Ensemble-based simultaneous state and parameter estimation for treatment of mesoscale model error: A real-data study

Xiao Ming Hu, Fuqing Zhang, John W. Nielsen-Gammon

Research output: Contribution to journalArticle

49 Scopus citations

Abstract

This study explores the treatment of model error and uncertainties through simultaneous state and parameter estimation (SSPE) with an ensemble Kalman filter (EnKF) in the simulation of a 2006 air pollution event over the greater Houston area during the Second Texas Air Quality Study (TexAQS-II). Two parameters in the atmospheric boundary layer parameterization associated with large model sensitivities are combined with standard prognostic variables in an augmented state vector to be continuously updated through assimilation of wind profiler observations. It is found that forecasts of the atmosphere with EnKF/ SSPE are markedly improved over experiments with no state and/or parameter estimation. More specifically, the EnKF/SSPE is shown to help alleviate a near-surface cold bias and to alter the momentum mixing in the boundary layer to produce more realistic wind profiles.

Original languageEnglish (US)
Article numberL08802
JournalGeophysical Research Letters
Volume37
Issue number8
DOIs
StatePublished - Apr 1 2010

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Earth and Planetary Sciences(all)

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