Single‐Column Emulation of Reanalysis of the Northeast Pacific Marine Boundary Layer
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 cumul...
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Published in | Geophysical research letters Vol. 46; no. 16; pp. 10053 - 10060 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Washington
John Wiley & Sons, Inc
28.08.2019
American Geophysical Union (AGU) |
Subjects | |
Online Access | Get full text |
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Summary: | 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.
Key Points
A new machine learning boundary layer parameterization, MARBLE, is developed from ERA5 reanalysis data for the summertime northeast Pacific
Used in a single‐column model, MARBLE reproduces ERA5 thermodynamic structure and cloud properties over 7‐day simulation periods
MARBLE reproduces the regional climatology of the stratocumulus to cumulus transition |
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Bibliography: | USDOE SC0016433 |
ISSN: | 0094-8276 1944-8007 |
DOI: | 10.1029/2019GL083646 |