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Discussion papers | Copyright
https://doi.org/10.5194/amt-2018-168
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 Jun 2018

Research article | 28 Jun 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Application of High-Dimensional Fuzzy K-means Cluster Analysis to CALIOP/CALIPSO Version 4.1 Cloud-Aerosol Discrimination

Shan Zeng1,2, Mark Vaughan2, Zhaoyan Liu2, Charles Trepte2, Jayanta Kar1,2, Ali Omar2, David Winker2, Patricia Lucker1,2, Yongxiang Hu2, Brian Getzewich1,2, and Melody Avery2 Shan Zeng et al.
  • 1Science Systems and Applications, Inc., Hampton, 23666, USA
  • 2NASA Langley Research Centre, Hampton, 23666, USA

Abstract. This study applies fuzzy K-means cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products and compares the clustering results with cloud-aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. The selection of samples, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameter analyses in feature type discrimination are discussed. Statistical results show that the fuzzy K-means classification agrees with the CAD algorithm classification for more than 94% of the cases in troposphere. This is because the lidar-measured signatures of most clouds and aerosols are naturally different. Based on their different natures, objective methods can effectively separate clouds from aerosols in most cases. In addition to validating the current CAD algorithm, the fuzzy K-means clustering can also provide new insights and supplemental information to help better understand the driving parameters in the scene classification process.

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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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