李莘莘等:Estimation of GEOS-Chem and GOCART Simulated Aerosol Profiles Using CALIPSO Observations over the Contiguous United States
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2017-03-02
Estimation of GEOS-Chem and GOCART Simulated Aerosol Profiles Using CALIPSO Observations over the Contiguous United States
作者:Li, SS (Li, Shenshen)[ 1,2 ] ; Chen, LF (Chen, Liangfu)[ 1 ] ; Fan, M (Fan, Meng)[ 1 ] ; Tao, JH (Tao, Jinhua)[ 1 ] ; Wang, ZT (Wang, Zhongting)[ 3 ] ; Yu, C (Yu, Chao)[ 1 ] ; Si, YD (Si, Yidan)[ 1 ] ; Letu, H (Letu, Husi)[ 1 ] ; Liu, Y (Liu, Yang)[ 2 ]
AEROSOL AND AIR QUALITY RESEARCH
卷: 16  期: 12  页: 3256-3265
DOI: 10.4209/aaqr.2015.03.0173
出版年: DEC 2016
摘要
Model-simulated aerosol profiles can significantly improve a satellite's capability to estimate ground-level particle concentrations, but are difficult to validate due to the sparse network of ground-based lidars. We quantitatively evaluated aerosol vertical profiles simulated by the Global 3-D Atmospheric Chemical Transport model (GEOS-Chem) and the Goddard Chemistry Aerosol Radiation and Transport model (GOCART) over the contiguous United States using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Model-simulated and satellite-retrieved Aerosol Optical Depth (AOD) are first validated with the Aerosol Robotic Network (AERONET) data. The large discrepancies between satellite and model are due to underestimation of GEOS-Chem AOD in the West and GOCART AOD in the East, along with overestimation of CALIPSO AOD during winter and in the West. Model-simulated Aerosol Extinction Coefficients (AEC) at three layers are evaluated against CALIPSO. Aggregating the data from daily and 2 x 2.5 degree model resolutions to a national or annual scale can significantly improve the correlation coefficient and regression slope. Both GEOS-Chem and GOCART underestimate AEC in the lower troposphere in the East, and in the free troposphere in the West than CALIPSO. We attribute these differences to the coarse horizontal resolution, missing aerosol components, and inappropriate emission inventory of the models. Additionally, the low signal-to-noise ratio and cloud and bright surface reflectance interfere with the satellite's measurements.
 
通讯作者地址: Letu, H (通讯作者)
Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China.
通讯作者地址: Liu, Y (通讯作者)
Emory Univ, Rollins Sch Publ Hlth, Atlanta, GA 30322 USA.
地址:
[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
[ 2 ] Emory Univ, Rollins Sch Publ Hlth, Atlanta, GA 30322 USA
[ 3 ] Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China