Abstract:The aim of the research project "Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR)" is to substantially increase the understanding of the stable atmospheric boundary layer (SBL) through a combination of well-established and innovative observation methods as well as by models of different complexity. During three weeks in February 2017, a first field campaign was carried out over the sea ice of the Bothnian Bay in the vicinity of the Finnish island of Hailuoto. Observations were based on ground-based eddy-covariance (EC), automatic weather stations (AWS) and remote-sensing instrumentation as well as more than 150 flight missions by several different Unmanned Aerial Vehicles (UAVs) during mostly stable and very stable boundary layer conditions. The structure of the atmospheric boundary layer (ABL) and above could be resolved at a very high vertical resolution, especially close to the ground, by combining surface-based measurements with UAV observations, i.e., multicopter and fixed-wing profiles up to 200 m agl and 1800 m agl, respectively. Repeated multicopter profiles provided detailed information on the evolution of the SBL, in addition to the continuous SODAR and LIDAR windAtmosphere 2018, 9, 268; doi:10.3390/atmos9070268www.mdpi.com/journal/atmosphereAtmosphere 2018, 9, 268 2 of 29 measurements. The paper describes the campaign and the potential of the collected data set for future SBL research and focuses on both the UAV operations and the benefits of complementing established measurement methods by UAV measurements to enable SBL observations at an unprecedented spatial and temporal resolution.

The inverse radiative transfer equation to retrieve atmospheric ozone distribution from the UV‐visible satellite spectrometer Global Ozone Monitoring Experiment (GOME) has been modeled by means of a feed forward neural network. This Neural Network Ozone Retrieval System (NNORSY) was trained exclusively on a data set of GOME radiances collocated with ozone measurements from ozonesondes, Halogen Occultation Experiment, Stratospheric Aerosol and Gas Experiment II, and Polar Ozone and Aerosol Measurement III. Network input consists of a combination of spectral, geolocation, and climatological information (time and latitude). In the stratosphere the method globally reduces standard deviation with respect to an ozone climatology by around 40%. Tropospheric ozone can also be retrieved in many cases with corresponding reduction of 10–30%. All GOME data from January 1996 to July 2001 were processed. In a number of case studies involving comparisons with ozonesondes from Hohenpeissenberg, Syowa, and results from the classical Full Retrieval Method, we found good agreement with our results. The neural network was found capable of implicitly correcting for instrument degradation, pixel cloudiness, and scan angle effects. Integrated profiles generally agree to within ±5% with the monthly Total Ozone Mapping Spectrometer version 7 total ozone field. However, some problems remain at high solar zenith angles and very low ozone values, where local deviations of 10–20% have been observed in some cases. In order to better characterize individual ozone profiles, two local error estimation methods are presented. Vertical resolution of the profiles was assessed empirically and seems to be of the order of 4–6 km. Since neural network retrieval is a mathematically simple, one‐step procedure, NNORSY is about 103–105 times faster than classical retrieval techniques based upon optimal estimation.

Abstract. In June and July 2011 the RPAS (Remotely Piloted Aircraft System) SUMO (Small Unmanned Meteorological Observer) performed a total number of 299 scientific flights during the BLLAST (Boundary Layer Late Afternoon and Sunset Turbulence) campaign in southern France. Three different types of missions were performed: vertical profiling of the mean meteorological parameters (temperature, humidity and wind), horizontal surveys of the surface temperature and horizontal transects for the estimation of turbulence. The manuscript provides an introduction to the corresponding SUMO operations, including regulatory issues and the coordination of manned and unmanned airborne operations for boundary-layer research that have been pioneered during the BLLAST campaign.The main purpose of the SUMO flight strategy was atmospheric profiling at high temporal resolution. A total of 168 profile flights were performed during the campaign with typically more than 10 flights per Intensive Observational Period (IOP) day. The collected data allow for a detailed study of boundary-layer structure and dynamics and will be used for further analysis, e.g. the determination of profiles of sensible and latent heat fluxes. First, tests of a corresponding method have shown very promising results and have provided surface-flux values in close agreement with those from ground-based eddy-covariance measurements. In addition, 74 horizontal surveys of the IR emission of the surface were performed at altitudes of around 65 m. Each of those surveys covers a typical area of around 1 km 2 and allows for an estimation of the surface-temperature variability, important information for the assessment of the heterogeneity of the surface forcing as a function of soil and vegetation properties. The comparison with other surface-temperature measurements shows that the raw data of the airborne and ground observations can differ considerably, but that even a very simple multiple regression method can reduce those differences to a large degree. Finally, 49 flight missions for the measurement of velocity variance have been realized during the BLLAST campaign. For that, SUMO has been equipped with a 5-hole probe (5HP) sensor for the determination of the flow vector at 100 Hz. In particular, for this application there is still need for further improvement, both with respect to the aircraft and sensor hard-and software, and the algorithms and methods for data analysis and interpretation. Nevertheless, the SUMO operations during the BLLAST campaign have shown the vast potential of small and lightweight RPA systems with low infrastructural demand for atmospheric boundary-layer research.

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In this paper, the first-ever measurements of the wake of a full-scale wind turbine using an instrumented uninhabited aerial vehicle (UAV) are reported. The key enabler for this novel measurement approach is the integration of fast response aerodynamic probe technology with miniaturized hardware and software for UAVs that enable autonomous UAV operation. The measurements, made to support the development of advanced wind simulation tools, are made in the near-wake (0.5D–3D, where D is rotor diameter) region of a 2 MW wind turbine that is located in a topography of complex terrain and varied vegetation. Downwind of the wind turbine, profiles of the wind speed show that there is strong three-dimensional shear in the near-wake flow. Along the centerline of the wake, the deficit in wind speed is a consequence of wakes from the rotor, nacelle, and tower. By comparison with the profiles away from the centerline, the shadowing effects of nacelle and tower diminish downstream of 2.5D. Away from the centerline, the deficit in wind speed is approximately constant ≈ 25%. However, along the centerline, the deficit is ≈ 65% near to the rotor, 0.5D–1.75D, and only decreases to ≈ 25% downstream of 2.5D.