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Reducing the dependence on fossil fuels and utilizing the renewable energy have become essential due to the global resource exhaustion and unfriendly environmental impact from coal, petroleum and natural gas. Therefore, the rising attention has been paid to wave energy characterized by sustainability, clean, high energy density and extensive distribution. As one of the most important parameters of wave energy, significant wave height (SWH) is difficult to forecast accurately due to the complex marine condition and ubiquitous presence of chaos in nature. In this research, a novel hybrid model called STL–CNN–PE which combines seasonal-trend decomposition procedure based on loess (STL) and one-dimensional convolutional neural networks (CNN) with positional encoding (PE) was proposed to forecast SWH efficiently and accurately. To evaluate the proposed model comprehensively, the hourly standard meteorology data at station 44007, 46087 and 51000 from NOAA’s National Data Buoy Center were selected for model training and testing. The experimental results indicated that STL–CNN–PE provided more reliable forecasting values than the single model. Meanwhile, STL–CNN–PE had enormous advantage on speed and similar precision compared with EMD-LSTM. Finally, the experimental results revealed that the models provided better forecasting metrics at deeper waters.

For the survival and development of ‘One Belt, One Road’, the present work aimed to evaluate the current situation of wave energy resources around Sri Lankan (SL) waters. Thirty-year ERA-Interim wind data were used to drive the third-generation wave model WAVEWATCH-III, and the seasonal and regional distribution characteristics of wave energy resources in SL waters were analyzed. Furthermore, the optimal season and region that contribute most to wave power in the study area were determined. On the basis of 30-year hindcast wave data, the significant wave height and wave power density, the occurrence of available SWH and rich WPD, the effective storage of wave energy, and the contribution and stability of wave energy were also analyzed. Results show that extremely optimistic wave energy resources are found at the western, southern, and southeastern waters of SL; moreover, the period of June, July, August (JJA) has great advantages in terms of the overall level of WPD, wave energy effective storage, and the contribution rate of wave energy. In addition, the wave energy during JJA is more stable than that of other periods and thus is beneficial to the transformation and development of wave energy. This study also provides important guiding value for disaster prevention and reduction, coastal zone management, and coastal development in the crucial region of the 21st Century Maritime Silk Road.

Short-term forecasts of wave energy play a key role in the daily operation, maintenance planning, and electrical grid operation of power farms. In this study, we propose a short-term wave energy forecast scheme and use the North Indian Ocean (NIO) as a case study. Compared with the traditional forecast scheme, our proposed scheme considers more forecast elements. In addition to the traditional short-term forecast factors related to wave energy (wave power, significant wave height (SWH), wave period), our scheme emphasizes the forecast of a series of key factors that are closely related to the effectiveness of the energy output, capture efficiency, and conversion efficiency. These factors include the available rate, total storage, effective storage, cooccurrence of wave power-wave direction, co-occurrence of the SWH-wave period, and the wave energy at key points. In the regional nesting of numerical simulations of wave energy in the NIO, the selection of the southern boundary is found to have a significant impact on the simulation precision, especially during periods of strong swell processes of the South Indian Ocean (SIO) westerly. During tropical cyclone ‘VARDAH’ in the NIO, as compared with the simulation precision obtained with no expansion of the southern boundary (scheme-1), when the southern boundary is extended to the tropical SIO (scheme-2), the improvement in simulation precision is significant, with an obvious increase in the correlation coefficient and decrease in error. In addition, the improvement is much more significant when the southern boundary extends to the SIO westerly (scheme-3). In the case of strong swell processes generated by the SIO westerly, the improvement obtained by scheme-3 is even more significant.

With the commercialization of offshore wind and the continued advancement of wave energy technologies, the option of locating both in the same sea area has emerged. The joint development of offshore wind and wave energy can effectively address the challenges faced by offshore wind and wave energy development, reduce costs, and improve the stability of power generation and output. This article introduces the current status of sea area utilization and marine functional zoning in Zhejiang Province and proposes a site selection method to identify the most suitable sea area for the construction of co-located offshore wind and wave farms in Zhejiang. First, a geographic information systems database was developed to identify unsuitable areas for co-located offshore wind and wave farms. Then, a literature review was conducted to establish a system of resource, economic, and technical selection indicators, and the Delphi method was used to determine the weight of each indicator. Finally, the sea areas suitable for the construction of co-located offshore wind and wave farms were evaluated and ranked, and the order of power plant development was given. The results of the study illustrate the potential of developing co-located offshore wind and wave farms in Zhejiang, especially in the northern part of Zhoushan and the southern part of Taizhou.

Research on the diffusion characteristics of swells contributes positively to wave energy forecasting, swell monitoring, and early warning. In this work, the South Indian Ocean westerly index (SIWI) and Indian Ocean swell diffusion effect index (IOSDEI) are defined on the basis of the 45-year (September 1957–August 2002) ERA-40 wave reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the impact of the South Indian Ocean westerlies on the propagation of swell acreage. The following results were obtained: 1) The South Indian Ocean swell mainly propagates from southwest to northeast. The swell also spreads to the Arabian Sea upon reaching low-latitude waters. The 2.0-meter contour of the swell can reach northward to Sri Lankan waters. 2) The size of the IOSDEI is determined by the SIWI strength. The IOSDEI requires approximately 2–3.5 days to fully respond to the SIWI. The correlations between SIWI and IOSDEI show obvious seasonal differences, with the highest correlations found in December–January–February (DJF) and the lowest correlations observed in June–July–August (JJA). 3) The SIWI and IOSDEI have a common period of approximately 1 week in JJA and DJF. The SIWI leads by approximately 2–3 days in this common period.

In this study, a long-term assessment of the wave energy in the China Sea was performed for a 30-year time interval (1988–2017), using the model WAVEWATCH-III. The reliability of the wave simulation results was increased by means of longer time horizon data compared to other relevant studies in the China Sea. This analysis provided information on the regional distribution as well as on the monthly and seasonal variability. The exploitation and stability of wave energy were taken into consideration, so as to find the advantage of resource exploitation. Results indicated that values of significant wave height and wave power density had obviously differences compared with different months, especially in December with a maximum significant wave height of 2.7 m and 35 kW/m of wave power density. The minimum value of them appeared in May, was 1.0 m and 4.5 kW/m, respectively. The distribution of wave energy was abundant in winter and the poorest in summer. In winter, the significant wave height in most areas was above 1.8 m, while the maximum wave energy density in summer was only 1.2 m. As for the wave power density, in winter values in most areas were above 18 kW/m, while the maximum value in summer was only 12 kW/m. In sight of regional distribution, the highest wave energy potential was located in the Northern South China Sea, the East China Sea, the Ryukyu Islands waters, east of the Taiwan Island and the Luzon Strait, with coefficient of variation was within 2.0 and occurrence of exploitation was above 80%, whereas the Bohai Sea, the northern part of the Yellow sea, the Gulf of Thailand, and the Northern Bay were in poor contribution, with occurrence of exploitation was within 50%.

At home and abroad, this chapter carries out the first detailed investigation on the climate characteristics of wind energy resource along the Maritime Silk Road. A series of key parameters are defined: availability of wind energy (effective wind speed occurrence, EWSO), available level occurrence (ALO, occurrence of WPD greater than 100 W/m2) moderate level occurrence (MLO, occurrence of WPD greater than 150 W/m2) rich level occurrence (RLO, occurrence of WPD greater than 200 W/m2) excellent level occurrence (ELO, occurrence of WPD greater than 300 W/m2) superb level occurrence (SLO, occurrence of WPD greater than 400 W/m2), to describe the availability and richness of wind energy. Systematically considering the wind power density, energy availability, energy level occurrences, energy stability, monthly and seasonal variability of wind energy, energy storage, the temporal-spatial distribution characteristics of wind energy along the Maritime Silk Road are analyzed, to provide theoretical support for the energy classification, short-term forecast and long-term projection of wind energy.

The research on medium and long-term forecasting of wind energy is still extremely scarce until now, although this work is closely related to the medium and long-term planning of wind energy development and power dispatching. This chapter used the CMIP data to carry out the long-term wind energy projection along the Maritime Silk Road for the next 40 years, which includes a series of key indicators, such as: the wind power density, energy availability, energy level occurrences, energy stability, monthly and seasonal variability, energy storage for the next 40 years, to provide reference for the long-term planning of wind energy utilization.

Currently, the studies on the wind power along the Road area are still scarce, let alone those on its climatic trends. However, these studies are the theoretical basis which is crucial to improving the accuracy of the mid- and long-term forecast and could further impact the mid- and long-term planning of wind power exploitation. Besides, nowadays there is abundant research on the evolution trends of the meteorology and ocean, but little on that of the wind power. In this chapter, the annual trend, and regional and seasonal difference of the trends of a series of key indicators of wind energy resource are calculated and analyzed for the first time, to provide a theoretical foundation for the improvement of medium – and long-term projection capacity for wind energy projection.

Currently, the marine and meteorological raw data are relatively abundant. However, the offshore wind energy data is scarce. How to extracted the useful information about wind energy development from the original big data with large volume and low information density, and then building a wind energy resource dataset has become the key support to the industrialization and efficient deployment of wind energy. It is also a common challenge for global colleague. In this chapter, the first open-ended and non-profit dataset of spatial-temporal characteristics of wind energy resource for the Maritime Silk Road at home and abroad was established, to provide scientific reference and data support for offshore wind energy resource evaluation. A wind energy resource, which should include the temporal-spatial characteristics, energy classification, climatic variation of wind energy, long-term projection of wind energy, wind climate, etc., was prospected to be established to provide a systematic data support for power plant site selection, daily operation, long-term planning, environmental safety guarantee for resource development.

The short-term wind power forecasting is closely linked to the collection and transformation of resources and their applications such as unit scheduling and power trading. Previous researchers have done tremendous work on the short-term forecasting of offshore wind power across the globe. However, there are no such studies specifically targeting the countries and regions along the Maritime Silk Road. Besides, current forecasting products mainly focuses on parameters such as wind speed and density while key indicators like usability, ELO, and storage amount have not been covered. In this chapter, an all-element short-term forecasting system of offshore wind energy resource was designed, comprehensively including the wind field, wind power density, energy availability, energy level occurrences, energy storage, etc., as well as the forecasting of wind energy of key nodes (hourly wind power density and wind direction, wind energy rose, etc.), to provide reference for the daily operation of wind power generation and so on.

The offshore wind energy can provide strong power support to overcome the energy crisis, as well as good opportunity for international exchange and cooperation, an antidote to climate change and conventional energy shortage, and important support to achieve the carbon neutrality target. Evaluation of resources and planning should go first. However, such evaluations are still relatively few owing to the difficulties faced by marine observation and shortage of research resources, which has been a barrier to developing offshore wind power into a bigger scale as an industry. Firstly, this chapter discussed the research status and difficulties (detail research on the climatic characteristics of wind energy, macro and micro-scale energy classification, relationship between wind energy and key indexes, short-term forecast of wind energy, climatic variation of wind energy, mid-long term projection of wind energy, energy evaluation on key nodes). Then the countermeasures to deal with the difficulties are provided.

Sri Lanka is located near the center of the main channel of the Indian Ocean. It is also an important place for the Maritime Silk Road initiative. This chapter carried out a technology of offshore wind energy evaluation on key island or vital region, with the Sri Lankan waters as a case study. Systematically considering the wind power density, energy availability, energy level occurrences, energy direction (co-occurrence of wind power density-wind direction), climatic variation of wind energy, wind class, the wind energy of Sri Lankan waters for the future and for the past are analyzed and compared, to provide reference for the long-term planning of wind energy development and technical approach for the mid-long term projection of wind energy resource.

How to extract useful and key information from the original big wave data with large volume and low information density and establish a wave climate datasets is still in a blank status, which is also urgently needed in the ocean navigation, deep sea development, marine resource development, offshore construction, and disaster prevention and mitigation, etc. Based on the ERA-interim wave data for 1979–2018 and ERA-40 wave reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF), this study adopts the climate statistical analysis methods to establish the first open and public wave climate dataset for the Maritime Silk Road, systematically including the prevailing wave direction and significant wave height (SWH), wave period, swell index (SI), rough sea occurrence, wave direction occurrence, co-occurrence of wave height and wave direction, co-occurrence of wave height and wave period, and extreme wave height. It is hoped that the results can provide a data support for the safe and efficient deployment of marine development and construction.

The oversampling sigma-delta analog-to-digital converter (ADC) is widely used in digital audio systems for its low hardware cost and high precision. The digital filter part of the ADC is the primary source of the silicon and power cost. In this work, we proposed an economical multistage decimation filter architecture to meet the requirements of commercial ADC specifications with a further novel multi-objective optimization strategy. We took group delay and computational cost as objective functions and used annealing algorithm to readjust the stopband attenuation, stopband edge and passband ripple of each filter. Our multistage architecture combines cascaded integrator–comb (CIC) filters, compensating filters and equiripple finite impulse response (FIR) filters at the different stages to balance the overall performance. The experimental results showed that our design approach worked with multi-objective optimization strategy could achieve low group delay with lower cost compared to traditional designs.

The oversampling sigma-delta based analog-to-digital converter and digital-to-analog converter (ADC/DAC) architecture can achieve high resolution with effective low hardware cost. It normally introduces latency due to the internal high-performance finite impulse response (FIR) digital filters. In this work, we proposed a novel approach to reduce latency and accurately predict latency caused by the group delay. We propose a filter structure applied to ADC, using the infinite impulse response (IIR) filter and half-band filter (HBF) to construct a hybrid half-band filter (HHBF). A novel modified Simulated Annealing (SA) algorithm is proposed to optimize the best distribution of passband ripple and transition bandwidth that is suitable for audio processing with low latency and computation cost. The experiments show that hybrid HBF proposed in this paper improves the efficiency of the filter in terms of filter cost and group delay comparing to traditional design.

With the advancement of sonar technology and inverse problem theory, the use of acoustic signals to retrieve the parameters of underwater acoustic environments has received more and more attention. Based on the parabolic equation (PE) solver and genetic algorithm, this paper studied the problem of jointly inverting the water sound speed profile (SSP) and geoacoustic parameters, where the dimension of water SSP was greatly reduced by the EOFs. Numerical experiments show that this method can provide a high accurately retrieved results with noise free case observations, as well as a favorable anti-noise ability for 3dB Gaussian noise case.

Macrophages participate in all stages of sepsis and affect immune homeostasis and inflammatory processes. Small ubiquitin-like modifier (SUMO) protease SENP1 plays an important role in cellular inflammation by regulating proteins in SUMOylation. However, the roles and related mechanisms of SENP1 in macrophage inflammation during sepsis are largely unknown. In the present study, SENP1 expression was significantly promoted in lipopolysaccharide (LPS)-induced RAW 264.7 cells; furthermore, the knock down of SENP1 reduced the expression of inflammatory cytokines interleukin-6 and tumor necrosis factor-α. Momordin Ic (MC), a new type of SENP1 inhibitor, reduces LPS-induced cellular inflammation by depressing SENP1 expression. Moreover, the effect of SENP1 on LPS-induced inflammatory response was dependent on SENP1-Sp3 interaction and the promotion of Sp3 expression via Sp3 deSUMOylation. Furthermore, MC-depressed Sp3 expression disturbed Sp3-nuclear factor (NF)-κB interaction and then alleviated LPS-induced cellular inflammation. These results suggest that SENP1 promotes LPS-induced macrophage inflammation by promoting Sp3 expression via deSUMOylation and Sp3-NF-κB interaction in sepsis.

Forecasting the significant wave heights (Hs) is indispensable in HS-related engineering studies and is exceedingly important in the assessment of wave energy in future. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to Hs has always been a vital research subject. In this paper, an optimized hybrid method based on the back propagation neural network (BP) and the cuckoo search algorithm (CS) is proposed to forecast the Hs in the South China Sea. This approach employs the CS as an intelligent optimization algorithm to optimize the parameters of the BP model, which develop a hybrid model that is suit for the data set, reducing the forecasting errors. The proposed method is subsequently tested based on nine prediction points selected in the South China Sea, where the proposed hybrid model is proved to perform effectively and steadily.

Nowadays, the world is facing the dual crisis of the energy and environment, and renewable energy, such as wave energy, can contribute to the improvement of the energy structure of the world, enhance energy supply and improve the environment in the framework of sustainable development. Long-term prediction of the significant wave height (SWH) is indispensable in SWH-related engineering studies and is exceedingly important in the assessment of wave energy in the future. In this paper, the spatial and temporal characteristics of wave energy in the South China Sea (SCS), and adjacent waters are analyzed. The results show that there are abundant wave energy resources in the waters around the Taiwan Strait, the Luzon Strait, and the north part of the SCS with annual average SWH (SWH) of over 1.4 m and obvious increasing trend. Then, the SARIMA approach considers the relationship between the current time and the values, residuals at some previous time and the periodicity of the SWH series are proposed to forecast the SWH in the SCS and adjacent waters. The results obtained are promising, showing good performance of the prediction of monthly average SWH in the SCS and adjacent waters.

Wave energy development will help ease resource crises. The projection of wave energy has practical value for the long-term planning of energy development (implementation of power generation, trading strategies, and so on). This paper proposed a wave energy projection program. South China Sea (SCS) and the East China Sea (ECS) in 2019 were carried out as case studies using the Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset to drive the WAVEWATCH-III (WW3) wave model. The multiyear average wave energy of the SCS and ECS was presented. A comparison of the projected values with multiyear averages of the wave energy could positively contribute to the planning of the wave energy development. The results show that the SCS possessed relatively rich energy for both the past and future and that January and October possessed the highest wave power density (WPD). The projected annual average WPD in 2019 was similar to the multiyear average WPD in the north and middle of the ECS, slightly higher than the multiyear average in the south of the ECS, and considerably greater than that in the SCS. The projected WPDs in January, April, and October 2019 were higher than the multiyear averages in the corresponding months. In July, the projected WPD in the SCS was smaller than the multiyear average, while the opposite was observed in the south of the ECS. The projected effective wave height occurrence (EWHO) and the occurrence of WPD >2 kW/m in 2019 were also superior to the multiyear average values.

Glass forming ability (GFA) is one of the key factors hindering the application of bulk metallic glasses (BMGs). In this paper, a cluster-related method was used to design good glass-formers in the complex Co-Fe-based system. A novel Co-centered Co-Mo binary topologically packed cluster Co-Co8Mo4 with 12-coordination number was found and used. The basic ternary composition in Co-Mo-B system is calculated based on an intersection of cluster lines B-B2Co8 and Co-Co8Mo4. Based on this, a series of novel Co-Fe-based glass-formers with high GFA, were quickly designed using the method of similar element replacement and microalloying. The best glass-former is (Co27.5Fe27.5Mo12.2Cr12.2B16P4.6)98Y2. The source of high GFA of designed glass-formers was also discussed. The results of this paper would offer researchers a novel insight in understanding the source of high GFA of Co/Fe-based system, and lay a solid foundation for exploring Co/Fe-Mo-based glass-formers via newly found Co-Mo binary cluster.

The Equatorial Undercurrent (EUC) plays an important role in ocean circulation and global climate change. Near the equator, as the Coriolis parameter goes to 0, equatorial currents cannot be described by geostrophy in which the pressure gradient force term is balanced by the Coriolis force term. Many previous studies focus on the relationships between the EUC and El Nino-Southern Oscillation (ENSO), the thermocline, sea surface topography, the distribution of equatorial wind stress and other atmosphere-ocean factors. However, little attention has been paid to the northward pressure gradient (NGT), which may also be important to the EUC. The pressure can be regarded as a complex nonlinear function of terms including temperature, salinity and density. This study attempts to reveal the connection between a function of the northward pressure gradient (FNP) and the EUC. The connection is derived from primitive equations, by simplifying the equations with using scaling analysis, and shows that the beta effect may be the main reason why the FNP is important to the EUC. The vertical structure of the EUC can be partially described by the FNP. The NGT has an obvious influence on the EUC while the eastward pressure gradient has a relatively smaller effect.

Against a backdrop of increasing energy demand, the development of wave energy technology is a logical means of both meeting this demand and mitigating the environmental degradation associated with conventional power generation. Previous research has made considerable progress in the climatic characterization and short-term forecasting of wave energy. However, medium- to long-term predictions of wave energy resources, which are central to the development of future operating and trading strategies, remain scarce. This study provides an overview of long-term climatic trends and medium- to long-term predictions of wave energy, before discussing the focus of future predictions. Finally, a new method is proposed for predicting wave energy resources on a medium- to long-term basis that incorporates the swell index and propagation characteristics of swell energy. This model was developed with the aim of improving the precision of wave energy predictions, thereby providing a reference for the effective utilization of wave resources. The results of this study demonstrate that long-term climatic trend analysis should include not only variations in wave power density (WPD), but also long-term variability in wave energy stability, energy level occurrence, and variability in the occurrence of effective significant wave height (SWH). The medium- to long-term prediction of wave energy should also synthetically consider the above factors. We conclude that monitoring the propagation of swell energy and calculating the swell index constitutes a robust theoretical basis for predicting the WPD of mixed wave.

Wind and waves are key components of the climate system as they drive air-sea interactions and influence weather systems and atmospheric circulation. In marine environments, understanding surface wind and wave fields and their evolution over time is important for conducting safe and efficient human activities, such as navigation and engineering. This study considers long-term trends in the sea surface wind speed (WS) and significant wave height (SWH) in the China Seas over the period 1988–2011 using the Cross-Calibrated Multi-Platform (CCMP) ocean surface wind product and a 24-year hindcast wave dataset obtained from the WAVEWATCH-III (WW3) wave model forced with CCMP winds. The long-term trends in WS and SWH in the China Seas are analyzed over the past 24 years to provide a reference point from which to assess future climate change and offshore wind and wave energy resource development in the region. Results demonstrate that over the period 1988–2011 in the China Seas: 1) WS and SWH showed a significant increasing trend of 3.38 cm s−1 yr−1 and 1.52 cm yr−1, respectively; 2) there were notable regional differences in the long-term trends of WS and SWH; 3) areas with strong increasing trends were located mainly in the middle of the Tsushima Strait, the northern and southern areas of the Taiwan Strait, and in nearshore regions of the northern South China Sea; and 4) the long-term trend in WS was closely associated with El Nino and a significant increase in the occurrence of gale force winds in the region.

The propagation characteristics of swell energy are important for applications such as swell wave power generation, ocean wave forecasting, disaster prevention and reduction, etc. However, the study on the propagation of swell energy is still relatively rare. This study proposed a new method to exhibit the exact propagation route and speed and intraseasonal oscillation of swell energy. The Indian Ocean is selected as a case study, using the 45-year (1957.09–2002.08) European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40) wave reanalysis dataset. The main results are as follows. (1) empirical orthogonal function (EOF) analysis and 6-hourly zonal averages of swell wave power density (WPD) show that the Indian Ocean swell energy has an obvious but fluctuating northeastward propagation. (2) The swell energy of the southwest Indian Ocean generally spreads to two regions: the main body propagates in a NE–NNE direction to Sri Lankan waters, while a small branch propagates to Christmas Island waters. (3) Swell energy in the west of the southern Indian Ocean westerly (SIOW), in Sri Lankan waters, and in Christmas Island waters share a common period of quasi-weekly oscillation. Swell energy takes ∼6 days to propagate from the west of the SIOW to Sri Lankan and Christmas Island waters. Swell energy propagates faster during June–September than in other months.

Through the work of this book, a series of core challenges in wave energy assessment of Maritime Silk Road have been solved, such as the climate background characteristics of the wave energy resources (spatial-temporal distribution of a series of key parameters of wave energy), the historical trend of each key parameters, the short-term forecast of wave energy, the long-term projection of wave energy, wave energy assessment and proposals for maritime key points, etc. Planning to build a wave energy resource dataset of the Maritime Silk Road, and the results can provide a scientific basis for the site selection, daily operation, and long-term planning of wave power generation, seawater desalination and other wave energy projects. In the future, it is necessary to strengthen study on the following aspects: wave energy classification, propagation characteristics of swell energy and swell monitoring and warning, risk assessment of marine environment, the construction of a comprehensive application platform for wave energy resources, to improve the support capability for wave energy development.

The application and sharing of scientific data has become an important indicator of the national science and technology level. With the rapid development of observation methods and numerical models, ocean data has also exploded, and “big data” has entered people’s sight. How to extract useful information of energy assessments from large ocean data with low information density, and establish the marine energy dataset is the key to rational and efficient development of wave energy. At present, the global marine energy dataset is rare, and the dataset of wave energy of the Maritime Silk Road is still in blank. Based on the previous research results, this chapter propose to build the wave energy resource dataset of Maritime Silk Road, which is close to actual demand, convenient to query and perfect in theoretical system. The data is also the first wave energy resource dataset of Maritime Silk Road at home and abroad. In the future, the program can be widely used in the construction of marine new energy datasets of offshore wind energy and ocean current energy, to provide data support for researchers and engineers actively participating in the marine new energy development of the Maritime Silk Road.

The maritime key points are important support for marching into the deep blue. They are usually based on the remote islands and reefs, and the power dilemma has always been a worldwide difficulty. Carrying out the wave power generation will help the islands and reefs to achieve electricity self-sufficiency. Evaluation always goes first, followed by resource development. Previous researchers have made great contributions to the study of wave energy in various sea areas around the world. But so far, the wave energy research on the maritime key points of the North Indian Ocean is almost blank. This chapter uses the ERA-interim wave data from the ECMWF to conduct a systematic analysis of the wave energy resources in the Sri Lankan waters (an important node of the Maritime Silk Road), comprehensively including the monthly WPD, monthly available rate, monthly energy level frequency, energy direction and sea state contribution in representative months, monthly energy stability, monthly exploitable storage and long-term trend of wave energy, to provide scientific and technological support and proposal for marching into the deep blue.

Current research on long-term predictions of meteorological and oceanic elements is abundant, but research on long-term prediction of wave energy is scarce, which is urgently needed in the long-term planning of resource development. The long-term projection of wave energy can be usually achieved by the following three methods: (1) Analyzing the relationship among wave energy, MJO (Madden-Julian Ocillation), nino3 and other important factors, combined with the long-term trend characteristics of wave energy, to achieve medium- and long-term prediction of wave energy; (2) with wave model driven by CMIP (Coupled Model Intercomparison Project) data, to project the future wave energy; (3) using methods of Hilbert Huang, artificial neural network (ANN), etc., combined with long-term sequence wave energy data to make long-term projection of wave energy. This chapter proposed a long-term projection scheme of wave energy, with the CMIP5 data to drive the current international advanced numerical wave model WW3, to simulate the global wave field from 2020 to 2059, and then project and analyze the wave energy of the Maritime Silk Road for the future 40 years. The projection parameters include the WPD, available rate, energy richness, stability, and monthly variation, to provide scientific evidence for long-term planning of resource development.

Lots of research on the long-term trends of meteorological and oceanic factors has been conducted. However, the research on the climatic trend of wave energy is extremely scarce, which is closely related to the medium- and long-term planning of resource development and predictive ability of wave energy. And the traditional analysis on long-term trends of resources was mainly concerned with the trend of WPD. In the actual wave energy development, energy stability (Cv, Mv, Sv) can affect acquisition and conversion efficiency, as well as equipment life. The EWHO reflects the available rate of wave energy, and the energy level frequencies (ALO, MLO, RLO) reflects the energy enrichment. This chapter analyzed the climatic trends of a series of key parameters of wave energy along the Maritime Silk Road for the first time at home and abroad, comprehensively including the trends of WPD, Cv, Mv, Sv, EWHO, ALO, MLO, RLO, etc. The analysis content of climatic trend includes the overall trend, seasonal difference and regional difference of the trend, dominant season and region of the trend, to lay a theoretical foundation for improving the medium- and long-term planning ability of wave energy development and predictive ability of wave energy.

Previous researchers have made great contribution to the analysis of the wave energy climatic characteristics of many sea areas globally. But there are a few researches about the Maritime Silk Road. In the systematic aspect of the study of wave energy climatic characteristics, the elements of analysis are still not comprehensive enough. Besides considering traditional wave power density (WPD), energy stability, energy storage, it is necessary to take full consideration of the available rate, energy richness, energy direction, and the contribution of different sea states to wave energy, which are also closely related to the development of the wave energy. Scarce materials, large reserves and huge amount calculations and the high technical requirements have greatly increased the difficulty of systematic research on wave energy. For the first time, this chapter presented the spatio-temporal distribution characteristics of a series of key parameters of wave energy of the Maritime Silk Road by utilizing ERA-interim wave data from the ECMWF, to provide technical support (site selection, daily operation, etc.) for the development of resource such as wave power generation and seawater desalination.

The rational development of wave energy will make a positive contribution to ease the resource crisis, protect the marine environment, improve the living quality of residents, develop tourism of the deep sea, etc. Detailed survey of resources is an important foundation in order and efficient development and utilization of wave energy. Looking for advantageous areas with abundant resources, high utilization rate, good stability and low frequency of severe weather can achieve very efficient results. This chapter analyzes the research progress of wave energy resource assessment at home and abroad, especially focuses on the current wave energy assessment along the Maritime Silk Road, hoping to find the method to improve the exploitation and conversion efficiency for wave energy. According to data sources, the wave energy assessment can be divided into the following stages: (1) observation stage: wave energy resource assessment based on limited observation data; (2) satellite-based stage: satellite-based wave data are applied to the wave energy resource assessment; (3) numerical simulation stage: the method of numerical wave simulation is used in wave energy resource evaluation; (4) reanalysis data stage: the reanalysis data are applied to wave energy resource assessment. At last, the organization of this book was introduced.

Previous researchers have made a lot of accumulation and contribution to the analysis of short-term forecast of oceanic and meteorological parameters and wind energy resource. However, researches on short-term forecasts of wave energy resources are extremely scarce, which is an urgent need in the business operation of wave energy engineering. In this chapter, based on the current international advanced wave model WW3, by comparing different boundary conditions, a short-term forecast scheme of wave energy that can fully consider the influence of swells generated from other oceans and is suitable for the Maritime Silk Road was proposed, which can provide scientific evidence for the business operation of wave energy development. The forecast parameters in this chapter are more abundant than the existing forecast scheme. It systematically covers the hourly significant wave height, wave period, WPD field, weekly availability, weekly richness, weekly energy storage, energy direction characteristics, sea state contribution to the wave energy, and wave energy forecast of key nodes, etc.

The liver cancer has become the sixth most common cancer and the second leading cause of cancer deaths worldwide. The incidence of liver cancer has characteristics of the geographical distribution, which vary from region to region. Based on the data from Cancer Incidence in Five Continents, this study analyzed the spatial distribution of liver cancer globally and explored the effect of meteorological factors of surface temperature and wind field on incidence of liver cancer, to find the geographical distribution characteristics of liver cancer and the impact of geographical environmental factors on the incidence of liver cancer, which will be of great significance to the epidemiological study of liver cancer and the prevention of liver cancer. The results show that the humid and hot oceanic climate may be conducive to the development of liver cancer. The environmental factors play an important role in the occurrence of liver cancer.

Under the background of energy crisis, the development of renewable energy will significantly alleviate the energy and environmental crisis. On the basis of the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-interim) wind data, the annual and seasonal grade divisions of the global offshore wind energy are investigated. The results show that the annual mean offshore wind energy has great potential. The wind energy over the westerly oceans of the Northern and Southern Hemispheres is graded as Class 7 (the highest), whereas that over most of the mid-low latitude oceans are higher than Class 4. The wind energy over the Arctic Ocean (Class 4) is more optimistic than the traditional evaluations. Seasonally, the westerly oceans of the Northern Hemisphere with a Class 7 wind energy are found to be largest in January, followed by April and October, and smallest in July. The area of the Class 7 wind energy over the westerly oceans of the Southern Hemisphere are found to be largest in July and slightly smaller in the other months. In July, the wind energy over the Arabian Sea and the Bay of Bengal is graded as Class 7, which is obviously richer than that in other months. It is shown that in this data set in April and October, the majority of the northern Indian Ocean are regions of indigent wind energy resource.

This study aims at explaining the relationship between thermodynamic characteristics and direction of rotation of mesoscale eddies (MEs). The geometric characteristics of the MEs are under the following assumptions: the structure of the MEs is symmetrical, and changes of oceanic physical variables are close to linear features in the radial direction in the ME regions. Based on these assumptions, by using primitive equations without friction under a cylindrical coordinate system, the thermodynamic characteristics of the MEs are derived, showing that the conventional relationship of warm anticyclonic eddies with high sea surface height (SSH) and cold cyclonic eddies with low SSH is not consistent with the SSH and sea surface temperature (SST) observations of eddies. The results show that the symmetrical form is an ideal approximation for the geometric characteristics of MEs. In consideration of the above assumptions, there are advantages for derivation of the characteristics of the MEs under a cylindrical coordinate.

Based on the 45-year (09/1957-08/2008) European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40) wave reanalysis dataset, this study analyzes interannual and interdecadal variabilities and intraseasonal oscillations of sea surface wind speed (WS), wind sea wave height (Hw), swell wave height (Hs) and significant wave height (Hs) in the Roaring Forties and tropical waters of the Indian Ocean, to determine swell propagation characteristics. The results show: (1) monthly variabilities of Hs in the Roaring Forties are in good agreement with those in tropical waters of the Indian Ocean; swell plays a dominant role in mixed waves throughout most of the Indian Ocean; and WS, Hw, Hs, and Hs exhibit a significant increasing trend over the 45-year study period. (2) Hs in the Roaring Forties and tropical waters of the Indian Ocean share a common period of 9.8–10.4 years on an interdecadal scale; and WS and Hs in the Roaring Forties and Hs in the tropical waters of the Indian Ocean share a common period of approximately 8 days (weekly oscillation) on an intraseasonal scale. (3) Swell of the Roaring Forties needs approximately 30 h to fully respond to the wind in this region. Approximately 84 h are required for Hs to propagate from the Roaring Forties to the tropical waters of the south Indian Ocean, while it takes approximately 132–138 h for Hs to propagate from the Roaring Forties to the tropical waters of the north Indian Ocean.

China is now the world’s largest user of coal and also has the highest greenhouse gas emissions associated with the mining and use of coal. Under today’s enormous pressures of the growing shortage of conventional energy sources and the need for emission reductions, the search for clean energy is the most effective strategy to address the energy crisis and global warming. This study utilized satellite remote sensing technology, geographic information system (GIS) technology, and simulated wave data for the South China Sea. The characteristic features of the wave energy were obtained by analysis through the wave resource assessment formula and the results were stored in a GIS database. Software for the evaluation of wave energy in the South China Sea was written. The results should provide accurate, efficient references for wave energy researchers and decision-makers. Based on a 24-year WW3 model simulation wave data and GIS technology, this study presented the characteristic of the wave energy in the SCS; results demonstrated that the SCS has the feasibility and viability for wave energy farming.

This study presents the climatic trend of the North Atlantic wind energy using cross-calibrated, multiplatform (CCMP) wind data for the period 1988–2011. Results show the following. (1) The North Atlantic WPD exhibited a significant increasing trend of 4.45 (W/m2)/yr over the past 24 years. (2) The variation in the North Atlantic Ocean WPD shows a noticeable regional difference. More than half of the North Atlantic Ocean has a significantly increasing trend in WPD. The increasing trend in the mid-high latitudes is stronger than that in the low latitudes, and the trend is stronger in the west than in the east. The area with the strongest increasing trend is located along the southern coast of Greenland of 35 (W/m2)/yr. (3) There is a noticeable seasonal difference in the variation of WPD. The strongest increasing trend occurs in December-January-February (DJF), followed by September-October-November (SON) and March-April-May (MAM), and the weakest occurs in June-July-August (JJA). The increasing trend in different areas is dominated by different seasons. (4) There is no leading or lagging correlation between WPD and the North Atlantic Oscillation (NAO). However, there is a noticeable negative correlation between the Niño3 index and WPD in most of the North Atlantic.

This study presents the regional and seasonal climatic trends of the global oceanic sea surface wind speed for the period 1988–2011 using Cross-Calibrated, Multi-Platform (CCMP) wind data. The relationship between important factors (nino3 and soi index) and occurrences of wind speed is also calculated, in hope of providing references for the analysis of global climate change, development of offshore wind energy resources, air–sea interactions, and the basic reference helping for the mid-long term prediction of sea surface wind speed and occurrences of wind speed. The analysis results show that (1) The global oceanic sea-surface wind speeds increased at a significant overall rate of 3.35 cm s−1 yr−1 for the period 1988–2011; the maximal rate of increase, 4.15 cm s−1 yr−1, was attained between 1991 and 2007. (2) Globally, the sea-surface wind speeds significantly increased from 1988 to 2011. Only a few sporadic small water bodies exhibited decreasing wind speeds without significant variation over the 24-year interval. (3) The sea-surface wind speeds displayed obvious regional variability. The increasing wind speeds were more noticeable over the Pacific low-latitude waters than over waters of higher latitude. Wind speeds trends over the western Atlantic waters were stronger than those over the eastern Atlantic waters, while the south Indian Ocean winds were stronger than that those over the north Indian Ocean. (4) The variation in sea-surface wind speeds demonstrated noticeable seasonal differences. The increasing wind speed trend over the winter hemisphere was stronger than the one over the summer hemisphere. The variation in wind speeds over different waters was seasonally dominated. The increasing trend over the coastal waters of southern Greenland was dominated by the MAM and SON, was dominated by DJF in the Davis Strait and the Mediterranean, and was dominated by the SON 10° N over the Pacific Ocean and over the high-latitude waters in the southern Indian Ocean.

Wave energy assessments related to wave energy convertors in the coastal waters of China are very scarce. Thus, based on ERA-Interim wave field data acquired over the past 40 years, we assessed the applicability of state-of-the-art wave energy convertors at key stations in the coastal waters of China and obtained some important conclusions that can provide guidance for the future siting of wave farms and the design, manufacturing, construction, operation, and maintenance of wave energy convertors. The results show that the Wave Star and Sharp Eagle Wanshan are the most suitable wave energy convertors according to the capacity factor, while the Wave Star and Wave Dragon are the most suitable devices according to the capture width. The dominant direction of wave energy transport was concentrated in the N-ENE direction. From the perspective of wave energy transport, the South China Sea is the most suitable sea area for establishing nearshore wave farms, and the percentages of energy available for continued development exceeded 66%. Nearly all wave conditions that yielded outstanding energy contributions were under the storm protection state, and the energy contribution percentages exceeded 74% at each station. These conditions are convenient for the concentrated collection and utilization of energy.

Zheng, C.W.; Liang, F.; Yao, J.L.; Dai, J.C.; Gao, Z.S.; Hou, T.T., and Xiao, Z.N., 2020. Seasonal extreme wind speed and gust wind speed: A case study of the China seas. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 435-438.Previous researches have made great contribution to the evaluation of annual extreme wind speed and annual gust wind speed. However, the research on the extreme wind speed and gust wind speed of different season is extremely scarce, which is urgently needed in disaster prevention and reduction, navigation, ocean exploitation, etc. This study calculates the extreme wind speed with return period of 50-yr (U50) and 100-yr (U100) in each season and annual value by using the ERA-interim gust data and the Gumbel curve method, and the China seas were taken as case studies. The results show that, (1) Both the annual U50 and U100 have a good consistency with the multi-year average maximum gust as a whole, with the large area mainly located in the mid-low latitude (15°∼30°N) and the large center is located in the broad ocean surrounding the Volcano Islands (35-55 m/s in maximum gust, 50-75 m/s in U50 and U100). (2) The maximum gust, U50 and U100 exhibit noticeable seasonal difference. The large areas in January are located in the mid-high latitude, about north of 20°N, including the Japan Sea, south waters of Japan, Ryukyu Islands. The large areas in April are located in the mid-high latitude (north of 25°N) and the south of Volcano Islands, while in the broad ocean between 15°N and 30°N in both July and October.