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The global-scale patterns and covariances of subtropical marine\udboundary layer (MBL) cloud fraction and spatial variability with atmospheric\udthermodynamic and dynamic fields remain poorly understood. We describe an\udapproach that leverages coincident NASA A-train and the Modern Era\udRetrospective-Analysis for Research and Applications (MERRA) data to quantify\udthe relationships in the subtropical MBL derived at the native pixel and grid\udresolution. A new method for observing four subtropical oceanic regions that\udcapture transitions from stratocumulus to trade cumulus is demonstrated,\udwhere stratocumulus and cumulus regimes are determined from\udinfrared-based thermodynamic phase. Visible radiances are normally\uddistributed within stratocumulus and are increasingly skewed away from the\udcoast, where trade cumulus dominates. Increases in MBL depth, wind speed, and\udeffective radius (re), and reductions in 700–1000 hPa moist\udstatic energy differences and 700 and 850 hPa vertical velocity correspond\udwith increases in visible radiance skewness. We posit that a more robust\udrepresentation of the cloudy MBL is obtained using visible radiance rather\udthan retrievals of optical thickness that are limited to a smaller subset of\udcumulus. The method using the combined A-train and MERRA data set has\uddemonstrated that an increase in re within shallow cumulus is\udstrongly related to higher MBL wind speeds that further correspond to\udincreased precipitation occurrence according to CloudSat, previously\uddemonstrated with surface observations. Hence, the combined data sets have\udthe potential of adding global context to process-level understanding of the\udMBL

An A-train and MERRA view of cloud, thermodynamic, and dynamic variability within the subtropical marine boundary layer