Route Selection Decision Support in Convective Weather: a Case Study of the Effects of Weather and Operational Assumptions on Departure Throughput

This paper presents a detailed study of a convective weather event affecting the northeastern United States on 19 April 2002: its impacts on departure throughput, the response of traffic managers and an analysis of the potential effects of decision support on system performance. We compare actual departure throughput to what may have been achieved using the Route Availability Planning Tool (RAPT), a prototype decision support tool. We examine two questions: Can decision support identify opportunities to release departures that were missed during the event? How is route selection guidance affected by the operational model incorporated into the decision support tool? By “operational model”, we mean three things: the choice of weather forecast information used to define hazards (precipitation, echo tops, etc.), the model for how airspace is used (route definition and allocation) and the assessment of the likelihood that a given route is passable. We focus our analysis on the operational model only; we eliminate weather forecast uncertainty as a factor in the analysis by running RAPT using the actual observed weather as the forecast (‘perfect’ forecast). Results show that decision support based on perfect forecasts is sensitive to all three elements of the operational model. The sensitivity to weather metrics became evident when we compared decision support based upon perfect forecasts of level 3 vertically integrated liquid (VIL) to that based upon VIL plus storm echo tops. Traffic managers were at times able to move more aircraft by abandoning nominal routing than if they had used nominal routing with perfect weather information. The assessment of route availability will, at times, be ambiguous; different interpretations of that assessment lead to decisions that result in significant differences in departure throughput. These results suggest that for traffic flow management tools, a realistic operational model may be at least as important as the frequently discussed problem of weather forecast uncertainty.