Articles | Volume 16, issue 12
https://doi.org/10.5194/acp-16-7545-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-7545-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
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
Department of Atmospheric Sciences, Texas A&M
University, College Station, Texas, USA
Ping Yang
Department of Atmospheric Sciences, Texas A&M
University, College Station, Texas, USA
Bryan A. Baum
Space Science and Engineering Center, University of
Wisconsin-Madison, Madison, Wisconsin, USA
Steven Platnick
Earth Science Division, NASA Goddard Space Flight Center,
Greenbelt, Maryland, USA
Kerry G. Meyer
Goddard Earth Sciences Technology and Research,
Universities Space Research Association, Columbia, Maryland, USA
Michael D. King
Laboratory for Atmospheric & Space Physics, University
of Colorado, Boulder, CO, USA
Jerome Riedi
Laboratoire d'Optique Atmosphérique, Université de
Lille – Sciences et Technologies, Villeneuve d'Ascq, France
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Short summary
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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