Submitted as: development and technical paper 16 Oct 2020

Submitted as: development and technical paper | 16 Oct 2020

Review status: this preprint is currently under review for the journal GMD.

Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation

Matthew Ozon1, Aku Seppänen1, Jari P. Kaipio1,3, and Kari E. J. Lehtinen1,2 Matthew Ozon et al.
  • 1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
  • 2Finnish Meteorological Institute, Kuopio, Finland
  • 3Department of Mathematics, Faculty of Science, University of Auckland, New Zealand

Abstract. The uncertainty in the radiative forcing caused by aerosols and its effect on the climate change calls for research to improve knowledge of the aerosol particle formation and growth processes. While the experimental research has provided large amount of high quality data on aerosols in the last two decades, the inference of the process rates is still inadequate, mainly due to limitations in the analysis of data. This paper focuses on developing computational methods to infer aerosol process rates from size distribution measurements. In the proposed approach, the temporal evolution of aerosol size distributions is modeled with the general dynamic equation equipped with stochastic terms that account for the uncertainties of the process rates. The time-dependent particle size distribution and the rates of the underlying formation and growth processes are reconstructed based on time series of particle analyzer data using Bayesian state estimation – which not only provides (point) estimates for the process rates but also enables quantifying their uncertainties. The feasibility of the proposed computational framework is demonstrated by a set of numerical simulation studies.

Matthew Ozon et al.

Status: open (extended)
Status: open (extended)
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Matthew Ozon et al.

Model code and software

Parameters Estimation for the General Dynamic Equation for aerosols Matthew Ozon

Matthew Ozon et al.


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Short summary
Experimental research has provided large amounts of high quality data on aerosol in the last two decades. However, the inference of the process rates, e.g. the rate at which particles are generated, is still typically done by simple curve-fitting methods, and does not assess the credibility of the estimation. The devised method takes advantage of the Baysesian framework to, not only retrieves the state of the observed aerosol system, but also estimates the process rates, e.g. growth rate.