Biofouling Effects on the Response of a Wave Measurement Buoy in Deep Water

Thomson, Jim; Talbert, Joe; Klerk, Alex de; Brown, Adam; Schwendeman, Michael; Goldsmith, Jarett; Thomas, Julie; Olfe, Corey; Cameron, Grant; Meinig, Christian

doi:10.1175/jtech-d-15-0029.1

Cited by 30 publications

(18 citation statements)

References 11 publications

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“…These changes significantly increased buoyancy and surface following ability. The heave response frequency (Thomson et al, ), the highest frequency the buoy is expected to closely follow the sea surface, was estimated to be 3.80 Hz, well above the highest frequency of interest. k e is a simple combination of auto‐spectra.…”

Section: Methodsmentioning

confidence: 97%

“…Specifically, we assume that buoy perfectly responds to the heave and slope of the sea surface, i.e., response amplitude operators = 1 (e.g., Masson & LeBlond, ), over the frequency range 0.05–0.50 Hz. When the buoy response is perfect, deviations of k e from the open water relation can be used as a quality control measure and as a flag for buoy response to currents, mooring forces, and bio‐fouling (Thomson et al, ; Tucker, ; Tucker & Pitt, ). In the absence of these effects and in the presence of ice, deviations of k e / k ow from 1 can be interpreted as the modification of waves due to ice.…”

Section: Methodsmentioning

confidence: 99%

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Observations of Surface Wave Dispersion in the Marginal Ice Zone

et al. 2018

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This study presents the most comprehensive set of in situ and remote sensing measurements of wave number, and hence the dispersion relation, in ice to date. A number of surface‐following buoys were deployed in sea ice from the R/V Sikuliaq, which also hosted an X‐band marine radar, during the ONR Arctic Sea State field experiment. The heave‐slope‐correlation method was used to estimate the root‐mean‐square wave number from the buoys. The method was highly sensitive to noise, and extensive quality control measures were developed to isolate real signals in the estimated wave number. The buoy measurements were complemented by shipboard marine X‐band radar dispersion measurements, which are limited to lower frequencies (<0.32 Hz). Overall, deviation from the linear open water dispersion relation was not significant, and matched the open water relation nearly exactly for the range 0.10–0.30 Hz. Isolating a subset of data during the strongest wave event showed evidence of increased wave numbers at frequencies greater than 0.30 Hz. The ice conditions and deviation from linear open water dispersion were qualitatively consistent with predictions from the mass loading model. However, the dispersion curves did not exactly follow the contours of the mass loading model, suggesting either measurement error or other processes at play.

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Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

Observations of Surface Wave Dispersion in the Marginal Ice Zone

et al. 2018

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“…The shape differences resulted in very different icing conditions. Changes of hydrodynamic response of buoys can alter the frequency response of a buoy, as studied in a biofouling case (Thomson et al, ). Without a thorough study of sources of the observed discrepancies between these two types of buoys, reliable cross‐calibration cannot be performed.…”

Section: The Measured Wave Characteristicsmentioning

confidence: 99%

Calibrating a Viscoelastic Sea Ice Model for Wave Propagation in the Arctic Fall Marginal Ice Zone

et al. 2017

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This paper presents a wave‐in‐ice model calibration study. Data used were collected in the thin ice of the advancing autumn marginal ice zone of the western Arctic Ocean in 2015, where pancake ice was found to be prevalent. Multiple buoys were deployed in seven wave experiments; data from four of these experiments are used in the present study. Wave attenuation coefficients are calculated utilizing wave energy decay between two buoys measuring simultaneously within the ice covered region. Wavenumbers are measured in one of these experiments. Forcing parameters are obtained from simultaneous in‐situ and remote sensing observations, as well as forecast/hindcast models. Cases from three wave experiments are used to calibrate a viscoelastic model for wave attenuation/dispersion in ice cover. The calibration is done by minimizing the difference between modeled and measured complex wavenumber, using a multi‐objective genetic algorithm. The calibrated results are validated using two methods. One is to directly apply the calibrated viscoelastic parameters to one of the wave experiments not used in the calibration and then compare the attenuation from the model with measured data. The other is to use the calibrated viscoelastic model in WAVEWATCH III® over the entire western Beaufort Sea and then compare the wave spectra at two remote sites not used in the calibration. Both validations show reasonable agreement between the model and the measured data. The completed viscoelastic model is believed to be applicable to the fall marginal ice zone dominated by pancake ice.

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“…In addition, the fluid structure interactions of a wave buoy can linearize the wave time series (James, ; Magnusson et al, ). Wave buoys are also subject to biofouling (Thomson et al, ), vandalism (Beets et al, ), and affected by tidal currents. These drawbacks in sampling using wave buoys are mitigated by the unparalleled spatial distribution, length of record, and consistency of continuous surface elevation measurement by the Datawell Waverider buoys (Casas‐Prat & Holthuijsen, ).…”

Section: Data Setmentioning

confidence: 99%

Can Rogue Waves Be Predicted Using Characteristic Wave Parameters?

et al. 2018

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Rogue waves are ocean surface waves larger than the surrounding sea that can pose a danger to ships and offshore structures. They are often deemed unpredictable without complex measurement of the wavefield and computationally intensive calculation, which is infeasible in most applications; consequently, there a need for fast predictors. Here we collate, quality control, and analyze the largest data set of single‐point field measurements from surface following wave buoys to search for predictors of rogue wave occurrence. We find that analysis of the sea state parameters in bulk yields no predictors, as the subset of seas containing rogue waves sits within the set of seas without. However, spectral bandwidth parameters of rogue seas display different probability distributions to normal seas, but these parameters are rarely provided in wave forecasts. When location is accounted for, trends can be identified in the occurrence of rogue waves as a function of the average sea state characteristics at that location. These trends follow a power law relationship with the characteristic sea state parameters: mean significant wave height and mean zero upcrossing wave period. We find that frequency of occurrence of rogue waves and their generating mechanism is not spatially uniform, and each location is likely to have its own unique sensitivities, which increase in the coastal seas. We conclude that forecastable predictors of rogue wave occurrence will need to be location specific and reflective of their generation mechanism. Therefore, given location and a sufficiently long historical record of sea state characteristics, the likelihood of occurrence can be obtained for mariners and offshore operators.

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Biofouling Effects on the Response of a Wave Measurement Buoy in Deep Water

Cited by 30 publications

References 11 publications

Observations of Surface Wave Dispersion in the Marginal Ice Zone

Observations of Surface Wave Dispersion in the Marginal Ice Zone

Calibrating a Viscoelastic Sea Ice Model for Wave Propagation in the Arctic Fall Marginal Ice Zone

Can Rogue Waves Be Predicted Using Characteristic Wave Parameters?

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