Using Flight Information to Improve Weather Avoidance Predictions

Stewart, Tim; DeArmon, James; Chaloux, David

doi:10.2514/6.2013-4377

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…where L is the horizontal width of the target within the radar beam [6][7][8] . Since the horizontal size of the target is much wider than the beam width, h ψ always approximates to 1 and The vertical filling coefficient v ψ can be expressed as…”

Section: Filling Coefficient Estimationmentioning

confidence: 99%

Improved Retrieval of Reflectivity Factor for Airborne Weather Radar

Xiang

et al. 2014

AMR

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For the airborne weather radar signal processing, the intensity of the meteorological targets is represented by the physical quantity called reflectivity factor. When observing the middle-or long-range meteorological targets at low altitude from a high-altitude radar platform, due to the incomplete beam filling, the retrieval result of the reflectivity factor is much less than the actual value. In this paper, we firstly introduce a beam filling coefficient into the weather radar equation and then present its estimation method based on the real-time radar operating parameters. The validity of the proposed method is then verified by processing the raw-data of the simulated meteorological targets.

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Section: Filling Coefficient Estimationmentioning

confidence: 99%

Improved Retrieval of Reflectivity Factor for Airborne Weather Radar

Xiang

et al. 2014

AMR

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Identification Method of Typical Dynamic Time Series Weather Scenes in Terminal Area

Ligang

Zhang

2020

2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

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Prediction of Arrival Flight Operation Strategies under Convective Weather Based on Trajectory Clustering

Wang

Chu

et al. 2022

Aerospace

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An airport’s terminal area is the bottleneck of the air transport system. Convective weather can seriously affect the normal flight status of arrival and departure flights. At present, pilots take different flight operation strategies to avoid convective weather based on onboard radar, visual information, adverse weather experience, etc. This paper studies trajectory clustering based on the OPTICS algorithm to obtain the arrival of typical flight routes in the terminal area. Based on weather information of the planned typical flight route and flight plan information, Random Forest (RF), K-nearest Neighbor KNN (KNN), and Support Vector Machines (SVM) algorithms were used for training and establishing the Arrival Flight Operation Strategy Prediction Model (AFOSPM). In this paper, case studies of historical arrival flights in the Guangzhou (ZGGG) and Wuhan (ZHHH) terminal area were carried out. The results show that trajectory clustering results based on the OPTICS algorithm can more accurately reflect the regular flight routes of arrival flights in a terminal area. Compared to KNN and SVM, the prediction accuracy of AFOSPM based on RF is better, reaching more than 88%. On this basis, six features—including 90% VIL, weather coverage, weather duration, planned route, max VIL, and planned Arrival Gate (AF)—were used as the input features for AFOSPM, which can effectively predict various arrival flight operation strategies. For the most frequently used arrival flight operation strategies under convective weather conditions—radar guidance, AF changing, and diversion strategy—the prediction accuracy of the ZGGG and ZHHH terminal areas can exceed 95%, 85%, and 80%, respectively.

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Using Flight Information to Improve Weather Avoidance Predictions

Cited by 3 publications

References 4 publications

Improved Retrieval of Reflectivity Factor for Airborne Weather Radar

Improved Retrieval of Reflectivity Factor for Airborne Weather Radar

Identification Method of Typical Dynamic Time Series Weather Scenes in Terminal Area

Prediction of Arrival Flight Operation Strategies under Convective Weather Based on Trajectory Clustering

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