There is a critical need for improved handling of departures during convective weather events in the highly congested airspace in the Northeast and upper Midwest. The ability to predict impacts of convective weather on future departures is a fundamental need in departure management. The Route Availability Planning Tool (RAPT) is an automated decision support tool (DST) intended to help air traffic controllers and airline dispatchers determine the specific departure routes and departure times that will be affected by operationally significant convective weather up to 90 minutes into the future (30 minute departure window plus 60 minute flight time). RAPT helps users to determine when departure routes or fixes should be opened or closed and to identify alternative departure routes that are free of convective weather. RAPT assigns a status - RED (blocked), YELLOW (impacted), DARK GREEN (insignificant weather encountered) or GREEN (clear) - to specific departure routes for future departure times in the 30 minute departure time window. The status is calculated by combining the deterministic precipitation and echo top forecasts from the Corridor Integrated Weather System (CIWS) with an operational model for departure airspace usage. The operational model includes departure route definitions and a route blockage model that calculates the severity of convective weather impact on departure traffic along the first 60 minutes of flight time of the departure route.

RAPT became operational in August 2002, and has evolved in response to feedback from operational users and post event analysis of performance. The operational model and display were revised in 2007 to address shortcomings observed in the most recent RAPT performance evaluation and a ‘morning after' web site (RAPT Evaluation and Post-Event Analysis Tool, or REPEAT) was added to provide traffic and weather visualizations to support post-event analysis of New York area departure operations.

In this paper, the revised RAPT algorithm and display are described and evaluated. The fidelity of the RAPT operational model is assessed by comparing RAPT departure status with observed departure flows (i.e., trajectories, weather avoidance maneuvers and storm penetrations) on several days when convective weather SWAPs were in effect in New York. Real-time in situ observations at RAPT facilities (described in a companion paper at this conference) and user feedback from RAPT playbacks and the REPEAT web site are used to support this post-event evaluation. For instance, real time observations provide the time and operational rationale for a specific departure route closure identified in the traffic flow analysis, information that is necessary to identify closures or flow restrictions that are the result of factors outside of the current RAPT algorithm domain (e.g., traffic restrictions due to volume, downstream congestion, etc.). Real time observations are also used to identify specific times when critical, weather-related operational decisions were made. The RAPT guidance at these critical decision points is analyzed to determine if RAPT provided information that enabled (or could have enabled, had it been used) more timely or effective decisions.

The effect of forecast volatility and uncertainty on RAPT performance is also examined, particularly in convective weather situations where the location, severity and operational impact were difficult to predict. Strategies that mitigated risks associated with forecast uncertainty are presented. These include the use of additional information provided in the RAPT display (e.g., echo top heights encountered along the departure route) to confirm or modify RAPT guidance and, the consideration of the departure status of two or more adjacent routes to ‘average out' departure status volatility.

This work was sponsored by the Federal Aviation Administration under Air Force Contract No. FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government.