Generating day-of-operation probabilistic capacity scenarios from weather forecasts
This paper develops methodologies for generating probabilistic capacity scenarios from two terminal weather forecasts: The Terminal Aerodrome Forecast (TAF) and the San Francisco Marine Initiative (STRATUS) forecast. The scenarios are assessed by using them as inputs to a static stochastic Ground Delay Program (GDP) model to determine efficient Air Traffic Flow Management (ATFM) strategies, and determining the effectiveness of the strategies in reducing the realized cost of delay. We use San Francisco International airport as a case study to quantify the benefit of using weather forecasts in decision making. It is shown that capacity scenarios generated using day-of-operation weather forecasts can reduce the cost of delays by 17%-23% compared to scenarios that do not make use of this information. The paper also compares the cost of delays using strategies determined using the scenarios generated from TAF and STRATUS. It is shown that on average TAF-based scenarios result in delay costs of similar magnitudes to STRATUS-based ones. The methodologies developed using the TAF can be applied to other airports to better plan operations during a GDP.
probabilistic capacity scenarios terminal aerodrome forecast stratus ground delay program air traffic flow management k-means clustering