Abstract. Atmospheric aerosol microphysical processes are a
significant source of uncertainty in predicting climate change.
Specifically, aerosol nucleation, emissions, and growth rates, which are
simulated in chemical transport models to predict the particle size
distribution, are not understood well. However, long-term size distribution
measurements made at several ground-based sites across Europe implicitly
contain information about the processes that created those size
distributions. This work aims to extract that information by developing and
applying an inverse technique to constrain aerosol emissions as well as
nucleation and growth rates based on hourly size distribution measurements.
We developed an inverse method based upon process control theory into an
online estimation technique to scale aerosol nucleation, emissions, and
growth so that the model–measurement bias in three measured aerosol
properties exponentially decays. The properties, which are calculated from
the measured and predicted size distributions, used to constrain aerosol
nucleation, emission, and growth rates are the number of particles with
a diameter between 3 and 6 nm, the number with a diameter greater than 10 nm,
and the total dry volume of aerosol (N3–6, N10, Vdry),
respectively. In this paper, we focus on developing and applying the
estimation methodology in a zero-dimensional “box” model as a
proof of concept before applying it to a three-dimensional simulation in
subsequent work. The methodology is first tested on a dataset of synthetic
and perfect measurements that span diverse environments in which the true
particle emissions, growth, and nucleation rates are known. The inverse
technique accurately estimates the aerosol microphysical process rates with
an average and maximum error of 2 % and 13 %, respectively. Next, we
investigate the effect that measurement noise has on the estimated rates.
The method is robust to typical instrument noise in the aerosol properties
as there is a negligible increase in the bias of the estimated process rates.
Finally, the methodology is applied to long-term datasets of in situ size
distribution measurements in western Europe from May 2006 through June 2007.
At Melpitz, Germany, and Hyytiälä, Finland, the average diurnal
profiles of estimated 3 nm particle formation rates are reasonable, having
peaks near noon local time with average peak values of 1 and 0.15 cm−3 s−1, respectively. The
normalized absolute error in estimated N3–6, N10, and Vdry at
three European measurement sites is less than 15 %, showing that the
estimation framework developed here has potential to decrease
model–measurement bias while constraining uncertain aerosol microphysical
processes.
Establishment Size Dynamics in the Aggregate Economy
