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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2016-303
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-2016-303
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: research article 30 Nov 2016

Submitted as: research article | 30 Nov 2016

Review status
This discussion paper is a preprint. It has been under review for the journal Atmospheric Measurement Techniques (AMT). The revised manuscript was not accepted.

Low-Level, Liquid-Only and Mixed-Phase Cloud Identification by Polarimetric Lidar

Robert A. Stillwell1, Ryan R. Neely III2, Jeffrey P. Thayer1, Matthew D. Shupe3,4, and Michael O'Neill3 Robert A. Stillwell et al.
  • 1Aerospace Engineering Sciences, University of Colorado at Boulder, ECNT 320, 431 UCB, University of Colorado, Boulder, CO 80309, USA
  • 2National Centre for Atmospheric Science and the School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom
  • 3Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, 216 UCB, University of Colorado, Boulder, CO 80309, USA
  • 4Earth System Research Laboratory, National Oceanic and Atmospheric Administration, 325 Broadway, Boulder, CO 80309, USA

Abstract. The measurement of low-level, liquid-only and mixed-phase clouds in the polar regions is a necessary building block to understand the regional surface energy and mass budgets over ice sheets. The unambiguous retrieval of cloud phase from polarimetric lidar observations is dependent on the assumption that only cloud scattering processes alter the transmitted polarization. However, due to clouds varying in range, optical depth, and scatterer size and shape, most atmospheric lidar systems must observe high dynamic ranges in scattered signal strengths. Depending on the polarization component measured, these signals can far exceed the linear range of a detection system. Thus, due to the high optical thickness and predominately low-lying nature of liquid-only and mixed-phase clouds in the polar regions, relative to ice only clouds, a systematic overestimate of the traditional depolarization ratio, which uses the co-polarized and cross-polarized signals, can occur due to the large dynamic range signals. For both liquid-only and mixed-phase clouds, this results in a misidentification of liquid water in clouds as ice, which has broad implications on evaluating surface energy budgets. The Clouds Aerosol Polarization and Backscatter Lidar (CAPABL) at Summit, Greenland employs multiple planes of linear polarization, and photon counting and analog detection schemes, to self evaluate, correct, and optimize signal combinations to improve cloud classification. For example, an examination of observations of liquid-only and mixed-phase clouds at Summit shows as much as a 2 kilometer offset in median cloud height of those identified as liquid due to a systematic bias in photon counting signals. At a constant altitude, more than 94 % of the liquid pixels identified with analog signals can be misidentified as ice with photon counting signals. This results in a possible error of fractional occurrence of cloud liquid of approximately 30 %. It is shown that by observing polarization planes that are non-orthogonal, the dynamic range of observed signals is reduced, the coverage of the expected signal dynamic range is increased, and more linear response can be captured. Using non-orthogonal polarization observations is shown to enhance measurement sensitivity increasing the effective sampling range for CAPABL by as much as 18 % or approximately 1.5 km.

Robert A. Stillwell et al.
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Robert A. Stillwell et al.
Robert A. Stillwell et al.
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Short summary
This work explores the observation of Arctic mixed phase clouds by lidar and the consequences of mishandling lidar signals linking the signals to their geophysical interpretation. It concludes 3 points: 1) cloud phase identification is not only linked to cloud phase but other cloud properties, 2) having more than two polarization signals can be used to quality control data not possible with only two signals, and 3) phase retrievals with more than two polarizations enhance retrieval flexibility.
This work explores the observation of Arctic mixed phase clouds by lidar and the consequences of...
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