Modeling and Performance of NAS in Inclement Weather

Ramamoorthy, Kris; Hunter, George; Klein, Alexander

doi:10.2514/6.2006-7758

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Nilim et al [16] have developed a method in which convective weather is modeled as a dynamic, probabilistic process, and flight-specific solutions are found via dynamic programming. Ramamoorthy and Hunter [17] have built a framework to estimate probabilistic TFM decision-making benefits at the NAS level. Algorithms and an early prototype for solving en route congestion probabilistically in realtime, at a single point, have been presented by Wanke et al [18,19].…”

Section: Probabilistic Decision Makingmentioning

confidence: 99%

Incremental, Probabilistic Decision Making for En Route Traffic Management

Wan

Greenbaum

2007

Air Traffic Control Quarterly

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En route airspace congestion, often due to convective weather, causes system-wide delays and disruption in the U.S. National Airspace System (NAS). Today's methods for managing congestion are mostly manual, based on uncertain forecasts of weather and traffic demand, and often involve rerouting or delaying entire flows of aircraft. A new, incremental decision-making approach is proposed, in which prediction uncertainty is explicitly used to develop effective and efficient congestion resolution actions. Decisions are made based on a quantitative evaluation of the expected delay cost distribution, and resolution actions are targeted at specific flights, rather than flows. A massively-parallel simulation of the proposed method has been developed, and results for an operational-scale congestion problem are presented. 1 The alerts shown in Figure 1 are a composite of all predictions for the next 2 hours. The 15-minute predictions can also be viewed as a matrix (Figure 8).

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Section: Probabilistic Decision Makingmentioning

confidence: 99%

Incremental, Probabilistic Decision Making for En Route Traffic Management

Wan

Greenbaum

2007

Air Traffic Control Quarterly

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“…23 developed a method in which convective weather is modeled as a dynamic, probabilistic process, and flight-specific solutions are found via dynamic programming. Ramamoorthy and Hunter 24 have built a framework to estimate probabilistic TFM decision-making benefits at the NAS level. Algorithms and an early prototype for solving en route congestion probabilistically in real-time, at a single point, have been presented by Wanke, et.…”

Section: Probabilistic Decision Makingmentioning

confidence: 99%

Sequential Congestion Management with Weather Forecast Uncertainty

Wan

Greenbaum

2008

AIAA Guidance, Navigation and Control Conference and Exhibit

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En route airspace congestion, often due to convective weather, causes system-wide delays and disruption in the U.S. National Airspace System (NAS). Present-day methods for managing congestion are mostly manual, based on uncertain forecasts of weather and traffic demand, and often involve rerouting or delaying entire flows of aircraft. A sequential decision-making approach is proposed, in which traffic and weather forecast prediction uncertainty is quantified and explicitly used to develop efficient congestion resolution actions. The method is based on Monte Carlo simulation of traffic and weather outcomes from a specific forecast. Candidate sequential decision strategies are evaluated against the range of outcomes to determine the best course of action. Decisions are made based on a quantitative evaluation of the expected delay cost distribution, and resolution actions are targeted at specific flights, rather than flows. A weather-induced airspace congestion scenario is explored using the simulation, and three different levels of weather forecast uncertainty are postulated. Weather forecast uncertainty was shown to affect when and how aggressively to act to solve the congestion problem. Forecast uncertainty also increases the mean and variance of the congestion resolution cost. The simulation can be used both to learn about solving airspace congestion problems, and to do several types of cost benefit analyses. It is also a prototype of a future, real-time, probabilistic decision support aid for tactical traffic management.

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