Paper Info
Title
Diagnosis and Classification of Typhoon-Associated Low-Altitude Turbulence Using HKO-TDWR Radar Observations and Machine Learning
Abstract
Turbulence has been one of the major concerns for aviation safety. This paper applies evolutionary machine learning (ML) technology to turbulence level classification for civil aviation. An artificial neural network ML approach based on radar observation is developed for classifying the cubed root of the Eddy Dissipation Rate (EDR) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/3</sup> , an accepted measure of turbulence intensity. The approach is validated using Typhoon weather data collected by Hong Kong Observatory’s Terminal Doppler Weather Radar (TDWR) located near Hong Kong International Airport and comparing TDWR EDR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/3</sup> detections and predictions with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</italic> EDR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/3</sup> measured by commercial aircraft. The testing results verified that the ML approach performs reasonably well for both detecting and predicting tasks.
Year
DOI
Venue
2019
10.1109/TGRS.2018.2886070
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
Training,Radar measurements,Data integrity,Doppler radar,Signal to noise ratio,Training data
Radar,Observatory,Aviation safety,Typhoon,Remote sensing,Artificial intelligence,Turbulence kinetic energy,Artificial neural network,Terminal Doppler Weather Radar,Civil aviation,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
57
6
0196-2892
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Jingxiao Cai101.35
yan zhang241.55
Richard J. Doviak3274.61
Yunish Shrestha400.34
P. W. Chan531.84