A global study of temporal and spatial variation of SWH and wind speed and their correlation

The climatology of significant wave height（SWH） and sea surface wind speed are matters of concern in the fields of both meteorology and oceanography because they are very important parameters for planning offshore structures and ship routings. The TOPEX/Poseidon altimeter, which collected data for about 13 years from September 1992 to October 2005, has measured SWHs and surface wind speeds over most of the world＇s oceans. In this paper, a study of the global spatiotemporal distributions and variations of SWH and sea surface wind speed was conducted using the TOPEX/Poseidon altimeter data set. The range and characteristics of the variations were analyzed quantitatively for the Pacific, Atlantic, and Indian oceans. Areas of rough waves and strong sea surface winds were localized precisely, and the correlation between SWH and sea surface wind speed analyzed.

A Study on Reconstruction of Surface Wind Speed in China Due to Various Climate Variabilities

Using European Centre for Medium-Range Weather Forecasts Reanalysis V5(ERA5)reanalysis data,this study investigated the reconstruction effects of various climate variabilities on surface wind speed in China from 1979 to 2022.The results indicated that the reconstructed annual mean wind speed and the standard deviation of the annual mean wind speed,utilizing various climate variability indices,exhibited similar spatial modes to the reanalysis data,with spatial correlation coefficients of 0.99 and 0.94,respectively.In the reconstruction of six major wind power installed capacity provinces/autonomous regions in China,the effects were notably good for Hebei and Shanxi provinces,with the correlation coefficients for the interannual regional average wind speed time series being 0.65 and 0.64,respectively.The reconstruction effects of surface wind speed differed across seasons,with spring and summer reconstructions showing the highest correlation with reanalysis data.The correlation coefficients for all seasons across most regions in China ranged between 0.4 and 0.8.Among the reconstructed seasonal wind speeds for the six provinces/autonomous regions,Shanxi Province in spring exhibited the highest correlation with the reanalysis,with a coefficient of 0.61.The large-scale climate variability indices showed good reconstruction effects on the annual mean wind speed in China,and could explain the interannual variability trends of surface wind speed in most regions of China,particularly in the main wind energy provinces/autonomous regions.

Long-term Correlations and Extreme Wind Speed Estimations

In this paper, we use fluctuation analysis to study statistical correlations in wind speed time series. Each time series used here was recorded hourly over 40 years. The fluctuation functions of wind speed time series were found to scale with a universal exponent approximating to 0.7, which means that the wind speed time series are long-term correlated. In the classical method of extreme estimations, data are commonly assumed to be independent (without correlations). This assumption will lead to an overestimation if data are long-term correlated. We thus propose a simple method to improve extreme wind speed estimations based on correlation analysis. In our method, extreme wind speeds are obtained by simply scaling the mean return period in the classical method. The scaling ratio is an analytic function of the scaling exponent in the fluctuation analysis.

Correlation between sea surface temperature and wind speed in Greenland Sea and their relationships with NAO variability

The North Atlantic Oscillation （NAO） is one of the major causes of many recent changes in the Arctic Ocean. Generally, it is related to wind speed, sea surface temperature （SST）, and sea ice cover. In this study, we analyzed the distributions of and correlations between SST, wind speed, NAO, and sea ice cover from 2003 to 2009 in the Greenland Sea at 10°W to 10°E, 65°N to 80°N. SST reached its peak in July, while wind speed reached its minimum in July. Seasonal variability of SST and wind speed was different for different regions. SST and wind speed mainly had negative correlations. Detailed correlation research was focused on the 75~N to 80~N band. Regression analysis shows that in this band, the variation of SST lagged three months behind that of wind speed Ice cover and NAO had a positive correlation, and the correlation coefficient between ice cover and NAO in the year 2007 was 0.61 SST and NAO also had a positive correlation, and SST influenced NAO one month in advance. The correlation coefficients between SST and NAO reached 0.944 for the year 2005, 0.7 for the year 2008, and 0.74 for the year 2009 after shifting SST one month later. NAO also had a positive correlation with wind speed, and it also influenced wind speed one month in advance. The correlation coefficients between NAO and wind speed reached 0.783, 0.813, and 0.818 for the years 2004, 2005, and 2008, respectively, after shifting wind speed one month earlier.

Co-variation of the surface wind speed and the sea surface temperature over mesoscale eddies in the Gulf Stream region:momentum vertical mixing aspect

The co-variation of surface wind speed and sea surface temperature (SST) over the Gulf Stream frontal region is investigated using high-resolution satellite measurements and atmospheric reanalysis data. Results show that the pattern of positive SST-surface wind speed correlations is anchored by strong SST gradient and marine atmospheric boundary layer (MABL) height front, with active warm and cold-ocean eddies around. The MABL has an obvious transitional structure along the strong SST front, with greater (lesser) heights over the north (south) side. The significant positive SST-surface wind-speed perturbation correlations are mostly found over both strong warm and cold eddies. The surface wind speed increases (decreases) about 0.32 (0.41) m/s and the MABL elevates (drops) approximate 55 (54) m per 1℃ of SST perturbation induced by warm (cold) eddies. The response of the surface wind speed to SST perturbations over the mesoscale eddies is mainly attributed to the momentum vertical mixing in the MABL, which is confirmed by the linear relationships between the downwind (crosswind) SST gradient and wind divergence (curl).

Assessing the Variability of Extreme Weather Events and Its Influence on Marine Accidents along the Northern Coast of Tanzania

The marine accidents are among the main components of the Zanzibar Disaster Management Policy (2011) and the Zanzibar Blue Economy Policy (2020). These policies aimed to institute legal frame works and procedures for reducing both the frequency of marine accidents and their associated fatalities. These fatalities include deaths, permanent disabilities and loss of properties which may result into increased poverty levels as per the sustainable development goal one (SDG1) which stipulates on ending the poverty in all its forms everywhere. Thus, in the way to support these Government efforts, the influence of climate and weather on marine accidents along Zanzibar and Pemba Channels was investigated. The study used the 10 years (2013-2022) records of daily rainfall and hourly wind speed acquired from Tanzania Meteorological Authority (TMA) (for the observation stations of Zanzibar, Pemba, Dares Salaam and Tanga), and the significant wave heights data, which was freely downloaded from Globally Forecasting System (GFS-World model of 13 km resolution). The marine accident records were collected from TASAC and Zanzibar Maritime Authority (ZMA), and the anecdotal information was collected from heads of quay and boat captains in different areas of Zanzibar. The Mann Kendal test, was used to determine the slopes and trends direction of used weather parameters, while the Pearson correlations analysis and t-tests were used to understand the significance of the underlying relationship between the weather and marine accidents. The paired t-test was used to evaluate the extent to which weather parameters affect the marine accidents. Results revealed that the variability of extreme weather events (rainfall, ocean waves and wind speed) was seen to be among the key factors for most of the recorded marine accidents. For instance, in Pemba high rainfall showed an increasing trend of extreme rainfall events, while Zanzibar has shown a decreasing trend of these events. As for extreme wind events, results show that Dar es Salaam and Tanga had an increasing trend, while Zanzibar and Pemba had shown a decreasing trend. As for the monthly variability of frequencies of extreme rainfall events, March to May (MAM) season was shown to have the highest frequencies over all stations with the peaks at Zanzibar and Pemba. On the other hand, high frequency of extreme wind speed was observed from May to September with peaks in June to July, and the highest strength was observed during 09:00 to 15:00 GMT. Moreover, results revealed an increasing trend of marine accidents caused by bad weather except during November. Also, results showed that bad weather conditions contributed to 48 (32%) of all 150 recorded accidents. Further results revealed significant correlation between the extreme wind and marine accidents, with the highest strong correlation of r = 0.71 (at p ≤ 0.007) and r = 0.75 (at p ≤ 0.009) at Tanga and Pemba, indicating the occurrence of more marine accidents at the Pemba channel. Indeed, strong correlation of r = 0.6 between extreme rainfall events and marine accidents was shown in Pemba, while the correlations between extremely significant wave heights and marine accidents were r = 0.41 (at p ≤ 0.006) and r = 0.34 (p ≤ 0.0006) for Pemba and Zanzibar Channel, respectively. In conclusion, the study has shown high influence between marine accidents and bad weather events with more impacts in Pemba and Zanzibar. Thus, the study calls for more work to be undertaken to raise the awareness on marine accidents as a way to alleviate the poverty and enhance the sustainable blue economy.

A novel method for reliability and risk evaluation of wind energy conversion systems considering wind speed correlation

This paper investigates an analytical approach for the reliability modeling of doubly fed induction generator （DFIG） wind turbines. At present, to the best of the authors＇ knowledge, wind speed and wind turbine generator outage have not been addressed simultaneously. In this paper, a novel methodology based on the Weibull- Markov method is proposed for evaluating the probabil- istic reliability of the bulk electric power systems, including DFIG wind turbines, considering wind speed and wind turbine generator outage. The proposed model is presented in terms of appropriate wind speed modeling as well as capacity outage probability table （COPT）, considering component failures of the wind turbine generators. Based on the proposed method, the COPT of the wind farm has been developed and utilized on the IEEE RBTS to estimate the well-known reliability and sensitive indices. The simulation results reveal the importance of inclusion of wind turbine generator outage as well as wind speed in the reliability assessment of the wind farms. Moreover, the proposed method reduces the complexity of using analytical methods and provides an accurate reliability model for the wind turbines. Furthermore, several case studies are considered to demonstrate the effectiveness of the proposed method in practical applications.

Trend in pan evaporation and its attribution over the past 50 years in China

Trends in pan evaporation are widely relevant to the hydrological community as indicators of hydrological and climate change. Pan evaporation has been decreasing in the past few decades over many large areas with differing climates globally. This study analyzes pan evaporation data from 671 stations in China over the past 50 years in order to reveal the trends of it and the corresponding trend attribution. Mann-Kendall test shows a significant declining trend in pan evaporation for most stations, with an average decrease of 17.2 mm/10a in China as a whole, the rate of decline was the steepest in the humid region （29.7 mm/10a）, and was 17.6 mm/10a and 5.5 mm/10a in the semi-humid/semi-arid region and arid region, respectively. Complete correlation coefficients of pan evaporation with 7 climate factors were computed, and decreases in diurnal temperature range （DTR）, SD （sunshine duration） and wind speed were found to be the main attributing factors in the pan evaporation declines. Decrease in DTR and SD may relate to the increase of clouds and aerosol as well as the other pollutants, and decrease in wind speed to weakening of the Asian winter and summer monsoons under global climate warming.

Ambient noise spectral properties in the north area of Xisha

Ambient noise is very important in the prediction system of a sonar performance, because it determines the detection ranges always in a passive sonar and usually in an active sonar. In the uncertainty issue for the so-nar performance, it is necessary to know this factor＇s statistical characteristics that are only obtained by data processing from the underwater ambient noise measurements. Broad-band ambient noise signals from 16 hydrophones were amplified and recorded for 2 min every 1 h. The results show that the ambient noise is essentially depth independent. The cross correlation of the ambient noise levels （1, 6 and 12 h average） with a wind speed is presented. It was found that the correlation is excellent on the upper frequency band and the noise levels correlate better with high wind speed than with low wind speed.

Distributionally Robust Optimal Dispatch of Offshore Wind Farm Cluster Connected by VSC-MTDC Considering Wind Speed Correlation

Multi-terminal voltage source converter-based highvoltage direct current(VSC-MTDC)transmission technology has become an important mode for connecting adjacent offshore wind farms(OWFs)to power systems.Optimal dispatch of an OWF cluster connected by the VSC-MTDC can improve economic operation under the uncertainty of wind speeds.A two-stage distributionally robust optimal dispatch(DROD)model for the OWF cluster connected by VSC-MTDC is established.The first stage in this model optimizes the unit commitment of wind turbines to minimize mechanical loss cost of units under the worst joint probability distribution(JPD)of wind speeds,while the second stage searches for the worst JPD of wind speeds in the ambiguity set(AS)and optimizes active power output of wind turbines to minimize the penalty cost of the generation deviation and active power loss cost of the system.Based on the Kullback–Leibler(KL)divergence distance,a data-driven AS is constructed to describe the uncertainty of wind speed,considering the correlation between wind speeds of adjacent OWFs in the cluster by their joint PD.The original solution of the two-stage DROD model is transformed into the alternating iterative solution of the master problem and the sub-problem by the column-and-constraint generation(C&CG)algorithm,and the master problem is decomposed into a mixedinteger linear programming and a continuous second-order cone programming by the generalized Benders decomposition method to improve calculation efficiency.Finally,case studies on an actual OWF cluster with three OWFs demonstrate the correctness and efficiency of the proposed model and algorithm.

考虑时空相关性的风电机组风速清洗方法

为获得完整可靠的风速数据,提出一种考虑时空相关性的风电机组机舱风速清洗方法。利用图卷积神经网络(GCN)提取风速的空间相关信息、利用双向长短期记忆神经网络(Bi-LSTM)提取时间相关信息,建立GCN-LSTM模型重构各机组风速序列,实现对异常风速数据的识别和清洗。分析风速的时空特性及其对模型清洗精度的影响,确定最优时间尺度和机组节点数量2个重要的建模参数;以中国4个不同地形风电场为例对GCN-LSTM模型进行验证,结果表明考虑时空相关性可有效提高风速清洗精度,风速的时空相关性越高风速清洗误差越小,且该模型在不同地形风电场的风速清洗中表现出良好的鲁棒性。

一种分时段输电线路动态载流量计算时间点的确定方法

讨论了输电线路动态载流量计算的启动时间问题,基于影响输电线路载流量的2个主要因素——温度和风速,结合长序列的历史气象数据,通过对相邻时段风速的互信息分析,总结出不同季节条件下日内24 h风速的变化规律,根据这些变化规律,提出了在1条线路或1个区域内输电线路动态载流量日内计算时间点的设置方法。在某省1条220 kV输电线路的全年仿真验证中,验证了所提计算方法相对于静态环境算法对同一条输电线路载流量有较大的扩容能力。

稳态下击暴流风场竖向空间相关性试验研究

为研究下击暴流水平风速的竖向相关性,采用冲击射流装置模拟了稳态下击暴流风场,测量了不同径向位置、竖向距离、地貌条件以及射流速度下的水平风速,重点分析了水平风速的竖向相关性和相干曲线,提出了稳态下击暴流风场的相干函数模型。结果表明,相关系数随径向距离的增加而先增大后减小,最大相关系数出现在r=1.0 D_(jet)附近,地面粗糙度和射流风速对相关系数影响较小。相干函数与测点的竖向间距密切相关,径向位置和射流风速对相干函数影响较小,地表粗糙度的增加会降低近地测点的相干性。基于试验数据,建立了稳态下击暴流竖向相干函数模型,实现了与试验风场较好的吻合。

考虑时间相关性的风速和波浪高度短期预测

风与波浪高度是海洋工程设计的重要参数之一,其准确预测具有重要的工程意义。针对波浪高度复杂多变、难以预测的挑战,本文考虑时间相关性,利用长短时记忆(Long-Short-Term Memory,LSTM)神经网络方法建立风和波浪高度的短期预测模型。首先,对波浪高度和风速等海洋环境原始时间序列进行分析,并选取不同时间间隔对浪高和风速极值进行取值;其次,对风速与波浪高度开展相关性分析,并基于LSTM方法,构建风和波浪高度单步预测模型。在时间间隔为0.5 h时,波浪的预测误差为0.12 m;进而构建风和波浪联合预测模型,预测风速下的波高预测误差仅为0.12m,与实测风速下的波浪预测误差基本一致。

静止型雷暴冲击风作用下列车气动导纳函数试验研究

为研究静止型雷暴冲击风作用下列车的气动导纳函数,利用雷暴冲击风试验装置模拟雷暴冲击风场,基于刚性模型测压试验,并考虑列车模型对周围风场的影响,测试并分析距雷暴冲击风中心不同径向距离的列车抖振力谱、脉动风速谱以及脉动风速相干函数特性。通过拟合脉动风速相干函数获得考虑顺风向和竖向脉动风速展向相关性的传递函数,识别出考虑脉动风速展向相关性的列车气动导纳函数,并与未考虑脉动风速展向相关性的气动导纳函数进行对比。研究结果表明,当径向距离较小时,如r/D_(j)=0.5时,列车抖振力谱的能量主要集中在极值频率附近的窄带范围内;随着径向距离的增大,列车抖振力谱极值频率附近的能量逐渐向低频部分转移,同时,脉动风速谱的低频部分能量分布逐渐变均匀,而高频部分逐渐符合“-5/3”定律。当列车模型放入风场后,不同测点位置处的顺风向和竖向脉动风速谱的幅值随径向距离而变化,但距列车模型中心2B处的顺风向和竖向脉动风速谱的变化趋势与空风场的较接近。不同径向距离时,脉动风速相干函数都存在峰值,列车的侧向力和升力气动导纳函数在低频部分均趋近于一个常数;当径向距离r/D_(j)=1时,侧向力和升力气动导纳函数的幅值最大,而其他径向距离的侧向力和升力气动导纳函数幅值均基本相同。考虑传递函数后,识别出来的气动导纳函数幅值显著增大,同时高频部分的气动导纳函数幅值随着频率的增加其变化趋势也不同。采用频响函数拟合列车的气动导纳函数时,气动导纳函数低频部分的幅值由系数k决定,同时径向距离越大,拟合参数a、b和k值均越大。

基于二维风速修正和多重集成的两阶段迁移学习短期风电功率预测

为了在数据量不足的情况下增强新投运风电场的功率预测能力,提出了一种基于二维风速修正和多重集成的两阶段迁移学习短期风电功率预测方法。首先,在数据增强阶段,引入投运前气象站的测风数据,基于风电场的时空相关性关系,通过时序特征构建和场景匹配,从时空2个维度对预报风速进行初步修正。然后,对初步修正后的结果进行数据重构,以重构后的数据作为下一次集成的输入,构建多重集成模型对预报风速进行二次修正。最后,在功率预测阶段,基于一阶段的修正结果,通过门控循环单元(gate recurrent unit,GRU)得到预测功率。算例结果表明,所提方法使预报风速的均方根误差降低了1.038m/s,功率预测精度提升了4.718%。论文研究可为新投运风电场的短期功率预测提供参考。

基于空间相关性与Stacking集成学习的风电功率预测方法

针对目标气象预报数据缺失导致风电预测精度不足的问题,提出一种基于空间相关性和Stacking集成学习的风电功率预测方法。首先,分析目标风电场与相邻气象站点之间的空间相关性,根据相关系数极值点确定延迟时间,构建风速时移数据集;其次,利用Stacking集成方法融合多元算法,从多个数据观测角度预测目标风电场的风电功率,实现不同算法的优势互补,提升整体泛化能力,并采用粒子群优化算法搜索模型超参数,较好地平衡搜索时间与模型效果;最后,采用华东地区某风电场的实测数据验证了文中所提方法的有效性和准确性。结果表明,通过考虑不同位置的信息偏差,从数据输入和预测模型两方面可有效提高数据缺失情况下的风电预测精度。

改进MUSIC算法的超声波测风方法研究

针对传统超声波测风装置测风精度不高、抗噪声能力弱,提出了一种改进多重信号分类(multiple signal classification,MUSIC)算法的超声波测风方法。采用一种弧形6阵元超声波传感器阵列的测风结构,推导其阵列流型;在此基础上,添加小波阈值降噪算法提高信号信噪比,降低噪声信号协方差矩阵的秩;再使用PHAT加权广义互相关时延估计算法以提高时延估计的准确性,同时根据时延关系对传统MUSIC算法矢量矩阵进行改进;最后通过MUSIC算法实现对风速风向的测量。理论分析与仿真结果表明:改进后的MUSIC算法具有较好的抗噪性能和较高的风参数测量精度,测量风速绝对误差达到0.15 m/s,风向绝对误差达到2°,可以应用于对风参数要求较高的场景。

高海拔输电线路走廊风场实测及湍流特性研究

在海拔高度为4 500 m的西藏那曲镇高山平原地貌建立了两两间距为10 m、20 m和30 m的4点位同步脉动风速实测系统,获得了持续1.5年的高海拔地区连续大风风速样本,10 min平均风速和脉动风速最大值分别达到33.6 m/s和45.0 m/s以上,实测顺风向湍流强度和横风向湍流强度的均值分别为0.134和0.123,介于DL/T 5551-2018《架空输电线路荷载规范》规定的A类地貌和B类地貌湍流强度之间。基于任意两个测点的同步脉动风速,计算了顺风向脉动风速分量沿导线方向的空间相关系数及湍流积分尺度,广义极值模型可以较好地反映高风速样本湍流积分尺度的概率分布。实测样本风速越高,湍流积分尺度均值越大,当样本风速限值取8.0 m/s和20.0 m/s时,湍流积分尺度均值相差22.5%。20.0 m/s以上高风速样本的湍流积分尺度均值为106.96 m,约为DL/T 5551-2018《架空输电线路荷载规范》规定值50 m的2.1倍,导地线风荷载增大约6.1%,即高海拔高山平原地貌导地线风荷载可能会被低估。

基于最优Copula相关性分析的短期风速预测方法

准确剖析变量间的关联关系,深入挖掘数据的潜在价值,是提升一系列基于统计分析原理所构建风速预测模型精度的关键。为最大程度保留数据的潜在价值并剔除冗余信息,首先将待选输入变量用概率密度函数拟合,其次建立风速与其他变量间的最优Copula函数,再次基于最优Copula函数求解相关系数,明确影响风速预测精度的关键输入变量,最后基于长短期记忆网络模型输出预测结果。基于我国某地区的实测数据集对所提方法进行了验证。实验结果表明,所提出的方法可有效选取关键输入变量,在减少模型训练时间的同时提升预测精度。