Abstract. Atmospheric aerosols play a crucial role in the Earth's system, but their role is not completely understood, partly because of the large variability in their properties resulting from a large number of possible aerosol sources. Recently developed lidar-based techniques were able to retrieve the height distributions of optical and microphysical properties of fine-mode and coarse-mode particles, providing the types of the aerosols. One such technique is based on artificial neural networks (ANNs). In this article, a Neural Network Aerosol Typing Algorithm Based on Lidar Data (NATALI) was developed to estimate the most probable aerosol type from a set of multispectral lidar data. The algorithm was adjusted to run on the EARLINET 3β+2α(+1δ) profiles. The NATALI algorithm is based on the ability of specialized ANNs to resolve the overlapping values of the intensive optical parameters, calculated for each identified layer in the multiwavelength Raman lidar profiles. The ANNs were trained using synthetic data, for which a new aerosol model was developed. Two parallel typing schemes were implemented in order to accommodate data sets containing (or not) the measured linear particle depolarization ratios (LPDRs): (a) identification of 14 aerosol mixtures (high-resolution typing) if the LPDR is available in the input data files, and (b) identification of five predominant aerosol types (low-resolution typing) if the LPDR is not provided. For each scheme, three ANNs were run simultaneously, and a voting procedure selects the most probable aerosol type. The whole algorithm has been integrated into a Python application. The limitation of NATALI is that the results are strongly dependent on the input data, and thus the outputs should be understood accordingly. Additional applications of NATALI are feasible, e.g. testing the quality of the optical data and identifying incorrect calibration or insufficient cloud screening. Blind tests on EARLINET data samples showed the capability of NATALI to retrieve the aerosol type from a large variety of data, with different levels of quality and physical content.

On the morning of 23 March 2018, an unusual phenomenon was observed over Romania where the southeastern part of the country was covered in a fresh-layer of orange snow. The event was extensively reported in mass-media and social-media and raised questions about the origin and the possible impact of the orange snow. Even if this type of events, intrusions of Saharan dust, have been reported before in Romania, and in Europe in general, their occurrence during negative temperature conditions is very rare. Saharan dust intrusion occurs over Europe mainly during spring and, in general, is not accompanied by snow at low altitudes. In this article, for the first time, the synoptic-scale conditions leading to the Saharan dust intrusion over Romania and the chemical and physical properties of the deposited dust particles in a snow layer were analyzed. The Saharan dust event affected a permanent atmospheric measurement research infrastructure located southwest of Bucharest, the capital city of Romania. In-situ and remote sensing measurements conducted at this research infrastructure allowed the identification of the dust source as the north Sahara. The source was confirmed by the elemental ratios of the main components (e.g., Al, Ca, Mg, Fe, K). For example, the (Ca+Mg)/Fe ratio of 1.39 was characteristic for the north Sahara. The dust morphology and the minerals were analyzed by scanning electron microscopy with energy disperse X-ray spectrometry (SEM/EDX). The size distribution of the particle geometric diameter showed that they are centred on 1 μ m, but larger particles up to 40 μ m are also present. To visualize the minerals, an approach was developed which emphasized the presence of the calcite, quartz or clay minerals. The optical parameters of dust were measured by re-suspending the particles. Values of the optical parameters (i.e., asymmetry parameter at 550 nm was 0.604, single scattering albedo was 0.84–0.89) were similar to those measured for Saharan dust intrusions over the Iberian Peninsula. Also, the non-refractory particles found in the dust-contaminated snow layer were analyzed, indicating the presence of HULIS-like compounds, most probably advected from the Mediterranean sea.

The bioclimatology of thermal stress over Europe between 1979 and 2019 was analysed using the Universal Thermal Climate Index (UTCI) derived from ERA5‐HEAT reanalysis. The bioclimatology of different European regions was assessed using Köppen–Geiger climate classification. The annual number of hours with heat stress (UTCI > 32°C) increased significantly during the study period for all the analysed Köppen–Geiger climate subclasses, showing also a clear increase towards southern Europe. The highest percentage of hours (20% of all hours) with cold stress (UTCI < −13°C) occur over northern Europe. A significant increasing trend (>0.05 hr·year−1) in the number of hours with heat stress was observed for 23 out of 32 analysed European cities representative for the Köppen–Geiger climate subclasses. For these cities not only the number of hours with heat stress has increased but also the heat stress is more persistent, while the number of cases and the persistence of the periods with cold stress have decreased over the last four decades. The UTCI values showed a statistically significant increase between 0.6 and 3.2°C for all the analysed cities over the study period reflecting the rising of global mean temperatures.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; Atmospheric aerosols play a crucial role in the earth system, but their role is not completely understood, partly because of the large variability in their properties resulting from a large number of possible aerosol sources. Recently developed techniques were able to retrieve the height distributions of optical and microphysical properties of fine-mode and coarse-mode particles, providing the types of the aerosols depicted. One such technique for aerosol typing is based on artificial neural networks (ANNs). In this article, a Neural Network Aerosol Typing Algorithm Based on Lidar Data (NATALI) was developed to estimate the most probable aerosol type from a set of multispectral data. The algorithm has been adjusted for running on the EARLINET 3&amp;#223; + 2a (+1d) profiles. The NATALI algorithm is based on the ability of specialized ANNs to resolve the overlapping values of the intensive optical parameters calculated for each identified layer in the multiwavelength Raman lidar profiles. The ANNs were trained using synthetic data, for which a new aerosol model was developed. Two parallel typing schemes were implemented in order to accommodate datasets containing or not the measured linear particle depolarization ratios (LPDR): a) identification of mixtures from 14 aerosol mixtures (high-resolution typing) if the LPDR is available in the input data files, and b) identification of 5 predominant aerosol types (low-resolution typing) if the LPDR is not provided. For each scheme, three ANNs were run simultaneously, and a voting procedure selects the most probable answer. The whole algorithm has been integrated into a Python code. The main issue with the approached used in NATALI is that the results are strongly dependent on the input data, and thus the outputs should be understood accordingly. The algorithm has side-applications, for example, to test the quality of the optical data and identify incorrect calibration or incorrect cloud screening. Blind tests on EARLINET data samples showed the capability of this tool to retrieve the aerosol type from a large variety of data, with different quality and physical content.&lt;/p&gt;

In this study, AERONET (Aerosol Robotic Network) and EARLINET (European Aerosol Research Lidar Network) data from 17 collocated lidar and sun photometer stations were used to characterize the optical properties of aerosol and their types for the 2008–2018 period in various regions of Europe. The analysis was done on six cluster domains defined using circulation types around each station and their common circulation features. As concluded from the lidar photometer measurements, the typical aerosol particles observed during 2008–2018 over Europe were medium-sized, medium absorbing particles with low spectral dependence. The highest mean values for the lidar ratio at 532 nm were recorded over Northeastern Europe and were associated with Smoke particles, while the lowest mean values for the Angstrom exponent were identified over the Southwest cluster and were associated with Dust and Marine particles. Smoke (37%) and Continental (25%) aerosol types were the predominant aerosol types in Europe, followed by Continental Polluted (17%), Dust (10%), and Marine/Cloud (10%) types. The seasonal variability was insignificant at the continental scale, showing a small increase in the percentage of Smoke during spring and a small increase of Dust during autumn. The aerosol optical depth (AOD) slightly decreased with time, while the Angstrom exponent oscillated between “hot and smoky” years (2011–2015) on the one hand and “dusty” years (2008–2010) and “wet” years (2017–2018) on the other hand. The high variability from year to year showed that aerosol transport in the troposphere became more and more important in the overall balance of the columnar aerosol load.