The book presents a comprehensive overview of the current state-of-the-art in the atmospheric boundary layer (ABL) research. It focuses on experimental ABL research, while most of the books on ABL discuss it from a theoretical or fluid dynamics point of view. Experimental ABL research has been made so far by surface-based in-situ experimentation (tower measurements up to a few hundred meters, surface energy balance measurements, short aircraft experiments, short experiments with tethered balloons, constant-level balloons, evaluation of radiosonde data). Surface flux measurements are also discussed in the book. Although the surface fluxes are one of the main driving factors for the daily variation of the ABL, an ABL description is only complete if its vertical structure is analyzed and determined. Satellite information is available covering large areas, but it has only limited temporal resolution and lacks sufficient vertical resolution. Therefore, surface-based remote sensing is a large challenge to enlarge the database for ABL studies, as it offers nearly continuous and vertically highly resolved information for specific sites of interest. Considerable progress has been made in the recent years in studying of ground-based remote sensing of the ABL. The book discusses such new subjects as micro-rain radars and the use of ceilometers for ABL profiling, modern small wind lidars for wind energy applications, ABL flux profile measurements, RASS techniques, and mixing-layer height determination.

The primary goal of this research is advancement in the utilization of satellite remote sensing methods with mesoscale simulation models for improved prediction of marine stratus and boundary layer structure. Related goals include the study of marine stratus evolution and analysis of microwave refractivity at the interface of the cloudy marine layer and the free troposphere. High accuracy for short-term prediction of cloud and inversion structure in marine environments is required for Navy operations, particularly in the vicinity of stratus and fog decks. Knowledge related to the probable evolution of cloud fractional cover, cloud liquid water profiles, the presence of precipitation and microwave ducting conditions are essential for effective logistical decision-making. Our research objectives focus on the optimum utilization of parameter fields from the Navy's COAMPS and other mesoscale forecasting models with geostationary satellite data for monitoring and predicting the short-term physical characteristics of the marine boundary layer cloud and thermodynamic conditions.

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.

Active remote sensing of marine boundary-layer clouds is challenging as drizzle drops often dominate the observed radar reflectivity. We present a new method to simultaneously retrieve cloud and drizzle vertical profiles in drizzling boundary-layer clouds using surface-based observations of radar reflectivity, lidar attenuated backscatter, and zenith radiances under conditions when precipitation does not reach the surface. The vertical structure of droplet size and water content of both cloud and drizzle is characterised throughout the cloud. An ensemble optimal estimation approach provides full error statistics given the uncertainty in the observations. To evaluate the new method, we first perform retrievals using synthetic measurements from large-eddy simulation snapshots of cumulus under stratocumulus, where cloud water path is retrieved with an error of 31 g m-2. The method also performs well in non-drizzling clouds where no assumption of the cloud profile is required. We then apply the method to observations of marine stratocumulus obtained during the Atmospheric Radiation Measurement MAGIC deployment in the Northeast Pacific. Here, retrieved cloud water path agrees well with independent three-channel microwave radiometer retrievals, with a root mean square difference of 10-20 g m-2.

This book is a collection of the lectures, held at the International Summer School ISSAOS-2000 in L'Aquila (Italy), given by invited lecturers coming from both Europe and the USA. The goal of the book is to provide a broad panorama of spaceborne remote sensing techniques, at both microwave and visible-infrared bands and by both active and passive sensors, for the retrieval of atmospheric and oceanic parameters. A significant emphasis is given to the physical modeling background, instrument potential and limitations, inversion methods and applications. Topics on international remote sensing programs and assimilation techniques into numerical weather forecast models are also touched. The main purpose of the book is to offer to young scientists, Ph.D. or equivalent students, and to all who would like to have a broad-spectrum understanding of spaceborne remote sensing capabilities, introductory material to each remote sensing topic written by the most qualified experts in the field.