Abstract
This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records.
Highlights
Aerosols play an important role in the global energy budget, and atmospheric aerosol loading along with associated absorption and scattering properties is integral to the radiative forcing behind Earth’s changing climate [1,2,3,4], while modifying cloud properties and lifetimes [5,6]
aerosol optical depth (AOD) composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to Aerosol Robotic Network (AERONET) measurements, and the results showed that the correlation coefficient and root mean square error (RMSE)
We examine the effect of merging the Aerosol Robotic Network (AERONET)-based correction and fitting correction to improve AOD products and present a spatiotemporal data composite framework for merging multisensory data
Summary
Aerosols play an important role in the global energy budget, and atmospheric aerosol loading along with associated absorption and scattering properties is integral to the radiative forcing behind Earth’s changing climate [1,2,3,4], while modifying cloud properties and lifetimes [5,6]. This effect on the radiative energy balance is important for both estimating climate change and weather prediction [7,8]. In Northeast Asia, the traffic associated with rapidly urbanizing and expanding cities is a major regional source of anthropogenic
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