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UID:submissions.pasc-conference.org_PASC24_sess159_msa254@linklings.com
SUMMARY:Estimating Aerosol Process Rates Using Bayesian Inverse Methods
DESCRIPTION:Minisymposium\n\nTeemu Salminen, Aku Seppänen, Matti Niskanen,
  and Kari Lehtinen (University of Eastern Finland)\n\nThe last decade has 
 been a huge leap forward in atmospheric new particle formation (NPF) resea
 rch. Novel instrument development has allowed us to measure more and more 
 details of the dynamics of even the smallest clusters. At the same time, h
 owever, for example NPF and particle growth rates have been analyzed typic
 ally by very simple regression or balance equation approaches, permitting 
 no proper estimation of the uncertainties.  Here we combine a Bayesian app
 roach with finite element method (FEM) approximation of the size distribut
 ion to estimate unknown rate parameters in the aerosol general dynamic equ
 ation. The method is based on Kalman Filter and Kalman Smoother methods wh
 ich allow for the estimation of the parameters and their error covariance 
 matrices. The unknowns are modeled as random variables, and their prior pr
 obability distributions are incorporated in the solution of the inverse pr
 oblem. As a first step, we test our methodology with synthetic data, gener
 ated 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 a
 s the size dependence of the deposition and condensational growth rates.\n
 \nDomain: Climate, Weather, and Earth Sciences, Computational Methods and 
 Applied Mathematics\n\nSession Chairs: Lekha Patel (Sandia National Labora
 tories), Nicole Riemer (University of Illinois Urbana-Champaign), and Matt
 hew West (University of Illinois Urbana-Champaign)
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