The Role of Error Covariances in Estimation of Aerosol Number Concentrations
The evolution of error covariance and its impact in Kalman Filtering are examined. A synthetic aerosol size distribution and an associated error covariance matrix are used as an input for a tangent-linear box model simulating aerosol microphysics. The evolution of the error correlation structures are found to be robust with respect to changes in ambient vapor conditions and nucleation schemes. The near-diagonal error correlations evolve only modestly for particles larger than ∼20-30 nm, and the nucleation and condensation processes cause strong correlations between number density errors of small (below 10 nm) and large (above 100 nm) particles. The evolving error covariances significantly improve the estimation accuracy of the Kalman Filter in case of synthetic observations.
No References for this article.
No Supplementary Data.