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DOI | 10.5194/acp-19-13409-2019 |
Retrieval of aerosol components directly from satellite and ground-based measurements | |
Li L.; Dubovik O.; Derimian Y.; Schuster G.L.; Lapyonok T.; Litvinov P.; Ducos F.; Fuertes D.; Chen C.; Li Z.; Lopatin A.; Torres B.; Che H. | |
发表日期 | 2019 |
ISSN | 16807316 |
起始页码 | 13409 |
结束页码 | 13443 |
卷号 | 19期号:21 |
英文摘要 | This study presents a novel methodology for the remote monitoring of aerosol components over large spatial and temporal domains. The concept is realized within the GRASP (Generalized Retrieval of Aerosol and Surface Properties) algorithm to directly infer aerosol components from the measured radiances. The observed aerosols are assumed to be mixtures of hydrated soluble particles embedded with black carbon, brown carbon, iron oxide, and other (non-absorbing) insoluble inclusions. The complex refractive indices of the dry components are fixed a priori (although the refractive index of the soluble host is allowed to vary with hydration), and the complex refractive indices of the mixture are computed using mixing rules. The volume fractions of these components are derived along with the size distribution and the fraction of spherical particles, as well as the spectral surface reflectance in cases when the satellite data are inverted. The retrieval is implemented as a statistically optimized fit in a continuous space of solutions. This contrasts with most conventional approaches in which the type of aerosol is either associated with a preassumed aerosol model that is included in a set of lookup tables, or determined from the analysis of the retrieved aerosol optical parameters (e.g., single scattering albedo, refractive index, among others, provided by the AERONET retrieval algorithm); here, we retrieve the aerosol components explicitly. The approach also bridges directly to the quantities used in global chemical transport models. We first tested the approach with synthetic data to estimate the uncertainty, and then applied it to real ground-based AERONET and spaceborne POLDER/PARASOL observations; thus, the study presents a first attempt to derive aerosol components from satellite observations specifically tied to global chemical transport model quantities. Our results indicate aerosol optical characteristics that are highly consistent with standard products (e.g., R of ~ 0:9 for aerosol optical thickness) and demonstrate an ability to separate intrinsic optical properties of fine- and coarse-sized aerosols. We applied our method to POLDER/PARASOL radiances on the global scale and obtained spatial and temporal patterns of the aerosol components that agree well with existing knowledge on aerosol sources and transport features. Finally, we discuss limitations and perspectives of this new technique. © 2019 BMJ Publishing Group. All rights reserved. |
语种 | 英语 |
来源期刊 | Atmospheric Chemistry and Physics |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/144062 |
作者单位 | State Key Laboratory of Severe Weather (LASW), Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China; Univ. Lille, CNRS, UMR 8518, LOA - Laboratoire d'Optique Atmosphérique, Lille, 59000, France; NASA Langley Research Center, Hampton, VA 23681, United States; GRASP-SAS, Remote Sensing Developments, Cité Scientifique, Univ. Lille, Villeneuve d'Ascq, 59655, France; State Environmental Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China |
推荐引用方式 GB/T 7714 | Li L.,Dubovik O.,Derimian Y.,et al. Retrieval of aerosol components directly from satellite and ground-based measurements[J],2019,19(21). |
APA | Li L..,Dubovik O..,Derimian Y..,Schuster G.L..,Lapyonok T..,...&Che H..(2019).Retrieval of aerosol components directly from satellite and ground-based measurements.Atmospheric Chemistry and Physics,19(21). |
MLA | Li L.,et al."Retrieval of aerosol components directly from satellite and ground-based measurements".Atmospheric Chemistry and Physics 19.21(2019). |
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