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- ItemVertical profiles of aerosol mass concentration derived by unmanned airborne in situ and remote sensing instruments during dust events(Katlenburg-Lindau : Copernicus, 2018) Mamali, Dimitra; Marinou, Eleni; Sciare, Jean; Pikridas, Michael; Kokkalis, Panagiotis; Kottas, Michael; Binietoglou, Ioannis; Tsekeri, Alexandra; Keleshis, Christos; Engelmann, Ronny; Baars, Holger; Ansmann, Albert; Amiridis, Vassilis; Russchenberg, Herman; Biskos, GeorgeIn situ measurements using unmanned aerial vehicles (UAVs) and remote sensing observations can independently provide dense vertically resolved measurements of atmospheric aerosols, information which is strongly required in climate models. In both cases, inverting the recorded signals to useful information requires assumptions and constraints, and this can make the comparison of the results difficult. Here we compare, for the first time, vertical profiles of the aerosol mass concentration derived from light detection and ranging (lidar) observations and in situ measurements using an optical particle counter on board a UAV during moderate and weak Saharan dust episodes. Agreement between the two measurement methods was within experimental uncertainty for the coarse mode (i.e. particles having radii > 0.5 μm), where the properties of dust particles can be assumed with good accuracy. This result proves that the two techniques can be used interchangeably for determining the vertical profiles of aerosol concentrations, bringing them a step closer towards their systematic exploitation in climate models.
- ItemRetrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements(Katlenburg-Lindau : EGU, 2019) Marinou, Eleni; Tesche, Matthias; Nenes, Athanasios; Ansmann, Albert; Schrod, Jann; Mamali, Dimitra; Tsekeri, Alexandra; Pikridas, Michael; Baars, Holger; Engelmann, Ronny; Voudouri, Kalliopi-Artemis; Solomos, Stavros; Sciare, Jean; Groß, Silke; Ewald, Florian; Amiridis, VassilisAerosols 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 (n250,dry with an uncertainty of 20 % to 40 % and Sdry 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 nINP; 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 (nINP) 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.
- ItemEARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product(Katlenburg-Lindau : EGU, 2019) Proestakis, Emmanouil; Amiridis, Vassilis; Marinou, Eleni; Binietoglou, Ioannis; Ansmann, Albert; Wandinger, Ulla; Hofer, Julian; Yorks, John; Nowottnick, Edward; Makhmudov, Abduvosit; Papayannis, Alexandros; Pietruczuk, Aleksander; Gialitaki, Anna; Apituley, Arnoud; Szkop, Artur; Muñoz Porcar, Constantino; Bortoli, Daniele; Dionisi, Davide; Althausen, Dietrich; Mamali, Dimitra; Balis, Dimitris; Nicolae, Doina; Tetoni, Eleni; Liberti, Gian Luigi; Baars, Holger; Mattis, Ina; Stachlewska, Iwona Sylwia; Voudouri, Kalliopi Artemis; Mona, Lucia; Mylonaki, Maria; Perrone, Maria Rita; Costa, Maria João; Sicard, Michael; Papagiannopoulos, Nikolaos; Siomos, Nikolaos; Burlizzi, Pasquale; Pauly, Rebecca; Engelmann, Ronny; Abdullaev, Sabur; Pappalardo, GelsominaWe 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 50km 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 90min 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%. © Author(s) 2019.