气候变化领域集成服务门户(CCPortal): Hourly Aerosol Assimilation of Himawari-8 AOT Using the Four-Dimensional Local Ensemble Transform Kalman Filter

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DOI	10.1029/2018MS001475
	Hourly Aerosol Assimilation of Himawari-8 AOT Using the Four-Dimensional Local Ensemble Transform Kalman Filter
	Dai T.; Cheng Y.; Suzuki K.; Goto D.; Kikuchi M.; Schutgens N.A.J.; Yoshida M.; Zhang P.; Husi L.; Shi G.; Nakajima T.
发表日期	2019
ISSN	19422466
起始页码	680
结束页码	711
卷号	11 期号:3
英文摘要	The next-generation geostationary satellite Himawari-8 has a much higher observation frequency of the aerosol field than polar-orbiting satellites. Aerosol analyses with a geostationary satellite can advance our understanding of the rapid spatiotemporal evolution of aerosols, which is especially critical for studies of air pollution and its mechanisms. We present a one-monthlong hourly aerosol analysis using an aerosol data assimilation based on the local ensemble Kalman filter (LETKF), Himawari-8-retrieved hourly aerosol optical thicknesses (AOTs), and a global model named Non-hydrostatic Icosahedral Atmospheric Model coupled with an aerosol model named Spectral Radiation Transport Model for Aerosol Species (NICAM-SPRINTARS). To assimilate asynchronous observations and avoid frequent switching between the assimilation and ensemble aerosol forecasts, the LETKF is also extended to the four-dimensional LETKF (4D-LETKF). The hourly aerosol analyses are evaluated with both the assimilated Himawari-8 AOTs and independent Moderate Resolution Imaging Spectroradiometer (MODIS)- and AErosol RObotic NETwork (AERONET)-retrieved AOTs. All evaluations show that the assimilations positively affect the model performances and produce simulated AOTs that are closer to the observations. The analyses correctly reduce the significantly positive biases and root-mean-square errors of the control experiment, especially over East China and Australia. Our results also show that hourly aerosol analyses with more frequent Himawari-8 observations are superior to those using the polar satellite MODIS observations. The performances among the LETKF and 4D-LETKF experiments are generally not so different, but the computational load of the 4D-LETKF is much lighter than that of the LETKF. ©2019. The Authors.
语种	英语
scopus关键词	Aerosols; Geostationary satellites; Kalman filters; Mean square error; Orbits; Radiometers; Aerosol robotic networks; Computational loads; Ensemble Kalman Filter; Moderate resolution imaging spectroradiometer; Observation frequencies; Polar-orbiting satellites; Root mean square errors; Spatiotemporal evolution; Air pollution; AERONET; aerosol; atmospheric pollution; data assimilation; geostationary satellite; Kalman filter; MODIS; observational method; spatiotemporal analysis; Australia; China
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156944
作者单位	State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China; Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan; National Institute for Environmental Studies, Tsukuba, Japan; Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, Japan; Faculty of Science, Free University of Amsterdam, Amsterdam, Netherlands; National Satellite Meteorological Center, China Meteorological Administration, Beijing, China; State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Dai T.,Cheng Y.,Suzuki K.,et al. Hourly Aerosol Assimilation of Himawari-8 AOT Using the Four-Dimensional Local Ensemble Transform Kalman Filter[J],2019,11(3).
APA	Dai T..,Cheng Y..,Suzuki K..,Goto D..,Kikuchi M..,...&Nakajima T..(2019).Hourly Aerosol Assimilation of Himawari-8 AOT Using the Four-Dimensional Local Ensemble Transform Kalman Filter.Journal of Advances in Modeling Earth Systems,11(3).
MLA	Dai T.,et al."Hourly Aerosol Assimilation of Himawari-8 AOT Using the Four-Dimensional Local Ensemble Transform Kalman Filter".Journal of Advances in Modeling Earth Systems 11.3(2019).