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, 491 – 503 . Krishnamurti , T. N. , C. M. Kishtawal , Z. Zhang , T. LaRow , D. Bachiochi , E. Williford , S. Gadgil , and S. Surendran , 2000 : Multimodel ensemble forecasts for weather and seasonal climate. J. Climate , 13 , 4196 – 4216 . Meng , Z. , and F. Zhang , 2007 : Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments. Mon. Wea. Rev. , 135 , 1403 – 1423 . Meng , Z. , and F. Zhang
, 491 – 503 . Krishnamurti , T. N. , C. M. Kishtawal , Z. Zhang , T. LaRow , D. Bachiochi , E. Williford , S. Gadgil , and S. Surendran , 2000 : Multimodel ensemble forecasts for weather and seasonal climate. J. Climate , 13 , 4196 – 4216 . Meng , Z. , and F. Zhang , 2007 : Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments. Mon. Wea. Rev. , 135 , 1403 – 1423 . Meng , Z. , and F. Zhang
1. Introduction The ensemble Kalman filter (EnKF) technique introduced by Evensen (1994) has inspired numerous studies on the development of flow-dependent data assimilation schemes ( Evensen 2003 ). The technique uses short-range ensemble forecasts to provide time- and space-dependent error structures, resulting in potentially more accurate representations of the background error covariance. A fundamental difficulty in applying ensemble data assimilation techniques to complex
1. Introduction The ensemble Kalman filter (EnKF) technique introduced by Evensen (1994) has inspired numerous studies on the development of flow-dependent data assimilation schemes ( Evensen 2003 ). The technique uses short-range ensemble forecasts to provide time- and space-dependent error structures, resulting in potentially more accurate representations of the background error covariance. A fundamental difficulty in applying ensemble data assimilation techniques to complex
quantified. If this PDF is available, it can be used to examine which parameters have the most significant influence on the model, the relationships between parameters, and which model output variables are most sensitive to changes in the parameters. The resulting information can then be used to determine how to best perturb parameters in an ensemble forecasting or assimilation context. Ensemble Kalman filter–type data assimilation methods address this problem by assuming each space is characterized
quantified. If this PDF is available, it can be used to examine which parameters have the most significant influence on the model, the relationships between parameters, and which model output variables are most sensitive to changes in the parameters. The resulting information can then be used to determine how to best perturb parameters in an ensemble forecasting or assimilation context. Ensemble Kalman filter–type data assimilation methods address this problem by assuming each space is characterized
