Abstract.A global vertically resolved aerosol data set covering more than 10 years of observations at more than 20 measurement sites distributed from 63 • N to 52 • S and 72 • W to 124 • E has been achieved within the Raman and polarization lidar network Polly NET . This network consists of portable, remote-controlled multiwavelength-polarization-Raman lidars (Polly) for automated and continuous 24/7 observations of clouds and aerosols. Polly NET is an independent, voluntary, and scientific network. All Polly lidars feature a standardized instrument design with different capabilities ranging from single wavelength to multiwavelength systems, and now apply unified calibration, quality control, and data analysis. The observations are processed in near-real time without manual intervention, and are presented online at polly.tropos.de. The paper gives an overview of the observations on four continents and two research vessels obtained with eight Polly systems. The specific aerosol types at these locations (mineral dust, smoke, dust-smoke and other dusty mixtures, urban haze, and volcanic ash) are identified by their Ångström exponent, lidar ratio, and depolarization ratio. The vertical aerosol distribution at the Polly NET locations is discussed on the basis of more than 55 000 automatically retrieved 30 min particle backscatter coefficient profiles at 532 nm as this operating wavelength is available for all Polly lidar systems. A seasonal analysis of measurements at selected sites revealed typical and extraordinary aerosol conditions as well as seasonal differences. These studies show the potential of Polly NET to support the establishment of a global aerosol climatology that covers the entire troposphere.

Six months of stratospheric aerosol observations with the European Aerosol Research Lidar Network (EAR-LINET)

Abstract. The CALIPSO Level 3 (CL3) product is the most recent data set produced by the observations of the CloudAerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the Cloud-Aerosol Lidar and Pathfinder Satellite Observations (CALIPSO) space platform. The European Aerosol Research Lidar Network (EARLINET), based mainly on multi-wavelength Raman lidar systems, is the most appropriate ground-based reference for CALIPSO calibration/validation studies on a continental scale. In this work, CALIPSO data are compared against EARLINET monthly averaged profiles obtained by measurements performed during CALIPSO overpasses. In order to mitigate uncertainties due to spatial and temporal differences, we reproduce a modified version of CL3 data starting from CALIPSO Level 2 (CL2) data. The spatial resolution is finer and nearly 2 • × 2 • (latitude × longitude) and only simultaneous measurements are used for ease of comparison. The CALIPSO monthly mean profiles following this approach are called CALIPSO Level 3 * , CL3 * . We find good agreement on the aerosol extinction coefficient, yet in most of the cases a small CALIPSO underestimation is observed with an average bias of 0.02 km −1 up to 4 km and 0.003 km −1 higher above. In contrast to CL3 standard product, the CL3 * data set offers the possibility to assess the CALIPSO performance also in terms of the particle backscatter coefficient keeping the same quality assurance criteria applied to extinction profiles. The mean relative difference in the comparison improved from 25 % for extinction to 18 % for backscatter, showing better performances of CALIPSO backscatter retrievals. Additionally, the aerosol typing comparison yielded a robust identification of dust and polluted dust. Moreover, the CALIPSO aerosol-type-dependent lidar ratio selection is assessed by means of EARLINET observations, so as to investigate the performance of the extinction retrievals. The aerosol types of dust, polluted dust, and clean continental showed noticeable discrepancy. Finally, the potential improvements of the lidar ratio assignment have been examined by adjusting it according to EARLINET-derived values.

[1] In this paper we present in situ and tropospheric column measurements of NO 2 in the Po river basin (northern Italy). The aim of the work is to provide a quantitative comparison between ground-based and satellite measurements in order to assess the validity of spaceborne measurements for estimating NO 2 emissions and evaluate possible climatic effects. The study is carried out using in situ chemiluminescent instrumentation installed in the Po valley, a UV/Vis spectrometer installed at Mount Cimone (44.2°N, 10.7°E, 2165 m asl), and tropospheric column measurements obtained from the Global Ozone Monitoring Experiment (GOME) spectrometer. Results show that the annual cycle in surface concentrations and also some specific pollution periods observed by the air quality network are well reproduced by the GOME measurements. However, tropospheric columns derived from the surface measurements assuming a well-mixed planetary boundary layer (PBL) are much larger than the GOME columns and also have a different seasonal cycle. This is interpreted as indication of a smaller and less variable mixing height for NO 2 in the boundary layer. Under particular meteorological conditions the agreement between UV/Vis tropospheric column observations and GOME measurements in the Mount Cimone area is good (R 2 = 0.9) with the mixing properties of the atmosphere being the most important parameter for a valid comparison of the measurements. However, even when the atmospheric mixing properties are optimal for comparison, the ratio between GOME and ground-based tropospheric column data may not be unity. It is demonstrated that the values obtained (less than 1) are related to the fraction of the satellite ground pixel occupied by the NO 2 hot spot.

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 lit-erature 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 Published by Copernicus Publications on behalf of the European Geosciences Union. 15880 N. Papagiannopoulos et al.: Automatic aerosol classification extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite.