Two Random Forest Models for the Non‐Iterative Parametrization of Surface‐Layer Turbulent Fluxes

• Published in: Geophysical research letters Vol. 50; no. 21
• Main Authors: Yu, Yingxin, Gao, Chloe Yuchao, Li, Yubin, Gao, Zhiqiu
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 16.11.2023; Wiley
• Subjects: Accuracy; Algorithms; Artificial intelligence; Atmosphere; Atmospheric boundary layer; Decision trees; Efficiency; Errors; Fluxes; Heat; Heat transfer; Heat transfer coefficients; Machine learning; Momentum; Parameter estimation; Parameterization; Turbulent fluxes; Weather forecasting

This study investigated two random forest (RF) models for the non‐iterative parametrization of surface‐layer turbulent fluxes: (a) the RF scheme, a calculation model that is directly trained using correlated variables, and (b) the RF_Li10 scheme, a random forest correction model based on the Li10 sc...