Ensemble-based simultaneous state and parameter estimation with MM5

Details Ensemble-based simultaneous state and parameter estimation with MM5 Ensemble-based simultaneous state and parameter estimation with MM5

Jun 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006georl..3312801a&link_type=abstract

Geophysical Research Letters, Volume 33, Issue 12, CiteID L12801

Computer Science

Sound

21

Atmospheric Processes: Boundary Layer Processes, Atmospheric Processes: Data Assimilation, Atmospheric Processes: Mesoscale Meteorology

Scientific paper

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.

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