University of Hertfordshire


  • 907119

    Final published version, 1.24 MB, PDF document

  • P. Achtert
  • Matthias Tesche
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Original languageEnglish
Pages (from-to)1386-1405
Number of pages20
JournalJournal of Geophysical Research: Atmospheres
Early online date3 Feb 2014
Publication statusPublished - 16 Feb 2014


Lidar measurements of polar stratospheric clouds (PSCs) are commonly analyzed in classification schemes that apply the backscatter ratio and the particle depolarization ratio. This similarity of input data suggests comparable results of different classification schemes - despite measurements being performed with a variety of mostly custom-made instruments. Based on a time series of 16 years of lidar measurements at Esrange (68°N, 21°E), Sweden, we show that PSC classification differs substantially depending on the applied scheme. The discrepancies result from varying threshold values of lidar-derived parameters used to define certain PSC types. The resulting inconsistencies could impact the understanding of long-term PSC observations documented in the literature. We identify two out of seven considered classification schemes that are most likely to give reliable results and should be used in future lidar-based studies. Using polarized backscatter ratios gives the advantage of increased contrast for observations of weakly backscattering and weakly depolarizing particles. Improved confidence in PSC classification can be achieved by a more comprehensive consideration of the effect of measurement uncertainties. The particle depolarization ratio is the key to a reliable identification of different PSC types. Hence, detailed information on the calibration of the polarization-sensitive measurement channels should be provided to assess the findings of a study. Presently, most PSC measurements with lidar are performed at 532 nm only. The information from additional polarization-sensitive measurements in the near infrared could lead to an improved PSC classification. Coincident lidar-based temperature measurements at PSC level might provide useful information for an assessment of PSC classification. Key Points Assessment of PSC classification schemes Statistical analysis of PSC observations Recommendations for lidar-based PSC studies


This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License CC BY-NC-ND 3.0, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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