Generating Probabilistic Capacity Profiles from Weather Forecast: A Design-of-experiment Approach

Abstract-It is common understanding that weather plays an important role in determining the capacity of an airport. Severe weather causes capacity reductions, creating a capacity demand imbalance, leading to delays. The role of air traffic flow management (ATFM) measures is to reduce these delay costs by aligning the demand with the capacity. Ground delay program (GDP) is one such measure. Though the GDP is initiated in poor weather conditions, and weather forecasts are subject to errors, present GDP planning procedures are essentially deterministic in nature. Forecast weather is translated into deterministic capacity predictions on which GDP planning is based. Models which employ probabilistic capacity profiles for planning GDPs have been developed, but their application has been limited by the inability to create such profiles from weather forecasts. This paper develops probabilistic profiles for three airports, BOS, LAX and SFO using the Terminal Aerodrome Forecast and San Francisco Marine Initiative. The profiles are inputs to a static stochastic GDP model to simulate ATFM strategies. A design of experiments approach has been employed to determine profiles which minimize the total average costs. The average cost of the methodologies is evaluated against realized capacities to determine the benefit of the forecast. It is shown that inclusion of weather forecasts reduces the cost of delays. It is shown for SFO that on average TAF offers similar benefit in controlling cost of delay when compared to STRATUS. Careful use of the TAF indicates that other airports would also benefit from using TAF in planning of operations