Forward looking evidence based decision making for operational environmental modeling with an application to ensemble modeling

Evidence-based decision making is critical for improving operational environmental numerical models. This is particularly important because the development of such models moves more and more into a community-based open-source and open-science environment. Present evidence used for operational implementation decisions generally considers the present model performance only. This essay presents a simple model to assess impacts of different modeling strategies in the future. This model require estimates of present performance gaps and impacts of strategies on improvement rates of models. It is shown that such data are available for many operational (weather) models. An example application of this model to ensemble development strategies suggests that a focus on the development of a Unified (single model) Model Ensemble in an established development group is expected to provide better operational results than an Multi Model Ensemble (MME) development approach well within a typical 5 to 10 year strategic development period, whereas an MME of opportunity can still add skill at minimal costs if it consists of the combination of unified ensembles produced by different groups.