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AMT | Articles | Volume 12, issue 4
Atmos. Meas. Tech., 12, 2261-2285, 2019
https://doi.org/10.5194/amt-12-2261-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: CALIPSO version 4 algorithms and data products

Atmos. Meas. Tech., 12, 2261-2285, 2019
https://doi.org/10.5194/amt-12-2261-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Apr 2019

Research article | 12 Apr 2019

Application of high-dimensional fuzzy k-means cluster analysis to CALIOP/CALIPSO version 4.1 cloud–aerosol discrimination

Shan Zeng et al.
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Cited articles  
Avery, M. A., Ryan, R., Getzewich, B., Vaughan, M., Winker, D., Hu, Y., and Trepte, C.: Impact of Near-Nadir Viewing Angles on CALIOP V4.1 Cloud Thermodynamic Phase Assignments, in preparation, 2019. 
Bezdek, J. C.: Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981. 
Bezdek, J. C., Ehrlich, R., and Full, W.: FCM: the fuzzy c-means clustering algorithm, Comput. Geosci., 10, 191–203, 1984. 
Burrough P. A. and McDonnell R. A.: Principles of Geographic Information Systems, Oxford University Press, Oxford, 1998. 
Burrough, P. A., Van Gaans, P. F. M., and MacMillan, R. A.: High-resolution landform classification using fuzzy K-means, Fuzzy Set. Syst., 113, 37–52, 2000. 
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Short summary
We use a fuzzy k-means (FKM) classifier to assess the ability of the CALIPSO cloud–aerosol discrimination (CAD) algorithm to correctly distinguish between clouds and aerosols detected in the CALIPSO lidar backscatter signals. FKM is an unsupervised learning algorithm, so the classifications it derives are wholly independent from those reported by the CAD scheme. For a full month of measurements, the two techniques agree in ~ 95 % of all cases, providing strong evidence for CAD correctness.
We use a fuzzy k-means (FKM) classifier to assess the ability of the CALIPSO cloud–aerosol...
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