Abstract
Abstract In this study, a vertical correction method based on a two-layer aerosol model is proposed to estimate the surface-level visibility from satellite measurements of aerosol optical depth (AOD). The meteorological parameters from the re-analysis data of the National Centers for Environmental Prediction (NCEP) are applied to estimate the aerosol layer height (ALH) of the two-layer aerosol model via an automatic workflow. The estimated extinction coefficients near the surface by AOD/ALH over the single point of a lidar site in Shanghai agree well with those of the ground measurements from a visibility sensor, with a correlation coefficient of 0.86 and root mean squared error (RMS) of 0.19 km − 1 for the data set from April 18, 2008 to April 30, 2014. The season-long spatial comparison demonstrates that most of the correlation coefficients (90%) are > 0.6, and more than half of the samples (68%) have coefficients higher than 0.7 for the data set from January 1 to April 30, 2014. Dust transportation and higher relative humidity (RH) have been confirmed to be important factors in reducing the accuracy of estimated visibility, as these situations fail to meet the assumptions of the two-layer model. Additionally, the less-rigorous cloud mask algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS)/AOD might lead to overestimates of AOD, and further underestimating of the surface-level visibility. The spatial variation of temporal correlation coefficients shows that most comparison sites (> 74%) of satellite estimations agree well with the surface-level visibility measurements, with correlation coefficients up to 0.6 during the study period. The northern area of Eastern China presented better agreement than the southern area. This may be related to the complex underlying surface characteristics and higher RH in the southern part. This work will significantly improve the quality of climate simulations and air quality forecasts in Eastern China.
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