An algorithm for retrieving fine and coarse aerosol microphysical properties from AERONET-type... скачать в хорошем качестве

An algorithm for retrieving fine and coarse aerosol microphysical properties from AERONET-type...

2014 Fall Meeting Section: Atmospheric Sciences Session: Advances in Remote Sensing of Fires, Aerosols, and Trace Gases for Air Quality Applications IV Title: An algorithm for retrieving fine and coarse aerosol microphysical properties from AERONET-type photopolarimetric measurements Authors: Li, Z, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China, Beijing, China Liu, X, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, United States Zeng, J, University of Nebraska Lincoln, Lincoln, NE, United States Spurr, R J D, Rt Solutions Inc, Cambridge, MA, United States Li, L, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China, Beijing, China Dubovik, O, Laboratoire d'Optique Atmosphérique, University of Lille 1, Villeneuve d'Ascq, France Mishchenko, M I, NASA Goddard Institute for Space Studies, New York, NY, United States Holben, B N, NASA Goddard Space Flight Center, Greenbelt, MD, United States Wang, J, University of Nebraska Lincoln, Lincoln, NE, United States Xu, X, University of Nebraska Lincoln, Lincoln, NE, United States Abstract: A new retrieval algorithm has been developed to retrieve both fine and coarse modal aerosol properties from multi-spectral and multi-angular solar polarimetric radiation fields such as those measured by the AErosol RObotic NETwork (AERONET) but with additional channels of polarization observations (hereafter AEROENT-type measurements). Most AERONET sites lack the capability to measure light polarization, though a few measure polarization only at 870 nm. From both theory and real cases, we show that adding multi-spectral polarization data can allow a mode-resolved inversion of aerosol microphysical parameters. In brief, the retrieval algorithm incorporates AERONET-type measurements in conjunction with advanced vector radiative transfer model specifically designed for studying the inversion problems in aerosol remote sensing. It retrieves aerosol parameters associated to a bi-lognormal particle size distribution (PSD) including aerosol volume concentrations, effective radius and variance, and complex indices of aerosol refraction. Our algorithm differs from the current AERONET inversion algorithm in two major aspects. First, it retrieves effective radius and variance and total volume by assuming a bi-modal lognormal PSD, while AERONET one retrieves aerosol volumes of 22 size bins. Second, our algorithm retrieves spectral refractive indices for both fine and coarse modes. Mode-resolved refractive indices can improve the estimate of single scattering albedo (SSA) for each mode, which also benefits the evaluation for satellite products and chemistry transport models. While bi-lognormal PSD can well represent aerosol size spectrum in most cases, future research efforts will include implementation for tri-modal aerosol mixtures in situations of cloud-formation or volcanic aerosols. Applying the algorithm to a suite of real cases over Beijing_RADI site, we found that our retrievals are overall consistent with AERONET inversion products, but can offer mode-resolved refractive index and SSA. Theoretical analysis indicates that adding polarization can reduce the uncertainty by 50% in the retrieved fine modal aerosol parameters and SSA, although this reduction depends on fine/coarse mode AODs, specifics of instrumentation, and aerosol properties. Cite as: Author(s) (2014), Title, Abstract A54B-04 presented at 2014 Fall Meeting, AGU, San Francisco, Calif., 15-19 Dec. Learn more here: http://abstractsearch.agu.org/meeting...