87th AMS Annual Meeting

Thursday, 18 January 2007: 3:30 PM
Translating Weather Forecasts into Sector Capacity En Route Traffic Management
217A (Henry B. Gonzalez Convention Center)
Stephen M. Zobell, The MITRE Corporation, McLean, VA; and L. Song and C. Wanke
MITRE is developing concepts for predicting and managing congestion in en route airspace. These concepts include probabilistically forecasting traffic demand and system capacity, where forecast weather can decrease the system capacity. We are focusing on demand and capacity in sectors, where the workload limits of controller teams and the available airspace to maneuver and separate aircraft are important factors in system capacity. Many of these concepts have been incorporated into a Probabilistic, Automation-Assisted, Congestion Management for En Route (PACER) prototype.

We have recently added a probabilistic forecast of sector capacities that is based on a probabilistic convective weather forecast, the National Convective Weather Forecast 2 (NCWF-2). The goal of this sector capacity forecast is to predict a probability density function of capacities for every sector, for 8 15 minute look-ahead periods, out to the 2 hour maximum look-ahead of the weather forecast.

This initial capacity forecast is based on a preliminary historical study of how sector throughput changes with the percentage of area of weather coverage in the sector. This study shows a reasonable correlation between the percentages of area of weather coverage and the reductions in throughput. The capacity forecast is generated by probabilistically predicting the percent of weather coverage in each sector for each look-ahead-time, and correlating that with the throughput study results.

This capacity forecast is used to alert Traffic Managers to the sectors and times where the predicted traffic demand is predicted to exceed the weather impacted capacities of the sectors. PACER can also generate flight maneuvers to resolve these congestion problems. These maneuvers automatically reduce the traffic demand in sectors that are predicted to have weather, creating a capability to automatically manage forecast weather impacts without the need for human decision makers to estimate the weather impacts on the Air Traffic Control System.

This capacity forecast is an initial implementation, and the paper will discuss potential ways to improve these capabilities and areas for additional research. In creating this automation capability, we have identified areas where additional information from the weather forecasts may be useful.

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