Identifying meteorological influences on marine low-cloud mesoscale morphology using satellite classifications

Marine low-cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of classifications spanning 3 years generated by machine learning methods. Meteorological variables and cloud properties are composited by the mesoscale cloud type of the classification, 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, regardless of whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence, while suppressed regimes are associated with surface divergence.

Automated summary

Using machine learning methods, marine low-cloud mesoscale morphology in the southeastern Pacific Ocean was analyzed over a 3-year period. The study revealed distinct meteorological regimes of marine low-cloud organization from the tropics to the midlatitudes and detailed two tropical and subtropical cumuliform boundary layer regimes.