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Ensemble‐based simultaneous state and parameter estimation for treatment of mesoscale model error: A real‐data study
Author(s) -
Hu XiaoMing,
Zhang Fuqing,
NielsenGammon John W.
Publication year - 2010
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2010gl043017
Subject(s) - ensemble kalman filter , mesoscale meteorology , data assimilation , meteorology , kalman filter , environmental science , boundary layer , planetary boundary layer , state vector , estimation theory , air quality index , statistics , extended kalman filter , mathematics , physics , mechanics , classical mechanics
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.