Abstract: A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness ...

Abstract: AbstractNumerical weather prediction and climate models comprise a) a dynamical core describing resolved parts of the climate system and b) parameterizations describing unresolved components. Development of new subgrid-scale ...

Abstract: AbstractA new approach for improving the accuracy of data assimilation, by trading numerical precision for ensemble size, is introduced. Data assimilation is inherently uncertain because of the use of noisy observations ...

Abstract: his paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter ...

Abstract: his study quantifies, from a systematic set of regional ocean?atmosphere coupled model simulations employing various coupling intervals, the effect of subdaily sea surface temperature (SST) variability on the onset and ...

Abstract: new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) ...

Abstract: his study assesses the ability of the Community Climate System Model, version 4 (CCSM4) to represent the Madden?Julian oscillation (MJO), the dominant mode of intraseasonal variability in the tropical atmosphere. The U.S. ...