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

Zeng, S.; Vaughan, Mark; Liu, Z.; Trepte, Charles R.; Kar, Jayanta; Omar, Ali; Winker, David M.; Lucker, Patricia L.; Hu, Yongxiang; Getzewich, Brian; Avery, Melody

doi:10.5194/amt-2018-168

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Kmeans has already been applied in atmospheric research. For instance, it has been successfully used to distinguish clouds and aerosols in CALIOP/CALIPSO observations (Zeng et al, 2019). In this study, we apply the K-means algorithm to global aerosol simulations.…”

Section: Introductionmentioning

confidence: 99%

An aerosol classification scheme for global simulations using the K-means machine learning method

Hendricks

Righi

et al. 2022

Geosci. Model Dev.

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Abstract. The K-means machine learning algorithm is applied to climatological data of seven aerosol properties from a global aerosol simulation using EMAC-MADE3. The aim is to partition the aerosol properties across the global atmosphere in specific aerosol regimes; this is done mainly for evaluation purposes. K-means is an unsupervised machine learning method with the advantage that an a priori definition of the aerosol classes is not required. Using K-means, we are able to quantitatively define global aerosol regimes, so-called aerosol clusters, and explain their internal properties and their location and extension. This analysis shows that aerosol regimes in the lower troposphere are strongly influenced by emissions. Key drivers of the clusters' internal properties and spatial distribution are, for instance, pollutants from biomass burning and biogenic sources, mineral dust, anthropogenic pollution, and corresponding mixtures. Several continental clusters propagate into oceanic regions as a result of long-range transport of air masses. The identified oceanic regimes show a higher degree of pollution in the Northern Hemisphere than over the southern oceans. With increasing altitude, the aerosol regimes propagate from emission-induced clusters in the lower troposphere to roughly zonally distributed regimes in the middle troposphere and in the tropopause region. Notably, three polluted clusters identified over Africa, India, and eastern China cover the whole atmospheric column from the lower troposphere to the tropopause region. The results of this analysis need to be interpreted taking the limitations and strengths of global aerosol models into consideration. On the one hand, global aerosol simulations cannot estimate small-scale and localized processes due to the coarse resolution. On the other hand, they capture the spatial pattern of aerosol properties on the global scale, implying that the clustering results could provide useful insights for aerosol research. To estimate the uncertainties inherent in the applied clustering method, two sensitivity tests have been conducted (i) to investigate how various data scaling procedures could affect the K-means classification and (ii) to compare K-means with another unsupervised classification algorithm (HAC, i.e. hierarchical agglomerative clustering). The results show that the standardization based on sample mean and standard deviation is the most appropriate standardization method for this study, as it keeps the underlying distribution of the raw data set and retains the information of outliers. The two clustering algorithms provide similar classification results, supporting the robustness of our conclusions. The classification procedures presented in this study have a markedly wide application potential for future model-based aerosol studies.

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Section: Introductionmentioning

confidence: 99%

An aerosol classification scheme for global simulations using the K-means machine learning method

Hendricks

Righi

et al. 2022

Geosci. Model Dev.

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“…Its purpose was to develop a computer system abnormal state identification method based on fuzzy cluster analysis [8]. Zeng et al applied fuzzy k-mean clustering analysis to a subset of parameters reported in CALIPSO LIDAR Level 2 data products [9]. Vovan and Ledai presented a new fuzzy time series model that allowed efficient prediction of the future by interpolating the historical data [10].…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Clustering in Higher Education General Management Based on Internet Environment

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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General education is an important part of university education aiming at cultivating students’ comprehensive quality and sense of responsibility. As professional sports colleges and universities, higher sports colleges implement the curriculum reform of general education relatively late, and there is a problem of curriculum system construction in the process of implementing general education. The purpose of this paper is to research and discuss the application of general education management in higher education based on fuzzy cluster analysis in the Internet environment. This paper first discusses the teaching mode in the Internet environment and then analyzes the application of fuzzy cluster analysis in general education. Through fuzzy cluster analysis, the students’ understanding of general education before and after learning general education courses is compared, which paves the way for the promotion of general education in colleges and universities. Finally, this paper takes the general education curriculum system of higher physical education colleges as the research object and conducts fuzzy cluster analysis on it. The experimental results show that before studying general courses, 66.7% of the students hope to improve their language communication and expression skills, and 52.8% and 46% of the students hope to improve their practical ability and logical thinking ability. After receiving general education, only 23.5% of students have improved logical thinking ability, and only 13.7% of students have improved language communication and expression skills. The results show that general education does not meet the expectations of students, general education in higher physical education colleges is not effective, and the realization of general education needs to be further improved. To this end, schools can purchase network information resources, cooperate with well-known universities at home and abroad, develop general education courses with a global perspective, train students’ spirit of independent thinking, and master correct thinking methods.

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CALIPSO lidar calibration at 1064 nm: version 4 algorithm

et al. 2019

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Abstract. Radiometric calibration of space-based elastic backscatter lidars is accomplished by comparing the measured backscatter signals to theoretically expected signals computed for some well-characterized calibration target. For any given system and wavelength, the choice of calibration target is dictated by several considerations, including signal-to-noise ratio (SNR) and target availability. This paper describes the newly implemented procedures used to calibrate the 1064 nm measurements acquired by CALIOP (i.e., the Cloud-Aerosol Lidar with Orthogonal Polarization), the two-wavelength (532 and 1064 nm) elastic backscatter lidar currently flying on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. CALIOP's 532 nm channel is accurately calibrated by normalizing the molecular backscatter from the uppermost aerosol-free altitudes of the CALIOP measurement region to molecular model data obtained from NASA's Global Modeling and Assimilation Office. However, because CALIOP's SNR for molecular backscatter measurements is prohibitively lower at 1064 nm than at 532 nm, the direct high-altitude molecular normalization method is not a viable option at 1064 nm. Instead, CALIOP's 1064 nm channel is calibrated relative to the 532 nm channel using the backscatter from a carefully selected subset of cirrus cloud measurements. In this paper we deliver a full account of the revised 1064 nm calibration algorithms implemented for the version 4.1 (V4) release of the CALIPSO lidar data products, with particular emphases on the physical basis for the selection of “calibration quality” cirrus clouds and on the new averaging scheme required to characterize intra-orbit calibration variability. The V4 procedures introduce latitudinally varying changes in the 1064 nm calibration coefficients of 25 % or more, relative to previous data releases, and are shown to substantially improve the accuracy of the V4 1064 nm attenuated backscatter coefficients. By evaluating calibration coefficients derived using both water clouds and ocean surfaces as alternate calibration targets, and through comparisons to independent, collocated measurements made by airborne high spectral resolution lidar, we conclude that the CALIOP V4 1064 nm calibration coefficients are accurate to within 3 %.

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

Cited by 4 publications

References 22 publications

An aerosol classification scheme for global simulations using the K-means machine learning method

An aerosol classification scheme for global simulations using the K-means machine learning method

Application of Fuzzy Clustering in Higher Education General Management Based on Internet Environment

CALIPSO lidar calibration at 1064 nm: version 4 algorithm

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