Minisymposium Presentation

The last decade has been a huge leap forward in atmospheric new particle formation (NPF) research. Novel instrument development has allowed us to measure more and more details of the dynamics of even the smallest clusters. At the same time, however, for example NPF and particle growth rates have been analyzed typically by very simple regression or balance equation approaches, permitting no proper estimation of the uncertainties. Here we combine a Bayesian approach with finite element method (FEM) approximation of the size distribution to estimate unknown rate parameters in the aerosol general dynamic equation. The method is based on Kalman Filter and Kalman Smoother methods which allow for the estimation of the parameters and their error covariance matrices. The unknowns are modeled as random variables, and their prior probability distributions are incorporated in the solution of the inverse problem. As a first step, we test our methodology with synthetic data, generated by a detailed aerosol dynamics model. The advantage of this approach is that the ‘answers’ are known, i.e. we know in detail, for example, the time evolution of the nucleation and condensational growth rates as well as the size dependence of the deposition and condensational growth rates.