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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/acp-2020-1026
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-1026
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Nov 2020

03 Nov 2020

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This preprint is currently under review for the journal ACP.

Identifying meteorological influences on marine low cloud mesoscale morphology using deep learning classifications

Johannes Mohrmann1, Robert Wood1, Tianle Yuan2,3, Hua Song4, Ryan Eastman1, and Lazaros Oreopoulos2 Johannes Mohrmann et al.
  • 1Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
  • 2Earth Science Division, NASA Goddard Space Flight Center, Goddard, MD, USA
  • 3Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD, USA
  • 4Science Systems and Application, Inc., Lanham, MD, USA

Abstract. Marine low cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of machine-learning generated classifications spanning three years. Meteorological variables and cloud properties are composited by mesoscale cloud type, showing distinct meteorological regimes of marine low cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud types are found to be well-separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence and suppressed regimes are associated with surface divergence.

Johannes Mohrmann et al.

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Johannes Mohrmann et al.

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Latest update: 24 Nov 2020
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Short summary
Observations of marine boundary layer conditions are composited by cloud type, based on a new classification dataset. It is found that two cloud types, representing regions of clustered and suppressed low-level clouds, occur in very similar large-scale conditions, but are distinguished from each other by considering low-level circulation and surface wind fields, validating prior results from modelling.
Observations of marine boundary layer conditions are composited by cloud type, based on a new...
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