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

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1029/2019GL083646.

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Authors

Jeremy James McGibbon , Christopher S. Bretherton

Abstract

An artificial neural network is trained to reproduce thermodynamic tendencies and boundary layer properties from ERA5 HIRES 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 (MARBLE), 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. MARBLE more closely tracks the reanalysis than does a comparable configuration of the underlying forecast model.

DOI

https://doi.org/10.31223/osf.io/3bfps

Subjects

Atmospheric Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

Keywords

machine learning, Neural Network, boundary layer, emulation, ERA5, reanalysis

Dates

Published: 2019-05-30 14:37

Last Updated: 2019-07-26 18:10

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License

Academic Free License (AFL) 3.0

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