ALBERT

Description: 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.

Description: 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.

Description: In this study we present an evaluation of the Comprehensive Air Quality Model with extensions (CAMx) for Thessaloniki using radiometric and lidar data. The aerosol mass concentration profiles of CAMx are compared against the PM2.5 and PM2. 5−10 concentration profiles retrieved by the Lidar-Radiometer Inversion Code (LIRIC). The CAMx model and the LIRIC algorithm results were compared in terms of mean mass concentration profiles, center of mass and integrated mass concentration in the boundary layer and the free troposphere. The mean mass concentration comparison resulted in profiles within the same order of magnitude and similar vertical structure for the PM2. 5 particles. The mean centers of mass values are also close, with a mean bias of 0.57 km. On the opposite side, there are larger differences for the PM2. 5−10 mode, both in the boundary layer and in the free troposphere. In order to grasp the reasons behind the discrepancies, we investigate the effect of aerosol sources that are not properly included in the model's emission inventory and in the boundary conditions such as the wildfires and the desert dust component. The identification of the cases that are affected by wildfires is performed using wind backward trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model in conjunction with satellite fire pixel data from MODerate-resolution Imaging Spectroradiometer (MODIS) Terra and Aqua global monthly fire location product MCD14ML. By removing those cases the correlation coefficient improves from 0.69 to 0.87 for the PM2. 5 integrated mass in the boundary layer and from 0.72 to 0.89 in the free troposphere. The PM2.5 center of mass fractional bias also decreases to 0.38 km. Concerning the analysis of the desert dust component, the simulations from the Dust Regional Atmospheric Model (BSC-DREAM8b) were deployed. When only the Saharan dust cases are taken into account, BSC-DREAM8b generally outperforms CAMx when compared with LIRIC, achieving a correlation of 0.91 and a mean bias of −29.1 % for the integrated mass in the free troposphere and a correlation of 0.57 for the center of mass. CAMx, on the other hand, underestimates the integrated mass in the free troposphere. Consequently, the accuracy of CAMx is limited concerning the transported Saharan dust cases. We conclude that the performance of CAMx appears to be best for the PM2.5 particles, both in the boundary layer and in the free troposphere. Sources of particles not properly taken into account by the model are confirmed to negatively affect its performance, especially for the PM2. 5−10 particles.

Description: The gap in knowledge regarding the radiative effects of aerosols in the UV region of the solar spectrum is large, mainly due to the lack of systematic measurements of the aerosol single scattering albedo (SSA) and absorption optical depth (AAOD). In the present study, spectral UV measurements performed in Thessaloniki, Greece by a double monochromator Brewer spectrophotometer in the period 1998–2017 are used for the calculation of the aforementioned optical properties. The main uncertainty factors have been described and there is an effort to quantify the overall uncertainties in SSA and AAOD. Analysis of the results suggests that the absorption by aerosols is much stronger in the UV relative to the visible. SSA follows a clear annual pattern ranging from ~0.7 in winter to ~0.85 in summer at wavelengths 320–360 nm, while AAOD peaks in summer and winter. The average AAOD for 2009–2011 is ~50% above the 2003–2006 average, possibly due to increased emissions of absorbing aerosols related to the economic crisis and the metro-railway construction works in the city center.

Description: 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 %.

Description: 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.

Description: In this study, we present an aerosol classification technique based on measurements of a double monochromator Brewer spectrophotometer during the period 1998–2017 in Thessaloniki, Greece. A machine learning clustering procedure was applied based on the Mahalanobis distance metric. The classification process utilizes the UV Single Scattering Albedo (SSA) at 340 nm and the Extinction Angstrom Exponent (EAE) at 320–360 nm that are obtained from the spectrophotometer. The analysis is supported by measurements from a CIMEL sunphotometer that were deployed in order to establish the training dataset of Brewer measurements. By applying the Mahalanobis distance algorithm to the Brewer timeseries, we automatically assigned measurements in one of the following clusters: Fine Non Absorbing Mixtures (FNA): 64.7%, Black Carbon Mixtures (BC): 17.4%, Dust Mixtures (DUST): 8.1%, and Mixed: 9.8%. We examined the clustering potential of the algorithm by reclassifying the training dataset and comparing it with the original one and also by using manually classified cases. The typing score of the Mahalanobis algorithm is high for all predominant clusters FNA: 77.0%, BC: 63.9%, and DUST: 80.3% when compared with the training dataset. We obtained high scores as well FNA: 100.0%, BC: 66.7%, and DUST: 83.3% when comparing it with the manually classified dataset. The flags obtained here were applied in the timeseries of the Aerosol Optical Depth (AOD) at 340 nm of the Brewer and the CIMEL in order to compare between the two and also stress the future impact of the proposed clustering technique in climatological studies of the station.

Description: 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) onboard the International Space Station (ISS). The study employs correlative CATS and EARLINET backscatter measurements within 50 km distance between the ground station and the ISS overpass and as close in time as possible, typically within 90 min, from February 2015 to September 2016. The results demonstrate the good agreement of CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig-Germany, Évora-Portugal and Dushanbe-Tajikistan are analysed here to demonstrate the performance of CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions CATS is in good agreement with EARLINET, independently of daytime/nighttime conditions. CATS low negative biases, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds, 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, may lead to non-representative, unrealistic and cloud contaminated aerosol profiles. The distributions of backscatter coefficient biases show the relatively good agreement between the CATS and EARLINET measurements, although on average underestimations are observed, 22.3 % during daytime and 6.1 % during nighttime.

Description: In this study we compare two automatic algorithms for the characterization of the aerosol layers derived from a Raman lidar and we test their application over a specific environment in continental Thessaloniki. Both automated aerosol typing methods base their typing on aerosol intensive properties. The methodologies are briefly described and the application on two case studies is presented. The results are checked for their consistency with satellite products and model simulations. Further application of the two classification tools was conducted. The evaluated dataset corresponds to ACTRIS/EARLINET (European Aerosol Research Lidar NETwork) Thessaloniki data acquired during the period 2012–2015. 80 layers out of 116 (percentage of 69 %) were successfully typed by both algorithms and assigned to four major types of aerosols: Dust, Maritime, Polluted Smoke and Clean Continental. The analysis showed that the two algorithms are in a very good agreement, when applied to real atmospheric conditions, with an agreement percentage of 88.8 % for Dust, 93.7 % for Polluted Smoke and 70 % for Clean Continental. The Maritime category was the one with the largest spread. These differences are attributed to differences in defining the aerosol types for the two methods. The overall consistency of the aerosol typing between the two automatic procedures despite the different aerosol type definition, allows their applicability to lidar data for characterization purposes. The joint characterization shows the highest degree of confidence in identifying Dust and Polluted Smoke, and emphasizes the need of further investigation for Maritime and Clean Continental type.

Description: In this study we present an evaluation of the Comprehensive Air quality Model with extensions CAMx for Thessaloniki using radiometric and lidar data. The aerosol mass concentration profiles of CAMx are compared against the fine and coarse mode aerosol concentration profiles retrieved by the Lidar-Radiometer Inversion Code LIRIC. The CAMx model and the LIRIC algorithm results were compared in terms of mean mass concentration profiles, center of mass and integrated mass concentration in the boundary layer and the free troposphere The mean mass concentration comparison resulted in profiles within the same order of magnitude and similar vertical structure for the fine particles. The mean center of mass values are also close with a fractional bias of 24.8 %. On the opposite side, the coarse mode appears to be underestimated by the model below 4 km and overestimated above. In order to grasp the reasons behind the discrepancies, we investigate the effect of aerosol components that are not properly included in the model's emission inventory and boundary conditions such as the wildfires and the desert dust component. The identification of the cases that are affected by wildfires is performed using wind backward trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory Model HYSPLIT in conjunction with satellite fire pixel data from the MODIS Terra and Aqua Global Monthly Fire Location Product MCD14ML. By removing those cases the correlation coefficient improves from 0.44 to 0.86 for the fine mode integrated mass in the boundary layer. The fine mode center of mass fractional bias also decreases to 16.9 %. Concerning the analysis on the desert dust component, the simulations from the updated version of the former Dust Regional Atmospheric Model called BSC-DREAM8b were deployed. When only the desert dust cases are taken into account, BSC-DREAM8b generally outperforms CAMx when compared with LIRIC, achieving a correlation of 0.91 and a fractional bias of −18.9 % for the integrated mass in the free troposphere and a correlation of 0.44 for the center of mass. CAMx, on the other hand, both underestimates and anti-correlates the integrated mass in the free troposphere. Consequently, the accuracy of CAMx is limited concerning the transported dust cases. We conclude that the performance of CAMx appears to be best for the fine particles, especially in the boundary layer. At the same time it systematically fails to successfully predict the coarse mode. Sources of particles not properly taken into account by the model are confirmed to negatively affect its performance.