The European Aerosol Research Lidar Network, EARLINET, was founded in 2000 as a research project for establishing a quantitative, comprehensive, and statistically significant database for the horizontal, vertical, and temporal distribution of aerosols on a continental scale. Since then EARLINET has continued to provide the most extensive collection of ground-based data for the aerosol vertical distribution over Europe. This paper gives an overview of the network's main developments since 2000 and introduces the dedicated EARLINET special issue, which reports on the present innovative and comprehensive technical solutions and scientific results related to the use of advanced lidar remote sensing techniques for the study of aerosol properties as developed within the network in the last 13 years. Since 2000, EARLINET has developed greatly in terms of number of stations and spatial distribution: from 17 stations in 10 countries in 2000 to 27 stations in 16 countries in 2013. EARLINET has developed greatly also in terms of technological advances with the spread of advanced multiwavelength Raman lidar stations in Europe. The developments for the quality assurance strategy, the optimization of instruments and data processing, and the dissemination of data have contributed to a significant improvement of the network towards a more sustainable observing system, with an increase in the observing capability and a reduction of operational costs. Consequently, EARLINET data have already been extensively used for many climatological studies, long-range transport events, Saharan dust outbreaks, plumes from volcanic eruptions, and for model evaluation and satellite data validation and integration. Future plans are aimed at continuous measurements and near-real-time data delivery in close cooperation with other ground-based networks, such as in the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) www.actris.net, and with the modeling and satellite community, linking the research community with the operational world, with the aim of establishing of the atmospheric part of the European component of the integrated global observing system.

The spread of mineral particles over southwestern, western, and central Europe resulting from a strong Saharan dust outbreak in October 2001 was observed at 10 stations of the European Aerosol Research Lidar Network (EARLINET). For the first time, an optically dense desert dust plume over Europe was characterized coherently with high vertical resolution on a continental scale. The main layer was located above the boundary layer (above 1‐km height above sea level (asl)) up to 3–5‐km height, and traces of dust particles reached heights of 7–8 km. The particle optical depth typically ranged from 0.1 to 0.5 above 1‐km height asl at the wavelength of 532 nm, and maximum values close to 0.8 were found over northern Germany. The lidar observations are in qualitative agreement with values of optical depth derived from Total Ozone Mapping Spectrometer (TOMS) data. Ten‐day backward trajectories clearly indicated the Sahara as the source region of the particles and revealed that the dust layer observed, e.g., over Belsk, Poland, crossed the EARLINET site Aberystwyth, UK, and southern Scandinavia 24–48 hours before. Lidar‐derived particle depolarization ratios, backscatter‐ and extinction‐related Ångström exponents, and extinction‐to‐backscatter ratios mainly ranged from 15 to 25%, −0.5 to 0.5, and 40–80 sr, respectively, within the lofted dust plumes. A few atmospheric model calculations are presented showing the dust concentration over Europe. The simulations were found to be consistent with the network observations.

Six months of stratospheric aerosol observations with the European Aerosol Research Lidar Network (EARLINET) from August 2017 to January 2018 are presented. The decay phase of an unprecedented, record-breaking stratospheric perturbation caused by wildfire smoke is reported and discussed in terms of geometrical, optical, and microphysical aerosol properties. Enormous amounts of smoke were injected into the upper troposphere and lower stratosphere over fire areas in western Canada on 12 August 2017 during strong thunderstorm–pyrocumulonimbus activity. The stratospheric fire plumes spread over the entire Northern Hemisphere in the following weeks and months. Twenty-eight European lidar stations from northern Norway to southern Portugal and the eastern Mediterranean monitored the strong stratospheric perturbation on a continental scale. The main smoke layer (over central, western, southern, and eastern Europe) was found at heights between 15 and 20 km since September 2017 (about 2 weeks after entering the stratosphere). Thin layers of smoke were detected at heights of up to 22–23 km. The stratospheric aerosol optical thickness at 532 nm decreased from values > 0.25 on 21–23 August 2017 to 0.005–0.03 until 5–10 September and was mainly 0.003–0.004 from October to December 2017 and thus was still significantly above the stratospheric background (0.001–0.002). Stratospheric particle extinction coefficients (532 nm) were as high as 50–200 Mm⁻¹ until the beginning of September and on the order of 1 Mm⁻¹ (0.5–5 Mm⁻¹) from October 2017 until the end of January 2018. The corresponding layer mean particle mass concentration was on the order of 0.05–0.5 µg m⁻³ over these months. Soot particles (light-absorbing carbonaceous particles) are efficient ice-nucleating particles (INPs) at upper tropospheric (cirrus) temperatures and available to influence cirrus formation when entering the tropopause from above. We estimated INP concentrations of 50–500 L⁻¹ until the first days in September and afterwards 5–50 L⁻¹ until the end of the year 2017 in the lower stratosphere for typical cirrus formation temperatures of −55 ^∘C and an ice supersaturation level of 1.15. The measured profiles of the particle linear depolarization ratio indicated a predominance of nonspherical smoke particles. The 532 nm depolarization ratio decreased slowly with time in the main smoke layer from values of 0.15–0.25 (August–September) to values of 0.05–0.10 (October–November) and < 0.05 (December–January). The decrease of the depolarization ratio is consistent with aging of the smoke particles, growing of a coating around the solid black carbon core (aggregates), and thus change of the shape towards a spherical form. We found ascending aerosol layer features over the most southern European stations, especially over the eastern Mediterranean at 32–35^∘ N, that ascended from heights of about 18–19 to 22–23 km from the beginning of October to the beginning of December 2017 (about 2 km per month). We discuss several transport and lifting mechanisms that may have had an impact on the found aerosol layering structures.

In the framework of the European Aerosol Research Lidar Network to Establish an Aerosol Climatology (EARLINET), 19 aerosol lidar systems from 11 European countries were compared. Aerosol extinction or backscatter coefficient profiles were measured by at least two systems for each comparison. Aerosol extinction coefficients were derived from Raman lidar measurements in the UV (351 or 355 nm), and aerosol backscatter profiles were calculated from pure elastic backscatter measurements at 351 or 355, 532, or 1064 nm. The results were compared for height ranges with high and low aerosol content. Some systems were additionally compared with sunphotometers and starphotometers. Predefined maximum deviations were used for quality control of the results. Lidar systems with results outside those limits could not meet the quality assurance criterion. The algorithms for deriving aerosol backscatter profiles from elastic lidar measurements were tested separately, and the results are described in Part 2 of this series of papers [Appl. Opt. 43, 977–989 (2004)]. In the end, all systems were quality assured, although some had to be modified to improve their performance. Typical deviations between aerosol backscatter profiles were 10% in the planetary boundary layer and 0.1 × 10^-6 m^-1 sr^-1 in the free troposphere.

In an optical communication link between an optical ground station and a geostationary satellite the main problems appear in the uplink and are due to beam wander and to scintillation. Reliable methods for modeling both effects simultaneously are needed to provide an accurate tool with which the robustness of the communication channel can be tested. Numerical tools, especially the split-step method (also referred to as the fast-Fourier-transform beam propagation method), have demonstrated their ability to deal with problems of optical propagation during atmospheric turbulence. However, obtaining statistically significant results with this technique is computationally intensive. We present an analytical-numerical hybrid technique that provides good information on the variance in optical irradiance with an important saving of time and computational resources.

We present an automatic aerosol classification method based solely on the European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each unclassified measurement to a predefined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined 8 aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with literature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyze the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59 % (minimum) and 90 % (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in the literature further improves the accuracy with values for all the aerosol classes around 80 %. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite.