Are EARLINET and AERONET climatologies consistent? The case of Thessaloniki, Greece

Abstract

Abstract. In this study we investigate the climatological behavior of the aerosol optical properties over Thessaloniki during the years [2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016][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 (EAR-LINET) 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 EAR-LINET 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.

Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering

“…Data with AOD above 1.5, a limit that was set to exclude cases contaminated by thin cirrus clouds that have not been detected by the cloud screening algorithm, are not present in the Brewer timeseries. It is also worth mentioning, that such high AOD340 values are encountered very rarely in Thessaloniki (e.g., [38]). This filter removes less than 0.25% of the cases.…”

“…After 2005, the system was upgraded and three additional channels at 340 nm, 380 nm, and 500 nm were included. Further details on the timeseries of the sunphotometer can be found in [38]. Measurements are automatically gathered, processed, and distributed by the AERONET network [39].…”

“…These wavelengths are preferred as they are available for a longer time. Applying, a 2nd order interpolation that takes into account all available wavelengths is favored over a linear interpolation between the two closest ones because it results to a more accurate predicted value (e.g., [38,45]). Indeed, according to [45] the spectral dependence of the logarithm of the AOD is close to but not exactly linear.…”

“…We will refer to that dataset from now on as the reference clusters in the manuscript. Aerosol classification is often manually performed by taking into account satellite observations, modeled backward trajectories, aerosol transportation models, and measurements from other instruments (e.g., [38,[57][58][59]). Here we prefer to apply an automated threshold-based approach using measurements from a single instrument because it allows faster, more efficient, and less subjective classification of a large number of cases.…”

“…In order to perform the manual classification we have identified 6 cases per aerosol type (FNA, BC, DUST) based on measurements from a co-located multi-wavelength Raman system in the period 2008-2017. The aerosol characterization analysis was initially performed by [38]. They characterize aerosol profiles as desert dust or biomass burning if at least one such layer is present in the profile, otherwise the profile is flagged as continental.…”