ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-12-11781-2012 Estimation of aerosol particle number distribution with Kalman Filtering – Part 2: Simultaneous use of DMPS, APS and nephelometer measurements Viskari T. 1 2 Asmi E. 1 Virkkula A. 1 Kolmonen P. 1 Petäjä T. 2 Järvinen H. 1 Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland University of Helsinki, Department of Physics, P.O. Box 64, 00014 Helsinki, Finland 17 12 2012 12 24 11781 11793 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/12/11781/2012/acp-12-11781-2012.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/12/11781/2012/acp-12-11781-2012.pdf

Extended Kalman Filter (EKF) is used to estimate particle size distributions from observations. The focus here is on the practical application of EKF to simultaneously merge information from different types of experimental instruments. Every 10 min, the prior state estimate is updated with size-segregating measurements from Differential Mobility Particle Sizer (DMPS) and Aerodynamic Particle Sizer (APS) as well as integrating measurements from a nephelometer. Error covariances are approximate in our EKF implementation. The observation operator assumes a constant particle density and refractive index. The state estimates are compared to particle size distributions that are a composite of DMPS and APS measurements. The impact of each instrument on the size distribution estimate is studied. Kalman Filtering of DMPS and APS yielded a temporally consistent state estimate. This state estimate is continuous over the overlapping size range of DMPS and APS. Inclusion of the integrating measurements further reduces the effect of measurement noise. Even with the present approximations, EKF is shown to be a very promising method to estimate particle size distribution with observations from different types of instruments.

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