Single-Column Emulation of Reanalysis of the Northeast Pacific Marine Boundary Layer

Abstract

An artificial neural network is trained to reproduce thermodynamic tendencies and boundary layer properties from European Center for Medium-Range Weather Forecasts Reanalysis 5th Generation high resolution realization reanalysis data over the summertime northeast Pacific stratocumulus to trade cumulus transition region. The network is trained prognostically using 7-day forecasts rather than using diagnosed instantaneous tendencies alone. The resulting model, Machine-Assisted Reanalysis Boundary Layer Emulation, skillfully reproduces the boundary layer structure and cloud properties of the reanalysis data in 7-day single-column prognostic simulations over withheld testing periods. Radiative heating profiles are well simulated, and the mean climatology and variability of the stratocumulus to cumulus transition are accurately reproduced. Machine-Assisted Reanalysis Boundary Layer Emulation more closely tracks the reanalysis than does a comparable configuration of the underlying forecast model.