Validation of MODIS aerosol optical depth product over China using CARSNET measurements

Abstract. Aerosol pollution in eastern China is an unfortunate consequence of the region's rapid economic and industrial growth. Here, sun photometer measurements from seven sites in the Yangtze River Delta (YRD) from 2011 to 2015 were used to characterize the climatology of aerosol microphysical and optical properties, calculate direct aerosol radiative forcing (DARF) and classify the aerosols based on size and absorption. Bimodal size distributions were found throughout the year, but larger volumes and effective radii of fine-mode particles occurred in June and September due to hygroscopic growth and/or cloud processing. Increases in the fine-mode particles in June and September caused AOD 440 nm > 1.00 at most sites, and annual mean AOD 440 nm values of 0.71-0.76 were found at the urban sites and 0.68 at the rural site. Unlike northern China, the AOD 440 nm was lower in July and August (∼ 0.40-0.60) than in January and February (0.71-0.89) due to particle dispersion associated with subtropical anticyclones in summer. Low volumes and large bandwidths of both fine-mode and coarsemode aerosol size distributions occurred in July and August because of biomass burning. Single-scattering albedos at 440 nm (SSA 440 nm ) from 0.91 to 0.94 indicated particles with relatively strong to moderate absorption. Strongly absorbing particles from biomass burning with a significant SSA wavelength dependence were found in July and August at most sites, while coarse particles in March to May were Published by Copernicus Publications on behalf of the European Geosciences Union. 406H. Che et al.: Aerosol optical properties and direct radiative forcing mineral dust. Absorbing aerosols were distributed more or less homogeneously throughout the region with absorption aerosol optical depths at 440 nm ∼ 0.04-0.06, but inter-site differences in the absorption Angström exponent indicate a degree of spatial heterogeneity in particle composition. The annual mean DARF was −93 ± 44 to −79 ± 39 W m −2 at the Earth's surface and ∼ −40 W m −2 at the top of the atmosphere (for the solar zenith angle range of 50 to 80 • ) under cloud-free conditions. The fine mode composed a major contribution of the absorbing particles in the classification scheme based on SSA, fine-mode fraction and extinction Angström exponent. This study contributes to our understanding of aerosols and regional climate/air quality, and the results will be useful for validating satellite retrievals and for improving climate models and remote sensing algorithms.

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“…Ground-based observations are also useful for the validation of satellite retrievals (Holben et al, 2017;Xie et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Aerosol optical properties and direct radiative forcing based on measurements from the China Aerosol Remote Sensing Network (CARSNET) in eastern China

et al. 2018

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“…The overpass of Terra and Aqua are around 11:00 a.m. and 14:00 p.m. local time (UTC+08:00) in Chongqing, respectively. Due to the enhanced cloud formation in the afternoon, there are fewer matchups for Aqua than Terra [25]. However, comparisons of the statistical results demonstrate that Aqua MODIS performs better than Terra MODIS.…”

Section: Validation Resultsmentioning

confidence: 91%

“…are fewer matchups for Aqua than Terra [25]. However, comparisons of the statistical results demonstrate that Aqua MODIS performs better than Terra MODIS.…”

Section: Validation Resultsmentioning

confidence: 93%

“…A widely used collocation method is 50 km by 50 km box with the ground site in the center and corresponding ±30 min of MODIS overpass assuming that the aerosol diffusion speed is 50 km/h. But the relatively large averaging box may reduce the representativeness of averaged MODIS AODs because of topographic and/or aerosol type heterogeneity [15,21,25]. As shown in Figure 1, the ground site is located in a trough valley that is surrounded by mountains exceeding 500 m in height.…”

Section: Comparison Methodsmentioning

confidence: 99%

“…In previous studies, several factors that could produce occasional large AOD differences had been identified, including topography [15], land use [25], and gradients of AOD or aerosol optical properties [21]. In Chongqing, the terrain is quite complicated, as shown in Figure 1.…”

Section: Influence Of Collocation Methodsmentioning

confidence: 99%

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Evaluation of the MODIS C6 Aerosol Optical Depth Products over Chongqing, China

Shi

Liu²,

Wang

et al. 2017

Atmosphere

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Abstract:The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) aerosol optical depth (AOD) products from the 10/3 km Dark Target (DT) and Deep Blue (DB) algorithms are firstly evaluated using ground observed AODs by the sun photometer in Chongqing, a mountainous mega-city in southwest China. The validation results show that MODIS AODs from 10/3 km DT algorithm are comparable with those of the sun photometer, although there are slight overestimations. However, the DB algorithm substantially underestimates MODIS AODs when comparing with those of the sun photometer. Error analyses imply that the bias of surface reflectance estimation is the main error source for both algorithms. The cloud screening scheme of the DT algorithm is more effective than the DB algorithm. The cloud vicinity effect should be considered in the quality control processes for both of the algorithms. A sensitivity test suggests that in complex terrain area, like Chongqing, the collocation method in the validation of satellite products should be carefully selected according to local circumstances. When comparing the monthly mean AODs of MODIS products with sun photometer observations, it shows that the Terra MODIS AOD products are valid to represent the mean statuses in summer and autumn, but the monthly mean of Aqua MODIS AODs are limited in Chongqing.

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Evaluation of MODIS aerosol retrieval algorithms over the Beijing‐Tianjin‐Hebei region during low to very high pollution events

2015

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This study evaluates the performance of different MODerate resolution Imaging Spectroradiometer (MODIS) aerosol algorithms during fine particle pollution events over the Beijing-Tianjin-Hebei region using Aerosol Robotic Network aerosol optical depth (AOD). These algorithms include the Deep Blue (DB) Collection 5.1 (C5) and Collection 6 (C6) algorithms at 10 km resolution, the Dark Target (DT) C5 and C6 algorithms at 10 km, the DT C6 algorithm at 3 km, and the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m, 3 km, and 10 km resolutions. The DB C6 retrievals have 34-39% less uncertainties, 2-3 times smaller root-mean-square error (RMSE), and 3-4 times smaller mean absolute error (MAE) than DB C5 retrievals. The DT C6 has 4-8% lower bias, 4-12% less overestimation, and smaller RMSE and MAE errors than DT C5. Due to underestimation of surface reflectance and the use of inappropriate aerosol schemes, 87-89% of the collocations of the DT C6 at 3 km fall above the expected error (EE), with overestimation of 64-79% which is 15-27% higher than that for the DT C6 at 10 km. The results suggest that the DT C6 at 3 km resolution is less reliable than that at 10 km. The SARA AOD has small RMSE and MAE errors with 90-96% of the collocations falling within the EE. Overall, the SARA showed 15-16% less uncertainty than the DB C6 (10 km), 69-72% less than the DT C6 (10 km), and 79-83% less than the DT C6 (3 km) retrievals.

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Validation of MODIS aerosol optical depth product over China using CARSNET measurements

Cited by 114 publications

References 26 publications

Aerosol optical properties and direct radiative forcing based on measurements from the China Aerosol Remote Sensing Network (CARSNET) in eastern China

Aerosol optical properties and direct radiative forcing based on measurements from the China Aerosol Remote Sensing Network (CARSNET) in eastern China

Evaluation of the MODIS C6 Aerosol Optical Depth Products over Chongqing, China

Evaluation of MODIS aerosol retrieval algorithms over the Beijing‐Tianjin‐Hebei region during low to very high pollution events

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