Mineral dust emission estimated from satellite data

Jen-Ping Chen; Meng-Lung Lin; Hsiang-He Lee

doi:10.1117/12.679895

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1 September 2006 Mineral dust emission estimated from satellite data

Jen-Ping Chen, Meng-Lung Lin, Hsiang-He Lee

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Proceedings Volume 6299, Remote Sensing of Aerosol and Chemical Gases, Model Simulation/Assimilation, and Applications to Air Quality; 62990I (2006) https://doi.org/10.1117/12.679895
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

East Asian dust sources are mostly located in remote areas where the geographical characteristics are not well understood. This imposes difficulties in the modeling of atmospheric mineral dust. Satellite remote sensing is a viable way of deriving temporal dynamics and spatial distributions of dust emission over large areas. Although the present-day satellites provide only column-integrated aerosol properties, it is possible to retrieve from them the dust emission strength, which is also a column denomination. In this study, dust emission over East Asia is estimated using aerosol optical depth retrieved from MODIS sensors with the aide of a regional meteorological and dust model. Differences of daily AOD and meteorological data are applied to the mass conservation equation to estimate net emission of dust over specified desert areas. The derived emission is then compared with the old data set used in the model for dust simulation in East Asia. The area of study covers northern and western China as well as Mongolia. Asian dust events identified during the period January 2003 to December 2004 are selected for emission retrieval and for modeling comparisons. The results show that the patterns of emission coefficient retrieved from Aqua-MODIS AOD data are consistent with spatial characteristics of land cover over Northern China and Mongolia.

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Jen-Ping Chen, Meng-Lung Lin, and Hsiang-He Lee "Mineral dust emission estimated from satellite data", Proc. SPIE 6299, Remote Sensing of Aerosol and Chemical Gases, Model Simulation/Assimilation, and Applications to Air Quality, 62990I (1 September 2006); https://doi.org/10.1117/12.679895

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