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Author: Baumgarten, G. × Has files: Yes × Sponsor: Leibniz_Fonds × Subject: lidar × Subject: polar mesospheric cloud ×

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    Solar and lunar tides in noctilucent clouds as determined by ground-based lidar
    (Göttingen : Copernicus GmbH, 2018) Fiedler, J.; Baumgarten, G.
    Noctilucent clouds (NLCs) occur during summer from midlatitudes to high latitudes. They consist of nanometer-sized ice particles in an altitude range from 80 to 90 km and are sensitive to ambient temperature and water vapor content, which makes them a suitable tracer for variability on all timescales. The data set acquired by the ALOMAR Rayleigh-Mie-Raman (RMR) lidar covers 21 years and is investigated regarding tidal signatures in NLCs. For the first time solar and lunar tidal parameters in NLCs were determined simultaneously from the same data. Several NLC parameters are subject to persistent mean variations throughout the solar day as well as the lunar day. Variations with lunar time are generally smaller compared to variations with solar time. NLC occurrence frequency shows the most robust imprint of the lunar semidiurnal tide. Its amplitude is about 50 % of the solar semidiurnal tide, which is surprisingly large. Phase progressions of NLC occurrence frequency indicate upward propagating solar tides. Below 84 km altitude the corresponding vertical wavelengths are between 20 and 30 km. For the lunar semidiurnal tide phase progressions vary symmetrically with respect to the maximum of the NLC layer. © Author(s) 2018.
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    A new description of probability density distributions of polar mesospheric clouds
    (Göttingen : Copernicus GmbH, 2019) Berger, U.; Baumgarten, G.; Fiedler, J.; Lübken, F.-J.
    In this paper we present a new description of statistical probability density functions (pdfs) of polar mesospheric clouds (PMCs). The analysis is based on observations of maximum backscatter, ice mass density, ice particle radius, and number density of ice particles measured by the ALOMAR Rayleigh-Mie-Raman lidar for all PMC seasons from 2002 to 2016. From this data set we derive a new class of pdfs that describe the statistics of PMC events that is different from previous statistical methods using the approach of an exponential distribution commonly named the g distribution. The new analysis describes successfully the probability distributions of ALOMAR lidar data. It turns out that the former g-function description is a special case of our new approach. In general the new statistical function can be applied to many kinds of different PMC parameters, e.g., maximum backscatter, integrated backscatter, ice mass density, ice water content, ice particle radius, ice particle number density, or albedo measured by satellites. As a main advantage the new method allows us to connect different observational PMC distributions of lidar and satellite data, and also to compare with distributions from ice model studies. In particular, the statistical distributions of different ice parameters can be compared with each other on the basis of a common assessment that facilitates, for example, trend analysis of PMC. © Author(s) 2019.