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.

Gridded analyses of observed precipitation are an important data resource for environmental modelling, climate model evaluation and climate monitoring.In Europe, datasets that resolve the rich mesoscale variations widely exist for the national territories, but similar datasets covering the entire continent are more recent. Here, we evaluate daily precipitation in two newly available pan-European datasets: E-OBS (v19.0e), a statistical analysis from rain-gauge data, and ERA5, the new global reanalysis from ECMWF. Special interest is on how the refinements of grid spacing, the methodological upgrades and the quantification of uncertainty (ensemble), bear on capabilities at the mesoscale.The evaluation is conducted in three subregions, the Alps, the Carpathians and Fennoscandia, and involves as reference high-quality regional datasets derived from dense rain-gauge data. The study suggests that E-OBS and ERA5 agree qualitatively well with the reference datasets. Major mesoscale patterns in the climatology (mean, wet-day frequency, 95% quantile) are reproduced.The improvement over earlier versions of the datasets is evident. ERA5 was found to overestimate mean precipitation in all regions, related to too many wet days. The accuracy of E-OBS was found to depend on station density, with spatial and temporal variations clearly less accurate in data sparse regions. In comparison, E-OBS turned out to be superior to ERA5 in regions with dense data, but the two datasets are on a par in regions with sparse data, and partly ERA5 has advantages. For both datasets we find that the spatial resolution is coarser than the grid spacing, with overly smooth fields and an underestimation of high quantiles. Also, both datasets were found to be clearly overconfident in their uncertainty characterization (too small ensemble spread).Overall, the two datasets advance the characterization of precipitation on a

Abstract. The micro-RPAS (remotely piloted aircraft system) SUMO (Small Unmanned Meteorological Observer) equipped with a five-hole-probe (5HP) system for turbulent flow measurements was operated in 49 flight missions during the BLLAST (Boundary-Layer Late Afternoon and Sunset Turbulence) field campaign in 2011. Based on data sets from these flights, we investigate the potential and limitations of airborne velocity variance and TKE (turbulent kinetic energy) estimations by an RPAS with a take-off weight below 1 kg.The integration of the turbulence probe in the SUMO system was still in an early prototype stage during this campaign, and therefore extensive post-processing of the data was required. In order to be able to calculate the threedimensional wind vector, flow probe measurements were first synchronized with the autopilot's attitude and velocity data. Clearly visible oscillations were detected in the resulting vertical velocity, w, even after correcting for the aircraft motion. The oscillations in w were identified as the result of an internal time shift between the inertial measurement unit (IMU) and the GPS sensors, leading to insufficient motion correction, especially for the vertical wind component, causing large values of σ w . Shifting the IMU 1-1.5 s forward in time with respect to the GPS yields a minimum for σ w and maximum covariance between the IMU pitch angle and the GPS climb angle.The SUMO data show a good agreement to sonic anemometer data from a 60 m tower for σ u , but show slightly higher values for σ v and σ w . Vertical TKE profiles, obtained from consecutive flight legs at different altitudes, show reasonable results, both with respect to the overall TKE level and the temporal variation. A thorough discussion of the methods used and the identified uncertainties and limitations of the system for turbulence measurements is included and should help the developers and users of other systems with similar problems.

Abstract. In science, poor quality input data will invariably lead to faulty conclusions, as in the spirit of the saying “garbage in, garbage out”. Atmospheric sciences make no exception and correct data is crucial to obtain a useful representation of the real world in meteorological, climatological and hydrological applications. Titan is a computer program for the automatic quality control of meteorological data that has been designed to serve real-time operational applications that process massive amounts of observations measured by networks of automatic weather stations. The need to quality control third-party data, such as citizen observations, within a station network that is constantly changing was an important motivation that led to the development of Titan. The quality control strategy adopted is a sequence of tests, where several of them utilize the expected spatial consistency between nearby observations. The spatial continuity can also be evaluated against independent data sources, such as numerical model output and remote sensing measurements. Examples of applications of Titan for the quality control of near-surface hourly temperature and precipitation over Scandinavia are presented. In the case of temperature, this specific application has been integrated into the operational production chain of automatic weather forecasts at the Norwegian Meteorological Institute (MET Norway). Titan is an open source project and it is made freely available for public download. One of the objectives of the Titan project is to establish a community working on common tools for automatic quality control, and the Titan program represents a first step in that direction for MET Norway. Further developments are necessary to achieve a solution that satisfies more users, for this reason we are currently working on transforming Titan into a more flexible library of functions.