黄海海浪季节变化的数值模拟研究

利用第三代海浪数值模式SWAN,研究了黄海海浪有效波高的季节变化特征及相关的物理过程。结果表明,在黄海的大部分区域,混合浪有效波高的最大值出现在冬季,而最小值则基本出现在夏季。北黄海北部和山东半岛南岸的近海海域呈现稍微不同的季节变化,有效波高的最大值出现在春季。全年4个季节中混合浪有效波高的空间分布基本一致:均在济州岛西南最大,沿黄海中部区域向北和由中部区域向近岸区域逐渐减小。黄海海浪为风浪占主,涌浪有效波高远小于风浪有效波高。在黄海的大部分区域,白冠耗散和四波非线性相互作用对黄海海浪的季节变化均至关重要;对于外海区域,四波非线性相互作用更为重要,而对于近海区域,白冠耗散则影响更大。本研究旨在研究黄海海浪的季节变化特征及其物理过程,为进一步探讨该海域海浪在其他时间尺度上的变异特征和动力学过程提供研究基础。     

1988-2002年黄海和渤海风浪后报

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。     

基于浮标资料的中国东部海域最大波高特征分析

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。     

南麂海域海浪分布基本特征

根据１９６０－１９８９年南麂海洋站的实测风浪资料，分析了该海域的风浪特征，结果认为：这个海域的浪通常是混合浪，常见浪是三级波高的浪；海浪要素的均值分布比较平稳，极值具有不均匀分布的特性，本区的波高和周期的联合分布表明，波高在０．５－１．９ｍ，周期为４．０－６．９ｓ类型的浪在该海域出现频率最高，此外，引用最大熵谱方法找出了本区波高，周期和风速的主要变化周期，还讨论了台风浪的周期与最大波高的经验关系。     

大西洋波高熵的分布变化规律研究

使用1992年10月-1998年12月连续75个月、230个重复周期的Topex／Poseidon卫星高度计有效波高资料，对南北大西洋波高熵的空间分布特征和时间变化规律进行了研究，统计分析了大西洋波高熵的多年的空间分布特征和多年各月的时间变化规律。结果表明，大西洋波高熵呈现出中间低、南北高的马鞍形空间分布特征和明显季节变化的规律，与大西洋的平均有效波高、气候的地理分布以及大气活动分布特征和变化规律相一致。     

基于浮标站和ERA5 再分析资料的浙江沿海台风浪特征分析

为了分析台风影响下浙江沿海风和浪的演变特点，利用浙江省海洋浮标站监测数据和欧洲中期天气预报中心第五代全球气候大气再分析数据（European Centre for Medium-Range Weather Forecasts Reanalysis v5，ERA5），选取2010年以来严重影响浙江的7次台风个例，对台风作用下浙江沿海海面风和浪的演变特点进行分析。结果表明：在台风影响过程中，海浪波型多数呈现混合浪-风浪-混合浪的演变规律；涌浪波型的出现与台风强度及其与浮标站的距离和方位有关，也与海洋潮汐现象紧密相关。台风影响期间，浙江沿海浪高的变化受风速和风向共同作用影响。在风向不变的情况下，持续风速增大对浪高的增大有明显作用；风向的变化也会对浪高变化产生影响，向岸风和离岸风的转变会造成浪高出现剧烈变化。ERA5 再分析资料有效波高在台风浪较大时会呈现偏小的趋势，分析订正后的ERA5 有效波高发现，台风浪有效波高大值区与台风中心位置相关。研究结果可为严重影响浙江沿海的台风浪预报服务提供参考。     

两类典型台风路径影响下的黄、渤海海浪场特征研究

台风引起的海浪灾害对我国黄、渤海沿岸影响巨大,严重威胁相关区域人民群众生命财产安全。本文主要利用ERA5(the fifth generation European Center for Medium-Range Weather forecasts atmospheric reanalysis of the global climate)风场研究了两类不同移动路径下的台风(1909号台风“利奇马”和1109号台风“梅花”)在黄、渤海区域的海浪场的时空分布特征及风-浪成长关系。结果表明:两个台风引起的海浪的有效波高空间分布明显不同,波高的分布和风速对应,而海浪周期与风速、波高的分布无明显相关性,波向较风向偏于台风移动方向且两者偏差较大;两个台风进入黄海之前就形成一个从黄海向渤海的“涌浪舌”。海浪成分方面,台风“利奇马”引起的沿海区大浪主要是风浪,而台风“梅花”移动路径的右侧以风浪为主,左侧则主要是涌浪;通过建立无因次波高与无因次周期的幂律关系、以及有效波高关于风速的二次多项式变化关系,研究了风-浪成长特性,结果发现,台风浪的成长特性与台风过程关系不明显,但与所处水域的水深和海底地形地貌有关,表现为两个台风在黄海区域的台风浪成长较渤海区域更为充分。     

台湾岛邻近海域台风浪的模拟研究

基于目前国际上较为先进的第三代近岸海浪数值模式SWAN（Simulation Waves Near-shore)。在充分考虑相关物理过程（风生浪，底摩擦，白帽耗散，深度诱导波破碎，非线性波－波相互作用）基础上，以较高的分辨率对影响台湾岛邻近海域的9015号台风浪过程进行了模拟研究。模式所需风场由藤田台风风场模型同化相应台风资料后提供；用自嵌套方式提供模式波谱边界条件。模拟结果与实际台风浪资料相符较好。台风过程模拟结果表明；台风中心位于台湾岛邻近海域的不同位置，台风浪有效波高的分布特征和传播方向都有着较大的差异。可以为整个台湾岛邻近海域台风浪分布特征的了解与认识提供较好的参考。     

石岛海域海浪数值模拟及特征分析

本文基于SWAN(Simulating Waves Nearshore)模式研究了2001～2016年石岛海浪有效波高的季节和年际变化特征,评估了不同区域风场对其贡献,并探讨了其与ENSO的关系。结果表明,石岛有效波高受黄海季风系统的影响呈现显著的季节变化:12月份最大, 6月份最小, 1%大波有效波高季节变化不显著。石岛有效波高年际变化信号显著,其与风速年际变化之间的关系存在季节性差异:石岛有效波高和石岛、黄海区域平均风速不同月份的年际异常分别在7、10月份相关性较高,而石岛1%大波有效波高和石岛、黄海区域平均1%大风风速不同月份的年际异常则在8月份左右相关性最高。不同区域风场对石岛有效波高场的影响也呈现了季节性差异:夏季,黄海南部区域风场对石岛海浪的贡献较大,而石岛风场的贡献较小;冬季,石岛风场的贡献较大。ENSO(El Ni?o-Southern Oscillation)事件会对石岛有效波高的年际变化产生一定的影响,但影响比较小。本研究旨在对石岛海浪在季节和年际尺度上的变化特征以及风场等影响因素进行研究,对该海域海浪场进行了详细的认识,对了解该海域海浪有重要意义。     

西太平洋赤道海域秋季混合浪特征及其谱拟合

根据西太平洋赤道海域秋季实测海浪资料,分析了该海域的混合浪特征,拟合出混合浪的波高、周期分布函数及波高-周期联合分布函数。应用文圣常等提出的深水风浪谱公式,对其稍作改变后拟合涌浪谱;应用他们的改进理论风浪谱公式拟合风浪谱,两分谱公式叠加为混合海浪谱公式。对混合海浪谱的拟合表明,拟合效果良好。     

Extreme significant wave height climate in the Gulf of Guinea

This article investigates spatio-temporal trends for different return periods of extreme significant wave height (SWH) in the Gulf of Guinea (GG), northeastern tropical Atlantic Ocean, based on a 37-year (1980–2016) wave hindcast. High-resolution reanalysis windfield datasets were used to force the spectral wave model WAVEWATCH III. The wave hindcast information was validated using data gathered from the US National Data Buoy Center. The model performance was adequate. In a spatial analysis, the trends were less than 0.3 m decade^?1 in all parts of the GG, and were increasingly positive westwards, extending to the far western part of the GG; trends below 0.01 m decade^?1 dominated in the eastern part and some areas of the northern part of the gulf. Temporal analysis showed that the trends were negative in all cases. Spatio-temporal trends in the return periods for the 99th-percentile wave height were generally weak. Also, trends in the yearly, seasonal and monthly means of extreme SWH all generally increased from east to west in the GG. Furthermore, temporal trend analysis showed that extreme SWH exhibited an increasing trend of 0.0041 m y^–1 throughout the 37-year period; by season, it exhibited a declining trend of ?0.0005 m y^–1 in winter, and an increasing trend of 0.0048 m y^?1 in summer. The observed increasing positive trend of extreme SWH westward in the GG, however, suggests an increasing storminess towards the western part of the gulf, with potential implications for coastal flooding and erosion, and consequences for coastal structures.     

New Zealand wave climate from satellite observations

Wave data derived from radar altimeters carried on four satellite missions are combined into a wave climatology for New Zealand waters. These data provide extensive observations of wave conditions around New Zealand, where the paucity of measurements has previously hindered definition of the wave climate. The data span the period 1985 to the present with the exception of a 2‐year gap in 1989–91. The spatial distribution of the long‐term mean of significant wave heights (SWH) indicates a strong latitudinal variation in the south‐west Pacific, with values of over 4 m at latitudes of 50–60°S and under 2.5 m towards the tropics. The shadowing of New Zealand is quite marked; a result of the dominant contribution of south‐westerly wave events. The annual range of the mean SWH also varies over the region; within 0.6 m in the north and 1.3 m in the south. A principal component analysis of the monthly anomalies in mean SWH identifies spatial patterns of variation. Some components vary with the local wind more than others suggesting that some anomalies are associated with wind sea and some with swell. Some patterns also appear to vary with the Southern Oscillation Index and can be related to the wind anomalies associated with El Nino events. Frequency distributions of SWH are also determined, and it is noted that in the north of the region the spatial pattern of the high waves differs considerably from the means.     

南海灾害性波浪基本特征研究

本文基于1991-2016年全球卫星高度计融合数据对南海灾害性波浪基本特征进行了分析，根据灾害性波浪诱发天气类型不同，将其分为"台风浪"和"非台风浪"。依此主线，对两类波浪在南海不同海域的特征进行了研究，并提出了用于定量研究两类波浪强度关系的台风浪权重系数（W），得到了两类波浪在南海相对强弱关系的分布规律，量化研究了南海灾害性波浪的特征。本文以卫星高度计波高数据为样本进行了极值分析，得到了南海重现期波浪要素整体分布规律，研究发现W值大小与广义极值曲线类型显著相关。     

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

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.     

中国海域有效波高、海表风速长期变化趋势的季节特征

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.     

多源卫星高度计海浪资料在中国近海的验证及应用

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%.     

台湾海峡有效波高的SWAN模式与ERA-Interim数据对比分析

有效波高是描述海浪的关键参数。欧洲中期天气预报中心（ECMWF）提供的ERA-Interim再分析数据提供了全球海浪的有效波高,本文选取该数据在台湾海峡2013年3月份的有效波高结果,分别与浮标观测数据以及海浪数值模式SWAN （Simulating Waves Nearshore）的数值模拟结果相对比,来分析其预报效果。结果显示:在浮标点,ERA-Interim数据和SWAN模拟浪高数据与浮标浪高数据的时间相关系数分别为0.94和0.98,ERA-Interim数据的浪高均值约为浮标的51%,为SWAN模拟数据的70%。在台湾海峡区域,ERA-Interim数据与SWAN模拟浪高之间的空间异常相关系数（ACC）月均值为0.51,时序ACC曲线显示,一般在海峡东北口风初起时刻ACC值最小,在风吹遍海峡并增长的过程中,ACC迅速增加,在风速达到最大值之后,ACC开始下降,但ERA-Interim数据与SWAN数值模拟结果在整个海峡区域的浪高最大值与最小值分布位置基本一致。综合分析,ERA-Interim数据能够反映台湾海峡区域此时间段的有效波高的时空变化趋势,在数值上有明显低估。