Search Results
. Miyoshi , T. , and S. Yamane , 2007 : Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev. , 135 , 3841 – 3861 . Molteni , F. , 2003 : Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments
. Miyoshi , T. , and S. Yamane , 2007 : Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev. , 135 , 3841 – 3861 . Molteni , F. , 2003 : Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments
solution is naturally connected to our knowledge of the error associated with the information sources. Based on the Gaussian hypothesis, such knowledge is expressed via error covariances and correlations. However, while an accurate estimate of the observation error covariance is usually at hand, more difficulties arise for the background and model error covariances. In the last decades, extensive researches have been devoted to improve the estimation of the background error covariance
solution is naturally connected to our knowledge of the error associated with the information sources. Based on the Gaussian hypothesis, such knowledge is expressed via error covariances and correlations. However, while an accurate estimate of the observation error covariance is usually at hand, more difficulties arise for the background and model error covariances. In the last decades, extensive researches have been devoted to improve the estimation of the background error covariance
1. Introduction Although ensemble-based Kalman filter (EnKF) data assimilation schemes were first proposed more than a decade ago ( Evensen 1994 ; Burgers et al. 1998 ; Houtekamer and Mitchell 1998 ) and several successful attempts at assimilating observations of the atmosphere have been reported in the last few years (e.g., Houtekamer et al. 2005 ; Whitaker et al. 2004 , 2008 ; Szunyogh et al. 2008 ; Miyoshi and Sato 2007 ; Miyoshi and Yamane 2007 ; Torn and Hakim 2008 ; Bonavita et
1. Introduction Although ensemble-based Kalman filter (EnKF) data assimilation schemes were first proposed more than a decade ago ( Evensen 1994 ; Burgers et al. 1998 ; Houtekamer and Mitchell 1998 ) and several successful attempts at assimilating observations of the atmosphere have been reported in the last few years (e.g., Houtekamer et al. 2005 ; Whitaker et al. 2004 , 2008 ; Szunyogh et al. 2008 ; Miyoshi and Sato 2007 ; Miyoshi and Yamane 2007 ; Torn and Hakim 2008 ; Bonavita et
a reduced analysis error when assimilating potential temperature and dewpoint instead of temperature and specific humidity at the surface, which is likely due to the larger variability and less Gaussian distribution of the latter variables ( Fujita et al. 2007 ). To describe more detailed mesoscale features, observations with higher resolution than conventional surface observations are required. More and more attention is being paid to the assimilation of remotely sensed observations for the LAM
a reduced analysis error when assimilating potential temperature and dewpoint instead of temperature and specific humidity at the surface, which is likely due to the larger variability and less Gaussian distribution of the latter variables ( Fujita et al. 2007 ). To describe more detailed mesoscale features, observations with higher resolution than conventional surface observations are required. More and more attention is being paid to the assimilation of remotely sensed observations for the LAM
