Preprints
https://doi.org/10.5194/acp-2021-99
https://doi.org/10.5194/acp-2021-99

  18 Mar 2021

18 Mar 2021

Review status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Aerosol formation and growth rates from chamber experiments using Kalman smoothing

Matthew Ozon1, Dominik Stolzenburg2, Lubna Dada2, Aku Seppänen1, and Kari E. J. Lehtinen1,3 Matthew Ozon et al.
  • 1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
  • 2Institute for Atmospheric and Earth System Research/ Physics, University of Helsinki, 00014 Helsinki, Finland
  • 3Atmospheric Research Centre of Eastern Finland, Finnish Meteorological Institute, Kuopio, Finland

Abstract. 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 remarkably good for all studied cases. The formation rates matched also well, especially considering the fact that they were estimated from data given by two different instruments, the other being the Particle Size magnifier (PSM). 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.

Matthew Ozon et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-99', Christina Williamson, 12 Apr 2021
    • AC1: 'Reply on RC1', Dominik Stolzenburg, 01 Jul 2021
  • RC2: 'Comment on acp-2021-99', Anonymous Referee #2, 21 Apr 2021
    • AC2: 'Reply on RC2', Dominik Stolzenburg, 01 Jul 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-99', Christina Williamson, 12 Apr 2021
    • AC1: 'Reply on RC1', Dominik Stolzenburg, 01 Jul 2021
  • RC2: 'Comment on acp-2021-99', Anonymous Referee #2, 21 Apr 2021
    • AC2: 'Reply on RC2', Dominik Stolzenburg, 01 Jul 2021

Matthew Ozon et al.

Model code and software

BAYROSOL Matthew Ozon, Dominik Stolzenburg, and Lubna Dada https://doi.org/10.5281/zenodo.4450492

Matthew Ozon et al.

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Latest update: 29 Jul 2021
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Short summary
Measuring the rate at which aerosol particles are formed is of importance for understanding climate change. We present an analysis method based on Kalman smoothing which retrieves new particle formation and growth rates from size-distribution measurements. We apply it to atmospheric simulation chamber experiments and show that it agrees well with traditional methods. In addition, it provides reliable uncertainty estimates and we suggest instrument design optimization for signal processing.
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