Estimating and Correcting Model Error

Christopher M. Danforth 

University of Maryland

With recent 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.