Abstract: The performance of an ensemble prediction system is inherently flow dependent. This paper investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations ...

Abstract: nsemble variances provide a prediction of the flow-dependent error variance of the ensemble mean or, possibly, a high-resolution forecast. However, small ensemble size, unaccounted for model error, and imperfections in ...

Abstract: conundrum of predictability research is that while the prediction of flow-dependent error distributions is one of its main foci, chaos fundamentally hides flow-dependent forecast error distributions from empirical observation. ...

Abstract: ata assimilation schemes combine observational data with a short-term model forecast to produce an analysis. However, many characteristics of the atmospheric states described by the observations and the model differ. ...

Abstract: he statistics of model temporal variability ought to be the same as those of the filtered version of reality that the model is designed to represent. Here, simple diagnostics are introduced to quantify temporal variability ...

Abstract: n Part I of this study, a model of the distribution of true error variances given an ensemble variance is shown to be defined by six parameters that also determine the optimal weights for the static and flow-dependent parts ...

Abstract: ppropriate specification of the error statistics for both observational data and short-term forecasts is necessary to produce an optimal analysis. Observation error stems from instrument error, forward model error, and ...

Abstract: AbstractBecause of imperfections in ensemble data assimilation schemes, one cannot assume that the ensemble-derived covariance matrix is equal to the true error covariance matrix. Here, we describe a simple and intuitively ...