Ensemble-based simultaneous state and parameter estimation with MM5

Altuǧ Aksoy, Fuqing Zhang, John W. Nielsen-Gammon

Research output: Contribution to journalArticlepeer-review

94 Scopus citations


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.

Original languageEnglish (US)
Article numberL12801
JournalGeophysical Research Letters
Issue number12
StatePublished - Jun 28 2006

All Science Journal Classification (ASJC) codes

  • Geophysics
  • General Earth and Planetary Sciences


Dive into the research topics of 'Ensemble-based simultaneous state and parameter estimation with MM5'. Together they form a unique fingerprint.

Cite this