DOAJ Open Access 2016

Wind Speed Inversion in High Frequency Radar Based on Neural Network

Yuming Zeng Hao Zhou Hugh Roarty Biyang Wen

Abstrak

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.

Topik & Kata Kunci

Electrical engineering. Electronics. Nuclear engineering Cellular telephone services industry. Wireless telephone industry

Penulis (4)

Yuming Zeng

Hao Zhou

Hugh Roarty

Biyang Wen

Format Sitasi

APA MLA BibTeX

Zeng, Y., Zhou, H., Roarty, H., Wen, B. (2016). Wind Speed Inversion in High Frequency Radar Based on Neural Network. https://doi.org/10.1155/2016/2706521

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Informasi Jurnal

Tahun Terbit: 2016
Sumber Database: DOAJ
DOI: 10.1155/2016/2706521
Akses: Open Access ✓