1 research outputs found
Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0
The uncertainty in the radiative forcing caused by aerosols and its effect on climate change calls for research to improve knowledge of the aerosol
particle formation and growth processes. While experimental research has
provided a large amount of high-quality data on aerosols over the last 2Â 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 (GDE) 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 quantification of
their uncertainties. The feasibility of the proposed computational framework
is demonstrated by a set of numerical simulation studies.</p