FAA NEXTOR III DO 20: Continued Data Analysis and Simulation of the Impact of Convective Weather on Airfield Efficiency

Faculty

Michael O. Ball

Funding Agency

Federal Aviation Administration

Year

2023

Descriptions

Michael Ball is working with a team from the University of South Florida to adapt and improve a simulation platform to quantify unused airfield capacity due to en-route convective weather while controlling for other influential factors This is a nine-month project worth $74,000.

In the US, airports are limited resources of the national airspace system (NAS). Underuse of existing airport capacity when demand is present can lead to reductions in efficiency and economic loss.

Improving trajectory predictions in light of airspace constraints around an airport can maximizing use of airport capacity. The goals of ground delay programs (GDPs) are to reduce demand on the airport to better match reduced capacity to avoid risky airborne delay in terminal area. Advanced GDP algorithm considers weather uncertainly into account and gets ready for possible earlier airport capacity recovery because of the improvement of adverse weather at the airport. The goal of these measures is to fully utilize airport capacity, or called slots, that are a perishable commodity, because unused slots drive larger delays and potential cancellations. However, it is observed that airport capacity could be underused because of convective weather conditions in en route airspace, which reduce the arrival throughput of the destination airport. Existing literature lacks studies exploring the magnitude of unused available runway capacity caused by en route convective weather.

Convective weather reduces airspace capacity and disrupt normal operations. It forces the rerouting of aircraft and elongates their flying time. How convective weather affects sector capacity has been extensively studied. However, a systematic method of understanding the impact of convective weather to airfield efficiency, indicated by the throughput or utilization of capacity, is lacking. In the previous study, University of South Florida (USF) Team leveraged learning- based models to predict airfield throughput and to identify critical airspace where its blockage due to en-route convective weather will lead to most significant loss of airfield efficiency. The USF Team also performed statistical analysis of airfield efficiency in hours with and without severe en-route convective weather.

Building off the previous study, USF Team will adapt and improve a simulation platform to quantify unused airfield capacity due to en-route convective weather while controlling for other influential factors. Furthermore, USF Team will analyze data on key South Florida airports and routings to measure the existing convective weather impacts on airport throughput when demand is high. USF will build more modules on the simulation platform to test how measures, such as earlier re-routes, reprioritizing internal departures, and potential speed increases, could reduce the loss of airfield efficiency. This work will build off the work in Phase 2 at the Atlanta International Airport will be used as the case study to demonstrate the operability of the simulation tool.

For more information, please contact Yu Yu Zhang, Ph.D., University of South Florida, at yuzhang@usf.edu.