Ensemble‐based simultaneous state and parameter estimation with MM5

] The performance of the ensemble Kalman filter(EnKF) under imperfect model conditions is investigatedthrough simultaneous state and parameter estimation for anumerical weather prediction model of operationalcomplexity (MM5). The source of model error is assumedto be the uncertainty in the vertical eddy mixing coefficient.Assimilations are performed with a 12-hour interval withsimulated sounding and surface observations of horizontalwinds and temperature. The mean estimated parametervalue nicely converges to the true value within a satisfactorylevel of variability due to sufficient model sensitivity toparameter uncertainty and detectable (relative to ensemblesampling noise) correlation signal between the parameterand observed variables.

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