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1.
应用WAVEWATCH-Ⅲ模式对南海的波浪场进行数值计算、统计分析和研究 总被引：11，自引：0，他引：11
以30a再分析风场数据为输入,采用WAVEWATCH-Ⅲ波浪模式,对南海海域1976—2005年的波浪场进行了数值计算。与大量的T/P高度计波浪资料和部分台风资料进行对比验证后发现,波浪计算结果较好。将计算结果进行统计和推算,得出南海海域波浪如下的主要特征:1)南海海域的常浪向基本为NE,出现频率占各向总数的40%;北部海域的强浪向主要为E,中部和南部海域的强浪向主要为NE;2)夏季的波高为全年最小,冬季受东北季风的影响,波高达到全年最大;3)100a一遇极值波高分布:海南岛东南附近海域最大,有效波高最大超过18m,中部海域的有效波高平均为14m左右,南部海域的有效波高平均约为9m。 相似文献
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通过在海口湾北部海域布置波浪观测站,对采集到的实测波浪资料进行统计和波谱分析,研究了琼州海峡波浪季节性变化特征。观测期间最大波高为5.6 m,发生在台风"莎莉嘉"经过期间。无台风影响的月份最大波高为3.0 m。年平均十分之一大波波高、年平均有效波高、年平均波高分别为0.5 m、0.4 m、0.3 m,该海域波高总体不大。波周期范围主要在2～7 s区间。研究结果表明:1)观测海区各月基本都受到东北风影响并存在东北向的波浪; 2)发现海区波浪类型主要是风浪为主的混合浪; 3)发现观测海区一直受到南海传入的长周期波影响; 4)海区风向与浪向的一致性在东北季风影响时段明显强于西南季风影响时段,风速与波高的相关性在东北季风影响时段明显强于西南季风影响时段,该现象在台风月份表现得尤其明显。 相似文献
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4.
利用WAVEWATCH Ⅲ(WW3)和SWAN海浪模式模拟了1949—2005年间对钦州湾海域影响较大的台风浪.以模拟结果为基础,利用皮尔逊Ⅲ(P-Ⅲ)频率适线法推算了钦州湾湾外深水的累积频率波高和平均波周期(Tm)的多年一遇极值,同时模拟了其间在百年一遇高潮水位条件下的台风浪,以及在百年一遇高潮水位、百年一遇风暴潮增水共同作用下对钦州湾影响较大的台风产生的波浪场.使用P-Ⅲ法推算了在极端天气下,钦州湾湾内统计点的累积波高和平均波周期的多年一遇极值.研究结果表明,由于湾内浅滩较多,波浪在传播过程中水体底部摩擦使能量耗损明显,所以湾内波高较小,湾口处的波高大于内湾处的波高.近岸海区的波浪耗散、破碎等物理过程比较强烈,因此近岸统计点C1处的波高极值最小,其最大波高向岸边快速减弱并沿东向传播.在极端天气情况下,波浪在传播过程中发生破碎,波高衰减显著. 相似文献
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渤海重现期波高的数值计算 总被引：2，自引：0，他引：2
利用RAMS大气模式给出的20年风场资料,利用SWAN近海波浪模式对渤海海域的波浪进行了20 a数值计算.通过与一般过程和大风过程的实测资料的对比后发现.波浪模拟值与实潮值符合地较好,SWAN模式适合渤海海域波浪的计算。通过分析发现.辽东湾常浪向为SSW。强浪向为SSW;渤海中部常浪向为S,强浪向为NE;渤海海峡常浪向为NNW,强浪向为NNW;莱州湾常浪向为S,强浪向为NNE;渤海湾常浪向为S.强浪向为NE。渤中偏东南海域(38°～39°N,119.5°～120.5°E)多年一遇有效波高最大.其中百年一遇有效波高最大值达到6.7m。 相似文献
6.
为了解海南东方市近岸海域波浪基本特征,根据东方海洋环境监测站使用的SZF型波浪浮标连续11 a的实测数据,进行了统计分析。首先对波浪要素进行统计,得到了各向各级波高的季节分布,以及波高和周期的均值与极值;再对波高和周期的联合分布、平均持续时间与波高的关系进行了探讨;最后选取一典型台风浪过程进行分析。结果表明本海域以S向浪出现频率最高,为14.3%,其次是N向和NNE向,频率均为11.9%;强浪向为S向和N向,浪向分布与东方市所处地理位置相符。该海域以有效波高小于1.3 m的小浪和轻浪为主,年出现频率为97.6%, 4级中浪占2.22%, 5级大浪仅占0.12%,只有在夏、秋季台风过境时才出现。累年有效波高平均值为0.49 m,最大值为3.2 m;最大波高为5.6 m,最大波高平均值为2.5 m;平均有效周期为4.2 s,最大有效波周期为9.5 s。有效波高在1.0 m以下,且周期在4～5 s的波浪出现频率最大,为80.5%。强台风“海燕”影响期间,波高具有明显的滞后特征, 5级大浪持续了10 h,浪向与风向基本一致,说明台风产生的波浪是以风浪为主,最大波高均出现于偏S向。通过波谱分析,... 相似文献
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1988-2002年黄海和渤海风浪后报 总被引：2，自引：1，他引：1
本文对黄海和渤海风浪开展长期后报实验,时间范围覆盖1988至2002年,并分析相应的区域波候特征。首先,模式输出的月平均有效波高和卫星数据比对一致。其次,我们讨论了气候态月平均有效波高和平均波周期的时空分布特征。有效波高和平均波周期的气候态空间分布都呈现出西北-东南、或由近岸向深水区增加的趋势,这种空间的分布特征和局地的风强迫和水深密切相关。同时,海浪参数的季节变化也较显著。进一步,我们统计分析了风场和有效波高的极值,给出并揭示了黄海和渤海多年一遇有效波高的空间结构,并讨论了有效波高极值和风强迫极值之间的联系。 相似文献
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响水近岸海域波浪特性研究 总被引：3，自引：0，他引：3
基于响水波浪站累计一整年的现场观测资料,分析了波高和波周期的年内变化特性,研究了波浪的统计特性和波谱特性,并总结归纳了该海域各特征波要素之间以及各波谱参数之间的转换关系。结果显示:响水海域全年有效波高的变化幅度在0.10~2.80 m之间,年平均值为0.56 m;最大波高的变化幅度在0.15~5.58 m之间,年平均值为0.93 m;平均波周期的变化范围为1.91~9.02 s,年平均值为3.90 s。夏季大波高发生频率明显要小于冬、春季节,波浪季节性变化较为显著。就波高和波周期分布而言,通过拟合得出的Weibull分布较为适合本海域实测波高分布和波周期分布。波谱特性方面,本海域双峰谱占到总数的62.5%,且低频谱峰值普遍高于高频谱峰值,其中低频谱峰出现在0.04 Hz左右,高频谱峰则出现在0.15~0.20 Hz之间,分别为本海域涌浪和风浪所集中的频率区间。采用回归分析方法进一步分析了各特征波要素之间以及各波谱参数之间的关系,发现多数波参数之间存在显著的相关性,但受波浪浅水变形影响,各参数之间的比值与理论深水关系有所区别。本文的研究成果可为沿海建筑物的设计以及防灾减灾提供参考和依据。 相似文献
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本文以WRF模式输出结果作为风场驱动条件,采用SWAN和WAVEWATCH III相嵌套的方法,对珠江口附近海域进行共20年(1991-2010年)的波浪场数值计算。根据计算结果,分析了该海域极值波浪特征,并使用Gumbel极值函数计算50年一遇和100年一遇波浪极值,结果显示:(1)1991-2010年间,珠江口区域最大有效波高为2.0m,万山群岛南侧可达5.0m,而外海最大有效波高可达10m以上,极值平均周期均在10s左右;(2)50年一遇和100年一遇波高在珠江口区域较小,而外海则相对较大,最大在14m以上;(3)50年一遇和100年一遇平均周期相差不大,不同海域之间相差也较小,大部分海域在10s左右,外海相对较大,可达12-14s之间。 相似文献
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渤海南部海域年极值波浪和设计波浪的特征研究 总被引：1，自引：0，他引：1
本文用统计计算和后报方法，获得了本海域不同海区多年年极值波高（Ｈ１／１０）资料。用Ｐ－Ⅲ型和短期测波资料频率分析方法，估算了各海区的设计波高，并依据文献［３］计算出对应的平均周期。用Ｋｏｌｍｏｇｏｒｏｆｆ适合度方法检验所得的结果表明，依Ｐ－Ⅲ型方法拟配的理论频率曲线与经验点十分吻合，从而确定了本海域不同海区最佳的设计波浪。分析本海域年极值波浪的基本特征表明，本海域除了渤海湾北部海区以外，主浪向一般为ＮＮＥ向，渤海海峡区的年极值波高和设计波高均为最大，而向莱州湾及渤海湾沿岸海区逐渐减小；在沿岸海区，由龙口至黄河口一带的极值波高较大。 相似文献
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利用原国家海洋局2010—2015年的浮标资料,计算渤、黄、东海有效波高和最大波高的线性关系,并通过1992—2011年共20 a的数值模拟有效波高资料计算中国东部海域各月的2.5 m、4 m、6 m以上最大波高频率和最大波高月极值分布。结果发现:中国东部海域由北至南,最大波高与有效波高的比值逐渐增大;最大波高频率和最大波高月极值空间分布均由渤海、黄海至东海逐渐增大,最大波高频率的极值12月最大,4或5月最小,最大波高月极值9月最大,4月最小。其时空分布表明:受不同天气系统影响,夏秋季台风较多,容易出现极值较大的最大波高;秋冬季冷空气较强,虽然最大波高极值相对较小,但大浪持续时间长、频率大、影响范围广。 相似文献
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Studies of offshore wave climate based on satellite altimeter significant wave height(SWH) have widespread application value. This study used a calibrated multi-altimeter SWH dataset to investigate the wave climate characteristics in the offshore areas of China. First, the SWH measurements from 28 buoys located in China's coastal seas were compared with an Ifremer calibrated altimeter SWH dataset. Although the altimeter dataset tended to slightly overestimate SWH, it was in good agreement with the in situ data in general. The correlation coefficient was 0.97 and the root-mean-square(RMS) of differences was 0.30 m. The validation results showed a slight difference in different areas. The correlation coefficient was the maximum(0.97) and the RMS difference was the minimum(0.28 m) in the area from the East China Sea to the north of the South China Sea.The correlation coefficient of approximately 0.95 was relatively low in the seas off the Changjiang(Yangtze River) Estuary. The RMS difference was the maximum(0.32 m) in the seas off the Changjiang Estuary and was0.30 m in the Bohai Sea and the Yellow Sea. Based on the above evidence, it is confirmed that the multialtimeter wave data are reliable in China's offshore areas. Then, the characteristics of the wave field, including the frequency of huge waves and the multi-year return SWH in China's offshore seas were analyzed using the23-year altimeter wave dataset. The 23-year mean SWH generally ranged from 0.6–2.2 m. The greatest SWH appeared in the southeast of the China East Sea, the Taiwan Strait and the northeast of the South China Sea.Obvious seasonal variation of SWH was found in most areas; SWH was greater in winter and autumn than in summer and spring. Extreme waves greater than 4 m in height mainly occurred in the following areas: the southeast of the East China Sea, the south of the Ryukyu Islands, the east of Taiwan-Luzon Island, and the Dongsha Islands extending to the Zhongsha Islands, and the frequency of extreme waves was 3%–6%. Extreme waves occurred most frequently in autumn and rarely in spring. The 100-year return wave height was greatest from the northwest Pacific seas extending to southeast of the Ryukyu Islands(9–12 m), and the northeast of the South China Sea and the East China Sea had the second largest wave heights(7–11 m). For inshore areas, the100-year return wave height was the greatest in the waters off the east coast of Guangdong Province and the south coast of Zhejiang Province(7–8 m), whereas it was at a minimum in the area from the Changjiang Estuary to the Bohai Sea(4–6 m). An investigation of sampling effects indicates that when using the 1°×1°grid dataset, although the combination of nine altimeters obviously enhanced the time and space coverage of sampling, the accuracy of statistical results, particularly extreme values obtained from the dataset, still suffered from undersampling problems because the time sampling percent in each 1°×1°grid cell was always less than33%. 相似文献
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联合利用中国沿岸长期验潮站实测资料和全球海潮模型NAO.99b在中国海域的结果,进行潮汐非调和常数的计算.分别对渤海、黄海、东海和南海进行分析,结果表明,中国海域潮汐类型复杂,渤海、黄海、东海以半日潮性质为主,南海以日潮性质为主;渤海、南海平均大潮差多分布在0.42～2.09 m,平均小潮差分布在0.27～1.33 m,东海、黄海平均大潮差多分布在1.12～4.44 m,平均小潮差多分布在0.41～2.41 m;渤海、黄海平均大潮高潮位分布在0.48～1.77 m,东海在0.42～2.41 m,南海在0.21～1.35 m;渤海、东海以及南海北部浅海海域潮高日不等现象显著. 相似文献
14.
基于1982–2019年美国国家海洋和大气管理局最优插值海表温度资料,运用多种统计方法分析了渤、黄海海洋热浪（频次、持续时间、强度）的时空分布特征及与之相关的环流背景。结果表明:（1）海洋热浪具有一定的区域性差异,更强、更持久和更多的海洋热浪多集中在渤海和北黄海海区;（2）近38年来,渤、黄海海洋热浪变化趋势也具有明显的区域性差异,频次、年平均持续时间、年平均平均强度和最大强度总体呈增多、增强趋势,但朝鲜半岛沿岸海域没有显著变化,这与该地区的平均海温变化密切相关;（3）根据日平均海表面温度将海洋热浪分为中等、强、严重和极端4种等级,结果表明,除极端海洋热浪外,其他3种不同等级海洋热浪发生频次和增长趋势均存在显著的地理差异,中等强度海洋热浪在渤、黄海所有区域均频次偏多且有显著的增加趋势,而强和严重海洋热浪主要集中在我国的渤海海域,但渤、黄海区域极端海洋热浪几乎没有发生;（4）就渤、黄海区域平均而言,38年间,共发生83次海洋热浪,平均每年2.2次;海洋热浪具有明显的季节差异,不同等级强度的海洋热浪的多发季节均在夏季;（5）合成分析结果表明,夏季渤、黄海海洋热浪与大气环流密切相关,当从高层到低层贝加尔湖区域上空表现为大范围的相当正压结构的暖性高压异常时,盛行的下沉运动和高空西北气流,带来了晴朗的天气和更多的地面净太阳短波辐射,有利于渤、黄海海洋热浪的形成和维持。 相似文献
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A numerical method is designed to examine the response properties of real sea areas to open ocean forcing. The application of this method to modeling the China’s adjacent seas shows that the Bohai Sea has a highest peak response frequency(PRF) of 1.52 d-1; the northern Yellow Sea has a PRF of 1.69 d-1; the Gyeonggi Bay has a high amplitude gain plateau in the frequency band roughly from 1.7 to 2.7 d-1; the Yellow Sea(including the Gyeonggi Bay), the East China Sea shelf and the Taiwan Strait have a common high amplitude gain band with frequencies around 1.76 to 1.78 d-1 and are shown to be a system that responds to the open ocean forcing in favor of amplifying the waves with frequencies in this band; the Beibu Gulf, the Gulf of Thailand and the South China Sea deep basin have PRFs of 0.91, 1.01 and 0.98 d-1 respectively. In addition, the East China Sea has a Poincare mode PRF of 3.91 d-1. The PRFs of the Bohai Sea, the northern Yellow Sea, the Beibu Gulf and the South China Sea can be explained by a classical quarter(half for the Bohai Sea) wavelength resonance theory. The results show that further investigations are needed for the response dynamics of the Yellow Sea-East China Sea-Taiwan Strait system, the East China Sea Poincare mode, the Taiwan Strait, and the Gulf of Thailand. 相似文献
16.
以欧洲中期天气预报中心的23年再分析风场数据为基础,采用HIRHAM风场模式和SWAN海浪模型对南海北部海域的波浪场进行推算,并将南海北部海域的有效波高与厄尔尼诺指数作对比,探究两者的关系,分析结论如下：（1）南海海域波高具有较强的季节性变化特征,冬季波高大于夏季波高;（2）南海北部海域月平均波高与Niño3.4指数呈负相关,大部分海域呈中度相关,台湾和菲律宾之间的部分海域呈高度相关;（3）在强厄尔尼诺年,南海北部海域的有效波高明显偏小,且厄尔尼诺指数变化越大,波高越小;反之,在强拉尼娜年,南海北部海域的有效波高较大。 相似文献
17.
基于多源融合卫星高度计观测数据的南海海浪有效波高的季节变化研究 总被引：2，自引：0，他引：2
The seasonal variability of the significant wave height(SWH) in the South China Sea(SCS) is investigated using the most up-to-date gridded daily altimeter data for the period of September 2009 to August 2015. The results indicate that the SWH shows a uniform seasonal variation in the whole SCS, with its maxima occurring in December/January and minima in May. Throughout the year, the SWH in the SCS is the largest around Luzon Strait(LS) and then gradually decreases southward across the basin. The surface wind speed has a similar seasonal variation, but with different spatial distributions in most months of the year. Further analysis indicates that the observed SWH variations are dominated by swell. The wind sea height, however, is much smaller. It is the the largest in two regions southwest of Taiwan Island and southeast of Vietnam Coast during the northeasterly monsoon, while the largest in the central/southern SCS during the southwesterly monsoon. The extreme wave condition also experiences a significant seasonal variation. In most regions of the northern and central SCS, the maxima of the 99 th percentile SWH that are larger than the SWH theoretically calculated with the wind speed for the fully developed seas mainly appear in August–November, closely related to strong tropical cyclone activities.Compared with previous studies, it is also implied that the wave climate in the Pacific Ocean plays an important role in the wave climate variations in the SCS. 相似文献
18.
本文基于第3代海浪模式WAVEWATCH Ⅲ (WW3）模拟的1996–2015年海浪后报数据,分析了南海北部有效波高及其极值的时空变化特征,并采用Pearson-Ⅲ和Gumbel两种极值分布方法对该区极值波高重现期进行了估算。结果表明,南海北部有效波高的季节变化和空间分布与季风风场基本一致,呈现秋冬高春夏低,并自吕宋海峡西侧向西南降低的特征,与ERA5再分析数据结果高度相似。有效波高极值（简称极值波高）的时空分布特征受时间分辨率强烈影响,采用极值数据的分辨率越高（如逐小时）,所展现的台风型波浪特征越显著。扣除季节变化信号后的有效波高和年极值波高均体现出较强的线性增高趋势,近20年升高的比例分别为7.7%和31.6%,值得警惕和关注。该区多年一遇极值波高存在若干个大值区,且与台风的路径、强度有直接联系,表明台风是引发该区域极端大浪的最主要机制。对比Pearson-Ⅲ和Gumbel极值分布估算结果发现：若极值波高较低,频率随极值波高升高缓慢降低,此时两种极值分布的估算都比较准确,差异极小,可忽略不计;但当研究时间范围内,某年极值波高远超其他年份时,Pearson-Ⅲ极值分布估算结果明显高... 相似文献
19.
Long-term variations in a sea surface wind speed(WS) and a significant wave height(SWH) are associated with the global climate change, the prevention and mitigation of natural disasters, and an ocean resource exploitation,and other activities. The seasonal characteristics of the long-term trends in China's seas WS and SWH are determined based on 24 a(1988–2011) cross-calibrated, multi-platform(CCMP) wind data and 24 a hindcast wave data obtained with the WAVEWATCH-III(WW3) wave model forced by CCMP wind data. The results show the following.(1) For the past 24 a, the China's WS and SWH exhibit a significant increasing trend as a whole, of3.38 cm/(s·a) in the WS, 1.3 cm/a in the SWH.(2) As a whole, the increasing trend of the China's seas WS and SWH is strongest in March-April-May(MAM) and December-January-February(DJF), followed by June-July-August(JJA), and smallest in September-October-November(SON).(3) The areal extent of significant increases in the WS was largest in MAM, while the area decreased in JJA and DJF; the smallest area was apparent in SON. In contrast to the WS, almost all of China's seas exhibited a significant increase in SWH in MAM and DJF; the range was slightly smaller in JJA and SON. The WS and SWH in the Bohai Sea, the Yellow Sea, East China Sea, the Tsushima Strait, the Taiwan Strait, the northern South China Sea, the Beibu Gulf, and the Gulf of Thailand exhibited a significant increase in all seasons.(4) The variations in China's seas SWH and WS depended on the season. The areas with a strong increase usually appeared in DJF. 相似文献