Preprints
https://doi.org/10.5194/amtd-6-8371-2013
https://doi.org/10.5194/amtd-6-8371-2013

  12 Sep 2013

12 Sep 2013

Review status: this preprint was under review for the journal AMT. A revision for further review has not been submitted.

An assessment of cloud top thermodynamic phase products obtained from A-Train passive and active sensors

S. Zeng1,*, J. Riedi1, F. Parol1, C. Cornet1, and F. Thieuleux1 S. Zeng et al.
  • 1Laboratoire d'Optique Atmosphérique, UMR8518, CNRS – Université de Lille 1, Sciences et Technologies, Lille, France
  • *now at: NASA Langley Research Center, Hampton, Virginia, USA

Abstract. The A-Train observations provide an unprecedented opportunity for the production of high quality dataset describing cloud properties. We illustrate in this study the use of one year of coincident POLDER (Polarization and Directionality of the Earth Reflectance), MODIS (MODerate Resolution Imaging Spectroradiometer) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations to establish a reference dataset for the description of cloud top thermodynamic phase at global scale. We present the results of an extensive comparison between POLDER and MODIS cloud top phase products and discuss those in view of cloud vertical structure and optical properties derived simultaneously from collocated CALIOP active measurements. These results allow to identify and quantify potential biases present in the 3 considered dataset. Among those, we discuss the impacts of observation geometry, thin cirrus in multilayered and single layered cloud systems, supercooled liquid droplets, aerosols, fractional cloud cover and snow/ice or bright surfaces on global statistics of cloud phase derived from POLDER and MODIS passive measurements. Based on these analysis we define criteria for the selection of high confidence cloud phase retrievals which in turn can serve for the establishment of a reference cloud phase product. This high confidence joint product derived from POLDER/PARASOL and MODIS/Aqua can be used in the future as a benchmark for the evaluation of other cloud climatologies, for the assessment of cloud phase representation in models and the development of better cloud phase parametrization in the general circulation models (GCMs).

S. Zeng et al.

 
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

S. Zeng et al.

S. Zeng et al.

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