Retrieval of Aerosol Optical Depth from Optimal Interpolation Approach Applied to SEVIRI Data
AbstractThis paper presents two algorithms used to derive Aerosol Optical Depth (AOD) from a synergy of satellite and ground-based observations, as well as aerosol transport model output. The Spinning Enhanced Visible Infrared Radiometer (SEVIRI) instrument on board Meteosat Second Generation (MSG) allows us to monitor aerosol loading over land at high temporal and spatial resolution. We present the algorithms which were fed with the data acquired via the SEVIRI channel 1, and also channels 1 and 3 in conjunction. In both cases, the surface reflectance is the most important parameter that should be estimated during the retrieval process. The surface properties are estimated during days with a low AOD (less than 0.1 at 500 nm) based on the radiance measured by the SEVIRI detector and aerosol optical properties modeled with the aerosol transport model or measured by the MODIS sensor. For data from the model and the MODIS, ground-based stations equipped with a sun photometer have been applied to correct the AOD fields using the optimal interpolation method. By assuming that surface reflectance at the SEVIRI resolution changes slowly over time, the AOD has been computed. Comparison of the SEVIRI AOD with the sun photometer observations shows good agreement/correlation. The mean bias is small (an order of 0.01–0.02) and the root mean square (rms) is about 0.05 for both one- and two-channel methods. In addition, the rms for the one-channel method does not change with the AOD. View Full-Text
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Zawadzka, O.; Markowicz, K. Retrieval of Aerosol Optical Depth from Optimal Interpolation Approach Applied to SEVIRI Data. Remote Sens. 2014, 6, 7182-7211.
Zawadzka O, Markowicz K. Retrieval of Aerosol Optical Depth from Optimal Interpolation Approach Applied to SEVIRI Data. Remote Sensing. 2014; 6(8):7182-7211.Chicago/Turabian Style
Zawadzka, Olga; Markowicz, Krzysztof. 2014. "Retrieval of Aerosol Optical Depth from Optimal Interpolation Approach Applied to SEVIRI Data." Remote Sens. 6, no. 8: 7182-7211.