A prime challenge to developing and fielding new weather decision support designed for air traffic management (ATM) applications is evaluating and demonstrating operational benefits which justify its procurement and fielding, particularly in this current climate of budget austerity. Claimed benefits must be clear, well articulated, robust and well exercised, and readily defendable for FAA Investment Reviews. Ideally, this is accomplished via NAS simulations, but historically, NAS models have not been able to combine weather (actual and forecasts), airspace resources, realistic traffic, and TMIs with both the needed resolution and sufficient computational capabilities to assess potential ranges of outcomes (and potential benefits) across perhaps hundreds of modified simulation scenarios and parameterizations.
The Dynamic Airspace Rerouting Tool (DART) is a weather-aware superfast-time NAS simulator that can intelligently model the impact of varied weather and forecast (convective and non-convective) and the system's response in the form of reroutes, delays, cancellations, or diversions for 50,000+ flights across a day of U.S. CONUS operations. This paper describes how DART is being used to evaluate and objectively estimate ATM benefits associated with 0-2 hour and 2-8 hour reflectivity and echo tops forecasts to be provided by the NextGen Weather Processor (NWP). A unique DART feature developed for this research is the hybrid simulation mode, where a set of historical TMIs or user-designed what-if alternative TMIs can be enforced while all other NAS events and responses are simulated by DART automatically. As a result, simulated NAS response differences (and differences in delay, cancellations, etc.) with and without NWP-derived TMI decisions can be objectively analyzed (and even reviewed in side-by-side playback mode) for targeted and robust benefits estimates.
The DART modeling approach and initial results for estimating NWP benefits based on the simulated NAS response to individual NWP-derived TMI decisions will be presented. Plans, methodologies, and initial results for extending DART-derived benefits by explicitly accounting for forecast uncertainty, user risk, batch random processing for varied and binned impact severity, and capturing detriments associated with potentially poor TMI decisions based on inaccurate forecasts will also be provided.