气候变化领域集成服务门户(CCPortal): Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter

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DOI	10.5194/acp-19-13445-2019
	Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter
	Cheng Y.; Dai T.; Goto D.; A J Schutgens N.; Shi G.; Nakajima T.
发表日期	2019
ISSN	16807316
起始页码	13445
结束页码	13467
卷号	19 期号:21
英文摘要	Aerosol vertical information is critical to quantify the influences of aerosol on the climate and environment; however, large uncertainties still persist in model simulations. In this study, the vertical aerosol extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are assimilated to optimize the hourly aerosol fields of the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) online coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) using a four-dimensional local ensemble transform Kalman filter (4-D LETKF). A parallel assimilation experiment using bias-corrected aerosol optical thicknesses (AOTs) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is conducted to investigate the effects of assimilating the observations (and whether to include vertical information) on the model performances. Additionally, an experiment simultaneously assimilating both CALIOP and MODIS observations is conducted. The assimilation experiments are successfully performed for 1 month, making it possible to evaluate the results in a statistical sense. The hourly analyses are validated via both the CALIOP-observed aerosol vertical extinction coefficients and the AOT observations from MODIS and the AErosol RObotic NETwork (AERONET). Our results reveal that both the CALIOP and MODIS assimilations can improve the model simulations. The CALIOP assimilation is superior to the MODIS assimilation in modifying the incorrect aerosol vertical distributions and reproducing the real magnitudes and variations, and the joint CALIOP and MODIS assimilation can further improve the simulated aerosol vertical distribution. However, the MODIS assimilation can better reproduce the AOT distributions than the CALIOP assimilation, and the inclusion of the CALIOP observations has an insignificant impact on the AOT analysis. This is probably due to the nadir-viewing CALIOP having much sparser coverage than MODIS. The assimilation efficiencies of CALIOP decrease with increasing distances of the overpass time, indicating that more aerosol vertical observation platforms are required to fill the sensor-specific observation gaps and hence improve the aerosol vertical data assimilation. © 2019 BMJ Publishing Group. All rights reserved.
语种	英语
scopus关键词	aerosol property; CALIOP; CALIPSO; data assimilation; extinction coefficient; Kalman filter
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/144060
作者单位	Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China; State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; National Institute for Environmental Studies, Tsukuba, Japan; Faculty of Science, Free University of Amsterdam, Amsterdam, Netherlands; Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, Japan
推荐引用方式 GB/T 7714	Cheng Y.,Dai T.,Goto D.,et al. Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter[J],2019,19(21).
APA	Cheng Y.,Dai T.,Goto D.,A J Schutgens N.,Shi G.,&Nakajima T..(2019).Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter.Atmospheric Chemistry and Physics,19(21).
MLA	Cheng Y.,et al."Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter".Atmospheric Chemistry and Physics 19.21(2019).