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

Zeng, Shan; Vaughan, Mark; Liu, Zhaoyan; Trepte, Charles; Kar, Jayanta; Omar, Ali; Winker, David; Lucker, Patricia; Hu, Yongxiang; Getzewich, Brian; Avery, Melody

doi:https://doi.org/10.5194/amt-12-2261-2019

Articles | Volume 12, issue 4

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

https://doi.org/10.5194/amt-12-2261-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 4

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, Mark Vaughan, Zhaoyan Liu, Charles Trepte, Jayanta Kar, Ali Omar, David Winker, Patricia Lucker, Yongxiang Hu, Brian Getzewich, and Melody Avery

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

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.