Impact of the Dust Aerosol Model on the VIIRS Aerosol Optical Depth (AOD) Product across China

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) has been observing aerosol optical depth (AOD), which is a critical parameter in air pollution and climate change, for more than 7 years since 2012. Due to limited and uneven distribution of the Aerosol Robotic Network (AERONET) station in China, the independent data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) was used to evaluate the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD products in six typical sites and analyze the influence of the aerosol model selection process in five subregions, particularly for dust. Compared with ground-based observations, the performance of all retrievals (except the Shapotou (SPT) site) is similar to other previous studies on a global scale. However, the results illustrate that the AOD retrievals with the dust model showed poor consistency with a regression equation as y = 0.312x + 0.086, while the retrievals obtained from the other models perform much better with a regression equation as y = 0.783x + 0.119. The poor AOD retrieval with the dust model was also verified by a comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product. The results show they have a lower correlation coefficient (R) and a higher mean relative error (MRE) when the aerosol model used in the retrieval is identified as dust. According to the Ultraviolet Aerosol Index (UVAI), the frequency of dust type over southern China is inconsistent with the actual atmospheric condition. In addition, a comparison of ground-based Ångström exponent (α) values yields an unexpected result that the dust model percentage exceed 40% when α < 1.0, and the mean α shows a high value of~0.75. Meanwhile, the α peak value (~1.1) of the "dust" model determined by a satellite retravel algorithm indicate there is some problem in the dust model selection process. This mismatching of the aerosol model may partly explain the low accuracy at the SPT and the systemic biases in regional and global validations.Although the Moderate Resolution Imaging Spectroradiometer (MODIS) has retrieved AOD for more than 15 years [9], this period is insufficient for climate research. As the successor of MODIS, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite will perform aerosol observations after the decommissioning of MODIS in the next few years [10]. Therefore, to quantify the accuracy and understand the consistency of the VIIRS AOD product is important. However, according to the recent preliminary validations [11,12], the VIIRS retrievals overestimate the AOD when the AOD is low and underestimate the AOD when the AOD is high, and the biases depend on the AOD range. Under the condition of heavy polluted aerosol loadings across China, the VIIRS AOD retrievals are unavailable because of strict cloud mask and ephemeral water test [13].According to radiation transfer theory, the top-of-atmosphere (TOA) radiance is a combination of the at...

Spatiotemporal Variations of Aerosols in China during the COVID-19 Pandemic Lockdown

“…However, there was a certain abnormal value in large AOD values, and there was a clear relationship with the water distribution, which verified the two reasons we summarized [35]. Therefore, we used the methods outlined in references [27,28] to calculate the AOD data for China. Figure 1 shows the AOD inversion algorithm used in this study.…”

“…Figure 1 shows the AOD inversion algorithm used in this study. To solve the aforementioned problems, Wang et al optimized the traditional algorithm in three aspects: the aerosol model, cloud detection, and surface reflectivity [28].…”

“…To verify the accuracy of the algorithm used in this manuscript, we refer the reader to the results given in references [27,28].…”

“…In this study, Hubei Province in China was used as our experimental area, and Himawari-8 satellite images were used as experimental data to analyze the change in AOD during the initial outbreak of COVID-19 and its relationship with human activities. However, two difficulties must be overcome before data analysis: (1) the Himawari-8 AOD algorithm has great uncertainty, and the results of the optimization algorithm of Wang et al are experimental data [27,28]; (2) the temporal and spatial distribution of aerosol optical depth is influenced by many complex factors such as human activities, temperature, and precipitation [29][30][31][32], the AOD in satellite image acquisition is only one component in the overall change rule, so long-term experimental data are mixed with discontinuous and abnormal values, significantly challenging data analysis. Here, we use E3D-LSTM to predict and analyze the long time-series data, correct the obvious errors in the raster data and make up for disordered missing values.…”

Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

“…In addition, aerosols impact air quality and thus affect human health (e.g., Pope and Dockery 2006). Satellite-retrieved aerosol optical depth (AOD), a quantitative measure of the amount of aerosols present in the atmosphere, is useful for evaluating aerosols' effect on climate change (e.g., Yu et al, 2006) and for estimating and forecasting ambient PM 2.5 concentrations (particulate matter with median diameter ≤ 2.5 µm; e.g., Hoff and Christopher, 2009).…”