Explore the vast range of titles available.

The fractional or percentage whitecap coverage of the ocean surface (W)$(W)$is often parameterized in terms of wind speed. Data sets of W$W$typically show order of magnitude scatter at a given wind speed value due to sea state variability. Here we compare modeled values of W$W$to measured W$W$values from the North Atlantic Ocean. The modeled W$W$is forced by the spectrally integrated whitecap dissipation source function in the European Center for Medium‐Range Weather Forecasts spectral wave model, ecWAM. Without tuning, best agreement is found for mature sea states, with an average modeled to measured W$W$ratio of 0.87. This ratio approaches unity with the introduction of a dissipation rate threshold value and an explicit wave‐age dependence. The study suggests that accurate estimates of W$W$can be routinely produced by ecWAM and opens new opportunities to model bubble‐mediated fluxes of CO2${\\text{CO}}_{2}$and sea spray aerosol with ecWAM. Plain Language Summary Predicting the frequency of occurrence and scale of wave breaking at the ocean surface is extremely difficult. In the past, many approaches have used wind speed as a predictor of the ocean surface coverage in whitecap foam (W)$(W)$generated by sufficiently energetic breaking waves. These relationships do not capture the inherent sea state‐dependent variability in wave breaking at a given wind speed and better approaches are needed. In this paper, we show how sophisticated wave models can be used to accurately reproduce measured W$W$values from the North Atlantic over a wind range of wind speeds and sea states. This result implies that we can move away from using wind speed to predict W$W$values. This has positive implications for how bubble‐mediated fluxes of greenhouse gases and sea spray aerosol particles can be more accurately parameterized in global climate models. Key Points Values of whitecap coverage made with the ECMWF spectral wave model, ecWAM, are in good agreement with observations from the North Atlantic Improved model estimates of W$W$are achieved by introducing a wave age dependency and an energy dissipation rate threshold Accurate estimates of whitecap coverage can be made with ecWAM

Whitecaps formed by breaking waves and air entrainment are readily visible on the ocean surface, with their high albedo significantly impacting the accuracy of remote sensing retrievals. While most traditional whitecap parameterizations rely only on wind speed, these approaches fail to explain complex variations in whitecap coverage. Satellite-derived whitecap data, based on brightness temperature variations from the WindSat radiometer, provide valuable global observations of whitecap coverage. To effectively utilize these satellite-derived data, we propose a transfer learning approach for predicting global whitecap coverage. The model is first pre-trained using modeling data based on statistical wave-breaking theory and subsequently fine-tuned with satellite-derived observations. The fine-tuned model demonstrates significant improvements over both the pre-trained model and traditional wind speed parameterizations when evaluated on independent satellite-derived test data. Through explainable deep learning methods, we identify that whitecap coverage is modulated by various atmospheric and wave parameters. The variable contribution analysis reveals the significant impacts of wind–wave interaction, wave states, and atmospheric stability on whitecap formation and coverage.

Oceanic whitecaps are caused by wave breaking and are very important in air–sea interactions. Usually, whitecap coverage is considered a key factor in representing the role of whitecaps. However, the accurate identification of whitecap coverage in videos under dynamic marine conditions is a tough task. An EMA-SE-ResUNet deep learning model was proposed in this study to address this challenge. Based on a foundation of residual network (ResNet)-50 as the encoder and U-Net as the decoder, the model incorporated efficient multi-scale attention (EMA) module and squeeze-and-excitation network (SENet) module to improve its performance. By employing a dynamic weight allocation strategy and a channel attention mechanism, the model effectively strengthens the feature representation capability for whitecap edges while suppressing interference from wave textures and illumination noise. The model’s adaptability to complex sea surface scenarios was enhanced through the integration of data augmentation techniques and an optimized joint loss function. By applying the proposed model to a dataset collected by a shipborne camera system deployed during a comprehensive fishery resource survey in the northwest Pacific, the model results outperformed main segmentation algorithms, including U-Net, DeepLabv3+, HRNet, and PSPNet, in key metrics: whitecap intersection over union (IoUW) = 73.32%, pixel absolute error (PAE) = 0.081%, and whitecap F1-score (F1W) = 84.60. Compared to the traditional U-Net model, it achieved an absolute improvement of 2.1% in IoUW while reducing computational load (GFLOPs) by 57.3% and achieving synergistic optimization of accuracy and real-time performance. This study can provide highly reliable technical support for studies on air–sea flux quantification and marine aerosol generation.

Whitecaps are crucial for understanding ocean-atmosphere interactions, particularly under high sea states, where quantifying whitecap coverage has long been a key research focus. This study aims to validate the Whitecap Statistical Physics Model (WSPM) under high sea states using observational data. Observational data from the High Wind Speed Gas Exchange Study (HiWinGS) was used to validate the WSPM. The model's performance was assessed across multiple sites under wind speeds exceeding 15 m/s and significant wave heights (SWH) up to 10 meters. The WSPM showed good agreement with observational data at most sites, accurately capturing variations in whitecap coverage. At the same time, discrepancies in the model results were observed, which were attributed to errors in the WSPM's data sources and complex sea conditions characterized by rapid shifts in wind direction and alternating dominance of wind waves and swell. This study highlights the advantages of physics-based models over simple wind-speed-dependent parameterizations in capturing the complexities of wave dynamics. The findings suggest that the WSPM is highly effective in capturing the dynamics of whitecap coverage across a range of high sea states, providing a detailed and robust reference for its application in real-world scenarios. Further research is needed to address the sources of error and improve the model's accuracy under complex sea conditions.

The results of field observations of breaking of surface spectral peak waves, taken from an oceanographic research platform, are presented. Whitecaps generated by breaking surface waves were detected using video recordings of the sea surface, accompanied by co-located measurements of waves and wind velocity. Whitecaps were separated according to the speed of their movement, c, and then described in terms of spectral distributions of their areas and lengths over c. The contribution of dominant waves to the whitecap coverage varies with the wave age and attains more than 50% when seas are young. As found, the whitecap coverage and the total length of whitecaps generated by dominant waves exhibit strong dependence on the dominant wave steepness, ϵp, the former being proportional to ϵp6. This result supports a parameterization of the dissipation term, used in the WAM model. A semi-empirical model of the whitecap coverage, where contributions of breaking of dominant and equilibrium range waves are separated, is suggested.

Research has shown considerable variability in whitecap coverage (W) under low to moderate wind conditions. During an expedition to the Northwestern Pacific, oceanographic variables and photographic measurements were collected to investigate the influence of wave-induced stress on W within these wind ranges. The friction velocity was recalculated based on turbulent stress, and wind profiles were modified to account for wave-induced stress and swell presence on the sea surface. The study examined W’s relationship with multiple parameters, including friction velocity (u*), breaking wave Reynolds numbers, wavesea Reynolds numbers, and wave age. The analysis utilized both conventional u* and turbulent stress-based friction velocity (u*turb). When utilizing u*turb rather than u*, the estimation model’s fitting results revealed an increase in correlation coefficient (R2) from 0.51 to 0.62, and a decrease in root mean square error (RMSE) from 0.0652 to 0.0574. Additionally, when parameterizing W using the windsea Reynolds number, with u*turb replacing u* and wind wave height substituting mixed wave height, the R2 increased from 0.38 to 0.53, and the RMSE decreased from 0.0737 to 0.0668. The results demonstrate that calculating u* using the turbulent stress-based method, along with wind wave height and peak wave speed of mixed waves, yields stronger correlation with W. This correlation improvement stems from the inhibition of wave breaking by swell and wave-induced stress. The integration of turbulent stress and wind wave field measurements enhances the understanding of relationships between W and various parameters. However, swell effects on wind profiles do not substantially affect W estimation using wind speed-related parameters.

This paper reports the results of field measurements of wave breaking modulations by dominant surface waves, taken from the Black Sea research platform at wind speeds ranging from 10 to 20 m/s. Wave breaking events were detected by video recordings of the sea surface synchronized and collocated with the wave gauge measurements. As observed, the main contribution to the fraction of the sea surface covered by whitecaps comes from the breaking of short gravity waves, with phase velocities exceeding 1.25 m/s. Averaging of the wave breaking over the same phases of the dominant long surface waves (LWs, with wavelengths in the range from 32 to 69 m) revealed strong modulation of whitecaps. Wave breaking occurs mainly on the crests of LWs and disappears in their troughs. Data analysis in terms of the modulation transfer function (MTF) shows that the magnitude of the MTF is about 20, it is weakly wind-dependent, and the maximum of whitecapping is windward-shifted from the LW-crest by 15 deg. A simple model of whitecaps modulations by the long waves is suggested. This model is in quantitative agreement with the measurements and correctly reproduces the modulations’ magnitude, phase, and non-sinusoidal shape.

The whitecap coverage at the sea surface is affected by the ratio of kinetic energy to potential energy, θ, the wave spectrum width parameter, ρ, and other factors. This paper validates an improved statistical theory for surface whitecap coverage. Based on the theoretical analysis, we find that the whitecap coverage is more sensitive to ρ than to θ, and the improved statistical theory for surface whitecap coverage is suitable in regions of rough winds and waves. The satellite-derived whitecap coverage data in the westerly wind zone is used to validate the improved theory. The comparison between the results from theory and observations displays a better performance from the improved theory relative to the other methods tested.

Callaghan and White (2009) put forward the automated whitecap extraction (AWE) technique to determine the whitecap coverage ( W ). An improved AWE was used to analyze images collected in the South China Sea during 2012 and 2013 and in western Pacific during 2015 to determine W . The influences of meteorological and oceanographic factors on whitecap coverage were investigated in this study. It is found that W increases with wind speed. Scale factor and exponent of parameterization for W (U 10 ) vary greatly in different models. Overall, there is a larger scatter of W at low wind speed than at high wind speed. W decreases with the increasing of wave age. Compared with wind speed, the scatter of W is smaller with wave age, which means the impact of wave age on the whitecap coverage is more robust under various environmental conditions. There is no significant dependence on SST and whitecap coverage seems to weakly decrease with SST. W decreases with the atmospheric stability. Relationship between W and wind speed change when swells are dominant. Swell can suppress wave breaking and decrease W . The effect is independent of the deflection angle between wind wave and swell.

An improvement was proposed for the statistical theory of breaking erttrainment depth and surface whitecap coverage of real sea waves in this study. The ratio of the kinetic and potential energy was estimated on a theoretical level, and optimal constants were determined to improve the statistical theory model for wave breaking. We also performed a sensitivity test to the model constants. A comparison between the model and in situ observations indicated that the level of agreement was better than has been achieved in previous studies.