A major concern in contemporary traffic flow management (TFM) is improving decision making when severe convective weather (Wx) impacts en route sectors throughout the National Airspace System (NAS). The FAA is currently seeking to reduce these convective weather delays through the use of multi-hour (e.g. 4 and 6 hour) Wx forecasts coupled with strategic planning by the FAA traffic flow managers and airline personnel to determine how en route traffic should be rerouted so as to avoid sector overloads and minimize the magnitude of the delays that occur [Huberdeau and Gentry (2004)]. One of the major challenges in the strategic planning process is the difficulty in converting the convective weather forecasts into forecasts of en route sector capacity. In this study, we explore the development of a model that can be combined with forecast validation data to translate probabilistic convective weather (Wx) forecasts into forecasts of a surrogate for sector capacity - the fraction of jet routes that would be blocked- within an en route sector.