Ocean Modelling最新文献

The Yellow Sea Cold Water Mass (YSCWM), surrounded by thermocline and fronts, is one of the most notable hydrological characteristics of the Yellow Sea (YS) in the summer. Temperature structure at the boundary of the YSCWM drives the Yellow Sea Cold Water Mass Circulation (YSCWMC). However, the 3D structure of YSCWMC remains unclear. The position, seasonal evolution, and dynamical mechanisms of the YSCWMC were examined by observations and high-resolution numerical model. It was found that the core of the YSCWMC is located at the junction of the fronts and the thermocline during the summer. Furthermore, the YSCWMC exhibits remarkable seasonal variations characterized by a progressive shrinking and deepening dependence on the position and strength of the fronts and thermocline. The YSCWMC is geostrophic at the basin scale, with the barotropic pressure term determining the direction of the circulation and the baroclinic pressure term controlling the vertical structure of the circulation. Fronts yield a baroclinic effect in the thermal field and affect the sea surface elevation in the barotropic process. Therefore, fronts are crucial to the formation of the YSCWMC. Nevertheless, under conditions of steep topography, tidal rectification is comparable to the frontal contribution to the circulation.

To fully resolve eight major tides from short-term records, classical harmonic analysis model usually infers unresolved constituents with the help of inference relationships from nearby long-term tide gauges. Our previous study developed a modified harmonic analysis model using the credo of smoothness (i.e., MHACS) which can achieve this without inference relationships. Via introducing the inherent natural links between major tides, MHACS breaks the restrictions of the Rayleigh criterion and requires only ∼9-day hourly records to resolve eight major tides. However, when data length is shorter than 9 days, the results of MHACS become problematic due to over-fitting. In this study, we introduce ridge regression to replace ordinary least squares (OLS) in the MHACS. Practical experiments on short-term hourly tide gauge records and satellite altimeter observations indicate that ridge regression can effectively eliminate meaningless mathematical artifacts obtained by OLS. The minimum length of records for MHACS to resolve eight major tides dramatically decreases from ∼210 h to ∼75 h as a result of using ridge regression. It is also found that ridge regression can notably reduce the uncertainties of tidal estimates from MHACS. Moreover, other modified harmonic analysis models such as NS_TIDE designed for river tides also suffer from over-fitting which can be solved by ridge regression in a similar way.

Here we address the problem of coherent interference that arises in double-sum wavemakers of wave-resolving models. Identified as a key problem for experimental and numerical simulations since the late 1970s, this problem induces spurious persistent longshore variability and affects nearshore dynamics. To overcome this problem, we present the implementation of a single-sum wavemaker in the 3D wave-resolving model CROCO. The new wavemaker, which assigns only one pair of direction and frequency values to each component of the wave spectrum, definitively prevents coherent interference, unlike a conventional double-sum wavemaker that allows waves of different direction to share the same frequency. Each wave component must also strictly comply with the periodicity rules, to avoid any spurious boundary dynamics. We validate the single-sum wavemaker with experimental data collected in a wave basin with longshore-uniform bathymetry and compare results with the double-sum wavemaker simulations. We show that the new wavemaker produces transient rips devoid of any coherent interference effect and that, consequently, the model statistics closely match the experimental data. The new wavemaker therefore guarantees statistical integrity while reducing computational costs, a necessary step for realistic wave-resolving studies of nearshore dynamics.

Thirteen laboratory and field tests were carried out with vibrating sea ice beams to study the dependence of the elastic modulus of sea ice in the spectral range from 1 Hz to 500 Hz. Six full-scale tests with floating fixed ends beams were carried out on the land fast ice of the Spitsbergen fjords. For laboratory testing, smaller ice beams were made sea ice of the same fjords. Three tests with columnar fresh lake ice S2 were conducted to validate the method for calculating of the added mass of a floating beam with fixed ends. A 60% increase in the elastic modulus of columnar sea ice S2 was found due to an increase in the frequency of flexural deformations in the range from 10 Hz to 500 Hz. The paper also discusses the influence of ice structure on the elastic modulus.

Auclair et al. (2021) analyzed the propagation of acoustic-gravity waves (AGWaves) in the ocean and showed that AGWaves dispersion can be described based on the inner and boundary dispersion relations. A major limitation to their two-dispersion-relation model is the assumption of a rigid bottom boundary since acoustic waves can cross the ocean bottom and propagate in the sediment. An extension of their AGWaves-dispersion model is consequently proposed toward a more realistic two-layers fluid model. This improvement enables the evaluation of the perspectives opened by the new generation of compressible ocean models for ocean-acoustics applications. The acoustic regimes in this resulting model are shown to be in agreement with underwater acoustics literature. In addition, the free-surface boundary condition is in turn compared to the pressure release boundary condition to establish a bridge with classical acoustic dispersion models.

The paper presents a morphodynamic model which can be coupled with any wave model capable of producing time/spectral averaged wave quantities. This model based on a wave energy minimization principle highlights the morphodynamic phenomenology, such as the sandbar creation. Such a model can be used in solving engineering optimization problems. It is also developed to illustrate the idea that beach sand transport can be thought as a non-local phenomenon. We used wave calculations from SWAN and XBeach in our model, and we compared the morphodynamic results to LIP and SANDS hydro-morphodynamic benchmark as well as open-sea simulations. Using supplementary mathematical development, we improved the minimization method using the Hadamard derivative.

In recent years, Portugal's coastal regions have experienced an increase in the frequency and intensity of severe weather events, including tropical cyclones and extratropical storms. This paper presents an analysis of Hurricane Leslie(2018)'s impact on Portugal, with a specific focus on the complex and often underestimated meteotsunami phenomena accompanying the storm system. Our analysis examines data collected from multiple sources, and employs advanced numerical simulations, integrated within the GeoClaw framework. These simulations encompass both storm surge and meteotsunami effects. One of the findings is the significant role played by meteotsunamis in amplifying coastal sea levels during extreme weather events. The observed sea-level fluctuations closely align with the combined surge-meteotsunami simulations, emphasizing the importance of considering these high-frequency phenomena in coastal hazard assessments.

Due to strong non-linearity, ocean surface gravity waves are difficult to directly and accurately predict, despite their importance for a wide range of coastal, nearshore, and offshore activities. To minimize forecast errors, a hybrid combined improved empirical wavelet transform decomposition (IEWT) and long-short term memory network (LSTM) model has been proposed. Data from National Data Buoy Center buoys deployed in the North Pacific Ocean are taken as an example to verify the models. Wave forecasts using the LSTM, EWT-LSTM, and IWET-LSTM models are compared with the observations at 6, 12, 18, 24 and 48 h forecast windows. Consequently, IEWT-LSTM is superior to EWT-LSTM or LSTM models, especially for larger waves at longer long forecast windows.

Internal solitary waves (ISWs) play a crucial role in the development of various physical and biological processes, and numerous high-precision two-dimensional or three-dimensional numerical models have been developed to simulate the generation and propagation processes of ISWs. However, these numerical models, especially when simulating the interaction between ISWs and ocean circulation, require substantial computational resources. This burden can make it challenging to apply them in real-time or short-term forecasting scenarios. In this study, we propose a new numerical model for ISWs by combining traditional one-dimensional ISW theory with wave refraction theory. The proposed model resolves the issues of ray crossing and divergence, which are commonly encountered in traditional refraction models, by employing equally spaced grids along the wave crest line. As a result, this model is capable of simulating the far-field propagation of ISWs. This model enables rapid prediction of the vertical structure and wave crest morphology of ISWs in specific current fields and at given time frames, and it is utilized to investigate the characteristics and propagation of ISWs generated by the nonlinear steepening of internal tide (IT) in the South China Sea. Comparative analysis with satellite imagery demonstrates the model's accurate representation of ISW processes and phenomena, such as wave crest line discontinuities, diffraction, and wave‒wave interactions when passing through Dongsha Island. Furthermore, propagation time estimates based on this model have errors of ±0.98 h (1σ) over which the ISWs are observed by a mooring system, and the average time difference is 0.81 h

Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only “learned” the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG.