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The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC), part of the larger experiment known as Elucidating the Role of Clouds-Circulation Coupling in Climate (EUREC4A), was held in the western Atlantic during the period 17 January–11 February 2020. This paper describes observations made during ATOMIC by the US National Oceanic and Atmospheric Administration's (NOAA) Lockheed WP-3D Orion research aircraft based on the island of Barbados. The aircraft obtained 95 h of observations over 11 flights, many of which were coordinated with the NOAA research ship R/V Ronald H. Brown and autonomous platforms deployed from the ship. Each flight contained a mixture of sampling strategies including high-altitude circles with frequent dropsonde deployment to characterize the large-scale environment, slow descents and ascents to measure the distribution of water vapor and its isotopic composition, stacked legs aimed at sampling the microphysical and thermodynamic state of the boundary layer, and offset straight flight legs for observing clouds and the ocean surface with remote sensing instruments and the thermal structure of the ocean with in situ sensors dropped from the plane. The characteristics of the in situ observations, expendable devices, and remote sensing instrumentation are described, as is the processing used in deriving estimates of physical quantities. Data archived at the National Center for Environmental Information include flight-level data such as aircraft navigation and basic thermodynamic information (NOAA Aircraft Operations Center and NOAA Physical Sciences Laboratory, 2020, https://doi.org/10.25921/7jf5-wv54); high-accuracy measurements of water vapor concentration from an isotope analyzer (National Center for Atmospheric Research, 2020, https://doi.org/10.25921/c5yx-7w29); in situ observations of aerosol, cloud, and precipitation size distributions (Leandro and Chuang, 2020, https://doi.org/10.25921/vwvq-5015); profiles of seawater temperature made with Airborne eXpendable BathyThermographs (AXBTs; NOAA Physical Sciences Laboratory, 2020a, https://doi.org/10.25921/pe39-sx75); radar reflectivity, Doppler velocity, and spectrum width from a nadir-looking W-band radar (NOAA Physical Sciences Laboratory, 2020c, https://doi.org/10.25921/n1hc-dc30); estimates of cloud presence, the cloud-top location, and the cloud-top radar reflectivity and temperature, along with estimates of 10 m wind speed obtained from remote sensing instruments operating in the microwave and thermal infrared spectral regions (NOAA Physical Sciences Laboratory, 2020b, https://doi.org/10.25921/x9q5-9745); and ocean surface wave characteristics from a Wide Swath Radar Altimeter (Prosensing, Inc., 2020, https://doi.org/10.25921/qm06-qx04). Data are provided as netCDF files following Climate and Forecast conventions.

Small unmanned aerial systems (sUASs) are becoming very popular as affordable and reliable observation platforms. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), conducted in the San Luis Valley (SLV) of Colorado (USA) between 14 and 20 July 2018, gathered together numerous sUASs, remote-sensing equipment, and ground-based instrumentation. Flight teams from the Finnish Meteorological Institute (FMI) and the Kansas State University (KSU) co-operated during LAPSE-RATE to measure and investigate the properties of aerosol particles and gases at the surface and in the lower atmosphere. During LAPSE-RATE the deployed instrumentation operated reliably, resulting in an observational dataset described below in detail. Our observations included aerosol particle number concentrations and size distributions, concentrations of CO2 and water vapor, and meteorological parameters. All datasets have been uploaded to the Zenodo LAPSE-RATE community archive (https://zenodo.org/communities/lapse-rate/, last access: 21 August 2020). The dataset DOIs for FMI airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3993996, Brus et al. (2020a), and those for KSU airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3736772, Brus et al. (2020b).

Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation—a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.

The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place from 7 January to 11 July 2020 in the tropical North Atlantic between the eastern edge of Barbados and 51∘ W, the longitude of the Northwest Tropical Atlantic Station (NTAS) mooring. Measurements were made to gather information on shallow atmospheric convection, the effects of aerosols and clouds on the ocean surface energy budget, and mesoscale oceanic processes. Multiple platforms were deployed during ATOMIC including the NOAA RV Ronald H. Brown (RHB) (7 January to 13 February) and WP-3D Orion (P-3) aircraft (17 January to 10 February), the University of Colorado's Robust Autonomous Aerial Vehicle-Endurant Nimble (RAAVEN) uncrewed aerial system (UAS) (24 January to 15 February), NOAA- and NASA-sponsored Saildrones (12 January to 11 July), and Surface Velocity Program Salinity (SVPS) surface ocean drifters (23 January to 29 April). The RV Ronald H. Brown conducted in situ and remote sensing measurements of oceanic and atmospheric properties with an emphasis on mesoscale oceanic–atmospheric coupling and aerosol–cloud interactions. In addition, the ship served as a launching pad for Wave Gliders, Surface Wave Instrument Floats with Tracking (SWIFTs), and radiosondes. Details of measurements made from the RV Ronald H. Brown, ship-deployed assets, and other platforms closely coordinated with the ship during ATOMIC are provided here. These platforms include Saildrone 1064 and the RAAVEN UAS as well as the Barbados Cloud Observatory (BCO) and Barbados Atmospheric Chemistry Observatory (BACO). Inter-platform comparisons are presented to assess consistency in the data sets. Data sets from the RV Ronald H. Brown and deployed assets have been quality controlled and are publicly available at NOAA's National Centers for Environmental Information (NCEI) data archive (https://www.ncei.noaa.gov/archive/accession/ATOMIC-2020, last access: 2 April 2021). Point-of-contact information and links to individual data sets with digital object identifiers (DOIs) are provided herein.

Because unmanned aircraft systems (UAS) offer new perspectives on the atmosphere, their use in atmospheric science is expanding rapidly. In support of this growth, the International Society for Atmospheric Research Using Remotely-Piloted Aircraft (ISARRA) has been developed and has convened annual meetings and “flight weeks.” The 2018 flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE), involved a 1-week deployment to Colorado’s San Luis Valley. Between 14 and 20 July 2018 over 100 students, scientists, engineers, pilots, and outreach coordinators conducted an intensive field operation using unmanned aircraft and ground-based assets to develop datasets, community, and capabilities. In addition to a coordinated “Community Day” which offered a chance for groups to share their aircraft and science with the San Luis Valley community, LAPSE-RATE participants conducted nearly 1,300 research flights totaling over 250 flight hours. The measurements collected have been used to advance capabilities (instrumentation, platforms, sampling techniques, and modeling tools), conduct a detailed system intercomparison study, develop new collaborations, and foster community support for the use of UAS in atmospheric science.

Unique data from seven flights of the Coyote small unmanned aircraft system (sUAS) were collected in Hurricanes Maria (2017) and Michael (2018). Using NOAA’s P-3 reconnaissance aircraft as a deployment vehicle, the sUAS collected high-frequency (>1 Hz) measurements in the turbulent boundary layer of hurricane eyewalls, including measurements of wind speed, wind direction, pressure, temperature, moisture, and sea surface temperature, which are valuable for advancing knowledge of hurricane structure and the process of hurricane intensification. This study presents an overview of the sUAS system and preliminary analyses that were enabled by these unique data. Among the most notable results are measurements of turbulence kinetic energy and momentum flux for the first time at low levels (

Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary-layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary-layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.

Between 14 and 20 July 2018, small unmanned aircraft systems (sUAS) were deployed to the San Luis Valley of Colorado (USA) alongside surface-based remote, in-situ sensors, and radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The measurements collected as part of LAPSE-RATE targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface-atmosphere exchange, and provide detailed information to support model evaluation and improvement work. Additionally, intensive intercomparison between the different unmanned aircraft platforms was completed. The current manuscript describes the observations obtained using three different types of surface-based mobile observing vehicles. These included the University of Colorado Mobile UAS Research Collaboratory (MURC), the National Oceanic and Atmospheric Administration National Severe Storms Laboratory Mobile Mesonet, and two University of Nebraska Combined Mesonet and Tracker (CoMeT) vehicles. Over the one-week campaign, a total of 143 hours of data were collected using this combination of vehicles. The data from these coordinated activities provide detailed perspectives on the spatial variability of atmospheric state parameters (air temperature, humidity, pressure, and wind) throughout the northern half of the San Luis Valley. These data sets have been checked for quality and published to the Zenodo data archive under a specific community set up for LAPSE-RATE (https://zenodo.org/communities/lapse-rate/) and are accessible at no cost by all registered users. The primary dataset DOIs are https://doi.org/10.5281/zenodo.3814765 (CU MURC measurements; de Boer et al., 2020d), https://doi.org/10.5281/zenodo.3738175 (NSSL MM measurements; Waugh, 2020) and https://doi.org/10.5281/zenodo.3838724 (UNL CoMeT measurements; Houston and Erwin., 2020).

Unmanned Aerial Systems (UASs) are increasingly being used as observation platforms for atmospheric applications. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) in Alamosa, CO, USA during July 14th–20th, 2018 investigated and validated different UASs, measurement sensors and setup configurations. Flight teams from the Finnish Meteorological Institute (FMI) and the Kansas State University (KSU) participated in LAPSE-RATE to measure and investigate properties of aerosol particles and gases in the lower atmosphere. During the experiment, the performance of different UAS configurations were investigated and confirmed to operate reliably resulting in a scientifically sound observational dataset. As an example, concentration of aerosols – including two new particle formation events, CO2 and water vapor, and meteorological parameters in the atmospheric vertical profile were measured during the short experiment. Such observations characterizing atmospheric phenomena of this specific environment would have not been possible in any other way, and thus, demonstrate power of UASs as new, promising tools in atmospheric and environmental research.

Thorough understanding of aerosols, clouds, boundary layer structure, and radiation is required to improve the representation of the Arctic atmosphere in weather forecasting and climate models. To develop such understanding, new perspectives are needed to provide details on the vertical structure and spatial variability of key atmospheric properties, along with information over difficult-to-reach surfaces such as newly forming sea ice. Over the last three years, the U.S. Department of Energy (DOE) has supported various flight campaigns using unmanned aircraft systems [UASs, also known as unmanned aerial vehicles (UAVs) and drones] and tethered balloon systems (TBSs) at Oliktok Point, Alaska. These activities have featured in situ measurements of the thermodynamic state, turbulence, radiation, aerosol properties, cloud microphysics, and turbulent fluxes to provide a detailed characterization of the lower atmosphere. Alongside a suite of active and passive ground-based sensors and radiosondes deployed by the DOE Atmospheric Radiation Measurement (ARM) program through the third ARM Mobile Facility (AMF-3), these flight activities demonstrate the ability of such platforms to provide critically needed information. In addition to providing new and unique datasets, lessons learned during initial campaigns have assisted in the development of an exciting new community resource.

Between 1 July and 30 September 2018, small unmanned aircraft systems (sUAS), tethered balloon systems (TBS), and additional radiosondes were deployed at Oliktok Point, Alaska to measure the atmosphere in support of the second special observing period for the Year of Polar Prediction (YOPP). These measurements, collected as part of the “Profiling at Oliktok Point to Enhance YOPP Experiments” (POPEYE) campaign, targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface-atmosphere exchange, and provide extra information for model evaluation and improvement work. Over the three-month campaign, a total of 59 DataHawk2 sUAS flights, 52 TBS flights, and 238 total radiosonde launches were completed as part of POPEYE. The data from these coordinated activities provide a comprehensive three-dimensional data set of the atmospheric state (air temperature, humidity, pressure, and wind), surface skin temperature, aerosol properties, and cloud microphysical information over Oliktok Point. These data sets have been checked for quality and submitted to the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program data archive (http://www.archive.arm.gov/discovery/) and are accessible at no cost by all registered users. The primary dataset DOIs are https://doi.org/10.5439/1418259 (DataHawk2 measurements; Atmospheric Radiation Measurement Program, 2016b), https://doi.org/10.5439/1426242 (TBS measurements; Atmospheric Radiation Measurement Program, 2017) and https://doi.org/10.5439/1021460 (radiosonde measurements; Atmospheric Radiation Measurement Program, 2013a).

This paper presents details of the University of Colorado (CU) “Pilatus” unmanned research aircraft, assembled to provide measurements of aerosols, radiation and thermodynamics in the lower troposphere. This aircraft has a wingspan of 3.2 m and a maximum take-off weight of 25 kg, and it is powered by an electric motor to reduce engine exhaust and concerns about carburetor icing. It carries instrumentation to make measurements of broadband up- and downwelling shortwave and longwave radiation, aerosol particle size distribution, atmospheric temperature, relative humidity and pressure and to collect video of flights for subsequent analysis of atmospheric conditions during flight. In order to make the shortwave radiation measurements, care was taken to carefully position a high-quality compact inertial measurement unit (IMU) and characterize the attitude of the aircraft and its orientation to the upward-looking radiation sensor. Using measurements from both of these sensors, a correction is applied to the raw radiometer measurements to correct for aircraft attitude and sensor tilt relative to the sun. The data acquisition system was designed from scratch based on a set of key driving requirements to accommodate the variety of sensors deployed. Initial test flights completed in Colorado provide promising results with measurements from the radiation sensors agreeing with those from a nearby surface site. Additionally, estimates of surface albedo from onboard sensors were consistent with local surface conditions, including melting snow and bright runway surface. Aerosol size distributions collected are internally consistent and have previously been shown to agree well with larger, surface-based instrumentation. Finally the atmospheric state measurements evolve as expected, with the near-surface atmosphere warming over time as the day goes on, and the atmospheric relative humidity decreasing with increased temperature. No directional bias on measured temperature, as might be expected due to uneven heating of the sensor housing over the course of a racetrack pattern, was detected. The results from these flights indicate that the CU Pilatus platform is capable of performing research-grade lower tropospheric measurement missions.

Macro- and microphysical properties of single-layer stratiform mixed-phase clouds are derived from multiple years of lidar, radar, and radiosonde observations. Measurements were made as part of the Mixed-Phase Arctic Clouds Experiment (MPACE) and the Study of Environmental Arctic Change (SEARCH) in Barrow, Alaska, and Eureka, Nunavut, Canada, respectively. Single-layer mixed-phase clouds occurred between 4% and 26% of the total time observed, varying with season and location. They had mean cloud-base heights between ∼700 and 2100 m and thicknesses between ∼200 and 700 m. Seasonal mean cloud optical depths ranged from 2.2 up. The clouds existed at temperatures of ∼242–271 K and occurred under different wind conditions, depending on season. Utilizing retrievals from a combination of lidar, radar, and microwave radiometer, mean cloud microphysical properties were derived, with mean liquid effective diameters estimated from 16 to 49 μm, mean liquid number densities on the order of 104–105 L−1, and mean water contents estimated between 0.07 and 0.28 g m−3. Ice precipitation was shown to have mean ice effective diameters of 50–125 μm, mean ice number densities on the order of 10 L−1, and mean water contents estimated between 0.012 and 0.031 g m−3. Mean cloud liquid water paths ranged from 25 to 100 g m−2. All results are compared to previous studies, and potential retrieval errors are discussed. Additionally, seasonal variation in macro- and microphysical properties was highlighted. Finally, fraction of liquid water to ice mass was shown to decrease with decreasing temperature.

Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) programme's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud-top temperature of −15 ◦ C. The average liquid water path of around 160 g m −2 was about two-thirds of the adiabatic value and far greater than the average mass of ice which when integrated from the surface to cloud top was around 15 gm −2 . Simulations of 17 single-column models (SCMs) and 9 cloud-resolving models (CRMs) are compared. While the simulated ice water path is generally consistent with observed values, the median SCM and CRM liquid water path is a factor-of-three smaller than observed. Results from a sensitivity study in which models removed ice microphysics suggest that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path. Despite this underestimate, the simulated liquid and ice water paths of several models are consistent with observed values. Furthermore, models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter exists. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics. Copyright c � 2009 Royal Meteorological Society

The phase composition and microphysical structure of clouds define the manner in which they modulate atmospheric radiation and contribute to the hydrologic cycle. Issues regarding cloud phase partitioning and transformation come to bear directly in mixed-phase clouds, and have been difficult to address within current modeling frameworks. Ground-based, remote-sensing observations of mixed-phase clouds can contribute a significant body of knowledge with which to better understand, and thereby more accurately model, clouds and their phase-defining processes. Utilizing example observations from the Mixed-Phase Arctic Cloud Experiment (M-PACE), which occurred at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program's Climate Research Facility in Barrow, Alaska, during autumn 2004, we review the current status of ground-based observation and retrieval methods used in characterizing the macrophysical, microphysical, radiative, and dynamical properties of stratiform mixed-phase clouds. In...

[1] The omnipresent existence of thin, mixed-phase clouds in northern polar latitudes presents special challenges to CloudSAT as it attempts to map radiatively relevant cloudiness around the globe. In this work, retrieved cloud properties of Arctic mixed-phase clouds observed simultaneously in Eureka, Canada by ground-based cloud radar and lidar, and by the CloudSAT Cloud-Profiling Radar (CPR) are compared. Through these comparisons, we evaluate the efficacy of identification of precipitation and assignment of cloud type by the 2B-CLDCLASS product, as well as the accuracy of microphysical retrievals from the 2B-CWC-RO product. These preliminary comparisons result in the following findings with regard to the CloudSAT retrievals: (1) The cloud detection algorithm worked well, detecting all clouds observed from surface based sensors. (2) Precipitation was not well identified, and was often mislabeled as cloud. (3) Both liquid and ice particle number densities retrieved by CloudSAT are found to be 1 to 2 orders of magnitude too high when compared to surface-based retrievals and previous studies of these cloud types. (4) CloudSAT particle effective sizes are often too large, with the exception of the largest particles, which are misidentified as liquid. (5) Water contents show the best agreement between the two retrieval types, as well as with measured values from outside studies. All comparisons were completed for raw liquid and ice retrievals, as well as for “composite” retrievals that partition liquid and ice contributions to measured reflectivity through a temperature-dependent algorithm. Differences found for these limited cases imply that careful analysis is required for application of these cloud products to mixed-phase cloud research. Furthermore, these differences help highlight specific assumptions within the CloudSAT algorithms that are in need of improvement to complete mixed-phase cloud retrievals.