Clustering analysis of typical aerosol characteristics based on AERONET Version 3 data
Abstract
Atmospheric aerosols affect human health and Earth radiation balance, and therefore it has been investigating their impact on climate change and air quality. The Aerosol Robotic Network(AERONET) program has been providing highly accurate ground-based Sun photometry measurements for globally distributed stations. Many studies based on AERONET data have been analysed the averaged characteristics of aerosol optical properties for a specific region or period. Often, the results do not sufficiently show the typical aerosol characteristics since the signals of exotic aerosols transported from the surrounding area could not be sufficiently removed by simple averaging for a specific region or period. There were several attempts for aerosol type classification in order to analyse the dominant aerosol types, but they cannot perfectly remove the bias caused by the threshold which is generally used. In this study, we use clustering analysis for understanding the dominant aerosol types and their temporal change on the representative aerosol source areas based on AERONET version 3 data, newly updated algorithm and adapted fully automatic cloud screening and controls and quality control checks system.
Acknowledgment This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019R1I1A1A01063751).- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A43P2933C
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0345 Pollution: urban and regional;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0394 Instruments and techniques;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 3394 Instruments and techniques;
- ATMOSPHERIC PROCESSES