Estimation of aerosol complex refractive indices for both fine and coarse modes simultaneously based on AERONET remote sensing products

Zhang, Ying; Li, Zhengqiang; Zhang, Yuhuan; Li, Donghui; Qie, Lili; Che, Huizheng; Xu, Hua

doi:10.5194/amt-10-3203-2017

Cited by 30 publications

(35 citation statements)

References 48 publications

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“…Considering that most of high absorption coarse‐particle components result from soot or flying ash related to coal burning, highly emitted during winter season in China, we think that the high‐absorbing c‐UHS is related to the coarse particles in north winter. In contrast, it is also reasonable to infer that the c‐ULW model represents the summer conditions, especially considering its high water content (i.e., small value n c ) as suggested in Zhang, Li, Zhang, et al (). The c‐UNW model shows an extraordinarily high volume concentration ( C c = 0.482) together with weak absorption and moderate refractivity, which implies the natural dust.…”

Section: Resultsmentioning

confidence: 90%

“…Scholars have suggested to mark these peaks as the standard fine mode (subscript f ), the standard coarse mode (subscript c ), the submicron fine mode (subscript SMF , i.e., particle radius larger than the standard fine mode but still in the fine particle size domain), and the super‐micron coarse mode (subscript SMC , i.e., particle radius smaller than the standard coarse mode but still in the coarse particle size domain; Li et al, ; Zhang et al, ). Zhang, Li, Zhang, et al () method (see Text S1 in the supporting information) is used to provide the individual fine‐/coarse‐mode aerosol products (as listed in Table ) from instantaneous measurements as the inputs of clustering analyses of this study. Considering that the optical parameters (e.g., AOD and single scattering albedo) can be derived from aerosol microphysical parameters, we only employ aerosol microphysical parameters in the clustering analyses.…”

Section: Methodsmentioning

confidence: 99%

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The Fundamental Aerosol Models Over China Region: A Cluster Analysis of the Ground‐Based Remote Sensing Measurements of Total Columnar Atmosphere

Zhang

et al. 2019

Geophysical Research Letters

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Ten fundamental aerosol models in China are derived from a cluster study based on the ground‐based remote sensing measurements of Sun‐sky radiometer Observation NETwork. The aerosol size distribution decomposition techniques are employed to yield individual fine and coarse mode size distribution functions with independent refractive indices. The total 10,773 records containing 18 kinds of aerosol microphysical parameters are used to yield 10 typical clusters with the verification of clustering robustness. Ten clusters suggest five typical fine particle aerosol models including urban polluted, secondary polluted, combined polluted, polluted fly ash, and continental background, as well as five coarse models including summer fly ash, winter fly ash, primary dust, transported dust, and background dust over China region. The representativeness and coappearance analyses again reveal five dominative aerosol patterns on the base of fundamental models. These models can be used in the chemical model simulation, satellite remote sensing, climate, and environment analyses.

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Section: Resultsmentioning

confidence: 90%

Section: Methodsmentioning

confidence: 99%

The Fundamental Aerosol Models Over China Region: A Cluster Analysis of the Ground‐Based Remote Sensing Measurements of Total Columnar Atmosphere

Zhang

et al. 2019

Geophysical Research Letters

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“…In this paper, the sub-mode VSDs and CRIs were retrieved by the aerosol products [24,26] , following by Zhang et al (2016) and Zhang et al (2017). The method developed by Cuesta et al (2008) is employed to separate VSD into a single Log-Normal Modes (LNM).…”

Section: Methodsmentioning

confidence: 99%

“…Most research analyzed aerosol total-columnar CRI, but fine-and coarse-mode particles are associated with different composition and source of pollution. So, in this study we recalculated the complex refractive indices for both of fine and coarse mode, following Zhang et al (2017). For the calculation of the sub-mode CRI, we also choose the same radius limit (1 μm) as the sub-mode VSD.…”

Section: Methodsmentioning

confidence: 99%

Sub-Mode Aerosol Volume Size Distribution and Complex Refractive Index from the Three-Year Ground-Based Measurements in Chengdu China

Zhang

et al. 2019

Atmosphere

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Chengdu is a typical basin city of Southwest China with rare observations of remote sensing measurements. To assess the climate change and establish a region aerosol model, a deeper understanding of the separated volume size distribution (VSD) and complex refractive index (CRI) is required. In this study, we employed the sub-mode VSD and CRI in Chengdu based on the three years observation data to investigate the sub-mode characteristics and climate effects. The annual average fraction of the fine-mode aerosol optical depth (AOD f ) is 92%, which has the same monthly tendency as the total AOD. But the coarse-mode aerosol optical depth (AOD c ) has little variation in different months. There are four distinguishing modes of VSD in Chengdu; the median radii are 0.17 µm ± 0.05, 0.31 µm ± 0.12, 1.62 µm ± 0.45, 3.25 µm ± 0.99, respectively. The multi-year average and seasonal variations of fine-and coarse-mode VSD and CRI are also analyzed to characterize aerosols over this region. The fine-mode single scattering albedos (SSAs) are higher than the coarse-mode ones, which suggests that the coarse-mode aerosols have a stronger absorbing effect on solar light than the small-size aerosol particles in Chengdu.is conducive to the formation of aerosol particulates, which has made Chengdu become one of the regions in China with serious haze pollution [7][8][9][10]. The special topography and climate in Sichuan basin leads to a kind of wet, rainy and cloudy weather condition [11]. Therefore, it is difficult to obtain a large number of measurements through optical remote sensing observations in Chengdu. Sampling method of atmospheric particulates is one kind of Chengdu air pollution study [7,9,10,12], which fails to acquire the aerosol's larger-scale spatial characteristics. The spatial characteristics of aerosols can be obtained by remote sensing, one kind of non-contact method [13][14][15]. For instance, some studies analyzed the spatial-temporal distribution of aerosol optical depth (AOD) in Sichuan basin using satellite remote sensing [16,17]. Besides, aerosol optical parameters obtained by the ground-based remote sensing observation were statistically analyzed in this area [18][19][20][21].Although the above-mentioned research studies focus on the total-column aerosol properties by remote sensing methods, it still remains a big challenge to obtain the characteristics of sub-mode aerosol (fine-and coarse-mode) in Chengdu. The fine-and coarse-mode particles are related to the different sources and compositions. Fine-mode aerosols are mainly dominated by anthropogenic emissions and are related to the fog or low-altitude cloud dissipated events [22,23]. Coarse-mode particles are determined by the sea salt, dust and other natural sources. Therefore, the knowledge of aerosol sub-mode properties plays a role in the research on regional climate and the improvement of the aerosol model used in the satellite retrieval algorithms.In this study, the sub-mode volume size distribution (VSD) and complex refractive index (CRI) in Chengdu a...

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“…The k c (675 nm) retrievals of SMART‐s were relatively higher than AERONET with a slightly larger variability even with their comparable estimated retrieval accuracy than fine mode as compared in Appendix A ( compare the black circles in between panels c and d in Figure ). Therefore, the significant variabilities of the n c and k c in Figure also can be attributed to diversity of dust composition and their microphysical properties (e.g., Gillespie & Lindberg, ; Sokolik & Toon, ; Zhang et al, ). In general, the retrievals of n and k are estimated to be highly uncertain, particularly for coarse mode (e.g., Sinyuk et al, ), which result in noisy values in Figures a and b.…”

Section: Algorithm Consistency Check Using Aeronet Measurementsmentioning

confidence: 99%

The SMART‐s Trace Gas and Aerosol Inversions: I. Algorithm Theoretical Basis for Column Property Retrievals

et al. 2020

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The SMART‐s (Spectral Measurements for Atmospheric Radiative Transfer—spectroradiometer) acquires Sun/sky observations for retrieving optimal information on trace gases and aerosols with minimal assumptions. Overall, the algorithm of SMART‐s incorporates a series of retrievals, from fundamental quantities (i.e., column abundance of trace gases and aerosol loading) to higher‐order geophysical parameters (e.g., aerosol physicochemical properties and vertical profiles), utilizing Sun/sky spectral radiance measurements. This paper describes the theoretical basis for column retrievals of trace gases and aerosols. Associated profile retrievals will be presented in follow‐up papers. The current algorithm retrieves the fine/coarse mode of the particle size distribution and spectral complex index of refraction and, thereby, the spectral aerosol single‐scattering albedo ω0. SMART‐s retrieval is unique particularly in its high spectral resolution of the complex index of refraction and ω0 from near‐ultraviolet to near‐infrared wavelengths, which is pivotal information for atmospheric chemistry, climate and other inversions. We theoretically assessed information content and retrieval accuracy of the algorithm and compared different type of measurements including the Aerosol Robotic Network (AERONET) and standard Pandora. For the same levels of radiometric accuracy, SMART‐s measurements provide the most informative aerosol retrievals based on theoretical error analyses. Higher spectral resolution measurements are particularly beneficial for particle size distribution and fine‐mode refractive index retrievals. We applied this algorithm to the AERONET Sun/sky measurements at Kanpur, India, in 2016 to assess algorithm consistency. Even with different assumptions and numerical methods for the inversion, SMART‐s retrieved aerosol parameters agreed well with the AERONET operational products (e.g., absolute mean bias errors less than 0.01 for ω0).

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Estimation of aerosol complex refractive indices for both fine and coarse modes simultaneously based on AERONET remote sensing products

Cited by 30 publications

References 48 publications

The Fundamental Aerosol Models Over China Region: A Cluster Analysis of the Ground‐Based Remote Sensing Measurements of Total Columnar Atmosphere

The Fundamental Aerosol Models Over China Region: A Cluster Analysis of the Ground‐Based Remote Sensing Measurements of Total Columnar Atmosphere

Sub-Mode Aerosol Volume Size Distribution and Complex Refractive Index from the Three-Year Ground-Based Measurements in Chengdu China

The SMART‐s Trace Gas and Aerosol Inversions: I. Algorithm Theoretical Basis for Column Property Retrievals

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