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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 12 | Copyright
Atmos. Chem. Phys., 16, 7545-7558, 2016
https://doi.org/10.5194/acp-16-7545-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Jun 2016

Research article | 21 Jun 2016

Degree of ice particle surface roughness inferred from polarimetric observations

Souichiro Hioki1, Ping Yang1, Bryan A. Baum2, Steven Platnick3, Kerry G. Meyer4, Michael D. King5, and Jerome Riedi6 Souichiro Hioki et al.
  • 1Department of Atmospheric Sciences, Texas A&M University, College Station, Texas, USA
  • 2Space Science and Engineering Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
  • 3Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 4Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, Maryland, USA
  • 5Laboratory for Atmospheric & Space Physics, University of Colorado, Boulder, CO, USA
  • 6Laboratoire d'Optique Atmosphérique, Université de Lille – Sciences et Technologies, Villeneuve d'Ascq, France

Abstract. The degree of surface roughness of ice particles within thick, cold ice clouds is inferred from multi-directional, multi-spectral satellite polarimetric observations over oceans, assuming a column-aggregate particle habit. An improved roughness inference scheme is employed that provides a more noise-resilient roughness estimate than the conventional best-fit approach. The improvements include the introduction of a quantitative roughness parameter based on empirical orthogonal function analysis and proper treatment of polarization due to atmospheric scattering above clouds. A global 1-month data sample supports the use of a severely roughened ice habit to simulate the polarized reflectivity associated with ice clouds over ocean. The density distribution of the roughness parameter inferred from the global 1-month data sample and further analyses of a few case studies demonstrate the significant variability of ice cloud single-scattering properties. However, the present theoretical results do not agree with observations in the tropics. In the extratropics, the roughness parameter is inferred but 74% of the sample is out of the expected parameter range. Potential improvements are discussed to enhance the depiction of the natural variability on a global scale.

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The degree of surface roughness of ice particles within thick, cold ice clouds is inferred from multi-directional, multi-spectral satellite polarimetric observations over oceans, assuming a column-aggregate particle habit. An improved roughness inference scheme is employed, which provides a more noise-resilient roughness estimate than the conventional approach. A global one-month data sample shows the use and the limit of a severely roughened ice habit to simulate the polarized reflectivity.
The degree of surface roughness of ice particles within thick, cold ice clouds is inferred from...
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