Real-Time Marine Radar Observations of Nearshore Waves and Flow Structures from Shore-based Towers

Haller, Merrick C.; Honegger, D.; Pittman, Randall; O’Dea, Annika; Simpson, Alexandra

doi:10.1109/cwtm43797.2019.8955152

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…The radar specifications and further details on deployment are described in Haller et al. (2019).…”

Section: Methodsmentioning

confidence: 99%

“…The radar was located onshore of several in situ sensor arrays (primarily temperature and velocity sensors) and a few wave buoys deployed by ISDE collaborators. The radar specifications and further details on deployment are described in Haller et al (2019).…”

Section: Inner Shelf Dynamics Experiments Observationsmentioning

confidence: 99%

“…10.1029/2020JC016619 2 of 27 Recent X-band radar observations collected on the south central California coast showed an active and extensive rip current field on an approximately 20-km stretch of relatively straight coastline (Haller et al, 2019). Analyses of persistent patterns in the time-averaged radar intensity images indicate that the surf zone bathymetry contained significant alongshore variability, and many of the observed rip current events appeared to be bathymetrically controlled.…”

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confidence: 99%

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Simulations of the Surf Zone Eddy Field and Cross‐Shore Exchange on a Nonidealized Bathymetry

2021

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The nearshore ocean spans the cross-shore region from the shoreline through the surf zone out to the inner shelf and is characterized dynamically by the importance of waves and wave-driven flows. Recent work indicates that exchange between surf zone and inner shelf is predominantly due to transient and morphologic rip currents (Brown et al., 2015;Hally-Rosendahl & Feddersen, 2016), which are offshore-oriented flows generated in the surf zone that extend out beyond the breaker line. Quantifying the hydrodynamic processes controlling surf zone mixing and cross-shore exchange via rip currents in different environmental conditions is necessary in order to predict the movement and dispersal of biological, chemical, and other types of tracers that have implications for both nearshore ecology (

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“…The radar specifications and further details on deployment are described in Haller et al. (2019).…”

Section: Methodsmentioning

confidence: 99%

Section: Inner Shelf Dynamics Experiments Observationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Simulations of the Surf Zone Eddy Field and Cross‐Shore Exchange on a Nonidealized Bathymetry

2021

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“…A Koden marine radar was installed on a United States Coast Guard watchtower near the North Pacific Ocean, providing realtime radar images 24 hours/day. A detailed description of the location information, radar and tower equipment can be found in [38]. In this study, 980 pulse images randomly collected from January 11 to July 18, 2019 with various time intervals are selected for wind parameter estimation.…”

Section: A Data Overviewmentioning

confidence: 99%

A Novel Scheme for Extracting Sea Surface Wind Information From Rain-Contaminated X-Band Marine Radar Images

Chen

Huang

Haller

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The presence of rain degrades the performance of sea surface parameter estimation using X-band marine radar. In this paper, a novel scheme is proposed to improve wind measurement accuracy from rain-contaminated X-band marine radar data. After extracting texture features from each image pixel, the rain-contaminated regions with blurry wave signatures are first identified using a self organizing map (SOM)-based clustering model. Then, a convolutional neural network (CNN) used for image haze removal, i.e., DehazeNet is introduced and incorporated into the proposed scheme for correcting the influence of rain on radar images. In order to obtain wind direction information, curve fitting is conducted on the average azimuthal intensities of rain-corrected radar images. On the other hand, wind speed is estimated from rain-corrected images by training a support vector regression (SVR)-based model. Experiments conducted using datasets from both shipborne and onshore marine radar show that compared to results obtained from images without rain correction, the proposed method achieves relatively high estimation accuracy by reducing measurement errors significantly.

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“…The tower was located in the north of the Yaquina Bay Inlet[44.624125, -124.062888]. A detailed description of the location information, radar and tower equipment can be found in[46]. The Koden image data used in this study were collected from 00:00, December 18 to 23:00, December 23 of 2019, at a rate of 45 images per minute.…”

mentioning

confidence: 99%

Rain-Contaminated Region Segmentation of X-Band Marine Radar Images With an Ensemble of SegNets

Chen

Huang

Haller

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Real-Time Marine Radar Observations of Nearshore Waves and Flow Structures from Shore-based Towers

Cited by 5 publications

References 9 publications

Simulations of the Surf Zone Eddy Field and Cross‐Shore Exchange on a Nonidealized Bathymetry

Simulations of the Surf Zone Eddy Field and Cross‐Shore Exchange on a Nonidealized Bathymetry

A Novel Scheme for Extracting Sea Surface Wind Information From Rain-Contaminated X-Band Marine Radar Images

Rain-Contaminated Region Segmentation of X-Band Marine Radar Images With an Ensemble of SegNets

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