progress in data assimilation, the accuracy in the initial conditions
of numerical weather forecasts has been substantially improved.
As a result, accounting for systematic errors associated with model
deficiencies has become even more important to ensemble prediction
and data assimilation applications. Leith (1978) proposed a statistical
method to account for model bias and systematic errors linearly
dependent on the flow anomalies. However, Leith's method is
computationally prohibitive for high-resolution operational models. This
talk will discuss whether other statistical correction approaches are
feasible and effective for possible operational use and compare the
impact of correcting the model integration with statistical corrections
performed a posteriori.