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 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.
Supplementary material to "Aerosol formation and growth rates from chamber experiments
using Kalman smoothing"
