Comparison of two automated aerosol typing methods and their application to an EARLINET station

Voudouri, Kalliopi Artemis; Σιώμος, Νικόλαος; Michailidis, Konstantinos; Papagiannopoulos, Nikolaos; Mona, Lucia; Rosati, Ilaria; Nicolae, Doina; Balis, Dimitris

doi:10.5194/acp-19-10961-2019

Cited by 18 publications

(10 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, their optical properties change as well. Taking into account that both NATALI and MD experience several limitations, as they request as input the aerosol intensive optical properties as stated previously (Nicolae et al, 2018b;Voudouri et al, 2019), a more generic aerosol classification code free from these defects is needed.…”

Section: Introductionmentioning

confidence: 99%

Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations

et al. 2021

Self Cite

View full text Add to dashboard Cite

Abstract. We introduce an automated aerosol type classification method, called Source Classification Analysis (SCAN). SCAN is based on predefined and characterized aerosol source regions, the time that the air parcel spends above each geographical region, and a number of additional criteria. The output of SCAN is compared with two independent aerosol classification methods, which use the intensive optical parameters from lidar data: (1) the Mahalanobis distance automatic aerosol type classification (MD) and (2) a neural network aerosol typing algorithm (NATALI). In this paper, data from the European Aerosol Research Lidar Network (EARLINET) have been used. A total of 97 free tropospheric aerosol layers from four typical EARLINET stations (i.e., Bucharest, Kuopio, Leipzig, and Potenza) in the period 2014–2018 were classified based on a 3β+2α+1δ lidar configuration. We found that SCAN, as a method independent of optical properties, is not affected by overlapping optical values of different aerosol types. Furthermore, SCAN has no limitations concerning its ability to classify different aerosol mixtures. Additionally, it is a valuable tool to classify aerosol layers based on even single (elastic) lidar signals in the case of lidar stations that cannot provide a full data set (3β+2α+1δ) of aerosol optical properties; therefore, it can work independently of the capabilities of a lidar system. Finally, our results show that NATALI has a lower percentage of unclassified layers (4 %), while MD has a higher percentage of unclassified layers (50 %) and a lower percentage of cases classified as aerosol mixtures (5 %).

show abstract

Section: Introductionmentioning

confidence: 99%

Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ref. [17] applied two aerosol classification algorithms for Thessaloniki and detected 3.5% and 0.0% of cases with pure maritime layers depending on the algorithm. Considering that the CIMEL performs columnar measurements, it should be even more rare to identify pure maritime aerosols in the whole column.…”

Section: The Reference Clustersmentioning

confidence: 99%

“…Finally, Refs. [14][15][16][17] all use machine learning techniques based on lidar measurements to automatically characterize individual aerosol layers.…”

Section: Introductionmentioning

confidence: 99%

Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering

et al. 2020

Self Cite

View full text Add to dashboard Cite

In this study, we present an aerosol classification technique based on measurements of a double monochromator Brewer spectrophotometer during the period 1998–2017 in Thessaloniki, Greece. A machine learning clustering procedure was applied based on the Mahalanobis distance metric. The classification process utilizes the UV Single Scattering Albedo (SSA) at 340 nm and the Extinction Angstrom Exponent (EAE) at 320–360 nm that are obtained from the spectrophotometer. The analysis is supported by measurements from a CIMEL sunphotometer that were deployed in order to establish the training dataset of Brewer measurements. By applying the Mahalanobis distance algorithm to the Brewer timeseries, we automatically assigned measurements in one of the following clusters: Fine Non Absorbing Mixtures (FNA): 64.7%, Black Carbon Mixtures (BC): 17.4%, Dust Mixtures (DUST): 8.1%, and Mixed: 9.8%. We examined the clustering potential of the algorithm by reclassifying the training dataset and comparing it with the original one and also by using manually classified cases. The typing score of the Mahalanobis algorithm is high for all predominant clusters FNA: 77.0%, BC: 63.9%, and DUST: 80.3% when compared with the training dataset. We obtained high scores as well FNA: 100.0%, BC: 66.7%, and DUST: 83.3% when comparing it with the manually classified dataset. The flags obtained here were applied in the timeseries of the Aerosol Optical Depth (AOD) at 340 nm of the Brewer and the CIMEL in order to compare between the two and also stress the future impact of the proposed clustering technique in climatological studies of the station.

show abstract

“…The system was finally upgraded during 2017, by changing the telescope setup. A detailed description of THELISYS technical specifications can be found in the works of Siomos et al [20] and Voudouri et al [21].…”

Section: Thessaloniki Lidar Systemmentioning

confidence: 99%

“…THALIA algorithm is been developed for THELISYS processing within LAP AUTH (Siomos et al [20]; Voudouri et al [21]) and has been applied in every uploaded profile in the EARLINET database up to 2019. THALIA algorithm products have been evaluated during the first intercomparison campaign [24].…”

Section: Thessaloniki Aerosol Lidar Algorithm-thaliamentioning

confidence: 99%

Consistency of the Single Calculus Chain Optical Products with Archived Measurements from an EARLINET Lidar Station

et al. 2020

Self Cite

View full text Add to dashboard Cite

A long-term analysis and climatology of aerosol backscatter and extinction coefficients profiles using a five-year study period lidar dataset derived from a multiwavelenth Raman lidar at Thessaloniki station is presented. All measurements have been processed with the latest version of the Single Calculus Chain (SCCv5.1.6) fully automated algorithm, which has been developed to provide a common lidar processing tool, within EARLINET (European Aerosol Research Lidar NETwork) stations. The optical products delivered by the SCC tool have already been compared with the optical products derived from the operational algorithm of Thessaloniki (THessaloniki Aerosol LIdar Algorithm-THALIA) and discussed in terms of inhomogeneities. In this contribution, we analyze these products for climatological purposes, in order to investigate the aerosol columnar properties over Thessaloniki lidar station, drawing conclusions about the issues to be considered when switching from the current operational algorithm to the SCCv5. The SCCv5 algorithm is evaluated for the AOD both for 355 and 532 nm. The agreement with THALIA algorithm seems promising with correlations of 0.89 and 0.84, respectively, and absolute deviations within the range of the EARLINET quality requirements. Time series of the AOD at 355 nm denote a decrease of 0.017 per year in the free troposphere, a trend that is also shown in the AOD values derived from the operational algorithm (0.014). A decrease of 0.01 per year in the lower troposphere is also noted from the SCC, whereas the corresponding AOD values derived from the operational algorithm denote a decrease of 0.017.

show abstract

Comparison of two automated aerosol typing methods and their application to an EARLINET station

Cited by 18 publications

References 54 publications

Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations

Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations

Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering

Consistency of the Single Calculus Chain Optical Products with Archived Measurements from an EARLINET Lidar Station

Contact Info

Product

Resources

About