Aerosol Optical Properties and Associated Direct Radiative Forcing over the Yangtze River Basin during 2001–2015

He, Ling‐Yun; Wang, Lunche; Lin, Aiming; Zhang, Ming; Bilal, Muhammad; Tao, Minghui

doi:10.3390/rs9070746

Cited by 34 publications

(25 citation statements)

References 61 publications

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“…While the low values (AOD < 0.4) are usually observed in high mountains of the upper reaches of the YRB. The result is consistent with previous findings over China [17,18]. However, over the areas of the YRB with high aerosol loadings, the VIIRS_EDR retrievals exhibit lower values than the aqua-MODIS C6 DT and DTB products.…”

Section: Discussionsupporting

confidence: 92%

“…Overall, for each AOD product at every ground site, the higher AOD values appear in spring and summer than the autumn and winter. Similar findings were observed by [35] at WH and by [18] over the YRB. The reason is likely that there are frequent dust events from North China in spring and local straw burnings in summer.…”

Section: Comparison Of Viirs and Aqua-modis C6 Aod Vs Ce318 Aodsupporting

confidence: 86%

“…According to previous research [18,35], the comparison procedures of satellite AOD retrievals against CE318 AOD observations are performed as follows. Firstly, the CE318 AOD values at 550 nm are calculated from Equations (1) and (2).…”

Section: Comparison Methodsmentioning

confidence: 99%

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Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin

Wang

Lin

et al. 2018

Remote Sensing

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Abstract:The visible infrared imaging radiometer suite (VIIRS) environmental data record aerosol product (VIIRS_EDR) and the Aqua-moderate resolution imaging spectroradiometer (MYD04) collection 6 (C6) aerosol optical depth (AOD) products are validated against the Cimel sun-photometer (CE318) AOD measurements during different air quality conditions over the Yangtze river basin (YRB) from 2 May 2012 to 31 December 2016. For VIIRS_EDR, the AOD observations are obtained from the scientific data set (SDS) "aerosol optical depth at 550 nm" at 6 km resolution, and for Aqua-MODIS, the AOD observations are obtained from the SDS "image optical depth land and ocean" at 3 km (DT3K) and 10 km (DT10K) resolutions, "deep blue aerosol optical depth 550 land" at 10 km resolution (DB10K), and "AOD 550 dark target deep blue combined" at 10 km resolution (DTB10K). Results show that the high-quality (QF = 3) DTB10K performs the best against the CE318 AOD observations, along with a higher R (0.85) and more retrievals within the expected error (EE) ± (0.05 + 15%) (55%). Besides, there is a 10% overestimation, but the positive bias does not exhibit obvious seasonal variations. Similarly, the DT3K and DT10K products overestimate AOD retrievals by 23% and 15%, respectively, all over the year, but the positive biases become larger in spring and summer. For the DB10K AOD retrievals, there is an overestimation (underestimation) in autumn and winter (spring and summer). Compared to the Aqua-MODIS AOD products, the VIIRS_EDR AOD retrievals are less correlated (R = 0.73) and only 44% of the retrievals fall within EE. Meanwhile, the VIIRS_EDR shows larger bias than the Aqua-MODIS C6 retrievals, and tends to overestimate AOD retrievals in summer and underestimate in winter. Additionally, there is an underestimation for the VIIRS_EDR AOD retrievals over the regions during high aerosol loadings. These indicate that the VIIRS_EDR retrieval algorithm needs to be improved in further applications over the YRB.

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Section: Discussionsupporting

confidence: 92%

Section: Comparison Of Viirs and Aqua-modis C6 Aod Vs Ce318 Aodsupporting

confidence: 86%

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Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin

Wang

Lin

et al. 2018

Remote Sensing

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“…For example, the Aerosol Robotic Network (AERONET) can provide continuous cloud-screened observations of spectral AOD values around the world [6,7]. In China, AOD values are routinely measured at about 50 sites within the China Aerosol Remote Sensing Network (CARSNET) [8][9][10]. However, the sites of AERONET and CARSNET are relatively sparse for AOD applications with high spatial resolutions [11].…”

Section: Introductionmentioning

confidence: 99%

Characteristic and Driving Factors of Aerosol Optical Depth over Mainland China during 1980–2017

et al. 2018

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Abstract:Since the reform and opening up of China, the increasing aerosol emissions have posted great challenges to the country's climate change and human health. The aerosol optical depth (AOD) is one of the main physical indicators quantifying the atmospheric turbidity and air pollution. In this study, 38-years (1980-2017) of spatial and temporal variations of AOD in China were analyzed using AOD records derived from MODIS atmosphere products and the MERRA-2 dataset. The results showed that the annual mean AOD values throughout China have gone through an increasing, but fluctuating, trend, especially in 1982 and in 1992 due to two volcano eruptions; the AOD values experienced a dramatically increasing period during 2000-2007 with the rapid economic development and "population explosions" in China/after 2008, the AOD values gradually decreased from 0.297 (2008) to 0.257 (2017). The AOD values in China were generally higher in spring than that in other seasons. The Sichuan Basin has always been an area with high AOD values owing to the strong human activity and the basin topography (hindering aerosol diffusions in the air). In contrast, the Qinghai Tibet Plateau has always been an area with low AOD values due to low aerosol emissions and clear sky conditions there. The trend analysis of AOD values during 1980-2017 in China indicated that the significant increasing trend was mainly observed in Southeastern China. By contrast, the AOD values in the northernmost of China showed a significant decreasing trend. Then, the contributions (AODP) of the AOD for black carbon aerosol (BCAOD), dust aerosol (DUAOD), organic carbon aerosol (OCAOD), sea salt aerosol (SSAOD), and SO 4 aerosol (SO 4 AOD) to the total AOD values were calculated. The results showed that DUAOD (25.43%) and SO 4 AOD (49.51%) were found to be the main driving factors for the spatial and temporal variations of AOD values. Finally, the effects of anthropogenic aerosol emissions, socioeconomic factors, and land-use and land coverage changes on AOD were analyzed. The GDP, population density, and passenger traffic volume were found to be the main socioeconomic drivers for AOD distributions. Relatively larger AOD values were mainly found in urban land and land covered by water, while lower AOD values were found in grassland and permanent glacier areas.

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“…), are used to conduct research on understanding the effects of aerosols [61] and clouds [62], which have large temporal and spatial variabilities, on radiation [18]. It can also be applied as basic input for numerical prediction and climate models, which will contribute to improving the accuracy of the calculation [63] and further monitoring of radiation balance to understand the mechanism of climate change [64].…”

Section: Discussionmentioning

confidence: 99%

Retrieval of Reflected Shortwave Radiation at the Top of the Atmosphere Using Himawari-8/AHI Data

et al. 2018

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This study developed a retrieval algorithm for reflected shortwave radiation at the top of the atmosphere (RSR). This algorithm is based on Himawari-8/AHI (Advanced Himawari Imager) whose sensor characteristics and observation area are similar to the next-generation Geostationary Korea Multi-Purpose Satellite/Advanced Meteorological Imager (GK-2A/AMI). This algorithm converts the radiance into reflectance for six shortwave channels and retrieves the RSR with a regression coefficient look-up-table according to geometry of the solar-viewing (solar zenith angle, viewing zenith angle, and relative azimuth angle) and atmospheric conditions (surface type and absence/presence of clouds), and removed sun glint with high uncertainty. The regression coefficients were calculated using numerical experiments from the radiative transfer model (SBDART), and ridge regression for broadband albedo at the top of the atmosphere (TOA albedo) and narrowband reflectance considering anisotropy. The retrieved RSR were validated using Terra, Aqua, and S-NPP/CERES data on the 15th day of every month from July 2015 to February 2017. The coefficient of determination (R 2 ) between AHI and CERES for scene analysis was higher than 0.867 and the Bias and root mean square error (RMSE) were −21.34-5.52 and 51.74-59.28 Wm −2 . The R 2 , Bias, and RMSE for the all cases were 0.903, −2.34, and 52.12 Wm −2 , respectively.

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Aerosol Optical Properties and Associated Direct Radiative Forcing over the Yangtze River Basin during 2001–2015

Cited by 34 publications

References 61 publications

Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin

Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin

Characteristic and Driving Factors of Aerosol Optical Depth over Mainland China during 1980–2017

Retrieval of Reflected Shortwave Radiation at the Top of the Atmosphere Using Himawari-8/AHI Data

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