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寒潮影响下江苏沿海风浪场数值模拟研究 总被引：2，自引：0，他引：2
基于第三代浅水波浪数值预报模型SWAN,建立自西北太平洋嵌套至东中国海、江苏沿海的三重嵌套模型,对2010年12月12日至15日江苏沿海寒潮大风引起的风浪过程进行了数值模拟研究。利用西北太平洋和江苏沿海实测数据对模型进行了验证,结果表明SWAN嵌套模型能较好地模拟江苏沿海寒潮风浪场的时空分布。通过响水站实测数据对江苏沿海底摩擦系数进行了率定,研究表明选取Collins拖曳理论中摩擦因数C_f=0.001时,有效波高模拟误差相对较小。寒潮风浪场的特征分析表明,有效波高分布与风场分布基本一致,寒潮风浪在江苏沿海北部影响较为显著,辐射沙洲附近由于其特殊地形影响相对较小。 相似文献
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台风引起的大浪对海岸带及海洋工程有很大的影响。进行长时间序列的台风资料分析和台风浪模拟,对海岸带规划及海洋工程防护有一定的指导意义。本文利用西北太平洋热带气旋最佳路径数据集(CMA-STI热带气旋最佳路径数据集)中提供的台风信息,分别统计和分析了1949—2010年和1981—2010年出现在东中国海海域的台风的时间分布特征和空间分布特征,并将2个时间段的分布特征进行比较。利用高桥公式和藤田公式计算了1981—2010年间92次台风过程的气压场分布,进而计算出风场,把经过验证的风场做为驱动,通过SWAN模式计算出有效波高。经过与B22001号浮标实测资料的对比,模型计算风速和有效波高均与实测值符合较好。根据模拟计算结果,分析了东中国海海域台风浪有效波高的分布特征。 相似文献
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利用卫星高度计资料进行多年一遇极值波高推算的方法研究 总被引：3，自引：0，他引：3
根据卫星高度计资料的特点 ,在对 3个海洋站的海浪极值推算中引用发展了尾部分布增强法 (Tail Distribution Method) ,并且与 3个海洋站实测资料推算结果相比较 ,结果良好。以此为基础对西北太平洋进行多年一遇极值推算 ,其多年一遇的极值分布与风场的分布是一致的 ,主要呈纬向带状分布特征 :从 35°N～ 55°N,1 4 5°E～ 1 80°E的长方形海域 ,长时间受到季风的作用并且海域开阔 ,所以它的百年一遇波高可以达到 1 3m以上 ;而在赤道无风带控制下的海域 ,它的百年一遇波高一般为 7m左右。 相似文献
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本文针对长江口深水航道工程建设前后航道内波浪特征的变化进行了分析研究,首先基于对长江口历史波浪资料分析,确定了E、NE和SE3个浪向为航道内典型代表,采用第3代波浪模型SWAN对工程建设前后的波浪进行模拟计算。研究表明,航道工程建设后航道内的有效波高和周期明显减小,但是其变化的规律基本一致。在不同天文潮影响时,航道内的有效波高和周期大不相同。E向和NE向的浪在大潮期间有效波高和周期均高于小潮时,SE向浪却相反。对于NE向的浪,航道工程对有效波高的影响大致呈现出从右向左逐渐减弱。对于E向和SE向的浪,航道工程对有效波高的影响大致呈现出从右向左先增强再减弱。航道工程对不同方向的浪在不同分段上也有所不同,在右段对NE向的消弱最强,对SE向最弱,在中段和左段对E向消弱最强,对NE向最弱。航道内流场与水位对波要素的影响也略有差别。 相似文献
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《海洋湖沼通报》2016,(1)
基于第三代海浪数值模式WAVEWATCH-Ⅲ(v 3.14),在CCMP风场驱动下,对2009年平常月份与台风"茉莉"作用期间涌浪场及混合浪场进行了数值模拟,探讨了东海E3海域的涌浪在不同气象条件下的产生机理及其演变特性。结果表明:平常月份东海E3海域的涌浪主要来自东海海域及西北太平洋海域,当涌浪来自东海海域,涌浪波高较大,涌浪波高多在0.7~2m,谱峰周期约为8~10s,混合浪中涌浪成分较高;当涌浪来自西北太平洋海域,涌浪的有效波高多在0.2~0.7m,谱峰周期约为8~10s,混合浪中涌浪成分较小。台风期间,东海E3海域主要受西北太平洋海域传来的涌浪影响,涌浪的有效波高及谱峰周期都较平常月份为大,有效波高主要分布在0.5~1.8m,谱峰周期主要分布在10~18s。 相似文献
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西北太平洋夏季海浪数值模拟研究 总被引：1，自引：0，他引：1
为了解第三代海浪模式SWAN在西北太平洋海浪模拟效果,利用2013年7月期间Jason-2卫星高度计观测资料,通过计算模拟值和观测值的绝对误差、均方根误差和进行逐日统计、分级统计、一次台风过程的统计,对FNL风场资料驱动SWAN模式的西北太平洋海浪数值模拟有效波高进行了检验。检验结果表明,模式对较小波高模拟效果较好,模拟波高与实测值误差在可接受的范围之内,可满足业务化预报的要求,但对较大波高的模拟存在一定的误差,且驱动风场的精细化水平直接影响模拟效果。 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
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When investigating the long-term variation of wave characteristics as associated with storm surges in the Bohai Sea,the Simulating Waves Nearshore(SWAN)model and Advanced CIRCulation(ADCIRC)model were coupled to simulate 32 storm surges between 1985 and 2014.This simulation was validated by reproducing three actual wave processes,showing that the simulated significant wave height(SWH)and mean wave period agreed well with the actual measurements.In addition,the long-term variations in SWH,pattems in SWH extremes along the Bohai Sea coast,the 100-year retum period SWH extreme distribution,and waves conditional probability distribution were calculated and analyzed.We find that the trend of SWH extremes in most of the coastal stations was negative,among which the largest trend was-0.03 m/a in the western part of Liaodong Bay.From the 100-year return period of the SWH distribution calculated in the Gumbel method,we find that the SWH extremes associated with storm surges decreased gradually from the center of the Bohai Sea to the coast.In addition,the joint probability of wave and surge for the entire Bohai Sea in 100-year return period was determined by the Gumbel logistic method.We therefore,assuming a minimum surge of one meter across the entire Bohai Sea,obtained the spatial SWH distribution.The conclusions of this study are significant for offshore and coastal engineering design. 相似文献
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The NW Pacific Ocean is not onIy the only transportation way between America andAsia, but also the source influencing on inIand climate and marine variability of adjacentseas in China. Based on ship observation data during l950 - l995 in the NW Pacific,with data from several hundreds to 30 thousand in every 5"x5" grid network, throughanalyzing the monthly mean directions of prevailing wind, wave and swelI, wind speed,pressure, wave height and frequencies of gaIe of 6 and 8 sca1e, high sea… 相似文献
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1988—2009年中国海波候、风候统计分析 总被引：3，自引：0，他引：3
利用高精度、高时空分辨率、长时间序列的CCMP(Cross-Calibrated,Multi-Platform)风场,驱动国际先进的第三代海浪模式WAVEWATCH-Ⅲ(WW3),得到中国海1988年1月～2009年12月的海浪场。对中国海的波候(风候)进行精细化的统计分析,分析了海表风场和浪场的季节特征、极值风速与极值波高、风力等级频率和浪级频率、海表风速和波高的逐年变化趋势,结果显示:(1)中国海的海浪场与海表风场具有较好的一致性,尤其是在DJF(December,January,February)期间;海表风速和波高在MAM(March,April,May)期间为全年最低,在DJF期间达到全年最大;MAM和JJA(June,July,August)期间,中国海大部分海域的波周期在3～5.5s,SON(September,October,November)和DJF期间为4.5～6.5s。(2)中国海极值风速、极值波高的大值区分布于渤海中部海域、琉球群岛附近海域和台湾以东广阔洋面、台湾海峡、东沙群岛附近海域、北部湾海域、中沙群岛南部海域。(3)吕宋海峡在MAM、SON、DJF期间均为6级以上大风和4m以上大浪的相对高频海域,JJA期间,6级以上大风的高频海域位于中国南半岛东南部海域,4m以上大浪主要出现在10°N以北。(4)在近22a期间,中国海大部分海域的海表风速、有效波高呈显著性逐年线性递增趋势,风速递增趋势约0.06～0.15m.s-1.a-1,波高递增趋势约0.005～0.03m.a-1。 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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Evaluation of GEV model for frequency analysis of annual maximum water levels in the coast of United States 总被引：3，自引：0，他引：3
The three-parameter generalized-extreme-value (GEV) model has been recommended by FEMA [FEMA (Federal Emergency Management Agency of the United States), 2004. Final Draft Guidelines for Coastal Flood Hazard Analysis and Mapping for the Pacific Coast of the United States. http://www.fema.gov/library/viewRecord.do?id=2188] for frequency analysis of annual maximum water levels in the Pacific coast of the United States. Yet, the GEV model's performance in other coastal areas still needs to be evaluated. The GEV model combines three types of probability distributions into one expression. The probability distributions can be defined by one of the three parameters of the GEV model. In this study, annual maximum water levels at nine water-level stations with long history data (more than 70 years) were chosen for analysis in five coastal areas: Pacific, Northeast Atlantic, East Atlantic, Southeast Atlantic, and Gulf of Mexico coasts. Parameters of the GEV model are estimated by the maximum likelihood estimation (MLE) method. Results indicate that probability distributions are characterized by the GEV Type III model at stations in the Pacific, Northeast, and East Atlantic coastal areas, while they are described by GEV Type II in stations of the Southeast Atlantic and Gulf of Mexico coastal areas. GEV model predictions of extreme water levels show good correlation to observations with correlation coefficients of 0.89 to 0.99. For predictions of 10% annual maximum water levels, the GEV model predictions are very good with errors equal to or less than 5% for all nine stations. Comparison of observations and GEV model estimations of annual maximum water levels for the longest recorded return periods, close to 100 years, revealed errors equal to or less than 5% for stations in the Pacific and Northeast Atlantic coastal areas. However, the errors range from 10% to 28% for other stations located in the East and Southeast Atlantic coasts as well as Gulf of Mexico coastal areas. Findings from this study suggest caution regarding the magnitudes of errors in applying the GEV model to the East and Southeast Atlantic coasts and Gulf of Mexico coast for estimating 100-year annual maximum water levels for coastal flood analysis. 相似文献
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1957～2002年南海—北印度洋海浪场波候特征分析 总被引：2，自引：0，他引：2
利用ERA-40海表10 m风场驱动第三代海浪数值模式WAVEWATCH-Ⅲ,得到南海—北印度洋1957年9月至2002年8月的海浪场,并分析其波候(风候)特征.研究发现如下主要特征:(1)该海域的波高波向、风速风向受季风影响显著;(2)北印度洋大部分海域的海表风速呈显著性逐年线性递增趋势,大约0.01～0.02 m/(s·a),南海线性递增的区域则较少,有效波高呈显著性逐年线性递增的区域主要集中在低纬度中东印度洋(约0.003～0.006 m/a)、索马里附近海域(大约0.002～0.005 m/a)、南海大部分海域(约0.002～0.004 m/a),线性递减的区域主要集中在孟加拉湾海域(约-0.002 m/a);(3)Nino3指数与南海—北印度洋的海表风场、浪场存在密切的关系;(4)南海—北印度洋的海表风速与有效波高存在5.2a左右的共同周期,南海的海表风速、有效波高还存在2.0a左右的共同周期,北印度洋的海表风速、有效波高还存在26.0a的长周期震荡. 相似文献
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利用WAVEWATCHⅢ海浪模式模拟的1993-2011年中国东部海域19 a冬季逐日海浪场资料以及同期CCMP逐日风场资料,采用奇异值分解(SVD)的方法分析了冬季中国东部海域海浪场与提前0~5 d的东亚大陆地面风场的关联特征。结果发现:海浪场与提前1 d的地面风场的关联更有意义;SVD第一模态和第二模态分别反映了贝加尔湖以东南下的反气旋式偏北扰动大风(或气旋式偏南扰动大风)和中国东部平原入海的气旋式扰动风场(或反气旋式扰动风场)对中国东部海域海浪的扰动影响。此外,SVD分析还揭示了冬季影响中国东部海域海浪的大风关键区和移动路径;随着时间的推移,大风关键区从贝加尔湖以东逐步由蒙古南下影响中国东北和华北地区,最后到达中国东部海域。 相似文献
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Long term wave climate of both extreme wave and operational wave height is essential for planning and designing coastal structures.
Since the field wave data for the waters around Korean peninsula is not enough to provide reliable wave statistics, the wave
climate information has been generated by means of long-term wave hindcasting using available meteorological data. Basic data
base of hindcasted wave parameters such as significant wave height, peak period and direction has been established continuously
for the period of 25 years starting from 1979 and for major 106 typhoons for the past 53 years since 1951 for each grid point
of the North East Asia Regional Seas with grid size of 18 km. Wind field reanalyzed by European Center for Midrange Weather
Forecasts (ECMWF) was used for the simulation of waves for the extratropical storms, while wind field calculated by typhoon
wind model with typhoon parameters carefully analyzed using most of the available data was used for the simulation of typhoon
waves. Design wave heights for the return period of 10, 20, 30, 50 and 100 years for 16 directions at each grid point have
been estimated by means of extreme wave analysis using the wave simulation data. As in conventional methodsi of design criteria
estimation, it is assumed that the climate is stationary and the statistics and extreme analysis using the long-term hindcasting
data are used in the statistical prediction for the future. The method of extreme statistical analysis in handling the extreme
events like typhoon Maemi in 2003 was evaluated for more stable results of design wave height estimation for the return periods
of 30–50 years for the cost effective construction of coastal structures. 相似文献