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Discussion papers
https://doi.org/10.5194/acp-2018-642
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-642
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Jul 2018

Research article | 20 Jul 2018

Review status
This discussion paper is a preprint. A revision of the manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP).

A new Description of Probability Density Distributions of Polar Mesospheric Clouds (PMC)

Uwe Berger, Gerd Baumgarten, Jens Fiedler, and Franz-Josef Lübken Uwe Berger et al.
  • Leibniz-Institute of Atmospheric Physics, Rostock University, Kühlungsborn, Germany

Abstract. In this paper we present a new description about statistical probability density distributions (pdfs) of Polar Mesospheric Clouds (PMC) and noctilucent clouds (NLC). 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 RMR-lidar for all NLC seasons from 2002 to 2016. From this data set we derive a new class of pdfs that describe the statistics of PMC/NLC events which is different from previously statistical methods using the approach of an exponential distribution commonly named g-distribution. The new analysis describes successfully the probability statistic 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 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 facilitate, for example, trend analysis of PMC/NLC.

Uwe Berger et al.
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Status: final response (author comments only)
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Uwe Berger et al.
Uwe Berger et al.
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In this paper we present a new description about statistical probability density distributions (pdfs) of Polar Mesospheric Clouds (PMC). We derive a new class of pdfs that describes successfully the probability statistic of ALOMAR lidar observations of different ice parameters. As a main advantage the new method allows to connect different observational PMC distributions of lidar, and satellite data, and also to compare with distributions from ice model studies.
In this paper we present a new description about statistical probability density distributions...
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