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The dataset collected during the Radar Snow Experiment (RadSnowExp) presents the first-ever airborne triple-frequency radar observations combined with almost perfectly co-located and coincident airborne microphysical measurements from a single platform, the National Research Council Canada (NRC) Convair-580 aircraft. The potential of this dataset is illustrated using data collected from one flight during an Arctic storm, which covers a wide range of snow habits from pristine ice crystals and low-density aggregates to heavily rimed particles with maximum size exceeding 10 mm. Three different flight segments with well-matched in situ and radar measurements were analyzed, giving a total of 49 min of triple-frequency observations. The in situ particle imagery data for this study include high-resolution imagery from the Cloud Particle Imager (CPI) probe, which allows accurate identification of particle types, including rimed crystals and large aggregates, within the dual-frequency ratio (DFR) plane. The airborne triple-frequency radar data are grouped based on the dominant particle compositions and microphysical processes (level of aggregation and riming). The results from this study are consistent with the main findings of previous modeling studies, with specific regions of the DFR plane associated with unique scattering properties of different ice habits, especially in clouds where the radar signal is dominated by large aggregates. Moreover, the analysis shows close relationships between the triple-frequency signatures and cloud microphysical properties (particle characteristic size, bulk density, and level of riming).

Measurements of the spectrum of the atmospheric emission in the far-infrared (FIR) range, between 100 and 667 cm−1 (100–15 µm) are scarce because of the detection complexity and of the strong absorption of air at ground level preventing the sounding of the FIR from low altitude. Consequently, FIR measurements need to be made from high-altitude sites or on board airborne platforms or satellites. This paper describes the dataset of FIR spectral radiances of the atmosphere and snow surface emission measured in the 100–1000 cm−1 range by the Far-Infrared Radiation Mobile Observation System (FIRMOS) instrument during a 2-month campaign carried out from the ground at about 3000 m of altitude on the top of Mt. Zugspitze in the German Alps in 2018–2019. This campaign is part of the preparatory activity of a new space FIR mission, named Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM), which is under development by the European Space Agency (ESA). The dataset acquired during the campaign also includes all the additional measurements needed to provide a full characterisation of the observed atmospheric state and the local atmospheric and surface conditions. It includes co-located spectral measurements in the infrared range from 400 to 1800 cm−1; lidar backscatter profiles; radio soundings of temperature, humidity and aerosol backscatter profiles; local weather parameters; and snow/ice microphysical properties. These measurements provide a unique dataset that can be used to perform radiative closure experiments to improve modelling parameters in the FIR that are not well-characterised, such as water vapour spectroscopy, scattering properties of cirrus clouds, and the FIR emissivity of the surface covered by snow. The consolidated dataset is freely available via the ESA campaign dataset website at https://doi.org/10.5270/ESA-38034ee (Palchetti et al., 2020a).

The Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaigns took place in Romania in September 2014 and August 2015. They focused on two sites: the Bucharest urban area and large power plants in the Jiu Valley. The main objectives of the campaigns were to test recently developed airborne observation systems dedicated to air quality studies and to verify their applicability for the validation of space-borne atmospheric missions such as the TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P). We present the AROMAT campaigns from the perspective of findings related to the validation of tropospheric NO2, SO2, and H2CO. We also quantify the emissions of NOx and SO2 at both measurement sites. We show that tropospheric NO2 vertical column density (VCD) measurements using airborne mapping instruments are well suited for satellite validation in principle. The signal-to-noise ratio of the airborne NO2 measurements is an order of magnitude higher than its space-borne counterpart when the airborne measurements are averaged at the TROPOMI pixel scale. However, we show that the temporal variation of the NO2 VCDs during a flight might be a significant source of comparison error. Considering the random error of the TROPOMI tropospheric NO2 VCD (σ), the dynamic range of the NO2 VCDs field extends from detection limit up to 37 σ (2.6×1016 molec. cm−2) and 29 σ (2×1016 molec. cm−2) for Bucharest and the Jiu Valley, respectively. For both areas, we simulate validation exercises applied to the TROPOMI tropospheric NO2 product. These simulations indicate that a comparison error budget closely matching the TROPOMI optimal target accuracy of 25 % can be obtained by adding NO2 and aerosol profile information to the airborne mapping observations, which constrains the investigated accuracy to within 28 %. In addition to NO2, our study also addresses the measurements of SO2 emissions from power plants in the Jiu Valley and an urban hotspot of H2CO in the centre of Bucharest. For these two species, we conclude that the best validation strategy would consist of deploying ground-based measurement systems at well-identified locations.

We use the CloudSat 2006–2016 data record to estimate snowfall over the Greenland Ice Sheet (GrIS). We first evaluate CloudSat snowfall retrievals with respect to remaining ground-clutter issues. Comparing CloudSat observations to the GrIS topography (obtained from airborne altimetry measurements during IceBridge) we find that at the edges of the GrIS spurious high-snowfall retrievals caused by ground clutter occasionally affect the operational snowfall product. After correcting for this effect, the height of the lowest valid CloudSat observation is about 1200 m above the local topography as defined by IceBridge. We then use ground-based millimeter wavelength cloud radar (MMCR) observations obtained from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland (ICECAPS) experiment to devise a simple, empirical correction to account for precipitation processes occurring between the height of the observed CloudSat reflectivities and the snowfall near the surface. Using the height-corrected, clutter-cleared CloudSat reflectivities we next evaluate various Z–S relationships in terms of snowfall accumulation at Summit through comparison with weekly stake field observations of snow accumulation available since 2007. Using a set of three Z–S relationships that best agree with the observed accumulation at Summit, we then calculate the annual cycle snowfall over the entire GrIS as well as over different drainage areas and compare the derived mean values and annual cycles of snowfall to ERA-Interim reanalysis. We find the annual mean snowfall over the GrIS inferred from CloudSat to be 34±7.5 cm yr−1 liquid equivalent (where the uncertainty is determined by the range in values between the three different Z–S relationships used). In comparison, the ERA-Interim reanalysis product only yields 30 cm yr−1 liquid equivalent snowfall, where the majority of the underestimation in the reanalysis appears to occur in the summer months over the higher GrIS and appears to be related to shallow precipitation events. Comparing all available estimates of snowfall accumulation at Summit Station, we find the annually averaged liquid equivalent snowfall from the stake field to be between 20 and 24 cm yr−1, depending on the assumed snowpack density and from CloudSat 23±4.5 cm yr−1. The annual cycle at Summit is generally similar between all data sources, with the exception of ERA-Interim reanalysis, which shows the aforementioned underestimation during summer months.

We present an intercomparison study of four airborne imaging DOAS instruments, dedicated to the retrieval and high-resolution mapping of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs). The AROMAPEX campaign took place in Berlin, Germany, in April 2016 with the primary objective to test and intercompare the performance of experimental airborne imagers. The imaging DOAS instruments were operated simultaneously from two manned aircraft, performing synchronised flights: APEX (VITO–BIRA-IASB) was operated from DLR's DO-228 D-CFFU aircraft at 6.2 km in altitude, while AirMAP (IUP-Bremen), SWING (BIRA-IASB), and SBI (TNO–TU Delft–KNMI) were operated from the FUB Cessna 207T D-EAFU at 3.1 km. Two synchronised flights took place on 21 April 2016. NO2 slant columns were retrieved by applying differential optical absorption spectroscopy (DOAS) in the visible wavelength region and converted to VCDs by the computation of appropriate air mass factors (AMFs). Finally, the NO2 VCDs were georeferenced and mapped at high spatial resolution. For the sake of harmonising the different data sets, efforts were made to agree on a common set of parameter settings, AMF look-up table, and gridding algorithm. The NO2 horizontal distribution, observed by the different DOAS imagers, shows very similar spatial patterns. The NO2 field is dominated by two large plumes related to industrial compounds, crossing the city from west to east. The major highways A100 and A113 are also identified as line sources of NO2. Retrieved NO2 VCDs range between 1×1015 molec cm−2 upwind of the city and 20×1015 molec cm−2 in the dominant plume, with a mean of 7.3±1.8×1015 molec cm−2 for the morning flight and between 1 and 23×1015 molec cm−2 with a mean of 6.0±1.4×1015 molec cm−2 for the afternoon flight. The mean NO2 VCD retrieval errors are in the range of 22 % to 36 % for all sensors. The four data sets are in good agreement with Pearson correlation coefficients better than 0.9, while the linear regression analyses show slopes close to unity and generally small intercepts.

The Small Whiskbroom Imager for atmospheric compositioN monitorinG (SWING) is a compact remote sensing instrument dedicated to mapping trace gases from an unmanned aerial vehicle (UAV). SWING is based on a compact visible spectrometer and a scanning mirror to collect scattered sunlight. Its weight, size, and power consumption are respectively 920 g, 27 cm × 12 cm × 8 cm, and 6 W. SWING was developed in parallel with a 2.5 m flying-wing UAV. This unmanned aircraft is electrically powered, has a typical airspeed of 100 km h−1, and can operate at a maximum altitude of 3 km. We present SWING-UAV experiments performed in Romania on 11 September 2014 during the Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaign, which was dedicated to test newly developed instruments in the context of air quality satellite validation. The UAV was operated up to 700 m above ground, in the vicinity of the large power plant of Turceni (44.67° N, 23.41° E; 116 m a. s. l. ). These SWING-UAV flights were coincident with another airborne experiment using the Airborne imaging differential optical absorption spectroscopy (DOAS) instrument for Measurements of Atmospheric Pollution (AirMAP), and with ground-based DOAS, lidar, and balloon-borne in situ observations. The spectra recorded during the SWING-UAV flights are analysed with the DOAS technique. This analysis reveals NO2 differential slant column densities (DSCDs) up to 13±0.6×1016 molec cm−2. These NO2 DSCDs are converted to vertical column densities (VCDs) by estimating air mass factors. The resulting NO2 VCDs are up to 4.7±0.4×1016 molec cm−2. The water vapour DSCD measurements, up to 8±0.15×1022 molec cm−2, are used to estimate a volume mixing ratio of water vapour in the boundary layer of 0.013±0.002 mol mol−1. These geophysical quantities are validated with the coincident measurements.

The Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC) and the LIdar-Radiometer Inversion Code (LIRIC) provide the opportunity to study the aerosol vertical distribution by combining ground-based lidar and sun-photometric measurements. Here, we utilize the capabilities of both algorithms for the characterization of Saharan dust and marine particles, along with their mixtures, in the south-eastern Mediterranean during the CHARacterization of Aerosol mixtures of Dust and Marine origin Experiment (CHARADMExp). Three case studies are presented, focusing on dust-dominated, marine-dominated and dust–marine mixing conditions. GARRLiC and LIRIC achieve a satisfactory characterization for the dust-dominated case in terms of particle microphysical properties and concentration profiles. The marine-dominated and the mixture cases are more challenging for both algorithms, although GARRLiC manages to provide more detailed microphysical retrievals compared to AERONET, while LIRIC effectively discriminates dust and marine particles in its concentration profile retrievals. The results are also compared with modelled dust and marine concentration profiles and surface in situ measurements.

We report first chemistry mode retrieval results from the new airborne limb-imaging infrared FTS (Fourier transform spectrometer) GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) and comparisons with observations by the conventional airborne limb-scanning infrared FTS MIPAS-STR (Michelson Interferometer for Passive Atmospheric Sounding – STRatospheric aircraft). For GLORIA, the flights aboard the high-altitude research aircraft M55 Geophysica during the ESSenCe campaign (ESa Sounder Campaign 2011) were the very first in field deployment after several years of development. The simultaneous observations of GLORIA and MIPAS-STR during the flight on 16 December 2011 inside the polar vortex and under conditions of optically partially transparent polar stratospheric clouds (PSCs) provided us the first opportunity to compare the observations by two different infrared FTS generations directly. We validate the GLORIA results with MIPAS-STR based on the lower vertical resolution of MIPAS-STR and compare the vertical resolutions of the instruments derived from their averaging kernels. The retrieval results of temperature, HNO3, O3, H2O, CFC-11 and CFC-12 show reasonable agreement of GLORIA with MIPAS-STR and collocated in situ observations. For the horizontally binned hyperspectral limb images, the GLORIA sampling outnumbered the horizontal cross-track sampling of MIPAS-STR by up to 1 order of magnitude. Depending on the target parameter, typical vertical resolutions of 0.5 to 2.0 km were obtained for GLORIA and are typically a factor of 2 to 4 better compared to MIPAS-STR. While the improvement of the performance, characterization and data processing of GLORIA are the subject of ongoing work, the presented first results already demonstrate the considerable gain in sampling and vertical resolution achieved with GLORIA.

The three-dimensional quantification of small-scale processes in the upper troposphere and lower stratosphere is one of the challenges of current atmospheric research and requires the development of new measurement strategies. This work presents the first results from the newly developed Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) obtained during the ESSenCe (ESa Sounder Campaign) and TACTS/ESMVal (TACTS: Transport and composition in the upper troposphere/lowermost stratosphere, ESMVal: Earth System Model Validation) aircraft campaigns. The focus of this work is on the so-called dynamics-mode data characterized by a medium-spectral and a very-high-spatial resolution. The retrieval strategy for the derivation of two- and three-dimensional constituent fields in the upper troposphere and lower stratosphere is presented. Uncertainties of the main retrieval targets (temperature, O3, HNO3, and CFC-12) and their spatial resolution are discussed. During ESSenCe, high-resolution two-dimensional cross-sections have been obtained. Comparisons to collocated remote-sensing and in situ data indicate a good agreement between the data sets. During TACTS/ESMVal, a tomographic flight pattern to sense an intrusion of stratospheric air deep into the troposphere was performed. It was possible to reconstruct this filament at an unprecedented spatial resolution of better than 500 m vertically and 20 × 20 km horizontally.