Filters

2008 - 200912010 - 20182

More filters

2020 - 20233
Reset filters

Settings

Version: publishedVersion × Subject: Europe × Subject: lidar ×

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Airborne high spectral resolution lidar observation of pollution aerosol during EUCAARI-LONGREX
    (Göttingen : Copernicus, 2013) Groß, S.; Esselborn, M.; Abicht, F.; Wirth, M.; Fix, A.; Minikin, A.
    Airborne high spectral resolution lidar observations over Europe during the EUCAARI-LONGREX field experiment in May 2008 are analysed with respect to the optical properties of continental pollution aerosol. Continental pollution aerosol is characterized by its depolarisation and lidar ratio. Over all, the measurements of the lidar ratio and the particle linear depolarization ratio of pollution aerosols provide a narrow range of values. Therefore, this data set allows for a distinct characterization of the aerosol type "pollution aerosol" and thus is valuable both to distinguish continental pollution aerosol from other aerosol types and to determine mixtures with other types of aerosols.
  • Thumbnail Image
    Item
    Influence of tides and gravity waves on layering processes in the polar summer mesopause region
    (Göttingen : Copernicus, 2008) Hoffmann, P.; Rapp, M.; Fiedler, J.; Latteck, R.
    Polar Mesosphere Summer Echoes (PMSE) have been studied at Andenes (69° N, 16° E), Norway, using VHF radar observations since 1994. One remarkable feature of these observations is the fact that {during 50% of the time,} the radar echoes occur in the form of two or more distinct layers. In the case of multiple PMSE layers, statistical analysis shows that the lower layer occurs at a mean height of ∼83.4 km, which is almost identical to the mean height of noctilucent clouds (NLC) derived from observation with the ALOMAR Rayleigh/Mie/Raman lidar at the same site. To investigate the layering processes microphysical model simulations under the influence of tidal and gravity waves were performed. In the presence of long period gravity waves, these model investigations predict an enhanced formation of multiple PMSE layer structures, where the lower layer is a consequence of the occurrence of the largest particles at the bottom of the ice cloud. This explains the coincidence of the lowermost PMSE layers and NLC. During periods with enhanced amplitudes of the semidiurnal tide, the observed NLC and PMSE show pronounced tidal structures comparable to the results of corresponding microphysical simulations. At periods with short period gravity waves there is a tendency for a decreasing occurrence of NLC and for variable weak PMSE structures.
  • Thumbnail Image
    Item
    An automatic observation-based aerosol typing method for EARLINET
    (Katlenburg-Lindau : EGU, 2018) Papagiannopoulos, Nikolaos; Mona, Lucia; Amodeo, Aldo; D'Amico, Giuseppe; Gumà Claramunt, Pilar; Pappalardo, Gelsomina; Alados-Arboledas, Lucas; Guerrero-Rascado, Juan Luís; Amiridis, Vassilis; Kokkalis, Panagiotis; Apituley, Arnoud; Baars, Holger; Schwarz, Anja; Wandinger, Ulla; Binietoglou, Ioannis; Nicolae, Doina; Bortoli, Daniele; Comerón, Adolfo; Rodríguez-Gómez, Alejandro; Sicard, Michaël; Papayannis, Alex; Wiegner, Matthias
    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.