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Wind and Wave Climate
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Wind and Wave Climate

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 13706

Special Issue Editor

Dr. Christos Stefanakos

E-Mail Website
Guest Editor
Marine Modelling and Analysis, SINTEF Ocean, 7465 Trondheim, Norway
Interests: wave climate analysis (offshore and nearshore); stochastic (probabilistic) modelling of marine environmental parameters (e.g., waves, wind, currents, temperature, salinity, density); assessment of offshore wind and wave energy potential; extreme-value analysis; duration analysis; time series analysis and forecasting; fuzzy information systems

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to present recent advances in the field of Wind and Wave Climate. A good knowledge of Wind and Wave Climate is of paramount importance for the design and operation of various types of floating and coastal structures, as well as for environmental studies. The study of Wind and Wave Climate entails various tools of probability theory and mathematical statistics, including machine learning techniques.

In this respect, studies related either to methodological and/or application-oriented aspects are welcome in this Special Issue. The former includes—among others—topics related to probabilistic modelling, extreme-value analysis, machine learning techniques, spectral analysis, directional analysis, and forecasting techniques. Some examples for the latter include: wind and wave atlases (both local and global scale), climate change related studies, design of floating structures (offshore platforms, ships, marine renewable energy devices, offshore and nearshore fish farms etc.) and coastal structures, marine sea operations related to such structures, marine traffic, marine energy resource assessments, wind and wave reanalyses, and coastal morphodynamics.

Dr. Christos Stefanakos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Wind and wave climate
  • Offshore wind and wave energy
  • Numerical modelling
  • Forecasting methods
  • Extreme values
  • Directional statistics
  • Machine learning techniques
  • Reanalysis and hindcasts
  • Climate change
  • Probabilistic and statistical modelling

Published Papers (6 papers)

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Research

32 pages, 1037 KiB  
Article
Investment Evaluation and Partnership Selection Model in the Offshore Wind Power Underwater Foundations Industry
by Min-Yuan Cheng and Yung-Fu Wu
J. Mar. Sci. Eng. 2021, 9(12), 1371; https://doi.org/10.3390/jmse9121371 - 02 Dec 2021
Cited by 1 | Viewed by 1534
Abstract
With a plan to achieve a target of 5.7 GW offshore wind power capacity in 2025, Taiwan anticipates building a 36-billion USD industry, which makes Taiwan a center of attention in the global marketplace of civil engineering construction. Aimed at Taiwan’s underwater foundations [...] Read more.
With a plan to achieve a target of 5.7 GW offshore wind power capacity in 2025, Taiwan anticipates building a 36-billion USD industry, which makes Taiwan a center of attention in the global marketplace of civil engineering construction. Aimed at Taiwan’s underwater foundations industries, this study is the first to develop an investment evaluation model (IEM) by applying FPR to obtain risk factor weights and calculate the overall investment risk value with a numerical scoring method. In a context where no precedent exists for reference, this study provides auxiliary and supportive tools to help builders to make the decision, based on objective indicators, whether to undertake an investment. To date, no research has been conducted to introduce a reasonable mathematical model that discusses the issue of partner selection in the field of offshore wind power. This study is the first paper to construct a SWARA-FTOPSIS partner selection model, which enables underwater foundations builders to take specific Taiwanese characteristics into account in their selection of the best partners to meet transportation, construction, and installation requirements. Finally, the study uses the case of the Taipower Offshore Wind Power Project (2nd phase) to verify the feasibility of this model. Full article
(This article belongs to the Special Issue Wind and Wave Climate)
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19 pages, 28556 KiB  
Article
A Scheme for Estimating Time-Varying Wind Stress Drag Coefficient in the Ekman Model with Adjoint Assimilation
by Xinping Wu, Minjie Xu, Yanqiu Gao and Xianqing Lv
J. Mar. Sci. Eng. 2021, 9(11), 1220; https://doi.org/10.3390/jmse9111220 - 04 Nov 2021
Cited by 4 | Viewed by 1600
Abstract
In this study, the time-varying wind stress drag coefficient in the Ekman model was inverted by the cubic spline interpolation scheme based on the adjoint method. Twin experiments were carried out to investigate the influences of several factors on inversion results, and the [...] Read more.
In this study, the time-varying wind stress drag coefficient in the Ekman model was inverted by the cubic spline interpolation scheme based on the adjoint method. Twin experiments were carried out to investigate the influences of several factors on inversion results, and the conclusions were (1) the inverted distributions with the cubic spline interpolation scheme were in good agreement with the prescribed distributions of the wind stress drag coefficients, and the cubic spline interpolation scheme was superior to direct inversion by the model scheme and Cressman interpolation scheme; (2) the cubic spline interpolation scheme was more advantageous than the Cressman interpolation scheme even if there is moderate noise in the observations. The cubic spline interpolation scheme was further validated in practical experiments where Ekman currents and wind speed derived from mooring data of ocean station Papa were assimilated. The results demonstrated that the variation of the time-varying wind stress drag coefficient with time was similar to that of wind speed with time, and a more accurate inversion result could be obtained by the cubic spline interpolation scheme employing appropriate independent points. Overall, this study provides a potential way for efficient estimation of time-varying wind stress drag coefficient. Full article
(This article belongs to the Special Issue Wind and Wave Climate)
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17 pages, 3365 KiB  
Article
Assimilation Research of Wind Stress Drag Coefficient Based on the Linear Expression
by Junli Xu, Yuling Nie, Kai Ma, Wenqi Shi and Xianqing Lv
J. Mar. Sci. Eng. 2021, 9(10), 1135; https://doi.org/10.3390/jmse9101135 - 15 Oct 2021
Cited by 1 | Viewed by 1305
Abstract
The wind stress drag coefficient plays an important role in storm surge models. This study reveals the influences of wind stress drag coefficients, which are given in form of formulas and inverted by the data assimilation method, on the storm surge levels in [...] Read more.
The wind stress drag coefficient plays an important role in storm surge models. This study reveals the influences of wind stress drag coefficients, which are given in form of formulas and inverted by the data assimilation method, on the storm surge levels in the Bohai Sea, Yellow Sea, and East China Sea during Typhoon 7008. In the process of data assimilation, the drag coefficient is based on the linear expression Cd = (a + b × U10) × 10−3 (generally speaking, a and b are empirical parameters determined by observed data). The results showed that the performance of the data assimilation method was far superior to those of drag coefficient formulas. Additionally, the simulated storm surge levels obviously changed in the neighborhood of typhoon eye. Furthermore, the effect of initial values of a and b in the Cd expression on the storm surge levels was also investigated when employing the data assimilation method. The results indicated that the simulation of storm surge level was the closest to the observation when a and b were simultaneously equal to zero, whereas the simulations had slight differences when the initial values of a and b were separately equal to the drag coefficients from the work of Smith, Wu, and Geernaert et al.. Therefore, we should choose appropriate initial values for a and b by using the data assimilation method. As a whole, the data assimilation method is much better than drag coefficient parameterization formulas in the simulation of storm surges. Full article
(This article belongs to the Special Issue Wind and Wave Climate)
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18 pages, 5165 KiB  
Article
Global Wind and Wave Climate Based on Two Reanalysis Databases: ECMWF ERA5 and NCEP CFSR
by Christos Stefanakos
J. Mar. Sci. Eng. 2021, 9(9), 990; https://doi.org/10.3390/jmse9090990 - 11 Sep 2021
Cited by 6 | Viewed by 2352
Abstract
In the present work, the global wind and wave climate is studied on the basis of two well-known reanalysis products, namely ERA5 and CFSR-W (WW3 hereafter). Several statistical features of the datasets are assessed, such as seasonal variability, quantiles of the probability distribution, [...] Read more.
In the present work, the global wind and wave climate is studied on the basis of two well-known reanalysis products, namely ERA5 and CFSR-W (WW3 hereafter). Several statistical features of the datasets are assessed, such as seasonal variability, quantiles of the probability distribution, monthly, annual and inter-annual variability, and several error metrics. The time span covers a period of 31 years (1979–2009), a fact that assures that most of the long-scale features are equally present in both datasets. The analysis performed is depicted both on a global and regional scale. The results are also assessed by means of a global satellite altimeter dataset. Full article
(This article belongs to the Special Issue Wind and Wave Climate)
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20 pages, 25025 KiB  
Article
Verification and Validation of a Methodology to Numerically Generate Waves Using Transient Discrete Data as Prescribed Velocity Boundary Condition
by Rafael P. Maciel, Cristiano Fragassa, Bianca N. Machado, Luiz A. O. Rocha, Elizaldo D. dos Santos, Mateus N. Gomes and Liércio A. Isoldi
J. Mar. Sci. Eng. 2021, 9(8), 896; https://doi.org/10.3390/jmse9080896 - 19 Aug 2021
Cited by 7 | Viewed by 2240
Abstract
This work presents a two-dimensional numerical analysis of a wave channel and a oscillating water column (OWC) device. The main goal is to validate a methodology which uses transient velocity data as a means to impose velocity boundary condition for the generation of [...] Read more.
This work presents a two-dimensional numerical analysis of a wave channel and a oscillating water column (OWC) device. The main goal is to validate a methodology which uses transient velocity data as a means to impose velocity boundary condition for the generation of numerical waves. To achieve this, a numerical wave channel was simulated using regular waves with the same parameters as those used in a laboratory experiment. First, these waves were imposed as prescribed velocity boundary condition and compared with the analytical solution; then, the OWC device was inserted into the computational domain, aiming to validate this methodology. For the numerical analysis, computational fluid dynamics ANSYS Fluent software was employed, and to tackle with water–air interaction, the nonlinear multiphase model volume of fluid (VOF) was applied. Although the results obtained through the use of discrete data as velocity boundary condition presented a little disparity; in general, they showed a good agreement with laboratory experiment results. Since many studies use regular waves, there is a lack of analysis with ocean waves realistic data; thus, the proposed methodology stands out for its capacity of using realistic sea state data in numerical simulations regarding wave energy converters (WECs). Full article
(This article belongs to the Special Issue Wind and Wave Climate)
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27 pages, 38894 KiB  
Article
Enhanced Weight-Optimized Recurrent Neural Networks Based on Sine Cosine Algorithm for Wave Height Prediction
by Alawi Alqushaibi, Said Jadid Abdulkadir, Helmi Md Rais, Qasem Al-Tashi, Mohammed G. Ragab and Hitham Alhussian
J. Mar. Sci. Eng. 2021, 9(5), 524; https://doi.org/10.3390/jmse9050524 - 12 May 2021
Cited by 23 | Viewed by 2975
Abstract
Constructing offshore and coastal structures with the highest level of stability and lowest cost, as well as the prevention of faulty risk, is the desired plan that stakeholders seek to obtain. The successful construction plans of such projects mostly rely on well-analyzed and [...] Read more.
Constructing offshore and coastal structures with the highest level of stability and lowest cost, as well as the prevention of faulty risk, is the desired plan that stakeholders seek to obtain. The successful construction plans of such projects mostly rely on well-analyzed and modeled metocean data that yield high prediction accuracy for the ocean environmental conditions including waves and wind. Over the past decades, planning and designing coastal projects have been accomplished by traditional static analytic, which requires tremendous efforts and high-cost resources to validate the data and determine the transformation of metocean data conditions. Therefore, the wind plays an essential role in the oceanic atmosphere and contributes to the formation of waves. This paper proposes an enhanced weight-optimized neural network based on Sine Cosine Algorithm (SCA) to accurately predict the wave height. Three neural network models named: Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (VRNN), and Gated Recurrent Network (GRU) are enhanced, instead of random weight initialization, SCA generates weight values that are adaptable to the nature of the data and model structure. Besides, a Grid Search (GS) is utilized to automatically find the best models’ configurations. To validate the performance of the proposed models, metocean datasets have been used. The original LSTM, VRNN, and GRU are implemented and used as benchmarking models. The results show that the optimized models outperform the original three benchmarking models in terms of mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE). Full article
(This article belongs to the Special Issue Wind and Wave Climate)
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