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- Author or Editor: Alejandro Cifuentes-Lorenzen x
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Abstract
Obtaining accurate measurements of wave statistics from research vessels remains a challenge due to the platform motion. One principal correction is the removal of ship heave and Doppler effects from point measurements. Here, open-ocean wave measurements were collected using a laser altimeter, a Doppler radar microwave sensor, a radar-based system, and inertial measurement units. Multiple instruments were deployed to capture the low- and high-frequency sea surface displacements. Doppler and motion correction algorithms were applied to obtain a full 1D (0.035–1.3 ± 0.2 Hz) wave spectrum. The radar-based system combined with the laser altimeter provided the optimal low- and high-frequency combination, producing a frequency spectrum in the range from 0.035 to 1.2 Hz for cruising speeds ≤3 m s−1 with a spectral rolloff of f −4 Hz and noise floor of −20/−30 dB. While on station, the significant wave height estimates were comparable within 10%–15% among instrumentation. Discrepancies in the total energy and in the spectral shape between instruments arise when the ship is in motion. These differences can be quantified using the spectral behavior of the measurements, accounting for aliasing and Doppler corrections. The inertial sensors provided information on the amplitude of the ship’s modulation transfer function, which was estimated to be ~1.3 ± 0.2 while on station and increased while underway [2.1 at ship-over-ground (SOG) speed; 4.3 m s−1]. The correction scheme presented here is adequate for measurements collected at cruising speeds of 3 m s−1 or less. At speeds greater than 5 m s−1, the motion and Doppler corrections are not sufficient to correct the observed spectral degradation.
Abstract
Obtaining accurate measurements of wave statistics from research vessels remains a challenge due to the platform motion. One principal correction is the removal of ship heave and Doppler effects from point measurements. Here, open-ocean wave measurements were collected using a laser altimeter, a Doppler radar microwave sensor, a radar-based system, and inertial measurement units. Multiple instruments were deployed to capture the low- and high-frequency sea surface displacements. Doppler and motion correction algorithms were applied to obtain a full 1D (0.035–1.3 ± 0.2 Hz) wave spectrum. The radar-based system combined with the laser altimeter provided the optimal low- and high-frequency combination, producing a frequency spectrum in the range from 0.035 to 1.2 Hz for cruising speeds ≤3 m s−1 with a spectral rolloff of f −4 Hz and noise floor of −20/−30 dB. While on station, the significant wave height estimates were comparable within 10%–15% among instrumentation. Discrepancies in the total energy and in the spectral shape between instruments arise when the ship is in motion. These differences can be quantified using the spectral behavior of the measurements, accounting for aliasing and Doppler corrections. The inertial sensors provided information on the amplitude of the ship’s modulation transfer function, which was estimated to be ~1.3 ± 0.2 while on station and increased while underway [2.1 at ship-over-ground (SOG) speed; 4.3 m s−1]. The correction scheme presented here is adequate for measurements collected at cruising speeds of 3 m s−1 or less. At speeds greater than 5 m s−1, the motion and Doppler corrections are not sufficient to correct the observed spectral degradation.
Shipboard Wave Measurements in the Southern OceanAbstract
Surface wave measurements from ships pose difficulties because of motion contamination. Cifuentes-Lorenzen et al. analyzed laser altimeter and marine X-band radar (MR) wave measurements from the Southern Ocean Gas Exchange Experiment (SOGasEx). They found that wave measurements from both sensors deteriorate precipitously at ship speeds 
Abstract
Surface wave measurements from ships pose difficulties because of motion contamination. Cifuentes-Lorenzen et al. analyzed laser altimeter and marine X-band radar (MR) wave measurements from the Southern Ocean Gas Exchange Experiment (SOGasEx). They found that wave measurements from both sensors deteriorate precipitously at ship speeds
Novel Methods for Optically Measuring Whitecaps under Natural Wave-Breaking Conditions in the Southern OceanAbstract
Traditional methods for measuring whitecap coverage using digital video systems mounted to measure a large footprint can miss features that do not produce a high enough contrast to the background. Here, a method for accurately measuring the fractional coverage, intensity, and decay time of whitecaps using above-water radiometry is presented. The methodology was developed using data collected in the Southern Ocean under a wide range of wind and wave conditions. Whitecap quantities were obtained by employing a magnitude threshold based on the interquartile range of the radiance or reflectance signal from a single channel. Breaking intensity and decay time were produced from the integration of and the exponential fit to radiance or reflectance over the lifetime of the whitecap. When using the lowest magnitude threshold possible, radiometric fractional whitecap coverage retrievals were consistently higher than fractional coverage from high-resolution digital images, perhaps because the radiometer captures more of the decaying bubble plume area that is difficult to detect with photography. Radiometrically obtained whitecap measurements are presented in the context of concurrently measured meteorological (e.g., wind speed) and oceanographic (e.g., wave) data. The optimal fit of the radiometrically estimated whitecap coverage to the instantaneous wind speed, determined using robust linear least squares, showed a near-cubic dependence. Increasing the magnitude threshold for whitecap detection from 2 to 4 times the interquartile range produced a wind speed–whitecap relationship most comparable to the concurrently collected fractional coverage from digital imagery and previously published wind speed–whitecap parameterizations.
Abstract
Traditional methods for measuring whitecap coverage using digital video systems mounted to measure a large footprint can miss features that do not produce a high enough contrast to the background. Here, a method for accurately measuring the fractional coverage, intensity, and decay time of whitecaps using above-water radiometry is presented. The methodology was developed using data collected in the Southern Ocean under a wide range of wind and wave conditions. Whitecap quantities were obtained by employing a magnitude threshold based on the interquartile range of the radiance or reflectance signal from a single channel. Breaking intensity and decay time were produced from the integration of and the exponential fit to radiance or reflectance over the lifetime of the whitecap. When using the lowest magnitude threshold possible, radiometric fractional whitecap coverage retrievals were consistently higher than fractional coverage from high-resolution digital images, perhaps because the radiometer captures more of the decaying bubble plume area that is difficult to detect with photography. Radiometrically obtained whitecap measurements are presented in the context of concurrently measured meteorological (e.g., wind speed) and oceanographic (e.g., wave) data. The optimal fit of the radiometrically estimated whitecap coverage to the instantaneous wind speed, determined using robust linear least squares, showed a near-cubic dependence. Increasing the magnitude threshold for whitecap detection from 2 to 4 times the interquartile range produced a wind speed–whitecap relationship most comparable to the concurrently collected fractional coverage from digital imagery and previously published wind speed–whitecap parameterizations.
Abstract
Concurrent wavefield and turbulent flux measurements acquired during the Southern Ocean (SO) Gas Exchange (GasEx) and the High Wind Speed Gas Exchange Study (HiWinGS) projects permit evaluation of the dependence of the whitecap coverage W on wind speed, wave age, wave steepness, mean square slope, and wind-wave and breaking Reynolds numbers. The W was determined from over 600 high-frequency visible imagery recordings of 20 min each. Wave statistics were computed from in situ and remotely sensed data as well as from a WAVEWATCH III hindcast. The first shipborne estimates of W under sustained 10-m neutral wind speeds U 10N of 25 m s−1 were obtained during HiWinGS. These measurements suggest that W levels off at high wind speed, not exceeding 10% when averaged over 20 min. Combining wind speed and wave height in the form of the wind-wave Reynolds number resulted in closely agreeing models for both datasets, individually and combined. These are also in good agreement with two previous studies. When expressing W in terms of wavefield statistics only or wave age, larger scatter is observed and/or there is little agreement between SO GasEx, HiWinGS, and previously published data. The wind speed–only parameterizations deduced from the SO GasEx and HiWinGS datasets agree closely and capture more of the observed W variability than Reynolds number parameterizations. However, these wind speed–only models do not agree as well with previous studies than the wind-wave Reynolds numbers.
Abstract
Concurrent wavefield and turbulent flux measurements acquired during the Southern Ocean (SO) Gas Exchange (GasEx) and the High Wind Speed Gas Exchange Study (HiWinGS) projects permit evaluation of the dependence of the whitecap coverage W on wind speed, wave age, wave steepness, mean square slope, and wind-wave and breaking Reynolds numbers. The W was determined from over 600 high-frequency visible imagery recordings of 20 min each. Wave statistics were computed from in situ and remotely sensed data as well as from a WAVEWATCH III hindcast. The first shipborne estimates of W under sustained 10-m neutral wind speeds U 10N of 25 m s−1 were obtained during HiWinGS. These measurements suggest that W levels off at high wind speed, not exceeding 10% when averaged over 20 min. Combining wind speed and wave height in the form of the wind-wave Reynolds number resulted in closely agreeing models for both datasets, individually and combined. These are also in good agreement with two previous studies. When expressing W in terms of wavefield statistics only or wave age, larger scatter is observed and/or there is little agreement between SO GasEx, HiWinGS, and previously published data. The wind speed–only parameterizations deduced from the SO GasEx and HiWinGS datasets agree closely and capture more of the observed W variability than Reynolds number parameterizations. However, these wind speed–only models do not agree as well with previous studies than the wind-wave Reynolds numbers.
