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Original Articles

A new method for improving the retrieved aerosol fine-mode fraction from MODIS over ocean

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Pages 2551-2562 | Received 19 Jan 2011, Accepted 12 Aug 2011, Published online: 06 Oct 2011
 

Abstract

A new method for improving the retrieved aerosol fine-mode fraction (550) based on the current Moderate Resolution Imaging Spectroradiometer (MODIS) ocean algorithm is proposed. In the current MODIS ocean algorithm, the top of the atmosphere (TOA) apparent reflectance needs calculation from lookup tables (LUTs). The weighting parameters used in the calculation show an obvious spectral dependence, which is not taken into account in the current algorithm. The main measure taken in this study is to consider the spectral dependence of the weighting parameters. The MODIS aerosol products and the Aerosol Robotic Network (AERONET) data of Hong Kong Hok Tsui, Midway Island, Martha’s Vineyard Coastal Observatory (MVCO) and COVE, Virginia, where aerosols exhibit different loading and size distribution, are used to test the new method. The results show that the new method improves the retrieved fine-mode fraction, which is underestimated in anthropogenic-dominated aerosol conditions and overestimated in the sea salt-dominated aerosol conditions by the current algorithm. The correlation of the retrieved fine-mode fraction between the new method and AERONET is much higher (correlation coefficient, r = 0.92) than that between the current MODIS and AERONET (r = 0.80). The retrieved aerosol optical depth (AOD) is also improved. More AODs retrieved from the new method lie within the expected error bars.

Acknowledgements

We are grateful to the various MODIS software development and support teams for the production and distribution of the MODIS data, and the AERONET teams for collecting, processing and making available ground-based aerosol observations of MVCO, Midway Island, Hong Kong Hok Tsui and COVE. This work was funded by the National Natural Science Foundation of China through grant number 41005016, the project of 973 numbered 2010CB950803 and the Knowledge Innovation Programme of the Chinese Academy of Sciences Programme through grant number 083RC11125.

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