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Wind Speed Inversion in High Frequency Radar Based on Neural Network
Joint Authors
Zeng, Yuming
Zhou, Hao
Roarty, Hugh
Wen, Biyang
Source
International Journal of Antennas and Propagation
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-30
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Wind speed is an important sea surface dynamic parameter which influences a wide variety of oceanic applications.
Wave height and wind direction can be extracted from high frequency radar echo spectra with a relatively high accuracy, while the estimation of wind speed is still a challenge.
This paper describes an artificial neural network based method to estimate the wind speed in HF radar which can be trained to store the specific but unknown wind-wave relationship by the historical buoy data sets.
The method is validated by one-month-long data of SeaSonde radar, the correlation coefficient between the radar estimates and the buoy records is 0.68, and the root mean square error is 1.7 m/s.
This method also performs well in a rather wide range of time and space (2 years around and 360 km away).
This result shows that the ANN is an efficient tool to help make the wind speed an operational product of the HF radar.
American Psychological Association (APA)
Zeng, Yuming& Zhou, Hao& Roarty, Hugh& Wen, Biyang. 2016. Wind Speed Inversion in High Frequency Radar Based on Neural Network. International Journal of Antennas and Propagation،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1105106
Modern Language Association (MLA)
Zeng, Yuming…[et al.]. Wind Speed Inversion in High Frequency Radar Based on Neural Network. International Journal of Antennas and Propagation No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1105106
American Medical Association (AMA)
Zeng, Yuming& Zhou, Hao& Roarty, Hugh& Wen, Biyang. Wind Speed Inversion in High Frequency Radar Based on Neural Network. International Journal of Antennas and Propagation. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1105106
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1105106