Marine low clouds are an important feature of the climate system, cooling the planet due to their high albedo and warm temperatures. They display a variety of different mesoscale organizations, which are tied to the varying environmental conditions in which they occur. This dissertation explores the drivers of marine low cloud variability using an observational perspective that draws upon aircraft, satellite, and reanalysis data, and uses the application of a number of machine learning techniques. The focus is largely though not exclusively on mesoscale organization. In the first part of this work, data from the Cloud System Evolution over the Trades (CSET) campaign over the Pacific stratocumulus-to-cumulus transition are organized into 18 Lagrangian cases suitable for study and future modeling, made possible by the use of a track-and-resample flight strategy. Analysis of these cases shows that 2-day Lagrangian coherence of long-lived species (CO and O3) is high (r=0.93 and 0.73, respectively), but that of subcloud aerosol, MBL depth, and cloud properties is limited. Although they span a wide range in meteorological conditions, most sampled air masses show a clear transition when considering 2-day changes in cloudiness (-31%averaged over all cases), MBL depth (1560 m), estimated inversion strength (EIS; 22.2K), and decoupling, agreeing with previous satellite studies and theory. Changes in precipitation and droplet number were less consistent. The aircraft-based analysis is augmented by geostationary satellite retrievals and reanalysis data along Lagrangian trajectories between aircraft sampling times, documenting the evolution of cloud fraction, cloud droplet number concentration, EIS, and MBL depth. An expanded trajectory set spanning the summer of 2015 is used to show that the CSET-sampled air masses were representative of the season, with respect to EIS and cloud fraction. Two Lagrangian case studies attractive for future modeling are presented with aircraft and satellite data. The first features a clear Sc-Cu transition involving MBL deepening and decoupling with decreasing cloud fraction, and the second undergoes a much slower cloud evolution despite a greater initial depth and decoupling state. Potential causes for the differences in evolution are explored, including free-tropospheric humidity, subsidence, surface fluxes, and microphysics. The remaining work focuses on the mesoscale organization of marine low clouds. A convolutional neural network (CNN) model is trained to classify 128 km by 128 km scenes of marine low clouds into six categories: stratus, open-cellular mesoscale cellular convection (MCC), closed-cellular MCC, disorganized MCC, clustered cumulus, and suppressed cumulus. Overall model test accuracy was approximately 90%. This model is applied to three years of data in the southeast Pacific, as well as the 2015 northeast Pacific summer for comparison with the CSET campaign. Meteorological variables related to marine low cloud processes are composited by mesoscale cloud type, allowing for the identification of distinct meteorological regimes. Presentation of MCC is largely consistent with previous literature, both in terms of geographic distribution boundary layer structure, and cloud-controlling factors. The two more novel types, clustered and suppressed cumulus, are examined in more detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the presentation of marine shallow mesoscale convective self-aggregation found in previous large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud regimes are found to be well-separated by variables associated with a low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Divergence is consistent with near-surface inflow into clustered regimes and outflow from suppressed regimes. To further understand the dependencies of mesoscale cloud type on environmental factors, a second classification model is built. This uses a random forest of decision trees to predict cloud type, but instead of using an image of a cloud scene, mesoscale averages of meteorological variables are used as inputs. The model uses the three-year dataset output from the CNN model for training, and overall accuracy is approximately 50%. Rotated principal component analysis of the meteorological variables is used to create a set of decorrelated features on which to train the model, allowing for the application of certain statistical analyses which rely on uncorrelated data. Permutation feature importance is used to quantify which variables are most important for correct prediction of cloud mesoscale organization. Overall, temperature and stability are approximately equally important; for correctly distinguishing between open-MCC and closed-MCC, stability is the most important feature, and for correctly distinguishing between suppressed and clustered cumulus, surface divergence is the most important variable. Partial dependence analysis is used to show the relationship between each input variable and the likelihood of observing each cloud type, and 2-dimensional partial dependence analysis shows bimodal distributions of MCC types, consistent with their subtropical and midlatitude incarnations. The random forest model is able to reproduce the geographic distributions of cloud type occurrences.

This volume presents the history of marine fog research and applications, and discusses the physical processes leading to fog's formation, evolution, and dissipation. A special emphasis is on the challenges and advancements of fog observation and modeling as well as on efforts toward operational fog forecasting and linkages and feedbacks between marine fog and the environment.

Over complex terrain, spatial inhomogeneities of pre-convective atmospheric conditions occur due to convection and mesoscale transport processes. This thesis focuses on the identification of these processes over the mountainous island of Corsica and on the evaluation of their impact on the spatial variability of water vapour, convection-related parameters and the evolution of deep convection by means of observations.

Marine low clouds are a major determinant of the Earth?s albedo and are a major source of uncertainty in how the climate responds to changing greenhouse gas levels and anthropogenic aerosol. Marine low clouds are particularly difficult to simulate accurately in climate models, and their remote locations present a significant observational challenge. A complex set of interacting controlling processes determine the coverage, condensate loading, and microphysical and radiative properties of marine low clouds. Marine low clouds are sensitive to atmospheric aerosol in several ways. Interactions at microphysical scales involve changes in the concentration of cloud droplets and precipitation, which induce cloud dynamical impacts including changes in entrainment and mesoscale organization. Marine low clouds are also impacted by atmospheric heating changes due to absorbing aerosols. The response of marine low clouds to aerosol perturbations depends strongly upon the unperturbed aerosol-cloud state, which necessitates greater understanding of processes controlling the budget of aerosol in the marine boundary layer. Entrainment and precipitation mediate the response of low clouds to aerosols but these processes also play leading roles in controlling the aerosol budget. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Climate Research Facility and Atmospheric System Research (ASR) program are making major recent investments in observational data sets from fixed and mobile sites dominated by marine low clouds. This report provides specific action items for how these measurements can be used together with process modeling to make progress on understanding and quantifying the key cloud and aerosol controlling processes in the next 5-10 years. Measurements of aerosol composition and its variation with particle size are needed to advance a quantitative, process-level understanding of marine boundary-layer aerosol budget. Quantitative precipitation estimates that combine radar and lidar measurements are becoming available, and these could be used to test process models, quantify precipitation responses to aerosol, and constrain climate models. Models and observations can be used to constrain how clouds respond dynamically to changing precipitation. New measurements of turbulence from ground-based remote sensing could be used to attempt to relate entrainment to the vertical and horizontal structure of turbulence in the boundary layer. Cloud-top entrainment plays a major role in modulating how low clouds respond to both aerosols and to greenhouse gases, so investment in promising new observational estimates would be beneficial. Precipitation formation and radiative cooling both help marine low clouds to organize on the mesoscale. More work is needed to develop metrics to characterize mesoscale organization, to elucidate mechanisms that determine the type and spatial scale of mesoscale cellular convection, and to understand the role of mesoscale structures in the stratocumulus-to-cumulus transition.