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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2019-448
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-448
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 23 Jan 2020

Submitted as: research article | 23 Jan 2020

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This preprint is currently under review for the journal AMT.

Evaluation of the Aqua MODIS Collection 6.1 multilayer cloud detection algorithm through comparisons with CloudSat CPR and CALIPSO CALIOP products

Benjamin Marchant1,2, Steven Platnick1, Kerry Meyer1, and Galina Wind1,3 Benjamin Marchant et al.
  • 1NASA Goddard Space Flight Center
  • 2USRA Universities Space Research Association
  • 3SSAI: Science Systems and Applications, Inc.

Abstract. Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06/MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6.1 (C6.1) multilayer cloud detection algorithm through comparisons with active CPR and CALIOP products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers, and (3) the upper layer cloud optical thickness. The impact of including the Pavolonis-Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6.1 multilayer cloud detection algorithm identifies roughly 20 percent of all cloudy pixels as multilayer (decreasing to about 13 percent if the Pavolonis-Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP 2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar/lidar product, and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.

Benjamin Marchant et al.

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Benjamin Marchant et al.

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Latest update: 17 Feb 2020
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Short summary
Multilayer cloud scenes (such as an ice cloud overlapping a liquid cloud) are common in the Earth's atmosphere and are quite difficult to detect from space. The detection of multilayer clouds is important to better understand how they interact with the light and their impact on the climate. So, for the instrument MODIS an algorithm has been developed to detect those clouds and the following paper presents an evaluation of this algorithm by comparing with others instruments.
Multilayer cloud scenes (such as an ice cloud overlapping a liquid cloud) are common in the...
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