Large-eddy simulations (LES) with the newThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. R. Heinze et al.at building confidence in the model's ability to simulate small-to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high-resolution model matches the observed variability much better at small-to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time-scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high-resolution model.

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Abstract. Triple-wavelength polarization lidar measurements in Saharan dust layers were performed at Barbados (13.1 • N, 59.6 • W), 5000-8000 km west of the Saharan dust sources, in the framework of the Saharan Aerosol Longrange Transport and Aerosol-Cloud-Interaction Experiment (SALTRACE-1, June-July 2013, SALTRACE-3, June-July 2014). Three case studies are discussed. High quality was achieved by comparing the dust linear depolarization ratio profiles measured at 355, 532, and 1064 nm with respective dual-wavelength (355, 532 nm) depolarization ratio profiles measured with a reference lidar. A unique case of long-range transported dust over more than 12 000 km is presented. Saharan dust plumes crossing Barbados were measured with an airborne triple-wavelength polarization lidar over Missouri in the midwestern United States 7 days later. Similar dust optical properties and depolarization features were observed over both sites indicating almost unchanged dust properties within this 1 week of travel from the Caribbean to the United States. The main results of the triple-wavelength polarization lidar observations in the Caribbean in the summer seasons of 2013 and 2014 are summarized. On average, the particle linear depolarization ratios for aged Saharan dust were found to be 0.252 ± 0.030 at 355 nm, 0.280 ± 0.020 at 532 nm, and 0.225 ± 0.022 at 1064 nm after approximately 1 week of transport over the tropical Atlantic. Based on published simulation studies we present an attempt to explain the spectral features of the depolarization ratio of irregularly shaped mineral dust particles, and conclude that most of the irregularly shaped coarse-mode dust particles (particles with diameters > 1 µm) have sizes around 1.5-2 µm. The SALTRACE results are also set into the context of the SAMUM-1 (Morocco, 2006) and SAMUM-2 (Cabo Verde, 2008) depolarization ratio studies. Again, only minor changes in the dust depolarization characteristics were observed on the way from the Saharan dust sources towards the Caribbean.

Abstract. Continuous lidar observations of the top height of the boundary layer (BL top) have been performed at Leipzig (51.3° N, 12.4° E), Germany, since August 2005. The results of measurements taken with a compact, automated Raman lidar over a one-year period (February 2006 to January 2007) are presented. Four different methods for the determination of the BL top are discussed. The most promising technique, the wavelet covariance algorithm, is improved by implementing some modifications so that an automated, robust retrieval of BL depths from lidar data is possible. Three case studies of simultaneous observations with the Raman lidar, a vertical-wind Doppler lidar, and accompanying radiosonde profiling of temperature and humidity are discussed to demonstrate the potential and the limits of the four lidar techniques at different aerosol and meteorological conditions. The lidar-derived BL top heights are compared with respective values derived from predictions of the regional weather forecast model COSMO of the German Meteorological Service. The comparison shows a general underestimation of the BL top by about 20% by the model. The statistical analysis of the one-year data set reveals that the seasonal mean of the daytime maximum BL top is 1400 m in spring, 1800 m in summer, 1200 m in autumn, and 800 m in winter at the continental, central European site. BL top typically increases by 100–300 m per hour in the morning of convective days.

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.