Document Type
Article
Publication Date
2015
Abstract
Dynamical and physical features of a long range transported dust event originating in China affecting Korea early March 2008 are examined using an integrative multi-sensor and multi-algorithm approach. Aerosol loadings and their size mode were analyzed over both ocean and land surfaces using the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), employing standard dark target (DT) and deep blue (DB) algorithms, and the Ångström exponent (AE). Anthropogenic absorbing aerosols and smoke were found to be significant over the Indochina Peninsula, the Philippines and southern China, while a mixture of dust and pollution were predominant over central to northern China, as identified by the AE analysis and the Multi-angle Imaging SpectroRadiometer (MISR) spherecitiy and plume height. Remarkable aerosol absorptions in both the near ultraviolet (UV) and the visible were spread over central, central western and northern China, probably due to aerosol mixtures including desert dust and industrial pollution as well as smoke from biomass burning as evidenced from the Ozone Monitoring Instrument (OMI). Long range transport is validated as dust storm reached up to 4–5 km vertically and a mixed cloud layer was identified over the Yellow Sea as disclosed by the vertical structure of dust aerosols as well as observed aerosols subtypes from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The real time detection and monitoring of the dust outbreak and its subsequent evolution are available through the infrared optical depth index (IODI), developed from the MTSAT-1R geostationary satellite imager.
Recommended Citation
El-Askary, H., Park, S.K., Ahn, M.H., Prasad, A., and Kafatos, M. 2015. On the Detection and Monitoring of the Transport of an Asian Dust Storm Using Multi-Sensor Satellite Remote Sensing. Journal of Environmental Informatics 25, 99–116. doi:10.3808/jei.201500306
Peer Reviewed
1
Copyright
International Society for Environmental Information Sciences (ISEIS)
Comments
This article was originally published in Journal of Environmental Informatics, volume 25, issue 2, in 2015. DOI: 10.3808/jei.201500306