Measurements of significant wave height are made routinely throughout the world’s oceans, but a record of the sea surface elevation (
η
) is rarely kept. This is mostly due to memory limitations on ...data, but also, it is thought that buoy measurements of sea surface elevation are not as accurate as wave gauges mounted on stationary platforms. Accurate records of
η
which contain rogue waves (defined here as an individual wave at least twice the significant wave height) are of great interest to scientists and engineers. Using field data, procedures for tilt correcting and double integrating accelerometer data to produce a consistent record of
η
are given in this study. The data in this study are from experimental buoys deployed in the recent
Impact of Typhoons on the Ocean in the Pacific
(ITOP) field experiment which occurred in 2010. The statistics from the ITOP buoys is under that predicted by Rayleigh theory, but matches the distributions of Boccotti and others (Tayfun and Fedele) (Ocean Eng 34:1631-1649,
2007
). Rogue waves were recorded throughout the experiment under various sea state conditions. Recommendations, as a result of lessons learned during ITOP, are made for the routine recording of
η
which may not add significantly to the existing data burden. The hope is that we might one day collect a worldwide database of rogue waves from the existing buoy network, which would progress our understanding of the rogue wave phenomenon and make work at sea safer.
In this study, we present an algorithm for summer sea ice conditions that semi-automatically produces the floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture ...Radar data. Currently, floe size distribution data from satellite images are very rare in the literature, mainly due to the lack of a reliable algorithm to produce such data. Here, we developed the algorithm by combining various image analysis methods, including Kernel Graph Cuts, distance transformation and watershed transformation, and a rule-based boundary revalidation. The developed algorithm has been validated against the ground truth that was extracted manually with the aid of 1-m resolution visible satellite data. Comprehensive validation analysis has shown both perspectives and limitations. The algorithm tends to fail to detect small floes (mostly less than 100 m in mean caliper diameter) compared to ground truth, which is mainly due to limitations in water-ice segmentation. Some variability in the power law exponent of floe size distribution is observed due to the effects of control parameters in the process of de-noising, Kernel Graph Cuts segmentation, thresholds for boundary revalidation and image resolution. Nonetheless, the algorithm, for floes larger than 100 m, has shown a reasonable agreement with ground truth under various selections of these control parameters. Considering that the coverage and spatial resolution of satellite Synthetic Aperture Radar data have increased significantly in recent years, the developed algorithm opens a new possibility to produce large volumes of floe size distribution data, which is essential for improving our understanding and prediction of the Arctic sea ice cover.
The application of nautical X-Band radars to measure internal wave (IW) properties is investigated. A methodology based on the use of Radon transform (RT) techniques to detect internal wave related ...features from backscatter image sequences is introduced to compute properties such as direction of propagation, non-linear velocity (
c
0), distance between solitons (
L
cc
) and number of solitons per packet. The proposed methodology was applied to several events recorded by a ship-mounted X-Band radar system (WaMoS) during the NLIWI experiment in 2006. Results from the comparisons to simultaneous measurements taken at neighboring oceanographic moorings indicated that
c
0 can be estimated with a RMS error of 0.06
m
s
−1, which corresponds to a mean relative error of −1.4%. Similarly,
L
cc
can be estimated with a RMS error of 98
m, which is associated with a mean relative error of 14.6%. This latter error estimate however is likely to be overestimated, because it reflects strongly the separation between sampling stations as
L
cc
was shown to be highly dependent on propagation distance. The accuracy of the results shows that X-Band systems are well suited to measure internal wave properties offering some advantages over SAR and other
in situ devices.
Over the open ocean, the aerodynamic drag coefficient is typically well predicted; however, the impact depth‐limited processes have on the drag remains underexplored. A case study is presented here ...where winds, waves, and currents were simultaneously observed from a mobile platform that repeatedly transected the inner shelf of Monterey Bay, CA. Eddy covariance‐derived drag coefficients were compared to several bulk parameterizations, including all of the roughness variations of COARE 3.5 and two explicitly depth‐limited models. The analysis demonstrated that the drag was underestimated by O(2–4) times and the variability with wind speed or cross‐shore distance was not well predicted. The drag based on a recent depth‐limited roughness length model performed substantially better than the rest of the bulk estimates, which were all within 15% of each other and effectively equivalent given typical operational uncertainties. The measured friction velocity was compared to a wave‐dependent parameterization and generalizing the model to arbitrary water depth significantly improved the mean observation‐model difference to within 30%. Latent variability in the observation‐model comparison was associated with stability, wind direction, and wave steepness. The wind stress angle variability was also analyzed. Stress veering was correlated with the alongshore surface current within 2 km from shore (r2= 0.7–0.95, p < 0.05); offshore of this margin, consistent wind stress veering was observed and may be attributable to a secondary, low‐frequency swell system. These results demonstrate that it remains a persistent challenge to accurately predict wind stress variability in the nearshore, especially at locations with complex wave and current fields.
Plain Language Summary
As the wind blows over the ocean surface, the atmosphere experiences friction, or drag, as the air and water molecules interact. Small waves increase the roughness of the surface, which augments the drag felt by the atmosphere as the air flows over the waves. This physical interaction between atmosphere and ocean facilitates the exchange of energy and material (e.g., gas) across the ocean surface, as well as drives upper ocean currents. In the presence of large waves, or swell, this interaction becomes more complicated. The impact swell has on the atmosphere changes as these large waves travel into shallow water, thereby growing taller and steeper, however our understanding of this process is limited. We present an observational study that took place within Monterey Bay and our results suggest that typical models used to predict the ocean surface drag do not perform well in the nearshore zone. In fact, applying a shallow water model did not significantly improve model‐observation comparison. We demonstrate that the mechanisms that characterize air‐sea interaction in deep water, may not apply near shore. While coastal zones are limited, compared to the global ocean, their impacts on and response to human activity are profound and should be better understood.
Key Points
Measured drag coefficients were 2‐4 times larger than parameterized values, when comparing several models
Generalizing a wave‐dependent friction velocity model to arbitrary water depths improved model‐observation agreement to within 30%
Consistent wind stress veering off the wind direction was observed and, within 2 km of shore, was associated with surface current variance
Abstract
To better simulate the seasonal evolution of sea ice in the Arctic, with particular attention to the marginal ice zone, a sea ice model of the distribution of ice thickness, floe size, and ...enthalpy was implemented into the Pan-arctic Ice–Ocean Modeling and Assimilation System (PIOMAS). Theories on floe size distribution (FSD) and ice thickness distribution (ITD) were coupled in order to explicitly simulate multicategory FSD and ITD distributions simultaneously. The expanded PIOMAS was then used to estimate the seasonal evolution of the Arctic FSD in 2014 when FSD observations are available for model calibration and validation. Results indicate that the simulated FSD, commonly described equivalently as cumulative floe number distribution (CFND), generally follows a power law across space and time and agrees with the CFND observations derived from TerraSAR-X satellite images. The simulated power-law exponents also correlate with those derived using MODIS images, with a low mean bias of –2%. In the marginal ice zone, the modeled CFND shows a large number of small floes in winter because of stronger winds acting on thin, weak first-year ice in the ice edge region. In mid-spring and summer, the CFND resembles an upper truncated power law, with the largest floes mostly broken into smaller ones; however, the number of small floes is lower than in winter because floes of small sizes or first-year ice are easily melted away. In the ice pack interior there are fewer floes in late fall and winter than in summer because many of the floes are “welded” together into larger floes in freezing conditions, leading to a relatively flat CFND with low power-law exponents. The simulated mean floe size averaged over all ice-covered areas shows a clear annual cycle, large in winter and smaller in summer. However, there is no obvious annual cycle of mean floe size averaged over the marginal ice zone. The incorporation of FSD into PIOMAS results in reduced ice thickness, mainly in the marginal ice zone, which improves the simulation of ice extent and yields an earlier ice retreat.
•Swell is shown to modulate energy in the sea spectral tail.•The f−4 and f−5 power laws exist in spectra with swell.•The spectra of large swell with very low winds also display f−4 and f−5 power ...laws. The coefficients depend on swell steepness.•Direction of the waves in the high frequency range of the spectrum is that of the wind.•A simple equation for stress encompassing the swell effect is developed.
Investigation of 37,106 ocean surface wave spectra from the Pacific, Atlantic Ocean, and Gulf of Mexico demonstrate that swell modulates the energy level of the high frequency tail of the wind-sea wave spectrum, altering sea surface roughness. With a mixture of sea and swell, the wind-sea part of spectra follows the well-known f-4 (equilibrium range) and f-5 (saturation range) power laws. Swell modulates the energy levels but does not change the power-law structure. For swell with minimal winds, the spectra follow the −4, −5 power-law paradigm, but energy correlates to swell steepness not wind speed. Swell shifts the transition between the two sub-ranges towards lower frequencies. For sea-swell mixtures, a modulation factor λ is proposed that depends on wind speed and swell steepness which allows parameterization of the spectral tail. Comparison of large swell with little wind to wind-sea spectra of same height and period, indicates that there is little difference in spectral shape and suggests that the Hasselmann Snl source term is likely the mechanism by which energy is transferred into the wind-sea tail causing the modulation. Analysis of 33,000+ directional spectra at Ocean Station Papa shows that the mean direction for the wind-sea high frequency tail is strongly correlated to wind direction, no matter the swell direction or steepness or level of swell dominance.
An equation for the friction velocity of a sea state with swell (u*s) is developed, u∗s=λ1/2u∗o where u∗o is the friction velocity in the absence of swell, by neglect of the direct swell impact. Noting that this is only a partial estimate of the total measured stress, the prediction is evaluated for 3,000+ observed spectra yielding a correlation of 0.91 suggesting that it may be of consequence. Observations of u∗/u∗o suggest a dependence with swell steepness that is similar to that predicted by λ1/2. At low winds, λ1/2 overestimates the stress, but noting that it was derived absent the components from the swell frequencies. In the tail, the momentum transport is downward, while in the swell the transport is predominantly upward, suggests a possible correction for λ1/2. The case of a swell generated wind is discussed.
Abstract
The capability of phased-array HF radar systems to sample the spatial distribution of wave energy is investigated in different storm scenarios and coastal configurations. First, a ...formulation introduced by D. E. Barrick to extract significant wave height Hs from backscatter Doppler spectra was calibrated and subsequently tested (to assess bias and uncertainty) with data from seven different buoy/gauge stations collected during three different field experiments. Afterward, Hs observations were obtained for selected sampling locations within the radar effective domain (in all experiments), and a filtering technique based on wavelet transform characterization and decomposition was applied. The accuracy of the filtered radar-derived observations was assessed by comparing these estimates to results from independently calibrated wave propagation models. It was found that the HF radar accurately measured the energy field induced by different storm events. The filtering technique minimized the contribution of unrealistic features introduced by the presence of defective sampling, which is intrinsic to radar remote sensing at this frequency, and it proved to be central for the use of the HF radar as a tool to identify wave energy trends and potential zones of wave energy concentration in coastal areas. These findings show that the sampling capabilities of radar systems may be greatly enhanced because reliable wave energy estimates can be obtained in addition to conventional surface current measurements. This is particularly important in locations such as harbor entrances where in situ measuring devices cannot be deployed.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
We investigated the spectral structure and source term balance of short gravity waves, based on in situ observations of wave number spectra retrieved by air‐sea interaction spar (ASIS) buoys. The ...behaviors of wave number spectra up to 10 rad/m (the gravity wave regime) were analyzed for a wide range of wind and wave conditions. The observed wave number spectra showed the spectral power laws described by Toba (1973) and Phillips (1958) in addition to the characteristic nodal point at ∼10 rad/m where spectral energy becomes constant over the entire wind speed range. We also improved the third‐generation wave model using the nonlinear dissipation term. The wave model reproduced the spectral form in the higher wave number domain. In the equilibrium range, nonlinear transfer played a major role in maintaining equilibrium conditions. On the other hand, in the saturation range, which starts at the upper limit of the equilibrium range, the nonlinear transfer tended to be out of balance with other source terms, and the dissipation term was in balance with wind input.
Key Points
We investigated the characteristics of short gravity wind waves
The observed wave number spectra showed the spectral power laws
The nonlinear dissipation term significantly improved the model results
Internal gravity waves in an area northeast of Taiwan are characterized using data from multiple sensor types. The data set includes intermittent information collected from a ship and short time ...series from moorings. Modeled nonlinear waves are fitted to observed nonlinear waves to
provide self-consistent estimates of multiple wave parameters. A nonlinear internal wave of over 50 m amplitude, observed in deep water, is examined in detail. This wave was moving northward from the southern Okinawa Trough toward the continental shelf, and presumably formed from internal
tides propagating northward from the Ilan Ridge area. A possible scenario for the formation of this wave from the internal tide is compared to related behavior south of Taiwan. On the outer continental shelf, a few large internal waves with maximum displacement greater than one-quarter of
the water depth were measured with moorings. Sensors aboard ship and satellite recorded waves in this area traveling in many directions. Two possible causes (not mutually exclusive) for the multiple wave directions are scattering of nonlinear internal waves arriving from the south, and variable
local generation of nonlinear gravity waves by the strong tidal and internal tidal currents. Internal tides on the shelf are relatively strong, among the strongest measured, having about 10 times greater kinetic energy density than numerous low-energy sites, which is consistent with the strong
barotropic tides of the area. The ratio of diurnal baroclinic to barotropic kinetic energy found in this area is unusually high.
The validation of estimates of ocean surface current speed and direction from high‐frequency (HF) Doppler radars can be obtained through comparisons with measurements from moored near‐surface current ...meters, acoustic Doppler current profilers, or drifters. Expected differences between current meter (CM) and HF radar estimates of ocean surface vector currents depend on numerous sources of errors and differences such as instrument and sensor limitations, sampling characteristics, mooring response, and geophysical variability. We classify these sources of errors and differences as being associated exclusively with the current meter, as being associated exclusively with the HF radar, or as a result of differing methodologies in which current meters and HF radars sample the spatially and temporally varying ocean surface current vector field. In this latter context we consider three geophysical processes, namely, the Stokes drift, Ekman drift, and baroclinicity, which contribute to the differences between surface and near‐surface vector current measurements. The performance of the HF radar is evaluated on the basis of these expected differences. Vector currents were collected during the High Resolution Remote Sensing Experiment II off the coast of Cape Hatteras, North Carolina, in June 1993. The results of this analysis suggest that 40%–60% of the observed differences between near‐surface CM and HF radar velocity measurements can be explained in terms of contributions from instrument noise, collocation and concurrence differences, and geophysical processes. The rms magnitude difference ranged from 11 to 20 cm s−1 at the four mooring sites. The average angular difference ranged between 15° and 25° of which about 10° is attributed to the directional error of the radar current vector estimates due to the alignment of the radial beams.
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