PNNL

Abstract

Marine boundary layer clouds are crucial in Earth's climate system. They frequently manifest as closed or open cell mesoscale cellular convection (MCC). MCC clouds are challenging to represent accurately in current climate models, highlighting the need for detailed observational datasets and in-depth analyses. This study utilizes over eight years of observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U-Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground-based radar measurements. This method addresses some observational gaps in satellite data related to low temporal resolution, nighttime challenges, and limited vertical structure capture. The analysis of the resultant MCC cases shows distinct differences between closed and open MCC: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, and stronger atmospheric stability than open MCCs. They also exhibit higher cloud droplet number concentrations and a more homogeneous liquid water path. Finally, we demonstrate two potential applications of our radar-based MCC classifications: 1) facilitating the investigation of aerosol-cloud interactions and 2) exploring meteorological factors along with MCC’s evolution by integrating satellite imagery and back-trajectory analysis. The identified MCC cases offer a valuable resource for the scientific community to study MCC processes further and improve climate model accuracy.