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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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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.
Data sets

Cloud-Aerosol LIDAR Infrared Pathfinder Satellite Observations (CALIPSO) data management system data products catalog M. Vaughan, M. Pitts, C. Trepte, D. Winker, P. Detweiler, A. Garnier, B. Getzewich, W. Hunt, J. Lambeth, K.-P. Lee, P. Lucker, and T. Murray Rodier, S., Tremas, T., Bazureau, A., and Pelon, J. https://www-calipso.larc.nasa.gov/products/CALIPSO_DPC_Rev4x40.pdf

Model code and software

FuzME version 3.0 B. Minasny and A. B. McBratney https://sydney.edu.au/agriculture/pal/software/fuzme.shtml

Publications Copernicus
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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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