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Proceedings Paper

Aerosol models characterization in arctic region using cluster analysis based on long-term AERONET observations
Author(s): Chi Li; Yong Xue; Leiku Yang; Yingjie Li
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Paper Abstract

The Arctic region is especially sensitive to climate change; meanwhile atmospheric aerosol is one of the largest uncertainties geophysical factors in climate modeling, calling for aerosol database in Arctic regions with sufficient temporal and spatial coverage. Satellite remote sensing is the best approach to obtain the aerosol information over the Arctic region, for which appropriate aerosol models are required. In this study, five distinctive aerosol models are classified using cluster analysis from Level 1.5 data collected in Aerosol Robotic Network (AERONET) sites. More than 14,000 cases are collected over 17 AERONET sites in Arctic region from 1995 to 2012. For each case, 23 parameters, representing either optical properties or size distribution patterns are input into cluster analysis after abnormal records and outliers are discarded and data of different attributes are standardized. Averaged properties in each cluster are obtained then and we extensively study the absorptive, scattering, and size distributive characteristics along with the temporal and spatial distributions for each model. Aerosol optical properties are carried out for each model using Second Simulation of a Satellite Signal in the Solar Spectrum - Vector (6SV) code and we conclude that our models are representative of the major aerosol properties in the Arctic region and can be utilized in the retrieval algorithms designed for this area.

Paper Details

Date Published: 8 November 2012
PDF: 10 pages
Proc. SPIE 8523, Remote Sensing of the Atmosphere, Clouds, and Precipitation IV, 852310 (8 November 2012); doi: 10.1117/12.977185
Show Author Affiliations
Chi Li, Ctr. for Earth Observation and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Yong Xue, Ctr. for Earth Observation and Digital Earth (China)
London Metropolitan Univ. (United Kingdom)
Leiku Yang, Beijing Normal Univ. (China)
Yingjie Li, Institute of Remote Sensing Applications (China)
Univ. of Chinese Academy of Sciences (China)

Published in SPIE Proceedings Vol. 8523:
Remote Sensing of the Atmosphere, Clouds, and Precipitation IV
Tadahiro Hayasaka; Kenji Nakamura; Eastwood Im, Editor(s)

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