Yongzeng Yang

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.

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.

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.

Generally, wave energy resource assessment and characterization are performed based on an entire wave spectrum, ignoring the detailed energy features that belong to wave systems, i.e., wind waves and swells. In reality, the energy is separately possessed by multiple wave systems, propagating at different directions and velocities. Therefore, it is the wave system that is the most fundamental unit of the wave energy resource. Although detailed analyses of wind waves and swells can be conducted via wave system partitioning, operational assessment methods that can reveal the detailed wave energy characteristics of wave systems still deserve further development. Following a two-step partitioning procedure, this paper presents an improved approach to the characterization of wave energy resources based on grouped wave systems. Wave systems classified as the same group are consistent in terms of propagation direction, velocity, and other characteristics of wave energy, but these characteristics between the groups are obviously different. Therefore, in comparison with the traditional method, the new approach can reveal more comprehensive and more detailed characteristics of the wave energy resource in terms of (i) wind-sea and swell components, (ii) directionality, and (iii) wave conditions; details that represent valuable information for the improvement of the performance of wave energy converter devices and the optimization of the layout of device arrays in wave farms.

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.

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.

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.

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.

Turbulent motions in the thin ocean surface boundary layer control exchanges of momentum, heat and trace gases between the atmosphere and ocean. However, present parametric equations of turbulent motions that are applied to global climate models result in systematic or substantial errors in the ocean surface boundary layer. Significant mixing caused by surface wave processes is missed in most parametric equations. A Large Eddy Simulation model is applied to investigate the wave-induced mixed layer structure. In the wave-averaged equations, wave effects are calculated as Stokes forces and breaking waves. To examine the effects of wave parameters on mixing, a series of wave conditions with varying wavelengths and heights are used to drive the model, resulting in a variety of Langmuir turbulence and wave breaking outcomes. These experiments suggest that wave-induced mixing is more sensitive to wave heights than to the wavelength. A series of numerical experiments with different wind intensities-induced Stokes drifts are also conducted to investigate wave-induced mixing. As the wind speed increases, the influence depth of Langmuir circulation deepens. Additionally, it is observed that breaking waves could destroy Langmuir cells mainly at the sea surface, rather than at deeper layers.