Ensemble-based simultaneous state and parameter estimation with MM5 Academic Article uri icon

abstract

  • The performance of the ensemble Kalman filter (EnKF) under imperfect model conditions is investigated through simultaneous state and parameter estimation for a numerical weather prediction model of operational complexity (MM5). The source of model error is assumed to be the uncertainty in the vertical eddy mixing coefficient. Assimilations are performed with a 12-hour interval with simulated sounding and surface observations of horizontal winds and temperature. The mean estimated parameter value nicely converges to the true value within a satisfactory level of variability due to sufficient model sensitivity to parameter uncertainty and detectable (relative to ensemble sampling noise) correlation signal between the parameter and observed variables. Copyright 2006 by the American Geophysical Union.

published proceedings

  • Geophysical Research Letters

author list (cited authors)

  • Aksoy, A., Zhang, F., & Nielsen-Gammon, J. W

citation count

  • 88

complete list of authors

  • Aksoy, Altu─č||Zhang, Fuqing||Nielsen-Gammon, John W

publication date

  • June 2006