Scientific and computational limitations prevent us from constructing a perfect numerical model of real systems. In a chaotic system like the atmosphere, model related errors, just like errors in the initial conditions, contribute to the eventual loss of predictive skill. The spatial and temporal variations in skill are of great importance to users of weather forecasts. Ensemble forecasting, where not onldy one but a number of numerical integrations are carried out, was first introduced to assess initial error related variations in predictability. Model related errors, however, also contribute to variations in skill. Such variations can onldy be captured if all known model related uncertainty is simulated in the forecasts at their origin. Based on earlier methods, a comprehensive approach is proposed here to capture forecast uncertainty associated with the use of imperfect models, including that due to numerical, physical parameterization, and boundary condition related approximations. Each component and parameter of a model needs to be examined and possibly modified to ensure that all closure (due to the effect of unresolved processes) and other type of uncertainty is properly simulated. With these new requirements Numerical Weather Prediction (NWP) models are expected to exhibit more realistic spatio-temporal variance, with a lower level of predictability. This can lead to a degradation of skill when onldy single control forecasts are considered. Ensemble-based probabilistic forecasts, however, are expected to improve since not onldy initial value, but also model related variations in predictability will be captured. Ensembles with a perturbed model can also lead to a new, adaptive approach in NWP forecasting where the structure/parameters of a model can be initialized based on a systematic evaluation of the most recent short range ensemble forecasts. Scientific and computational limitations prevent us from constructing a perfect numerical model of real systems. In a chaotic system like the atmosphere, model related errors, just like errors in the initial conditions, contribute to the eventual loss of predictive skill. The spatial and temporal variations in skill are of great importance to users of weather forecasts. Ensemble forecasting, where not onldy one but a number of numerical integrations are carried out, was first introduced to assess initial error related variations in predictability. Model related errors, however, also contribute to variations in skill. Such variations can onldy be captured if all known model related uncertainty is simulated in the forecasts at their origin. Based on earlier methods, a comprehensive approach is proposed here to capture forecast uncertainty associated with the use of imperfect models, including that due to numerical, physical parameterization, and boundary condition related approximations. Each component and parameter of a model needs to be examined and possibly modified to ensure that all closure (due to the effect of unresolved processes) and other type of uncertainty is properly simulated. With these new requirements Numerical Weather Prediction (NWP) models are expected to exhibit more realistic spatio-temporal variance, with a lower level of predictability. This can lead to a degradation of skill when onldy single control forecasts are considered. Ensemble-based probabilistic forecasts, however, are expected to improve since not onldy initial value, but also model related variations in predictability will be captured. Ensembles with a perturbed model can also lead to a new, adaptive approach in NWP forecasting where the structure/parameters of a model can be initialized based on a systematic evaluation of the most recent short range ensemble forecasts. Scientific and computational limitations prevent us from constructing a perfect numerical model of real systems. In a chaotic system like the atmosphere, model related errors, just like errors in the initial conditions, contribute to the eventual loss of predictive skill. The spatial and temporal variations in skill are of great importance to users of weather forecasts. Ensemble forecasting, where not onldy one but a number of numerical integrations are carried out, was first introduced to assess initial error related variations in predictability. Model related errors, however, also contribute to variations in skill. Such variations can onldy be captured if all known model related uncertainty is simulated in the forecasts at their origin. Based on earlier methods, a comprehensive approach is proposed here to capture forecast uncertainty associated with the use of imperfect models, including that due to numerical, physical parameterization, and boundary condition related approximations. Each component and parameter of a model needs to be examined and possibly modified to ensure that all closure (due to the effect of unresolved processes) and other type of uncertainty is properly simulated. With these new requirements Numerical Weather Prediction (NWP) models are expected to exhibit more realistic spatio-temporal variance, with a lower level of predictability. This can lead to a degradation of skill when onldy single control forecasts are considered. Ensemble-based probabilistic forecasts, however, are expected to improve since not onldy initial value, but also model related variations in predictability will be captured. Ensembles with a perturbed model can also lead to a new, adaptive approach in NWP forecasting where the structure/parameters of a model can be initialized based on a systematic evaluation of the most recent short range ensemble forecasts