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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 12, issue 24
Atmos. Chem. Phys., 12, 11781-11793, 2012
https://doi.org/10.5194/acp-12-11781-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 12, 11781-11793, 2012
https://doi.org/10.5194/acp-12-11781-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 17 Dec 2012

Research article | 17 Dec 2012

Estimation of aerosol particle number distribution with Kalman Filtering – Part 2: Simultaneous use of DMPS, APS and nephelometer measurements

T. Viskari2,1, E. Asmi1, A. Virkkula1, P. Kolmonen1, T. Petäjä2, and H. Järvinen1 T. Viskari et al.
  • 1Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
  • 2University of Helsinki, Department of Physics, P.O. Box 64, 00014 Helsinki, Finland

Abstract. 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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