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

Abstract

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 scheme (Li et al., 2010, https://doi.org/10.1007/s10546-010-9523-y). A comparison between these two new models and the Li10 scheme against an iterative scheme revealed the hierarchy of maximum relative errors in estimating stability parameters, as well as momentum and heat transfer coefficients. This hierarchy is as follows: the Li10 scheme is the greatest, followed by the RF scheme, with the RF_Li10 scheme exhibiting the least errors.