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DOI	10.5194/acp-21-12595-2021
	Aerosol formation and growth rates from chamber experiments using Kalman smoothing
	Ozon M.; Stolzenburg D.; Dada L.; Seppänen A.; Lehtinen K.E.J.
发表日期	2021
ISSN	1680-7316
起始页码	12595
结束页码	12611
卷号	21 期号:16
英文摘要	Bayesian state estimation in the form of Kalman smoothing was applied to differential mobility analyser train (DMA-train) measurements of aerosol size distribution dynamics. Four experiments were analysed in order to estimate the aerosol size distribution, formation rate, and size-dependent growth rate, as functions of time. The first analysed case was a synthetic one, generated by a detailed aerosol dynamics model and the other three chamber experiments performed at the CERN CLOUD facility. The estimated formation and growth rates were compared with other methods used earlier for the CLOUD data and with the true values for the computer-generated synthetic experiment. The agreement in the growth rates was very good for all studied cases: Estimations with an earlier method fell within the uncertainty limits of the Kalman smoother results. The formation rates also matched well, within roughly a factor of 2.5 in all cases, which can be considered very good considering the fact that they were estimated from data given by two different instruments, the other being the particle size magnifier (PSM), which is known to have large uncertainties close to its detection limit. The presented fixed interval Kalman smoother (FIKS) method has clear advantages compared with earlier methods that have been applied to this kind of data. First, FIKS can reconstruct the size distribution between possible size gaps in the measurement in such a way that it is consistent with aerosol size distribution dynamics theory, and second, the method gives rise to direct and reliable estimation of size distribution and process rate uncertainties if the uncertainties in the kernel functions and numerical models are known. © 2021 Matthew Ozon et al.
语种	英语
scopus关键词	aerosol; computer simulation; formation mechanism; growth rate; Kalman filter; numerical model; particle size; size distribution; uncertainty analysis
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/246635
作者单位	Department of Applied Physics, University of Eastern Finland, Kuopio, 70210, Finland; Institute for Atmospheric and Earth System Research/Physics, University of Helsinki, Helsinki, 00014, Finland; EPFL, School of Architecture, Civil and Environmental Engineering, Sion, 1951, Switzerland; Paul Scherrer Institute, Laboratory of Atmospheric Chemistry, PSI, Villigen, 5232, Switzerland; Atmospheric Research Centre of Eastern Finland, Finnish Meteorological Institute, Kuopio, 70210, Finland
推荐引用方式 GB/T 7714	Ozon M.,Stolzenburg D.,Dada L.,et al. Aerosol formation and growth rates from chamber experiments using Kalman smoothing[J],2021,21(16).
APA	Ozon M.,Stolzenburg D.,Dada L.,Seppänen A.,&Lehtinen K.E.J..(2021).Aerosol formation and growth rates from chamber experiments using Kalman smoothing.ATMOSPHERIC CHEMISTRY AND PHYSICS,21(16).
MLA	Ozon M.,et al."Aerosol formation and growth rates from chamber experiments using Kalman smoothing".ATMOSPHERIC CHEMISTRY AND PHYSICS 21.16(2021).