[1] This paper focuses on optical and microphysical properties of long-range transported biomass burning (BB) aerosols and their variation with atmospheric evolution (ageing), as observed by a multiwavelength Raman lidar, part of EARLINET (European Aerosol LIdar NETwork). Chemical analysis of the atmospheric aerosol was done using a colocated aerosol mass spectrometer (AMS). One relevant optical parameter for the ageing process is the Ångström exponent. In our study, we find that it decreases from 2 for fresh to 1.4-0.5 for aged smoke particles. The ratio of lidar (extinction-to-backscatter) ratios (LR 532 /LR 355 ) changes rapidly from values <1 for fresh to >1 for aged particles. The imaginary part of the refractive index is the most sensitive microphysical parameter. It decreases sharply from 0.05 to less than 0.01 for fresh and aged smoke particles, respectively. Singlescattering albedo (SSA) varies from 0.74 to 0.98 depending on aerosol age and source. The AMS was used to measure the marker ions of wood-burning particles during 2 days of measurements when the meteorological conditions favored the downward mixing of aerosols from lofted layers. Particle size distribution and particle effective radius from both AMS and lidar are similar, i.e., particle effective radii were approximately 0.27 mm for fresh BB aerosol particles. Microphysical aerosol properties from inversion of the lidar data agree with similar studies carried out in different regions on the globe. Our study shows that the Ångström exponent LR 532 /LR 355 and the imaginary part of the refractive index can be used to clearly distinguish between fresh and aged smoke particles.

Abstract. This paper presents a detailed description of LIRIC (LIdar-Radiometer Inversion Code) algorithm for simultaneous processing of coincident lidar and radiometric (sun photometric) observations for the retrieval of the aerosol concentration vertical profiles. As the lidar/radiometric input data we use measurements from European Aerosol Research Lidar Network (EARLINET) lidars and collocated sun-photometers of Aerosol Robotic Network (AERONET). The LIRIC data processing provides sequential inversion of the combined lidar and radiometric data. The algorithm starts with the estimations of column-integrated aerosol parameters from radiometric measurements followed by the retrieval of height dependent concentrations of fine and coarse aerosols from lidar signals using integrated column characteristics of Published by Copernicus Publications on behalf of the European Geosciences Union. A. Chaikovsky et al.: Lidar-Radiometer Inversion Code (LIRIC)aerosol layer as a priori constraints. The use of polarized lidar observations allows us to discriminate between spherical and non-spherical particles of the coarse aerosol mode.The LIRIC software package was implemented and tested at a number of EARLINET stations. Intercomparison of the LIRIC-based aerosol retrievals was performed for the observations by seven EARLINET lidars in Leipzig, Germany on 25 May 2009. We found close agreement between the aerosol parameters derived from different lidars that supports high robustness of the LIRIC algorithm. The sensitivity of the retrieval results to the possible reduction of the available observation data is also discussed.

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.

Abstract. This paper introduces the recent European AerosolResearch Lidar Network (EARLINET) quality-assurance efforts at instrument level. Within two dedicated campaigns and five single-site intercomparison activities, 21 EAR-LINET systems from 18 EARLINET stations were intercompared between 2009 and 2013. A comprehensive strategy for campaign setup and data evaluation has been established. Eleven systems from nine EARLINET stations participated in the EARLINET Lidar Intercomparison 2009 (EARLI09). In this campaign, three reference systems were qualified which served as traveling standards thereafter. EARLINET systems from nine other stations have been compared against these reference systems since 2009. We present and discuss comparisons at signal and at product level from all campaigns for more than 100 individual measurement channels at the wavelengths of 355, 387, 532, and 607 nm. It is shown that in most cases, a very good agreement of the compared systems with the respective reference is obtained. Mean signal deviations in predefined height ranges are typically below ±2 %. Particle backscatter and extinction coefficients agree within ±2 × 10 −4 km −1 sr −1 and ± 0.01 km −1 , respectively, in most cases. For systems or channels that showed larger discrepancies, an in-depth analysis of deficiencies was performed and technical solutions and upgrades were proposed and realized. The intercomparisons have reinforced confidence in the EARLINET data quality and allowed us to draw conclusions on necessary system improvements for some instruments and to identify major challenges that need to be tackled in the future.