - 2018
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 - 10.1029/2018gl078510
Subject(s) - artificial neural network , diabatic , grid , climate model , scale (ratio) , meteorology , computer science , numerical weather prediction , machine learning , physics , climate change , adiabatic process , geology , geodesy , oceanography , quantum mechanics , thermodynamics
Weather and climate models approximate diabatic and sub‐grid‐scale processes in terms of grid‐scale variables using parameterizations. Current parameterizations are designed by humans based on physical understanding, observations, and process modeling. As a result, they are numerically efficient and interpretable, but potentially oversimplified. However, the advent of global high‐resolution simulations and observations enables a more robust approach based on machine learning. In this letter, a neural network‐based parameterization is trained using a near‐global aqua‐planet simulation with a 4‐km resolution (NG‐Aqua). The neural network predicts the apparent sources of heat and moisture averaged onto (160 km) 2 grid boxes. A numerically stable scheme is obtained by minimizing the prediction error over multiple time steps rather than single one. In prognostic single‐column model tests, this scheme matches both the fluctuations and equilibrium of NG‐Aqua simulation better than the Community Atmosphere Model does.