Search

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.

Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling 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 (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado’s San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. Observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events.

Uncrewed Systems (UxS), including uncrewed aerial systems (UAS) and tethered balloon/kite systems (TBS), are significantly expanding observational capabilities in atmospheric science. Rapid adaptation of these platforms and the advancement of miniaturized instruments have resulted in an expanding number of data sets captured under various environmental conditions by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. In 2021, observational data collected using ARM UxS platforms, including seven TigerShark UAS flights and 133 tethered balloon system (TBS) flights, were archived by the ARM Data Center (https://adc.arm.gov/discovery/#/) and made publicly available at no cost for all registered users (https://www.doi.org/10.5439/1846798) (Mei and Dexheimer, 2022). Note that a specific directory has been created for the anonymous reviewer to access the data at https://adc.arm.gov/essd/. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research. This manuscript describes the DOE UAS/TBS datasets, including information on the acquisition, collection, and quality control processes, and highlights the potential scientific contributions using UAS and TBS platforms.

Observations collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provide a detailed description of the impact of thermodynamic and kinematic forcings on atmospheric boundary layer (ABL) stability in the central Arctic. This study reveals that the Arctic ABL is stable and near-neutral with similar frequencies, and strong stability is the most persistent of all stability regimes. MOSAiC radiosonde observations, in conjunction with observations from additional measurement platforms including a 10 m meteorological tower, ceilometer, microwave radiometer, and radiation station, provide insight into the relationships between atmospheric stability and various atmospheric thermodynamic and kinematic forcings of ABL turbulence, and how these relationships differ by season. We found that stronger stability largely occurs in low wind (i.e., wind speeds are slow), low radiation (i.e., surface radiative fluxes are minimal) environments, a very shallow mixed ABL forms in low wind, high radiation environments, weak stability occurs in high wind, moderate radiation environments, and a near-neutral ABL forms in high wind, high radiation environments. Surface pressure (a proxy for synoptic staging) partially explains the observed wind speeds for different stability regimes. Cloud frequency and atmospheric moisture contribute to the observed surface radiation budget. Unique to summer, stronger stability may also form when moist air is advected from over the warmer open ocean to over the colder sea ice surface, which decouples the colder near-surface atmosphere from the advected layer, and is identifiable through observations of fog and atmospheric moisture.

This study analyzes turbulent energy fluxes in the Arctic atmospheric boundary layer (ABL) using measurements with a small uncrewed aircraft system (sUAS). Turbulent fluxes constitute a major part of the atmospheric energy budget and influence the surface heat balance by distributing energy vertically in the atmosphere. However, only few in situ measurements of the vertical profile of turbulent fluxes in the Arctic ABL exist. The study presents a method to derive turbulent heat fluxes from DataHawk2 sUAS turbulence measurements, based on the flux gradient method with a parameterization of the turbulent exchange coefficient. This parameterization is derived from high-resolution horizontal wind speed measurements in combination with formulations for the turbulent Prandtl number and anisotropy depending on stability. Measurements were taken during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the Arctic sea ice during the melt season of 2020. For three example cases from this campaign, vertical profiles of turbulence parameters and turbulent heat fluxes are presented and compared to balloon-borne, radar, and near-surface measurements. The combination of all measurements draws a consistent picture of ABL conditions and demonstrates the unique potential of the presented method for studying turbulent exchange processes in the vertical ABL profile with sUAS measurements.

Observations collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provide an annual cycle of the vertical thermodynamic and kinematic structure of the atmospheric boundary layer (ABL) in the central Arctic. A self-organizing map (SOM) analysis conducted using radiosonde observations shows a range in the Arctic ABL vertical structure from very shallow and stable, with a strong surface-based virtual potential temperature (θv) inversion, to deep and near-neutral, with a weak elevated θv inversion. Profile observations from the DataHawk2 uncrewed aircraft system between 23 March and 26 July 2020 largely sampled the same profile structures, which can be further analyzed to provide unique insight into the turbulent characteristics of the ABL. The patterns identified by the SOM allowed for the derivation of criteria to categorize stability within and just above the ABL, which reveals that the Arctic ABL is stable and near-neutral with similar frequencies. In conjunction with observations from additional measurement platforms, including a 10 m meteorological tower, ceilometer, and microwave radiometer, the radiosonde observations provide insight into the relationships between atmospheric stability and a variety of atmospheric thermodynamic and kinematic features. The average ABL height was found to be 150 m, and ABL height increases with decreasing stability. A low-level jet was observed in 76 % of the radiosondes, with an average height of 401 m and an average speed of 11.5 m s−1. At least one temperature inversion below 5 km was observed in 99.7 % of the radiosondes, with an average base height of 260 m and an average intensity of 4.8 °C. The only cases without a temperature inversion were those with weak stability aloft. Clouds were observed within the 30 minutes preceding radiosonde launch 64 % of the time. These were typically low clouds, and high clouds largely coincide with a stable ABL. The amount of atmospheric moisture present increases with decreasing stability.

The structure and evolution of the atmospheric boundary layer (ABL) under clear-sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground-based infrared spectrometers deployed in the East River Valley in Colorado’s Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from snow-free to snow-covered ground. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval TROPoe. A nocturnal surface inversion formed in the valley during clear-sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer during snow-free periods. When the ground was snow covered, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA’s operational High-Resolution-Rapid-Refresh (HRRR) model and find large warm biases on clear-sky days resulting from the model’s inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. A possible explanation for these model shortcomings is the influence of the model’s relatively coarse horizontal grid spacing (3 km) and its impact on the model’s ability to represent well-developed thermally driven flows, specifically nighttime drainage flows.

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.

The DataHawk2 (DH2) is a small, fixed-wing, uncrewed aircraft system, or UAS, developed at the University of Colorado (CU) primarily for taking detailed thermodynamic measurements of the atmospheric boundary layer. The DH2 weighs 1.7 kg and has a wingspan of 1.3 m, with a flight endurance of approximately 60 min, depending on configuration. In the DH2's most modern form, the aircraft carries a Vaisala RSS-421 sensor for pressure, temperature, and relative humidity measurements, two CU-developed infrared temperature sensors, and a CU-developed fine-wire array, in addition to sensors required to support autopilot function (pitot tube with pressure sensor, GPS receiver, inertial measurement unit), from which wind speed and direction can also be estimated. This paper presents a description of the DH2, including information on its design and development work, and puts the DH2 into context with respect to other contemporary UASs. Data from recent field work (MOSAiC, the Multidisciplinary drifting Observatory for the Study of Arctic Climate) is presented and compared with radiosondes deployed during that campaign to provide an overview of sensor and system performance. These data show good agreement across pressure, temperature, and relative humidity as well as across wind speed and direction. Additional examples of measurements provided by the DH2 are given from a variety of previous campaigns in locations ranging from the continental United States to Japan and northern Alaska. Finally, a look toward future system improvements and upcoming research campaign participation is given.

A polarimetric radar–based method for retrieving atmospheric ice particle shapes is applied to snowfall measurements by a scanning Ka-band radar deployed at Oliktok Point, Alaska (70.495°N, 149.883°W). The mean aspect ratio, which is defined by the hydrometeor minor-to-major dimension ratio for a spheroidal particle model, is retrieved as a particle shape parameter. The radar variables used for aspect ratio profile retrievals include reflectivity, differential reflectivity, and the copolar correlation coefficient. The retrievals indicate that hydrometeors with mean aspect ratios below 0.2–0.3 are usually present in regions with air temperatures warmer than approximately from −17° to −15°C, corresponding to a regime that has been shown to be favorable for growth of pristine ice crystals of planar habits. Radar reflectivities corresponding to the lowest mean aspect ratios are generally between −10 and 10 dBZ. For colder temperatures, mean aspect ratios are typically in a range between 0.3 and 0.8. There is a tendency for hydrometeor aspect ratios to increase as particles transition from altitudes in the temperature range from −17° to −15°C toward the ground. This increase is believed to result from aggregation and riming processes that cause particles to become more spherical and is associated with areas demonstrating differential reflectivity decreases with increasing reflectivity. Aspect ratio retrievals at the lowest altitudes are consistent with in situ measurements obtained using a surface-based multiangle snowflake camera. Pronounced gradients in particle aspect ratio profiles are observed at altitudes at which there is a change in the dominant hydrometeor species, as inferred by spectral measurements from a vertically pointing Doppler radar.

The number concentration of ice particles in Arctic mixed-phase clouds is a major controlling factor of cloud lifetime. The relationships between ice nucleation mode and ice crystal habit development are not yet constrained by observations. This study uses a habit-predicting microphysical scheme within a 3D large-eddy simulation model to evaluate the relationship between immersion freezing and ice habit in a simulated Arctic mixed-phase cloud case. Three immersion freezing parameterizations are considered: a volume-dependent freezing scheme (VF), a parameterization limited to activated droplets (C-AC), and a parameterization limited to coarse aerosol particles (C-CM). Both C-AC and C-CM are based on classical nucleation theory. The freezing rate with VF is found to be greater in downdraft regions than in updraft regions due to the downdraft having a higher number concentration of large droplets. The C-AC cases show active freezing of small droplets near cloud top, whereas in the C-CM cases, mainly the 8–32-μm-sized droplets freeze in updraft regions near the cloud base. Because the initial crystal size is assumed to affect the axis ratio of hexagonal plates, the VF cases produce crystals with larger axis ratios, resulting in smaller mode radii than the C-AC cases. In all cases, irregular polycrystals dominate near cloud top and a band-like structure develops within the cloud, which qualitatively agrees with previous observations. In the VF and C-CM cases, unactivated large droplets arising from coarse-mode aerosol particles contributed significantly to the freezing rate, producing an important influence on crystal habit.

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 (

During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, meteorological conditions over the lowest 1 km of the atmosphere were sampled with the DataHawk2 (DH2) fixed-wing uncrewed aircraft system (UAS). These in situ observations of the central Arctic atmosphere are some of the most extensive to date and provide unique insight into the atmospheric boundary layer (ABL) structure. The ABL is an important component of the Arctic climate, as it can be closely coupled to cloud properties, surface fluxes, and the atmospheric radiation budget. The high temporal resolution of the UAS observations allows us to manually identify the ABL height (ZABL) for 65 out of the total 89 flights conducted over the central Arctic Ocean between 23 March and 26 July 2020 by visually analyzing profiles of virtual potential temperature, humidity, and bulk Richardson number. Comparing this subjective ZABL with ZABL identified by various previously published automated objective methods allows us to determine which objective methods are most successful at accurately identifying ZABL in the central Arctic environment and how the success of the methods differs based on stability regime. The objective methods we use are the Liu–Liang, Heffter, virtual potential temperature gradient maximum, and bulk Richardson number methods. In the process of testing these objective methods on the DH2 data, numerical thresholds were adapted to work best for the UAS-based sampling. To determine if conclusions are robust across different measurement platforms, the subjective and objective ZABL determination processes were repeated using the radiosonde profile closest in time to each DH2 flight. For both the DH2 and radiosonde data, it is determined that the bulk Richardson number method is the most successful at identifying ZABL, while the Liu–Liang method is least successful. The results of this study are expected to be beneficial for upcoming observational and modeling efforts regarding the central Arctic ABL.

International audience; With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic.

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).

It is well known that light-absorbing particulate matter (PM) enhances absorption of sunlight when deposited on ice and snow. Such increased absorption is due to a reduction in surface albedo, resulting in accelerated melt of frozen surfaces. In isolation, earlier melt enhances Arctic warming since dark surfaces underlying snow and ice are exposed and absorb additional solar energy. Here, we combine various observational tools to demonstrate that aeolian deposition of PM along fluvial features on the North Slope of Alaska resulted in a notable reduction of surface albedo in the spring of 2016, from values typical for snow (~0.8) to around 0.35 on average. This reduction resulted in accelerated snow and ice melt by up to three weeks compared to unaffected areas. This phenomenon was observed to some degree in 12 other years dating back to 2003. Deposition generally was found to occur near particular sections of the rivers, with several areas affected by events in multiple years. In all years, the deposition is attributed to high wind events. The extreme case in 2016 is linked to unusually strong and extraordinarily persistent winds during April. The deposited material is thought to be the natural sediment carried by the rivers, resulting in a seasonally replenished source of PM. These findings indicate a previously unreported impact of both fluvial and atmospheric processes on the seasonal melt of northern Alaska rivers.

The influence of ice hydrometeor shape on the dual-wavelength ratio (DWR) of radar reflectivities at millimeter-wavelength frequencies is studied theoretically and on the basis of observations. Data from dual-frequency (Ka–W bands) radar show that, for vertically pointing measurements, DWR increasing trends with reflectivity Ze are very pronounced when Ka-band Ze is greater than about 0 dBZ and that DWR and Ze values are usually well correlated. This correlation is explained by strong relations between hydrometeor characteristic size and both of these radar variables. The observed DWR variability for a given level of reflectivity is as large as 8 dB, which is in part due to changes in mean hydrometeor shape as expressed in terms of the particle aspect ratio. Hydrometeors with a higher degree of nonsphericity exhibit lower DWR values when compared with quasi-spherical particles because of near-zenith reflectivity enhancements for particles outside the Rayleigh-scattering regime. When particle mass–size relations do not change significantly (e.g., for low-rime conditions), DWR can be used to differentiate between quasi-spherical and highly nonspherical hydrometeors because (for a given reflectivity value) DWR tends to increase as particles become more spherical. Another approach for differentiating among different degrees of nonsphericity for larger scatterers is based on analyzing DWR changes as a function of radar elevation angle. These changes are more pronounced for highly nonspherical particles and can exceed 10 dB. Measurements of snowfall spatiotemporally collocated with spaceborne CloudSat W-band radar and ground-based S-band operational weather radars also indicate that DWR values are generally smaller for ice hydrometeors with higher degrees of nonsphericity, which, for the same level of S-band reflectivity, exhibit greater differential reflectivity values.

Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Thus far, no single robust method exists for assessing the calibration of past cloud radar data sets. Here, we investigate whether observations of microphysical processes in liquid clouds such as the transition of cloud droplets to drizzle drops can be used to calibrate cloud radars. Specifically, we study the relationships between the radar reflectivity factor and three variables not affected by absolute radar calibration: the skewness of the radar Doppler spectrum (γ), the radar mean Doppler velocity (W), and the liquid water path (LWP). For each relation, we evaluate the potential for radar calibration. For γ and W, we use box model simulations to determine typical radar reflectivity values for reference points. We apply the new methods to observations at the Atmospheric Radiation Measurement (ARM) sites North Slope of Alaska (NSA) and Oliktok Point (OLI) in 2016 using two 35 GHz Ka-band ARM Zenith Radars (KAZR). For periods with a sufficient number of liquid cloud observations, we find that liquid cloud processes are robust enough for cloud radar calibration, with the LWP-based method performing best. We estimate that, in 2016, the radar reflectivity at NSA was about 1±1 dB too low but stable. For OLI, we identify serious problems with maintaining an accurate calibration including a sudden decrease of 5 to 7 dB in June 2016.

USDOE Office of Science (SC), Biological and Environmental Research (BER) U.S. Department of Energy, DOE/SC-ARM-18-005

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 University of Colorado, Boulder, deployed unmanned aerial systems (UAS) over the sea ice during Leg 4 (June-August 2020) of the MOSAiC expedition. Among the different UAS platforms operated, a hexacopter, the HELiX, was dedicated for characterizing the surface properties, such as the surface albedo and the sea ice/melt pond fractions. The HELiX was equipped with two pyranometers to measure incoming and reflected broadband shortwave irradiance, and a multispectral camera to map the surface of the ice floe. Three flight plans were conducted with this platform, including (1) grid patterns at 10 m.asl to map out the distribution of albedo at this altitude, (2) hovering flights at 3 m.asl over identified surfaces (sea ice, melt pond, ocean, ridge, etc.) to get a detailed look at the albedo of each surface individually, and (3) profiles up to 100 m.asl. to evaluate the convergence height where surface heterogeneity is obscured when using a hemispheric sensor. In total, 34 flights took place in varied weather conditions, from clear sky to foggy weather with very low visibility. The UAS observations bring complementary results to a variety of other albedo observations collected during MOSAiC (albedo lines, sled-based, tethered balloon-based, and ship-based measurements). These observations spanned the majority of the melt season, capturing seasonal evolution in surface reflectivity, as well as melt pond fraction and resulting impact on surface albedo. In this presentation, we will present results from these flight activities and offer perspectives on the evolving sea ice pack during the summer portion of the MOSAiC expedition.

During the 2020 Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) and ElUcidating the Role of Cloud- Circulation Coupling in ClimAte (EUREC4A) field campaigns, a team from the University of Colorado Boulder deployed the RAAVEN Remotely-Piloted Aircraft System (RPAS). The RAAVEN RPAS was equipped with the miniFlux measurement system to observe the marine boundary layer upwind of Morgan Lewis, Barbados. Over the course of 23 days, the team completed 39 flights covering nearly 80 flight hours. Flights were conducted in and just above the boundary layer at altitudes between 10 and 1000 m, with a focus on capturing regular thermodynamic and kinematic profiles of the lower atmosphere, along with statistics on vertical transport and spatial variability. In this presentation, we will give initial details on the observed state of the lower atmosphere. This includes information on the structure and internal variability of thermodynamic and kinematic properties, turbulence intensity, turbulent surface fluxes and their variability, and details on the structure of vertical velocities in the lower atmosphere.