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.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-12-2261-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Apr 2019
Research article |
| 12 Apr 2019
| 12 Apr 2019
Application of high-dimensional fuzzy k-means cluster analysis to CALIOP/CALIPSO version 4.1 cloud–aerosol discrimination
Shan Zeng
CORRESPONDING AUTHOR
Science Systems and Applications, Inc., Hampton, VA 23666, USA
NASA Langley Research Centre, Hampton, VA 23666, USA
Mark Vaughan
NASA Langley Research Centre, Hampton, VA 23666, USA
Zhaoyan Liu
NASA Langley Research Centre, Hampton, VA 23666, USA
Charles Trepte
NASA Langley Research Centre, Hampton, VA 23666, USA
Jayanta Kar
Science Systems and Applications, Inc., Hampton, VA 23666, USA
NASA Langley Research Centre, Hampton, VA 23666, USA
Ali Omar
NASA Langley Research Centre, Hampton, VA 23666, USA
David Winker
NASA Langley Research Centre, Hampton, VA 23666, USA
Patricia Lucker
Science Systems and Applications, Inc., Hampton, VA 23666, USA
NASA Langley Research Centre, Hampton, VA 23666, USA
Yongxiang Hu
NASA Langley Research Centre, Hampton, VA 23666, USA
Brian Getzewich
Science Systems and Applications, Inc., Hampton, VA 23666, USA
NASA Langley Research Centre, Hampton, VA 23666, USA
Melody Avery
NASA Langley Research Centre, Hampton, VA 23666, USA
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Cited
14 citations as recorded by crossref.
- Global aerosol vertical structure analysis by clustering gridded CALIOP aerosol profiles with fuzzy k-means L. Wang et al. 10.1016/j.scitotenv.2020.144076
- Spring Dust Mass Flux over the Tibetan Plateau during 2007–19 and Connections with North Atlantic SST Variability C. Xu et al. 10.1175/JCLI-D-19-0481.1
- Comparison of V4 and V3 CALIOP (L3) aerosol products: A global perspective V. Sreekanth & P. Kulkarni 10.1016/j.rsase.2020.100412
- Application of Fuzzy Clustering in Higher Education General Management Based on Internet Environment Q. Wang et al. 10.1155/2022/3438666
- Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data Q. Doan et al. 10.5194/gmd-16-2215-2023
- First assessment of Aeolus Standard Correct Algorithm particle backscatter coefficient retrievals in the eastern Mediterranean A. Gkikas et al. 10.5194/amt-16-1017-2023
- Classification of lidar measurements using supervised and unsupervised machine learning methods G. Farhani et al. 10.5194/amt-14-391-2021
- Fully Dynamic High–Resolution Model for Dispersion of Icelandic Airborne Mineral Dust B. Cvetkovic et al. 10.3390/atmos13091345
- Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland S. Yang et al. 10.3390/rs13132433
- Regional Spatial Mean of Ionospheric Irregularities Based on K-Means Clustering of ROTI Maps Y. Migoya-Orué et al. 10.3390/atmos15091098
- An aerosol classification scheme for global simulations using the K-means machine learning method J. Li et al. 10.5194/gmd-15-509-2022
- A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust E. Proestakis et al. 10.5194/amt-17-3625-2024
- Discriminating between clouds and aerosols in the CALIOP version 4.1 data products Z. Liu et al. 10.5194/amt-12-703-2019
- CALIPSO lidar calibration at 1064 nm: version 4 algorithm M. Vaughan et al. 10.5194/amt-12-51-2019
12 citations as recorded by crossref.
- Global aerosol vertical structure analysis by clustering gridded CALIOP aerosol profiles with fuzzy k-means L. Wang et al. 10.1016/j.scitotenv.2020.144076
- Spring Dust Mass Flux over the Tibetan Plateau during 2007–19 and Connections with North Atlantic SST Variability C. Xu et al. 10.1175/JCLI-D-19-0481.1
- Comparison of V4 and V3 CALIOP (L3) aerosol products: A global perspective V. Sreekanth & P. Kulkarni 10.1016/j.rsase.2020.100412
- Application of Fuzzy Clustering in Higher Education General Management Based on Internet Environment Q. Wang et al. 10.1155/2022/3438666
- Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data Q. Doan et al. 10.5194/gmd-16-2215-2023
- First assessment of Aeolus Standard Correct Algorithm particle backscatter coefficient retrievals in the eastern Mediterranean A. Gkikas et al. 10.5194/amt-16-1017-2023
- Classification of lidar measurements using supervised and unsupervised machine learning methods G. Farhani et al. 10.5194/amt-14-391-2021
- Fully Dynamic High–Resolution Model for Dispersion of Icelandic Airborne Mineral Dust B. Cvetkovic et al. 10.3390/atmos13091345
- Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland S. Yang et al. 10.3390/rs13132433
- Regional Spatial Mean of Ionospheric Irregularities Based on K-Means Clustering of ROTI Maps Y. Migoya-Orué et al. 10.3390/atmos15091098
- An aerosol classification scheme for global simulations using the K-means machine learning method J. Li et al. 10.5194/gmd-15-509-2022
- A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust E. Proestakis et al. 10.5194/amt-17-3625-2024
Latest update: 14 Dec 2024
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...
Special issue