Aerosols that are efficient ice-nucleating particles (INPs) are crucial for the formation of cloud ice via heterogeneous nucleation in the atmosphere. The distribution of INPs on a large spatial scale and as a function of height determines their impact on clouds and climate. However, in situ measurements of INPs provide sparse coverage over space and time. A promising approach to address this gap is to retrieve INP concentration profiles by combining particle concentration profiles derived by lidar measurements with INP efficiency parameterizations for different freezing mechanisms (immersion freezing, deposition nucleation). Here, we assess the feasibility of this new method for both ground-based and spaceborne lidar measurements, using in situ observations collected with unmanned aerial vehicles (UAVs) and subsequently analyzed with the FRIDGE (FRankfurt Ice nucleation Deposition freezinG Experiment) INP counter from an experimental campaign at Cyprus in April 2016. Analyzing five case studies we calculated the cloud-relevant particle number concentrations using lidar measurements (n_250,dry with an uncertainty of 20 % to 40 % and S_dry with an uncertainty of 30 % to 50 %), and we assessed the suitability of the different INP parameterizations with respect to the temperature range and the type of particles considered. Specifically, our analysis suggests that our calculations using the parameterization of Ullrich et al. (2017) (applicable for the temperature range −50 to −33 ^∘C) agree within 1 order of magnitude with the in situ observations of n_INP; thus, the parameterization of Ullrich et al. (2017) can efficiently address the deposition nucleation pathway in dust-dominated environments. Additionally, our calculations using the combination of the parameterizations of DeMott et al. (2015, 2010) (applicable for the temperature range −35 to −9 ^∘C) agree within 2 orders of magnitude with the in situ observations of INP concentrations (n_INP) and can thus efficiently address the immersion/condensation pathway of dust and nondust particles. The same conclusion is derived from the compilation of the parameterizations of DeMott et al. (2015) for dust and Ullrich et al. (2017) for soot. Furthermore, we applied this methodology to estimate the INP concentration profiles before and after a cloud formation, indicating the seeding role of the particles and their subsequent impact on cloud formation and characteristics. More synergistic datasets are expected to become available in the future from EARLINET (European Aerosol Research Lidar Network) and in the frame of the European ACTRIS-RI (Aerosols, Clouds, and Trace gases Research Infrastructure). Our analysis shows that the developed techniques, when applied on CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) spaceborne lidar observations, are in agreement with the in situ measurements. This study gives us confidence for the production of global 3-D products of cloud-relevant particle number concentrations (n_250,dry, S_dry and n_INP) using the CALIPSO 13-year dataset. This could provide valuable insight into the global height-resolved distribution of INP concentrations related to mineral dust, as well as possibly other aerosol types.

We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) aboard the International Space Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter measurements within a 50 km distance between the ground station and the ISS overpass and as close in time as possible, typically with the starting time or stopping time of the EARLINET performed measurement time window within 90 min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are analyzed here to demonstrate the performance of the CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions, CATS is in good agreement with EARLINET, independent of daytime and nighttime conditions. CATS low negative biases are observed, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds; these are biases which may lead to systematic deviations and slight underestimations of the total aerosol optical depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low negative biases in CATS backscatter coefficient profiles, of the order of 6.1 %, indicate the good nighttime performance of CATS. During daytime, a reduced signal-to-noise ratio by solar background illumination prevents retrievals of weakly scattering atmospheric layers that would otherwise be detectable during nighttime, leading to higher negative biases, of the order of 22.3 %.

Measurements of geometrical and optical properties of cirrus clouds, performed with a multi-wavelength Polly^XT Raman lidar during the period 2008 to 2016, are analysed. The measurements were performed with the same instrument, during sequential periods, in three places at different latitudes, Gwal Pahari (28.43^∘ N, 77.15^∘ E; 243 m a.s.l.) in India, Elandsfontein (26.25^∘ S, 29.43^∘ E; 1745 m a.s.l.) in South Africa and Kuopio (62.74^∘ N, 27.54^∘ E; 190 m a.s.l.) in Finland. The lidar dataset was processed by an automatic cirrus cloud masking algorithm, developed in the frame of this work. In the following, we present a statistical analysis of the lidar-retrieved geometrical characteristics (cloud boundaries, geometrical thickness) and optical properties of cirrus clouds (cloud optical depth, lidar ratio, ice crystal depolarisation ratio) measured over the three areas that correspond to subtropical and subarctic regions as well as their seasonal variability. The effect of multiple scattering from ice particles to the derived optical products is also considered and corrected in this study. Our results show that cirrus layers, which have a noticeable monthly variability, were observed between 6.5 and 13 km, with temperatures ranging from −72 to −27 ^∘C. The observed differences on cirrus clouds' geometrical and optical properties over the three regions are discussed in terms of latitudinal and temperature dependence. The latitudinal dependence of the geometrical properties is consistent with satellite observations, following the pattern observed with CloudSat, with decreasing values towards the poles. The geometrical boundaries have their highest values in the subtropical regions, and overall, our results seem to demonstrate that subarctic cirrus clouds are colder, lower and optically thinner than subtropical cirrus clouds. The dependence of cirrus cloud geometrical thickness and optical properties on mid-cirrus temperatures shows a quite similar tendency for the three sites but less variability for the subarctic dataset. Cirrus clouds are geometrically and optically thicker at temperatures between −45 and −35 ^∘C, and a second peak is observed at lower temperatures $\sim -\mathrm{70}$ ^∘C for the subarctic site. Lidar ratio values also exhibit a pattern, showing higher values moving toward the poles, with higher mean values observed over the subarctic site. The dependency of the mid-cirrus temperatures on the lidar ratio values and the particle depolarisation values is further examined. Our study shows that the highest values of the cirrus lidar ratio correspond to higher values of cirrus depolarisation and warmer cirrus. The kind of information presented here can be rather useful in the cirrus parameterisations required as input to radiative transfer models and can be a complementary tool for satellite products that cannot provide cloud vertical structure. In addition, ground-based statistics of the cirrus properties could be useful in the validation and improvement of the corresponding derived products from satellite retrievals.

In the framework of the ESA-NASA Joint Aeolus Tropical Atlantic Campaign (JATAC), the ASKOS experiment was implemented during the summer and autumn of 2021 and 2022. ASKOS comprised roughly 9 weeks of measurements in the Saharan dust outflow towards the North Atlantic, with operations conducted from the Cabo Verde Islands. Through its unprecedented dataset of synergistic measurements in the region, ASKOS will allow for the calibration and validation of the aerosol/cloud product from Aeolus and the preparation of the terrain for EarthCARE cal/val activities. Moreover, ASKOS marks a turning point in our ability to study Saharan dust properties and the processes affecting its atmospheric transport, as well as the link to other components of the Earth’s system, such as the effect of dust particles on cloud formation over the Eastern Atlantic and the effect of large and giant particles on radiation. This is possible through the synergy of diverse observations acquired during the experiment, which include intense 24/7 ground-based aerosol, cloud, wind, and radiation remote sensing measurements, and UAV-based aerosol in situ measurements within the Saharan air layer, up to 5.3 km altitude, offering particle size-distributions up to 40 μm as well as sample collection for mineralogical analysis. We provide an outline of the novel measurements along with the main scientific objectives of ASKOS. The campaign data will be publicly available by September of 2023 through the EVDC portal (ESA Validation Data Center).

In this study we apply and compare two algorithms for the automated aerosol-type characterization of the aerosol layers derived from Raman lidar measurements over the EARLINET station of Thessaloniki, Greece. Both automated aerosol-type characterization methods base their typing on lidar-derived aerosol-intensive properties. The methodologies are briefly described and their application to three distinct cases is demonstrated and evaluated. Then the two classification schemes were applied in the automatic mode to a more extensive dataset. The dataset analyzed corresponds to ACTRIS/EARLINET (European Aerosol Research Lidar NETwork) Thessaloniki data acquired during the period 2012–2015. Seventy-one layers out of 110 (percentage of 65 %) were typed by both techniques, and 56 of these 71 layers (percentage of 79 %) were attributed to the same aerosol type. However, as shown, the identification rate of both typing algorithms can be changed regarding the selection of appropriate threshold criteria. Four major types of aerosols are considered in this study: Dust, Maritime, PollutedSmoke and CleanContinental. The analysis showed that the two algorithms, when applied to real atmospheric conditions, provide typing results that are in good agreement regarding the automatic characterization of PollutedSmoke, while there are some differences between the two methods regarding the characterization of Dust and CleanContinental. These disagreements are mainly attributed to differences in the definitions of the aerosol types between the two methods, regarding the intensive properties used and their range.

In this study we investigate the climatological behavior of the aerosol optical properties over Thessaloniki during the years 2003–2017. For this purpose, measurements of two independent instruments, a lidar and a sunphotometer, were used. These two instruments represent two individual networks, the European Lidar Aerosol Network (EARLINET) and the Aerosol Robotic Network (AERONET). They include different measurement schedules. Fourteen years of lidar and sunphotometer measurements were analyzed, independently of each other, in order to obtain the annual cycles and trends of various optical and geometrical aerosol properties in the boundary layer, in the free troposphere, and for the whole atmospheric column. The analysis resulted in consistent statistically significant and decreasing trends of aerosol optical depth (AOD) at 355 nm of −23.2 and −22.3 % per decade in the study period over Thessaloniki for the EARLINET and the AERONET datasets, respectively. Therefore, the analysis indicates that the EARLINET sampling schedule can be quite effective in producing data that can be applied to long-term climatological studies. It is also shown that the observed decreasing trend is mainly attributed to changes in the aerosol load inside the boundary layer. Seasonal profiles of the most dominant aerosol mixture types observed over Thessaloniki have been generated from the lidar data. The higher values of the vertically resolved extinction coefficient at 355 nm appear in summer, while the lower ones appear in winter. The dust component is more dominant in the free troposphere than in the boundary layer during summer. The biomass burning layers tend to arrive in the free troposphere during spring and summer. This kind of information can be quite useful for applications that require a priori aerosol profiles. For instance, they can be utilized in models that require aerosol climatological data as input, in the development of algorithms for satellite products, and also in passive remote-sensing techniques that require knowledge of the aerosol vertical distribution.