Abstract: nsemble Kalman filters perform data assimilation by forming a background covariance matrix from an ensemble forecast. Most of the literature on ensemble Kalman filters assumes that all ensemble members come from the same ...

Abstract: n ensemble Kalman filter (EnKF) is optimal only for linear models because it assumes Gaussian distributions. A new type of outer loop, different from the one used in 3D and 4D variational data assimilation (Var), is proposed ...

Abstract: Computational modeling is playing an increasingly vital role in the study of atmospheric?oceanic systems. Given the complexity of the models a fundamental question to ask is, How well does the output of one model agree ...

Abstract: Previously developed techniques that have been used to extract envelopes of Rossby wave packets are based on the assumption of zonally propagating waves. In this note a method that does not require such an assumption is ...

Abstract: Model error is the component of the forecast error that is due to the difference between the dynamics of the atmosphere and the dynamics of the numerical prediction model. The systematic, slowly varying part of the model ...

Abstract: Packets of Rossby waves play an important role in the transfer of kinetic energy in the extratropics. The ability to locate, track, and detect changes in the envelope of these wave packets is vital to detecting baroclinic ...

Abstract: In ensemble Kalman filter (EnKF) data assimilation, localization modifies the error covariance matrices to suppress the influence of distant observations, removing spurious long-distance correlations. In addition to allowing ...

Abstract: In this paper, the spatiotemporally changing nature of predictability is studied in a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), a state-of-the-art ...