Wind Information Requirements to Support Four Dimensional Trajectory-Based Operations

Glina, Yan; Troxel, Seth; Reynolds, Tom; McPartland, Michael

doi:10.2514/6.2012-5702

Cited by 10 publications

(7 citation statements)

References 1 publication

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“…4D-FMS中满足RTA要求的航迹规划计算是一个多约束动态优化过程, 其约束条件包括飞机性能、空管要求(高度、航线位置、速度、时间等)、成本限制、环境约束、航空运输法规要求等 [11,12] [9,13,14] . 目前航空领域使用的气象数据是世界气象组织发布的二进制格点(gridded binary, GRIB)码气象数据 [13,14] . GRIB是网格化数据…”

Section: 满足Rta要求的4d航迹规划unclassified

Flight guidance of aircrafts in continuous descent operation based on 4D trajectory

Jia

Lin

et al. 2017

Sci. Sin.-Tech.

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A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation SCIENCE CHINA Information Sciences 60, 112202 (2017); On the similarity/dissimilarity of DNA sequences based on 4D graphical representation Chinese Science Bulletin 55, 701 (2010); Vision navigation for aircrafts based on 3D reconstruction from real-time image sequences SCIENCE CHINA Technological Sciences 58, 1196 (2015); A survey of numerical algorithms for trajectory optimizationof flight vehicles

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Section: 满足Rta要求的4d航迹规划unclassified

Flight guidance of aircrafts in continuous descent operation based on 4D trajectory

Jia

Lin

et al. 2017

Sci. Sin.-Tech.

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“…The impact of wind information accuracy relative to the true wind in the environment on the FMS and its time estimation has been identified in the past studies [9]. There are various types of wind error that could negatively affect the FMS operation, such as wind forecast resolution errors, wind blending errors in the process of merging forecast wind in FMS and actual wind being sensed outside of aircraft [10]. In this study, wind forecast errors for each wind severity condition were used as the major source of wind error that affected the wind information inaccuracy on FMS operations.…”

Section: Wind Severity and Forecast Errormentioning

confidence: 99%

Required time of arrival as a control mechanism to mitigate uncertainty in arrival traffic demand management

Yoo

Möhlenbrink

Brasil

et al. 2016

2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)

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the objective of this study is to explore the use of Required Time of Arrival (RTA) capability on the flight deck as a control mechanism on arrival traffic management to improve traffic delivery accuracy by mitigating the effect of traffic demand uncertainty. The uncertainties are caused by various factors, such as departure error due to the difference between scheduled departure and the actual take-off time. A simulation study was conducted using the Multi Aircraft Control System (MACS) software, a comprehensive research platform developed in the Airspace Operations Laboratory (AOL) at NASA Ames Research Center. The Crossing Time (CT) performance (i.e. the difference between target crossing time and actual crossing time) of the RTA for uncertainty mitigation during cruise phase was evaluated under the influence of varying two main factors: wind severity (heavy wind vs. mild wind), and wind error (1 hour, 2 hours, and 5 hours wind forecast errors). To examine the CT performance improvement made by the RTA, the comparison to the CT of the aircraft that were not assigned with RTA (Non-RTA) under the influence of the selected factors was also made. The Newark Liberty International Airport (EWR) was chosen for this study. A total 66 inbound traffic to the EWR (34 of them were airborne when the simulation was initiated, 32 were predepartures at that time) was simulated, where the pre-scripted departure error was assigned to each pre-departure (61 % conform to their Expected Departure Clearance Time, which is +/-300 seconds of their scheduled departure time). The results of the study show that the delivery accuracy improvement can be achieved by assigning RTA, regardless of the influence of the selected two factors (the wind severity and the wind information inaccuracy). Across all wind variances, 66.9% (265 out of 396) of the CT performance of the RTA assigned aircraft was within +/-60 seconds (i.e. target tolerance range) and 88.9% (352 out of 396) aircraft met +/-300 seconds marginal tolerance range, while only 33.6% (133 out of 396) of the Non-RTA assigned aircraft's CT performance achieved the target tolerance range and 75.5% (299 out of 396) stayed within the marginal. Examination of the impact of different error sources -i.e. departure error, wind severity, and wind error -suggest that although large departure errors can significantly impact the CT performance, the impacts of wind severity and errors were modest relative the targeted +/-60 second conformance range.

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“…Moreover, observations are mainly gathered from radiosondes, which are launched at specific times, no more than four times per day. All these factors cause using NWP to be inadequate for TBO [3,4]. Therefore, using aircraft-derived data could improve the spatial and temporal resolution of wind forecasts [5].…”

Section: Introduction 1motivationmentioning

confidence: 99%

Polynomial Chaos Expansion-Based Enhanced Gaussian Process Regression for Wind Velocity Field Estimation from Aircraft-Derived Data

et al. 2023

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This paper addresses the problem of spatiotemporal wind velocity field estimation for air traffic management applications. Using data obtained from aircraft, the eastward and northward components of the wind velocity field inside a specific air space are calculated as functions of time. Both short-term wind velocity field forecasting and wind velocity field reconstruction are performed. Wind velocity data are indirectly obtained from the states of the aircraft flying in the relevant airspace, which are broadcast by the ADS-B and Mode-S aircraft surveillance systems. The wind velocity field is estimated by combining two data-driven techniques: the polynomial chaos expansion and the Gaussian process regression. The former approximates the global behavior of the wind velocity field, whereas the latter approximates the local behavior. The eastward and northward wind components of the wind velocity field must be estimated, which causes the problem to be a multiple-output problem. This method enables the estimation of the wind velocity field at any spatiotemporal location using wind velocity observations from any spatiotemporal location, eliminating the need for spatial and temporal grids. Moreover, since the method proposed in this article allows for the probability distributions of the estimates to be computed, it causes the computation of the confidence intervals to be possible. Furthermore, since the method presented in this paper allows for data assimilation, it can be used online to continuously update the wind velocity field estimation. The method is tested on different wind scenarios and different training-test data configurations, by means of which the consistency between the results of the wind velocity field forecasting and the wind velocity field reconstruction is checked. Finally, the ERA5 meteorological reanalysis data of the European Centre for Medium-Range Weather Forecasts are used to validate the proposed technique. The results show that the method is able to reliably estimate the wind velocity field from aircraft-derived data.

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Wind Information Requirements to Support Four Dimensional Trajectory-Based Operations

Cited by 10 publications

References 1 publication

Flight guidance of aircrafts in continuous descent operation based on 4D trajectory

Flight guidance of aircrafts in continuous descent operation based on 4D trajectory

Required time of arrival as a control mechanism to mitigate uncertainty in arrival traffic demand management

Polynomial Chaos Expansion-Based Enhanced Gaussian Process Regression for Wind Velocity Field Estimation from Aircraft-Derived Data

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