An approach is presented for using the probabilistic forecast of stratus clearing time at San Francisco (SFO) to achieve more efficient Ground Delay Programs (GDPs) by better determining GDP end time and scope. Given a probabilistic forecast, we use a Monte-Carlo simulation approach to generate many stratus clearing times for each discrete GDP end time and scope under consideration. Various key measures are evaluated such as unnecessary ground delay if the GDP ends later than stratus clearing and the risk of airborne holding at the end of the GDP if the GDP ends earlier than stratus clearing. An objective function that includes each of the key metrics captures the cost of each scenario under consideration, and the optimal GDP parameters can then be selected. Results show reductions of 29% for unnecessary issued ground delay and reductions of 39% for unnecessarily delayed flights over the SFO GDPs during the severe weather seasons in 2006 and 2007.

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The Ration by Schedule (RBS) algorithm has been accepted by the aviation community as the basis of Collaborative Decision Making (CDM) in strategic Traffic Flow Management (TFM) initiatives such as Ground Delay Programs (GDPs) and Airspace Flow Programs (AFPs). However, applications of RBS to date have been limited to strategic metering applications in which the slots to be allocated to flights are all of equal duration, and the flights to be assigned to slots do not need to be differentiated. Decision support capabilities for the general Air Traffic Management (ATM) problem require planning and scheduling algorithms that properly recognize different flight characteristics such as weight class, runway, departure direction and other Air Traffic Control (ATC) procedural issues in determining the slot size that is required for each flight. In this paper, we describe our design and implementation of the Generalized RBS algorithm for a surface traffic management scheduling application which involves detailed flight-by-flight slot time assignment. This algorithm has been designed and developed to support the Collaborative Departure Queue Management (CDQM) component of the Surface Trajectory Based Operations (STBO) project. CDQM provides an equitable allocation of departure capacity to each flight operator at an airport for dynamic use in management of departure queues. The principles of the RBS algorithm are achieved through an initial planning pass based on scheduled data, followed by a second planning pass in which dynamic flight status is incorporated into the planning results.

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During summer, marine stratus encroaches into the approach to San Francisco International Airport (SFO) bringing low ceilings. Low ceilings restrict landings and result in a high number of arrival delays, thus impacting the National Air Space (NAS). These delays are managed by implementation of ground delay programs (GDPs), which hold traffic on the ground at origination airports in anticipation of insufficient arrival capacity at SFO. In an effort to reduce delays and improve both airport and NAS efficiency, the Federal Aviation Administration (FAA) funded a research effort begun in 1995 to develop an objective decision support system to aid forecasters in the prediction of stratus clearing times. By improving forecasts at this major airport, the scope and duration of ground and airborne holds can be reduced. The Marine Stratus Forecast System (MSFS) issues forecasts both deterministically and probabilistically. Following transition to NWS operations in 2004, the system continued to provide reliable forecasts but showed no significant improvement in delay reduction. Changes to the FAA GDP issuance procedures in 2008 allowed them to utilize the improved forecasts, leading to quantifiable reductions in ground and airborne holds for SFO equating to dollars saved. To further reduce delays, a refined statistically based model, the Ground Delay Parameters Selection Model (GPSM) for selecting an optimal ground delay strategy has been developed, utilizing the available archive of objective MSFS probabilistic forecasts and accompanying traffic flow data. This effort represents one of the first systematic attempts to integrate objective probabilistic weather information into the air traffic flow decision process, which is a cornerstone element of the FAA's visionary NextGen program.