Surface Current Characteristics in the Taiwan Strait Observed by High-Frequency Radars

Lai, Yeping; Zhou, Hao; Wen, Biyang

doi:10.1109/joe.2016.2572818

Cited by 23 publications

(17 citation statements)

References 23 publications

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“…Finally, in winter, the residual surface current responding to the northeast monsoon has some special features. Previous work has demonstrated the relationship between residual current and wind in northeast direction at one single point, and suggested that wind plays a key role in current dynamics (Lai et al., 2016). These authors found a high correlation between (0.80) eastward currents and winds, which inspired us to further investigate wind‐current correlation especially in some specific directions.…”

Section: Discussion and Summarymentioning

confidence: 99%

Wintertime Variability of Currents in the Southwestern Taiwan Strait

Wang

Pawlowicz

et al. 2021

JGR Oceans

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The Taiwan Strait (TS; Figure 1) is an important channel for seawater transport and exchange between the East and South China Seas (Chuang, 1986;Jan et al., 2006). Bounded on the west by mainland China, and on the east by the island of Taiwan, it is a region about 180 km wide by 350 km long, with a relatively shallow average depth of 60 m (Chung et al., 2001). Numerous researchers have studied this area to improve not only fishing, commercial shipping, pollution monitoring and marine rescue, but also to understand the physical processes of air/sea exchange (

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Section: Discussion and Summarymentioning

confidence: 99%

Wintertime Variability of Currents in the Southwestern Taiwan Strait

Wang

Pawlowicz

et al. 2021

JGR Oceans

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“…Different from the previous detection work, all of our data were collected from observations by HFRs. In the experiment, two OSMAR-S radars [ 32 ] were depolyed at Shanliao and Xiaan in Fujian province of China to jointly measure the sea surface current field. The distance between them is about 60 km.…”

Section: Data Preparationmentioning

confidence: 99%

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Liu

Zhou

Wen

2020

Sensors

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Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo–Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert’s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.

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“…This observation illustrates that multifrequency HFGWR is better for target detection than single-frequency HFGWR, particularly for the case in which a target disappears at one frequency. The theoretical RCS ratio for one target of the two frequencies is 0.8714 as calculated by (1). The RCS ratio in Figure 3 is calculated and shown in Figure 4 values on the edge are excluded.…”

Section: Description Of Experimental System and Radar Data Processingmentioning

confidence: 99%

“…Over-the-horizon radar, HF ground-wave radar (HFGWR), has emerged in the past few decades for both monitoring ocean kinetic parameters (e.g., current, wave, and wind) and detecting moving targets on the ocean surface [1][2][3][4][5]. Such systems are relatively inexpensive and convenient.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Ship RCS Detected by Multifrequency HFGWR

Sun

Wen

Wang

2017

International Journal of Antennas and Propagation

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One of the important applications of high frequency-ground wave radar (HFGWR) is to detect offshore ships. A proper method should be used to obtain the ship radar cross section (RCS), which is a key parameter of the ship. This paper proposes a method based on an automatic information system (AIS). The relationship of the ship RCS versus bearing for different frequencies is analyzed by processing multifrequency HFGWR data. With this new method, bearing information is taken into consideration, which is not the case in traditional empirical formulas. The results provide prior knowledge for ship detection and tracking; therefore, the probability of detection is significantly improved.

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Surface Current Characteristics in the Taiwan Strait Observed by High-Frequency Radars

Cited by 23 publications

References 23 publications

Wintertime Variability of Currents in the Southwestern Taiwan Strait

Wintertime Variability of Currents in the Southwestern Taiwan Strait

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Analysis of Ship RCS Detected by Multifrequency HFGWR

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