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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.

Accurate numerical simulation of ocean waves is one of the most important measures to ensure shipping safety, offshore engineering construction, etc. The use of wave observations from satellite is an efficient way to correct model results. The goal of this paper is to assess the performance of assimilation in the MASNUM wave model for the Indian Ocean. The assimilation technique is based on Ensemble Adjusted Kalman Filter, with a variable ensemble constructed by the dynamic sampling method rather than ensemble members of wave model. Observations of significant wave height from satellites Jason-3 and CFOSAT are regarded as assimilation data and independent validation data, respectively. The results indicate good performance in terms of absolute mean error for significant wave height. Model error decreases by roughly 20–40% in high-sea conditions.

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.

Typically, storing a two-dimensional wave spectrum could occupy more than one thousand storage units, making saving and reading boundary spectra computationally burdensome in nested wave simulations. This paper proposes a new approach for preservation of a wave spectrum that can reduce the required number of storage units to dozens. Using a corresponding reconstruction approach, the spectrum can then be rebuilt with intact spectral characteristics. Experimental application confirmed that the reconstructed spectra could be adopted as boundary conditions in nested wave modeling. The newly proposed approach for preservation and reconstruction of spectra allows long-term spectral information covering the entire simulated domain to be saved with more acceptable storage consumption, and such information can then be adopted as nesting conditions for nested-child simulations without the limitations of predefined boundaries. The above-mentioned properties of the new method could help support engineering projects concerning wave environments, research focused on wave climatology, and studies associated with wave energy assessment.

To investigate the effect of wave-induced mixing on the upper ocean structure, especially under typhoon conditions, an ocean-wave coupled model is used in this study. Two physical processes, wave-induced turbulence mixing and wave transport flux residue, are introduced. We select tropical cyclone (TC) Nepartak in the Northwest Pacific ocean as a TC example. The results show that during the TC period, the wave-induced turbulence mixing effectively increases the cooling area and cooling amplitude of the sea surface temperature (SST). The wave transport flux residue plays a positive role in reproducing the distribution of the SST cooling area. From the intercomparisons among experiments, it is also found that the wave-induced turbulence mixing has an important effect on the formation of mixed layer depth (MLD). The simulated maximum MLD is increased to 54 m and is only 1 m less than the observed value. The wave transport flux residue shows a dominant role in the mixed layer temperature (MLT) changing. The mean error of the MLT is reduced by 0.19 °C compared with the control experiment without wave mixing effects. The study shows that the effect of wave mixing should be included in the upper ocean structure modeling.

Marine dynamic disasters, such as storm surges and huge waves, can cause large economic and human losses. The assessment of marine dynamic disasters is, thus, important, but improvements to its reliability are needed. The current study improved and integrated the assessment from the perspective of multi-factor coupling. Using a weighted index system, a marine dynamic disaster assessment indicator system suitable for China’s coastal waters was established, and a method for calculating the weight of disaster indicators was proposed from the perspective of rapid assessment. To reduce the assessment deviation in coastal waters, a multi-factor coupling algorithm was proposed. This algorithm obtained amplitude variations of wave orbital motion in horizontal and vertical directions, which was used to evaluate the influence of background current and terrain slope on coastal ocean waves. Landsat 8 remote sensing images were used to carry out an object-oriented extraction of raft and cage aquaculture areas in China’s coastal waters. The aquaculture density was then used as the main basis for a vulnerability assessment. Finally, the whole assessment system was integrated and verified during a typical storm surge process in coastal waters around the Shandong Peninsula in China. The coupled variations were also added to the assessment process and increased the risk value by an average of 12% in the High Sea States of the case study.

The quasi-linear or nonlinear interactions among different ocean motions dominate the system internal structure and appearance feature presented in spatial and temporal evolution. However, deficiency of the characteristics set computation model for internal wavenumber spectra proves to be a serious barrier to derive interaction mechanisms of internal waves with large or small scale ocean motions. In this study, a characteristics set computation model for internal wavenumber spectra is proposed for complicated offshore environments. The refraction of current shear instability, bottom topography and the reflection at surface and bottom are attentively considered in the complicated characteristics inlaid scheme. Model results are validated with MODIS retrieved internal wave parameters in the Sulu Sea and Celebes Sea. This original characteristics set computation model for internal wavenumber spectra can be used widely and can further improve the understandings of generation, dissipation, nonlinear wave-wave interaction and mixing process of internal waves.

The receptive field (RF) plays a crucial role in convolutional neural networks (CNNs) because it determines the amount of input information that each neuron in a CNN can perceive, which directly affects the feature extraction ability. As the number of convolutional layers in CNNs increases, there is a decay of the RF according to the two-dimensional Gaussian distribution. Thus, an effective receptive field (ERF) can be used to characterize the available part of the RF. The ERF is calculated by the kernel size and layer number within the neural network architecture. Currently, ERF calculation methods are typically applied to single-channel input data that are both independent and identically distributed. However, such methods may result in a loss of effective information if they are applied to more general (i.e., multi-channel) datasets. Therefore, we proposed a multi-channel ERF calculation method. By conducting a series of numerical experiments, we determined the relationship between the ERF and the convolutional kernel size in conjunction with the layer number. To validate the new method, we used the recently published global wave surrogate model for climate simulation (GWSM4C) and its accompanying dataset. According to the newly established relationship, we refined the kernel size and layer number in each neural network of the GWSM4C to produce the same ERF but lower RF attenuation rates than those of the original version. By visualizing the gradient map at several points in West African and East Pacific areas, the high gradient value regions confirmed the known swell sources, which indicated effective feature extraction in these areas. Furthermore, the new version of the GWSM4C yielded better prediction accuracy for significant wave height in global swell pools. The root mean square errors in the West African and East Pacific regions reduced from approximately 0.3 m, in the original model to about 0.15 m, in the new model. Moreover, these improvements were attributed to the higher efficiency of the newly modified neural network structure that allows the inclusion of more historical winds while maintaining acceptable computational consumption.

Wave–current interaction in coastal regions is significant and complicated. Most wave models consider the influence of ocean current and water depth on waves, while the influence of the gradient of the sea bottom slope is not taken into account in most research. This study aimed to analyze and quantify the contribution of storm tidal currents to coastal ocean waves in a case where sea bottom slope was not ignored. Fourier analysis was applied to solve the governing equation and boundary conditions, and an analytic model for the calculation of the variation of amplitude of wave orbital motion was proposed. Ocean currents affect ocean waves through resonance. In this paper, an implemented instance of this analytic model was given, using the Shengsi area during Typhoon Malakas as an example. The results suggest that vertical variation in the amplitude of wave orbital motion is remarkable. The impact of wave–current interaction is noticeable where the gradient of the sea bottom slope is relatively large.

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.