Estimation of Air Traffic Delay Using Three Dimensional Weather Information

2012

Journal of Aircraft

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This paper describes a class of strategies for reducing persistent contrail formation with the capability of trading off between contrails and aircraft-induced emissions. The concept of contrail-frequency index is defined and used to quantify the contrail activities. The contrail-reduction strategies reduce the contrail-frequency index by altering aircraft's cruising altitude with consideration to extra emissions. The strategies use a user-defined factor to trade off between contrail reduction and extra emissions. The analysis shows that contrails can be reduced with extra emissions and without adding congestion to airspace. For a day with high contrail activities, the results show that the maximal contrail-reduction strategy can achieve a contrail reduction of 88%. When a tradeoff factor is used, the strategy can achieve less contrail reduction while emitting less emissions compared to the maximal contrail-reduction strategy. The user-defined tradeoff factor provides a flexible way to trade off between contrail reduction and extra emissions. Better understanding of the tradeoffs between contrails and emissions and their impact on the climate need to be developed to fully use this class of contrail-reduction strategies. The strategies provide a starting point for developing operational policies to reduce the impact of aviation on climate.

Section: B Contrail-frequency Indexmentioning

confidence: 99%

“…(20) and (23), and is a user-defined tradeoff factor. It can be interpreted as the equivalent emissions in kg that has the same environmental impact as the contrail-frequency index of 1.…”

Section: B Tradeoff Between Contrails and Emissionsmentioning

confidence: 99%

Tradeoff Between Contrail Reduction and Emissions in United States National Airspace

2012

Journal of Aircraft

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“…Auto-regressive models have been used for short-term hourly air traffic delay prediction. 9,10 This research extends the delay prediction approach to sector demand prediction.…”

Section: Iib Demand Prediction Modelmentioning

confidence: 98%

Weather-Weighted Periodic Auto Regressive Models for Sector Demand Prediction

AIAA Guidance, Navigation, and Control Conference

2009

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This paper describes a class of weather-weighted periodic auto-regressive sector demand prediction models. The periodic auto-regressive model captures both the mid-term (30 minutes to 2 hours) trend based on the historical data, and the short-term (less than 30 minutes) transient response based on recent observations. For severe weather days, the model uses the three-dimensional weather information, considering both storm locations and echo tops, to form a weather factor to adjust the predictions. Unlike the traditional trajectory-based sector demand prediction methods, which predict the behavior of the National Airspace System adequately for short durations of up to 20 minutes and are vulnerable to the weather uncertainties, this class of models provides reliable short to mid-term sector demand predictions which account for the weather uncertainty.

“…Autoregressive models have been used for short-term hourly air traffic delay prediction [9,10]. This research extends the delay prediction approach to open-loop sector-demand prediction.…”

Section: Periodic Autoregressive Sector-demand Modelmentioning

confidence: 99%

“…Assume at time k, the predicted sector weather index at time k p is w kp , the PAR sector-demand prediction model in Eq. 7can be rewritten as (10) Using the echo tops information provides a more representative weather index, especially for the high sectors. If there are storms with low echo tops located at some high sectors, the weather might have minimal impact on the sector demand.…”

Section: Weather Factormentioning

confidence: 99%

Management-Action-Embedded Sector-Demand Prediction Models

Journal of Guidance, Control, and Dynamics

2010

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This paper describes a class of traffic flow management-action-embedded sector-demand prediction models. The models consist of the open-loop prediction, which is the prediction without management action, and the management-action model. The use of periodic autoregressive modeling approach enables the model to capture both the midterm (30 min to 2 h) trend based on the historical data and the short-term (less than 30 min) transient response based on recent observations. For severe weather days, both storm precipitation and echo tops were used to form a weather index to approximate the management actions due to reduced capacity. In addition to traditional trajectorybased sector-demand prediction methods, which predict only the open-loop behavior of the National Airspace System adequately for short durations of up to 20 min and are vulnerable to uncertainties, this class of models provides a reliable short-to midterm (both open-and closed-loop) sector-demand prediction that accounts for various traffic flow management actions. A combination of closed-loop and open-loop models provide decisionmakers the full range of traffic behavior.