Abstract
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.
Highlights
This is the Part 2 of papers describing the application a data assimilation of in situ multi-instrument aerosol measurements
Particle size distributions measured by TwinDMPS and Aerodynamic Particle Sizer (APS) have been effectively combined by varying particle density until the measurements are in agreement in the overlapping measurement range (Pitz et al, 2008)
The Extended Kalman Filter (EKF) implementation, introduced in Part 1 and extended in Part 2, was used to simultaneously estimate particle size number distributions based on measurements from Differential Mobility Particle Sizer (DMPS), APS and nephelometer
Summary
This is the Part 2 of papers describing the application a data assimilation of in situ multi-instrument aerosol measurements. Hand and Kreidenweis, 2002; Guyon et al, 2003; Virkkula et al, 2006; Muller et al, 2009; Petzold et al, 2009) As an another example, in Pitz et al (2008), measurements from two different sizesegregating measurement instruments, Differential Mobility Particle Sizer (DMPS) and Aerodynamic Particle Sizer (APS), are combined by modifying the particle density. In Pitz et al (2008), measurements from two different sizesegregating measurement instruments, Differential Mobility Particle Sizer (DMPS) and Aerodynamic Particle Sizer (APS), are combined by modifying the particle density It is difficult, for this approach to properly account for the uncertainties in the different observations. This article extends EKF to estimate the particle number size distribution based on information from several different types of instruments. The method was tested with size distribution and light scattering measurements from a boreal forest site in the South-Western Finland (Virkkula et al, 2011)
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