1. Models for model error –Additive noise. What is Q(x 1, x 2, t 1, t 2 )? –Covariance inflation –Multiplicative noise? –Parameter uncertainty –“Structural” uncertainty, i.e. multi-model –In strong-constraint 4DVar, fixed B acts in part as parameterization of model error? Relation to: –Model-error treatments in ensemble forecasting –Model evaluation, tuning and development WWRP/THORPEX WORKSHOP on 4D-VAR and ENSEMBLE KALMAN FILTER INTER-COMPARISONS Model Error Discussion

2. Importance of model error relative to other aspects of assimilation system? Characteristics of model error –Magnitude? –Temporal and spatial scales and their interdependence? –Flow dependence? Gaussian stats sufficient? –Main sources? –Available information is insufficient to estimate characteristics? (Dee 1995) Distinguishing among different sources of error (ICs, obs, model, DA scheme itself) WWRP/THORPEX WORKSHOP on 4D-VAR and ENSEMBLE KALMAN FILTER INTER-COMPARISONS Model Error Discussion

3. Analyses that produce best forecasts vs. analyses that are closest to true state –NWP vs. other applications, e.g. reanalyses Relative merits of variational and ensemble DA –Flexibility in specification of model error in ensemble methods –Ease in utilizing information from multiple times in variational methods, especially estimating temporal covariances –Nonlinear and non-Gaussian effects? E.g. parameter estimation WWRP/THORPEX WORKSHOP on 4D-VAR and ENSEMBLE KALMAN FILTER INTER-COMPARISONS Model Error Discussion