Characterizing Mesoscale Cellular Convection in Marine Cold Air
Outbreaks with a Machine Learning Approach
Abstract
During marine cold-air outbreaks (MCAOs), when cold polar air moves over
warmer ocean, a well-recognized cloud pattern develops, with open or
closed mesoscale cellular convection (MCC) at larger fetch over open
water. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment
(COMBLE) provided a comprehensive set of ground-based in-situ and remote
sensing observations of MCAOs at a coastal location in northern Norway.
MCAO periods that unambiguously exhibit open or closed MCC are
determined. Individual cells observed with a profiling Ka-band radar are
identified using a watershed segmentation method. Using self-organizing
maps (SOMs), these cells are then objectively classified based on the
variability in their vertical structure. The SOM nodes contain some
information about the location of the cell transect relative to the
center of the MCC. This adds classification noise, requiring numerous
cell transects to isolate cell dynamical information. The SOM-based
classification shows that comparatively intense convection occurs only
in open MCC. This convection undergoes an apparent lifecycle. Developing
cells are associated with stronger updrafts, large spectrum width,
larger amounts of liquid water, lower surface precipitation rates, and
lower cloud tops than mature and weakening cells. The weakening of these
cells is associated with the development of precipitation-induced cold
pools. The SOM classification also reveals less intense convection, with
a similar lifecycle. More stratiform vertical cloud structures with weak
vertical motions are common during closed MCC periods and are separated
into precipitating and non-precipitating stratiform cores. Convection is
observed only occasionally in the closed MCC environment.