Wind Speed Inversion in High Frequency Radar Based on Neural Network

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 pape...

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Main Authors: Yuming Zeng, Hao Zhou, Hugh Roarty, Biyang Wen
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:International Journal of Antennas and Propagation
Online Access:http://dx.doi.org/10.1155/2016/2706521
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author Yuming Zeng
Hao Zhou
Hugh Roarty
Biyang Wen
author_facet Yuming Zeng
Hao Zhou
Hugh Roarty
Biyang Wen
author_sort Yuming Zeng
collection DOAJ
description 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.
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institution OA Journals
issn 1687-5869
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language English
publishDate 2016-01-01
publisher Wiley
record_format Article
series International Journal of Antennas and Propagation
spelling doaj-art-1f0cff01de1e49afb858804ccdcadb4d2025-08-20T02:04:44ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772016-01-01201610.1155/2016/27065212706521Wind Speed Inversion in High Frequency Radar Based on Neural NetworkYuming Zeng0Hao Zhou1Hugh Roarty2Biyang Wen3School of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaInstitute of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ 08901, USASchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaWind 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.http://dx.doi.org/10.1155/2016/2706521
spellingShingle Yuming Zeng
Hao Zhou
Hugh Roarty
Biyang Wen
Wind Speed Inversion in High Frequency Radar Based on Neural Network
International Journal of Antennas and Propagation
title Wind Speed Inversion in High Frequency Radar Based on Neural Network
title_full Wind Speed Inversion in High Frequency Radar Based on Neural Network
title_fullStr Wind Speed Inversion in High Frequency Radar Based on Neural Network
title_full_unstemmed Wind Speed Inversion in High Frequency Radar Based on Neural Network
title_short Wind Speed Inversion in High Frequency Radar Based on Neural Network
title_sort wind speed inversion in high frequency radar based on neural network
url http://dx.doi.org/10.1155/2016/2706521
work_keys_str_mv AT yumingzeng windspeedinversioninhighfrequencyradarbasedonneuralnetwork
AT haozhou windspeedinversioninhighfrequencyradarbasedonneuralnetwork
AT hughroarty windspeedinversioninhighfrequencyradarbasedonneuralnetwork
AT biyangwen windspeedinversioninhighfrequencyradarbasedonneuralnetwork