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Understanding the role of lake breeze in vertical ozone profiles using unmanned aerial systems at a shoreline location. Vertical profiles show gradients in ozone with higher ozone in areas of steep temperature inversion.

Observing thermodynamic profiles within the planetary boundary layer is essential to understanding and predicting atmospheric phenomena because of the significant exchange of sensible and latent heat between the land and atmosphere within that layer. The Atmospheric Emitted Radiance Interferometer (AERI) is a ground-based infrared spectrometer used to obtain the vertical profiles of temperature and water vapor mixing ratio. Most AERIs are only capable of zenith views, although the Marine AERI (M-AERI) has a design that allows it to view various elevation angles. In this study, we quantify the improvement in the information content and accuracy of the retrieved profiles when nonzenith angles are included, as is common with microwave radiometer profilers. The impacts of the additional scan angles are quantified through both a synthetic study and with M-AERI observations from the ARM Cloud Aerosol Precipitation Experiment (ACAPEX) campaign. The simulation study shows that low elevation angles contain more information content for temperature whereas high elevation angles have more information content for water vapor. Outside of very humid environments, the addition of low elevation angles also results in lower root-mean-square errors when compared with high angles for both temperature and water vapor mixing ratio, although this is primarily a result of averaging multiple observations together to reduce instrument noise. Real-world results from the ACAPEX dataset indicate similar results as were found for the simulation study, although not all predicted benefits are realized because of the small sample size and observational uncertainties.

Profiles of atmospheric temperature and water vapor from remotely sensed platforms provide critical observations within the temporal and spatial gaps of the radiosonde network. The 2017 National Academies of Science Decadal Survey highlighted that observations of the planetary boundary layer (PBL) from the current space-based observing system are not of the necessary accuracy or resolution for monitoring and predicting high-impact weather phenomena. One possible solution to improving observations of the PBL is supplementing the existing space-based observing system with a network of ground-based profilers. A synthetic information content study is developed utilizing profiles from the Atmospheric Radiation Measurement (ARM) program sites at the Southern Great Plains (SGP), east North Atlantic (ENA), and North Slope of Alaska (NSA) to assess the benefits, in terms of degrees of freedom (DOF), vertical resolution, and uncertainties, of a synergy between the ground-based Atmospheric Emitted Radiance Interferometer (AERI) with space-based hyperspectral infrared (IR) sounders. A combination of AERI with any of the three polar-orbiting IR sounders: the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), or the Infrared Atmospheric Sounding Interferometer (IASI), results in a DOF increase of 30%–40% in the surface-to-700-hPa layer compared to the space-based instrument alone. Introducing AERI measurements to the observing system also results in significant improvements to vertical resolution and uncertainties in the bottom 1000 m of the atmosphere compared to CrIS measurements alone. A synergy of CrIS and AERI exceeds the 1-km-vertical-resolution goal set by the Decadal Survey in the lowest 1000 m.

Ground-based thermodynamic and kinematic profilers were placed adjacent to the western shore of Lake Michigan at two sites as part of the 2017 Lake Michigan Ozone Study. The southern site near Zion, Illinois, hosted a microwave radiometer (MWR) and a sodar wind profiler, while the northern site in Sheboygan, Wisconsin, featured an Atmospheric Emitted Radiance Interferometer (AERI), a Doppler lidar, and a High Spectral Resolution Lidar (HSRL). Each site experienced several lake-breeze events during the experiment. Composite time series and time–height cross sections were constructed relative to the lake-breeze arrival time so that commonalities across events could be explored. The composited surface observations indicate that the wind direction of the lake breeze was consistently southeasterly at both sites regardless of its direction before the arrival of the lake-breeze front. Surface relative humidity increased with the arriving lake breeze, though this was due to cooler air temperatures as absolute moisture content stayed the same or decreased. The profiler observations show that the lake breeze penetrated deeper when the local environment was unstable and preexisting flow was weak. The cold air associated with the lake breeze remained confined to the lowest 200 m of the troposphere even if the wind shift was observed at higher altitudes. The evolution of the lake breeze corresponded well to observed changes in baroclinicity and calculated changes in circulation. Collocated observations of aerosols showed increases in number and mass concentrations after the passage of the lake-breeze front.

Observations of thermodynamic and kinematic parameters associated with derivatives of the thermodynamics and wind fields, namely, advection, vorticity, divergence, and deformation, can be obtained by applying Green’s theorem to a network of observing sites. The five nodes that comprise the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) profiling network, spaced 50–80 km apart, are used to obtain measurements of these parameters over a finite region. To demonstrate the applicability of this technique at this location, it is first applied to gridded model output from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model, using profiles from the locations of ARM network sites, so that values calculated from this method can be directly compared to finite difference calculations. Good agreement is found between both approaches as well as between the model and values calculated from the observations. Uncertainties for the observations are obtained via a Monte Carlo process in which the profiles are randomly perturbed in accordance with their known error characteristics. The existing size of the ARM network is well suited to capturing these parameters, with strong correlations to model values and smaller uncertainties than a more closely spaced network, yet it is small enough that it avoids the tendency for advection to go to zero over a large area.

Low-powered commercially-available coherent Doppler lidar (CDL) provides continuous measurement of vertical profiles of wind in the lower troposphere, usually close to or up to the top of the planetary boundary layer. The vertical extent of these wind profiles is limited by the availability of scatterers, and thus varies substantially throughout the day and from one day to the next. This makes it challenging to develop continuous products that rely on CDL-observed wind profiles. In order to overcome this problem, we have developed a new method for wind profile retrievals from CDL that combines the traditional velocity-azimuth display (VAD) technique with optimal estimation (OE) to provide continuous wind profiles up to 3 km. The new method exploits the level-to-level covariance present in the wind profile to fill in the gaps where the signal to noise ratio of the CDL return is too low to provide reliable results using the traditional VAD method. Another advantage of the new method is that it provides the full error covariance matrix of the solution and profiles of information content, which more easily facilitates the assimilation of the observed wind profiles into numerical weather prediction models. This method was tested using CDL measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility. Comparison with the ARM operational CDL wind profile product and collocated radiosonde wind measurements shows excellent agreement (R2 > 0.99) with no degradation in results where the traditional VAD provided a valid solution. In the region where traditional VAD do not provide results, the OE wind speed has uncertainty of 4.5 m/s. As a result, the new method provides additional information over the standard technique and increases the effective range of existing CDL systems without the need for additional hardware.

The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multiagency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes; the role of lake breezes; contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management; and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 aircraft capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9–12 June, and 14–16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings.

A multisensor snowfall observational suite has been deployed at the Marquette, Michigan, National Weather Service Weather Forecast Office (KMQT) since 2014. Micro Rain Radar (MRR; profiling radar), Precipitation Imaging Package (PIP; snow particle imager), and ancillary ground-based meteorological observations illustrate the unique capabilities of these combined instruments to document radar and concomitant microphysical properties associated with northern Great Lakes snowfall regimes. Lake-effect, lake-orographic, and transition event case studies are presented that illustrate the variety of snowfall events that occur at KMQT. Case studies and multiyear analyses reveal the ubiquity of snowfall produced by shallow events. These shallow snowfall features and their distinctive microphysical fingerprints are often difficult to discern with conventional remote sensing instruments, thus highlighting the scientific and potential operational value of MRR and PIP observations. The importance of near-surface lake-orographic snowfall enhancement processes in extreme snowfall events and regime-dependent snow particle microphysical variability controlled by regime and environmental factors are also highlighted.

The addition of moisture observations via the Water Vapor Sensing System (WVSS) from about 150 aircraft available operationally through the World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) program now provides highly reliable thermodynamic profiles of the troposphere. The nearly 900 profiles available daily provide greater temporal and spatial density than the operational radiosonde network over many parts of the United States. Previous studies comparing WVSS reports with specially collocated radiosondes have documented the quality and consistency of the WVSS observations. These studies, however, have been limited for short periods at a single location. This study expands on the earlier evaluations by using operational U.S. radiosondes from 2015 in a variety of locations, seasons, and climates. Comparison profiles at radiosonde sites were calculated in pressure layers and then interpolated to terrain-following sigma coordinates to account for the differences in elevations of comparison sites and provide a better means of integrating the higher vertical resolution of AMDAR observations taken in the boundary layer. Overall, systematic differences between the WVSS and radiosondes are smallest just above the surface, with the WVSS observations being slightly moister than the radiosondes aloft, with WVSS reports being moister during ascent than descent—possibly the result of small hysteresis effects. Standard deviations averaged 1.3 g kg−1 near the surface over the yearlong period. Differences varied by season and region. Overall, the results indicate that WVSS observations are compatible with radiosonde reports and can be used with high confidence to fill temporal and spatial data gaps.

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, USA, active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include radar wind profilers, microwave radiometers, atmospheric emitted radiance interferometers, ceilometers, high spectral resolution lidars, Doppler lidars, and collaborative lower-atmospheric mobile profiling systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary layer depth estimations is also investigated. We found that while all instruments are overall able to provide reasonable boundary layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, radar wind profilers perform well during cloud-free conditions, and microwave radiometers and atmospheric emitted radiance interferometers have a very good agreement during all conditions but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from ceilometers and high spectral resolution lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the collaborative lower atmospheric mobile profiling systems can be constricted to a limited height range in low-aerosol conditions.

Presented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annual snow accumulation). In 2014 the site was augmented with a Micro Rain Radar (MRR) and a Precipitation Imaging Package (PIP). MRR observations are utilized to partition large-scale synoptically driven (deep) and surface-forced (shallow) snow events. Coincident PIP and NWS MQT meteorological surface observations illustrate different characteristics with respect to snow event category. Shallow snow events are often extremely shallow, with MRR-indicated precipitation heights of less than 1500 m above ground level. Large vertical reflectivity gradients indicate efficient particle growth, and increased boundary layer turbulence inferred from observations of spectral width implies increased aggregation in shallow snow events. Shallow snow events occur 2 times as often as deep events; however, both categories contribute approximately equally to estimated annual accumulation. PIP measurements reveal distinct regime-dependent snow microphysical differences, with shallow snow events having broader particle size distributions and comparatively fewer small particles and deep snow events having narrower particle size distributions and comparatively more small particles. In addition, coincident surface meteorological measurements indicate that most shallow snow events are associated with surface winds originating from the northwest (over Lake Superior), cold temperatures, and relatively high surface pressures, which are characteristics that are consistent with cold-air outbreaks. Deep snow events have meteorologically distinct conditions that are accordant with midlatitude cyclones and frontal structures, with mostly southwest surface winds, warmer temperatures approaching freezing, and lower surface pressures.

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, U.S.A., active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include Radar Wind Profilers, Microwave Radiometers, Atmospheric Emitted Radiance Interferometers, Ceilometers, High Spectral Resolution Lidars, Doppler Lidars, and Collaborative Lower Atmospheric Modelling Profiling Systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary-layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary-layer depth is also investigated. We found that while overall all instruments are able to provide reasonable boundary-layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, Radar Wind Profilers perform well during cloud free conditions, and Microwave Radiometers and Atmospheric Emitted Radiance Interferometers have a very good agreement during all conditions, but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from Ceilometers and High Spectral Resolution Lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the Collaborative Lower Atmospheric Modelling Profiling Systems can be constricted to a limited height range in low aerosol conditions.

The meso-scale meteorology of lake breezes along Lake Michigan impacts local observations of high ozone events. Previous manned aircraft and UAS observations have demonstrated non-uniform ozone concentrations within and above the marine layer over water and within shoreline environments. During the 2021 Wisconsin’s Dynamic Influence of Shoreline Circulations on Ozone (WiscoDISCO-21) campaign, two UAS platforms, a fixed-wing (University of Colorado RAAVEN) and a multirotor (Purdue University DJI M210), were used simultaneously to capture lake breeze during forecasted high ozone events at Chiwaukee Prairie State Natural Area in southeastern Wisconsin from May 21–26, 2021. The RAAVEN platform (data DOI: 10.5281/zenodo.5142491) measured temperature, humidity, and 3-D winds during 2-hour flights following two separate flight patterns up to 3 times per day at altitudes reaching 500 m above ground level. The M210 platform (data DOI: 10.5281/zenodo.5160346) measured vertical profiles of temperature, humidity and ozone during 15-minute flights up to 6 times per day at altitudes reaching 120 m above ground level (AGL) near to a WI-DNR ground monitoring station (AIRS ID: 55-059-0019). This campaign was conducted in conjunction with the Enhanced Ozone Monitoring plan from WI-DNR that included Doppler lidar wind profiler observations at the site (data DOI:10.5281/zenodo.5213039).

Atmospheric bores have been shown to have a role in the initiation and maintenance of elevated convection. Previous observational studies of bores have been case studies of more notable events. However, this creates a selection bias toward extraordinary cases, while discussions of the differences between bores that favor convective initiation and maintenance and bores that do not are lacking from the literature. This study attempts to fill that gap by analyzing a high-temporal-resolution thermodynamic profile composite of eight bores observed by multiple platforms during the Plains Elevated Convection at Night (PECAN) campaign in order to assess the impact of bores on the environment. The time–height cross section of the potential temperature composite displays quasi-permanent parcel displacements up to 900 m with the bore passage. Low-level lifting is shown to weaken the capping inversion and reduce convective inhibition (CIN) and the level of free convection (LFC). Additionally, low-level water vapor increases by about 1 g kg−1 in the composite mean. By assessing variability across the eight cases, it is shown that increases in low-level water vapor result in increases to convective available potential energy (CAPE), while drying results in decreased CAPE. Most cases resulted in decreased CIN and LFC height with the bore passage, but only some cases resulted in increased CAPE. This suggests that bores will increase the potential for convective initiation, but future research should be directed toward better understanding cases that result in increased CAPE as those are the types of bores that will increase severity of convection.

Mobile systems equipped with remote sensing instruments capable of simultaneous profiling of temperature, moisture, and wind at high temporal resolutions can offer insights into atmospheric phenomena that the operational network cannot. Two recently developed systems, the Space Science and Engineering Center (SSEC) Portable Atmospheric Research Center (SPARC) and the Collaborative Lower Atmosphere Profiling System (CLAMPS), have already experienced great success in characterizing a variety of phenomena. Each system contains an Atmospheric Emitted Radiance Interferometer for thermodynamic profiling and a Halo Photonics Stream Line Doppler wind lidar for kinematic profiles. These instruments are augmented with various in situ and remote sensing instruments to provide a comprehensive assessment of the evolution of the lower troposphere at high temporal resolution (5 min or better). While SPARC and CLAMPS can be deployed independently, the common instrument configuration means that joint deployments with well-coordinated data collection and analysis routines are easily facilitated.In the past several years, SPARC and CLAMPS have participated in numerous field campaigns, which range from mesoscale campaigns that require the rapid deployment and teardown of observing systems to multiweek fixed deployments, providing crucial insights into the behavior of many different atmospheric boundary layer processes while training the next generation of atmospheric scientists. As calls for a nationwide ground-based profiling network continue, SPARC and CLAMPS can play an important role as test beds and prototype nodes for such a network.

Routine in situ observations of the atmosphere taken in flight by commercial aircraft provide atmospheric profiles with greater temporal density and, in many parts of the country, at more locations than the operational radiosonde network. Thousands of daily temperature and wind observations are provided by largely complementary systems, the Airborne Meteorological Data Relay (AMDAR) and the Tropospheric Airborne Meteorological Data Reporting (TAMDAR). All TAMDAR aircraft also measure relative humidity while a subset of AMDAR aircraft are equipped with the Water Vapor Sensing System (WVSS) measure specific humidity.One year of AMDAR/WVSS and TAMDAR observations are evaluated against operational National Weather Service (NWS) radiosondes to characterize the performance of these systems in similar environments. For all observed variables, AMDAR reports showed both smaller average differences and less random differences with respect to radiosondes than the corresponding TAMDAR observations. Observed differences were not necessarily consistent with known radiosonde biases. Since the systems measure different humidity variables, moisture is evaluated in both specific and relative humidity using both aircraft and radiosonde temperatures to derive corresponding moisture variables. Derived moisture performance is improved when aircraft-based temperatures are corrected prior to conversion. AMDAR observations also show greater consistency between different aircraft than TAMDAR observations do. The small variability in coincident WVSS humidity observations indicates that they may prove more reliable than humidity observations from NWS radiosondes.

Large-eddy simulations are useful tools to study transport processes by mesoscale structures in the atmospheric boundary layer, since in contrast to single-tower eddy covariance measurements, they provide not only temporally but also spatially highly resolved information. Therefore, they are well suited to study the energy balance closure problem, for which the mesoscale transport of latent and sensible heat, triggered by heterogeneous ecosystems, is suspected to be a major cause. However, this requires simulations that are as realistic as possible and thus allow a comparison of real measurements in the field and virtual measurements in the simulation.During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD) experiment in the summer of 2019, a heterogeneous 10x10 square km domain was intensively sampled across scales. This data offers a unique possibility to set up large-eddy simulations with realistic surface heterogeneity. We use PALM to simulate two days and an area of 40 by 40 square kilometers incorporating the CHEESEHEAD site. The large scale atmospheric forcings to inform the boundary conditions are determined from the NCEP HRRR product. As the lower boundary condition, we use a soil and land-surface model coupled with a plant-canopy model, which we adapt to the CHEESEHEAD area based on ground-based and airborne measurements of plant physiological data.In this study, we investigate how well the simulations match with real measurements by comparing simulated profiles and virtual tower measurements with field measurements from radiosonde ascents, lidar measurements of three-dimensional wind and water vapor, eddy-covariance measurements from the 400 meter tower in the center of the study domain, as well as from typical eddy-covariance stations distributed through the study area. This way, we investigate how realistic the simulations actually are and to what extent the knowledge gained from them concerning the energy balance closure problem can be transferred to field measurements.

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

The Lake Michigan Ozone Study 2017 (LMOS 2017) in May and June 2017 enabled study of transport, emissions, and chemical evolution related to ozone air pollution in the Lake Michigan airshed. Two highly instrumented ground sampling sites were part of a wider sampling strategy of aircraft, shipborne, and ground-based mobile sampling. The Zion, Illinois site (on the coast of Lake Michigan, 67 km north of Chicago) was selected to sample higher NOx air parcels having undergone less photochemical processing. The Sheboygan, Wisconsin site (on the coast of Lake Michigan, 211 km north of Chicago) was selected due to its favorable location for observation of photochemically aged plumes during ozone episodes involving southerly winds with lake breeze. The study encountered elevated ozone during three multiday periods. Daytime ozone episode concentrations at Zion were 60 ppb for ozone, 3.8 ppb for NOx, 1.2 ppb for nitric acid, and 8.2 µg/m3 for fine particulate matter. At Sheboygan daytime ozone episode concentrations were 60 ppb for ozone, 2.5 ppb for NOx, and 2.9 ppb for NOy. To facilitate informed use of the LMOS 2017 data repository, we here present comprehensive site description, including airmass influences during high ozone periods of the campaign, overview of meteorological and pollutant measurements, analysis of continuous emission monitor data from nearby large point sources, and characterization of local source impacts from vehicle traffic, large point sources, and rail. Consistent with previous field campaigns and the conceptual model of ozone episodes in the area, trajectories from the southwest, south, and lake breeze trajectories (south or southeast) were overrepresented during pollution episodes. Local source impacts from vehicle traffic, large point sources, and rail were assessed and found to represent less than about 15% of typical concentrations measured. Implications for model-observation comparison and design of future field campaigns are discussed. Implication StatementThe Lake Michigan Ozone Study 2017 (LMOS 2017) was conducted along the western shore of Lake Michigan, and involved two well-instrumented coastal ground sites (Zion, IL, and Sheboygan, WI). LMOS 2017 data is publicly available, and this paper provides detailed site characterization and measurement summary to enable informed use of repository data. Minor local source impacts were detected but were largely confined to nighttime conditions of less interest for ozone episode analysis and modeling. The role of these sites in the wider field campaign and their detailed description facilitates future campaign planning, informed data repository use, and model-observation comparison. Implication Statement The Lake Michigan Ozone Study 2017 (LMOS 2017) was conducted along the western shore of Lake Michigan, and involved two well-instrumented coastal ground sites (Zion, IL, and Sheboygan, WI). LMOS 2017 data is publicly available, and this paper provides detailed site characterization and measurement summary to enable informed use of repository data. Minor local source impacts were detected but were largely confined to nighttime conditions of less interest for ozone episode analysis and modeling. The role of these sites in the wider field campaign and their detailed description facilitates future campaign planning, informed data repository use, and model-observation comparison.

Water vapor plays a crucial role for several processes on almost every scale of Earth's atmosphere. However, its turbulent transport throughout the PBL is at the same time particularly important and not very well understood. With the increasing resolution of numerical models arises the need for improved representations of the small-scale and nonlinear turbulent processes inside the PBL. Recent papers have shown that these refinements, predominantly on water vapor transport, are urgently needed as they are a limiting factor in the process of lifting numerical weather prediction to the next level. The CHEESEHEAD campaign, carried out in summer 2019, especially addresses the complex, turbulent land-atmosphere interactions over heterogeneous terrain and aims to close the energy balance. Therefore, a dense network of sensors has been installed measuring throughout the scales of the PBL with a multiple set of different measurement techniques. Within this proposed contribution, first results from the CHEESEHEAD measurements with a water vapor DIAL in combination with several Doppler wind LiDARs will be presented. The synergy of a virtual tower scanning geometry of the Doppler LiDARs right next to the water vapor DIAL delivers highly resolved data throughout the entire PBL. Therefore, special focus of this work lies on turbulent fluctuations inside vertical water vapor columns during the measuring time, spanning the entire PBL. This gives the additional opportunity to observe important entrainment processes at the very top of the PBL. Furthermore, this work deals with the calculation of vertical fluxes of latent heat. Therefore, the essential question whether this data is suitable for these calculations, which are highly sensitive towards temporal resolution, will be addressed. As a further step, aerosol and temperature data that have been measured with the same LiDAR system shall be integrated as well - aiming towards a comprehensive insight on relevant processes throughout the entire PBL.

Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current Earth system models to capture and simulate land–atmosphere feedback. To facilitate its realistic simulation in next-generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land–atmosphere (L–A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable.Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive datasets for characterizing L–A feedback independently of any model input. Several new applications are introduced, such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain; the testing of Monin–Obukhov similarity theory and turbulence parameterizations; the direct measurement of entrainment fluxes; and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE dataset are introduced. We envision the initiation of innovative L–A feedback studies in different climate regions to improve weather forecast, climate, and Earth system models worldwide.

While radiosondes have provided atmospheric scientists an accurate high-vertical-resolution profile of the troposphere for decades, they are unable to provide high-temporal-resolution observations without significant recurring expenses. Remote sensing technology, however, has the ability to monitor the evolution of the atmosphere in unprecedented detail. One particularly promising tool is the Atmospheric Emitted Radiance Interferometer (AERI), a passive ground-based infrared radiometer. Through a physical retrieval, the AERI can retrieve the vertical profile of temperature and humidity at a temporal resolution on the order of minutes. The synthesis of these two instruments may provide an improved diagnosis of the processes occurring in the atmosphere. This study provides a better understanding of the capabilities of the AERI in environments supportive of deep, moist convection. Using 3-hourly radiosonde launches and thermodynamic profiles retrieved from collocated AERIs, this study evaluates the accuracy of AERI-derived profiles over the diurnal cycle by analyzing AERI profiles in both the convective and stable boundary layers. Monte Carlo sampling is used to calculate the distribution of convection indices and compare the impact of measurement errors from each instrument platform on indices. This study indicates that the nonintegrated indices (e.g., lifted index) derived from AERI retrievals are more accurate than integrated indices (e.g., CAPE). While the AERI retrieval’s vertical resolution can inhibit precise diagnoses of capping inversions, the high-temporal-resolution nature of the AERI profiles overall helps in detecting rapid temporal changes in stability.

Ceilometer observations of cloud cover are an important component of the automated weather observation network. However, the accuracy of its measurements of cloud amount is impacted by the limited vertical range and areal extent of its observations. A multiyear collocated dataset of observations from a laser ceilometer, a total sky imager (TSI), and a micropulse lidar (MPL) at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility is used to simulate the observations of operational ceilometers and to analyze the magnitude of the errors associated with ceilometer-based observations of cloud amount. The limited areal coverage of ceilometers results in error when skies are heterogeneous, but these errors are small compared to those caused by the limited vertical range: observations of clear sky or few clouds are often in error as the instrument cannot detect the presence of upper-level clouds. The varying quantities of upper-level clouds mean that errors are diurnally and seasonally dependent, with the greatest error at the SGP site happening in the morning and summer, respectively. Overall, the spatial homogeneity and low base of stratus clouds means that ceilometer-based observations of overcast skies are the most accurate, with a root-mean-square error of cloud fraction in overcast conditions an order of magnitude lower than for the dataset as a whole.

While fractional entrainment rates for cumulus clouds have typically been derived from airborne observations, this limits the size and scope of available datasets. To increase the number of continental cumulus entrainment rate observations available for study, an algorithm for retrieving them from ground-based remote sensing observations has been developed. This algorithm, called the Entrainment Rate In Cumulus Algorithm (ERICA), uses the suite of instruments at the Southern Great Plains (SGP) site of the U.S. Department of Energy's Atmospheric Radiation Measurement Program (ARM) Climate Research Facility as inputs into a Gauss–Newton optimal estimation scheme, in which an assumed guess of the entrainment rate is iteratively adjusted through intercomparison of modeled cloud attributes to their observed counterparts. The forward model in this algorithm is the explicit mixing parcel model (EMPM), a cloud parcel model that treats entrainment as a series of discrete entrainment events. A quantified value for the uncertainty in the retrieved entrainment rate is also returned as part of the retrieval. Sensitivity testing and information content analysis demonstrate the robust nature of this method for retrieving accurate observations of the entrainment rate without the drawbacks of airborne sampling. Results from a test of ERICA on 3 months of shallow cumulus cloud events show significant variability of the entrainment rate of clouds in a single day and from one day to the next. The mean value of 1.06 km−1 for the entrainment rate in this dataset corresponds well with prior observations and simulations of the entrainment rate in cumulus clouds.

[1] Different turbulent entrainment-mixing processes (e.g., homogeneous and inhomogeneous) occur in clouds; accurate representation of these processes is critical for improving cloud-related parameterizations in large-scale models, but poorly understood and quantified. Using in situ aircraft observations over the U. S. Department of Energy's Atmospheric Radiation Measurement Southern Great Plains site during the March 2000 Cloud Intensive Observation Period and numerical simulations with the Explicit Mixing Parcel Model (EMPM), here we explore the potential of using degree of homogeneous mixing as a measure to quantify these different mechanisms and examine various microphysical measures of homogeneous mixing degree and their relationships to entrainment-mixing dynamics as measured by transition scale numbers. Three different microphysical measures for the homogeneous mixing degree are newly defined and each is coupled with one of two different transition scale numbers. Both observations and simulations show that all the combinations have positive correlated relationships; simulations further show that the tightest relationship is between the measure of homogeneous mixing degree considering adiabatic number concentration and the transition scale number accounting for mixing fraction of dry air. A parameterization of the entrainment-mixing processes is advanced according to the relationships of homogeneous mixing degree measures to transition scale numbers.

Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.

This study investigates the relationship of health locus of control, health beliefs, social support, use of nonsmoking areas, and objecting to another person's smoking to long-term abstinence and relapse following a minimal intervention for smoking cessation. Subjects participated in a single session group hypnosis treatment for smoking cessation. Questionnaires were completed by participants pretreatment and at a 1-year follow-up. Ex-smoker, recidivist, and continuing smoker groups were defined using follow-up data from 219 participants (70 males and 149 females). The data were analyzed using univariate and multiple discriminant analysis techniques. The results show that the three smoking status groups could be discriminated. Ex-smokers actively coped with smokers in their environment, avoided other smokers in public places, and received considerable support from spouses and friends. In contrast, recidivists prior to treatment had been unable to quit smoking for extended periods of time and placed greater responsibility on powerful others for their health. Following treatment, recidivists did not actively cope with smokers, were more likely to participate in additional hypnosis, and placed less responsibility on either powerful others or themselves for their own health. It was concluded that posttreatment factors appear to be more important for long-term maintenance of nonsmoking than entry characteristics of participants. Recommendations include incorporation of coping skills training into cessation programs and restrictions on smoking in the ex-smokers' environments to prevent relapse.