| description abstract | The possible economic value of the quantification of uncertainty in future ensemble-based surface weather forecasts is investigated using a formal, idealized decision model. Current, or baseline, weather forecasts are represented by probabilistic forecasts of moderate accuracy, as measured by the ranked probability score. Hypothetical ensemble-based forecasts are constructed by supplementing the baseline set of probabilistic forecasts with lower- and higher-skill forecasts. These are chosen in such a way that mixtures of the forecasts including the lower- and higher-skill subsets with equal frequency exhibit the same accuracy overall as the moderately accurate (conventional, baseline) forecasts. For both simple one-time decisions (static situation) and related sequences of decisions (dynamic situation), these hypothetical ensemble-based forecasts are found to lead to greater economic value in the idealized decision problem when protective actions are relatively inexpensive, corresponding to real-world problems. However, for some decision problems considered, the ensemble-based forecasts are slightly less valuable than the baseline forecasts. This result derives at least in part from the (probably unrealistic) assumption that the ensemble-based forecasts are no more skillful in aggregate than their conventional counterparts, but implies that positive economic value for ensemble forecasts with respect to this baseline will not be automatic. Rather, for ensemble-based forecasts to be at least as valuable for all decision problems, they will need to exhibit sufficiently higher skill in aggregate than the conventional forecasts that could have been produced in their place | |
