集合最优插值方法在北印度洋海浪同化中的应用

基于第三代海浪模式WaveWatch III,采用集合最优插值(EnOI)方法对北印度洋海浪进行同化数值实验研究。在集合样本选取方案上,针对不同的实验分别选取有效波高(SWH)的历史后报场(样本A)、24h变化(样本B)以及以同一时刻72h预报时效和24h预报时效的差异(样本C)用于估计背景误差协方差。样本A和样本B是为海浪模拟而设计,样本C是为海浪预报而设计;通过与由高度计数据确定的模式背景误差进行比较,认为样本B优于样本A。采用样本B对2011年北印度洋海浪场进行同化模拟,结果表明2011-03-11相对误差改进都在5%及以上,其中7月份改进效果最佳。采用样本C对2013-07的有效波高进行0~72h预报,发现同化使0~24h预报改进最明显:均方根误差改进0.12m,相对误差改进5%。浮标检验结果支持上述结论。     

基于EC集合预报的渤海灾害性大风风速预报方法研究

利用2014—2015年渤海埕北A平台逐小时风场特征对7级以上且持续6 h以上的大风个例进行统计,共出现36次大风过程。利用欧洲中心集合预报51个预报成员分别比较最优成员和最优百分位预报产品在预报海上大风风速预报的效果,结果表明:EC集合预报对于7级以上大风有低估的效果,从优选成员来看,集合预报最优成员并不唯一确定;6种(最大值、95、90、85、80、75百分位)集合预报百分位预报的10 m风速,多数预报与实况相比是偏小的,利用历史偏差对EC集合预报的误差进行校准,反算校准后误差缩小。总之,集合灾害性大风风速的预报有低估的效果,反算后预报水平有明显提高,可利用EC集合预报最优百分位订正后的10 m风场产品应用于渤海灾害性大风的预报。     

卫星高度计波高数据同化对西北太平洋海浪数值预报的影响评估

通过数值试验验证卫星高度计波高数据同化对西北太平洋3 d海浪预报的改进效果。驱动海浪模式的强迫场采用国家海洋环境预报中心基于MM5模式预报的风场,波高数据同化使用的观测数据是Jason-1卫星高度计有效波高。用最优插值数据同化方法获得海浪有效波高的最优估计并重构相应的海浪方向谱,以此为初始场进行为期3 d的数值预报试验。与没有同化的预报进行了比较和分析,结果表明卫星高度计海浪数据同化对0~72 h预报有不同程度的明显改善,改进程度随预报时效的增加而减少。     

基于海浪谱分解与重构的资料同化试验

针对有效波高资料提出一种海浪谱分解与重构的资料同化方案:利用历史时段内的有效波高观测资料和模式计算波高场,采用最优插值方法得到分析波高场;在WAVEWATCH-Ⅲ模式的波浪能量密度谱和有效波高分析值之间引入一个变异系数矩阵,描述模式的误差,以此为状态向量构建卡尔曼滤波系统,对分解过的海浪谱进行修正和重构,得到同化后的海浪谱初始场。利用美国阿拉斯加湾北部海域的7个浮标站进行同化和72 h预报试验,对连续1个月的预报结果进行统计表明:采用该同化方案后24 h预报结果的有效波高均方根误差比未同化的结果降低了0.13 m;同化方案对预报效果的影响可持续36 h左右,随着预报时效延长,同化的效果减弱。     

一种利用浮标站资料改进海浪模式有效波高预报的方法

基于浮标站海浪历史数据,利用回归分析方法建立了海浪数值模式有效波高预报产品的一元二次回归方程订正统计模型。通过2017年7月1日-2018年10月10日期间业务试运行结果发现:订正方程能有效改善有效波高数值预报产品的预报精度,且预报时效越短订正效果越显著。其中,第6~11 h预报时效内的订正前后平均绝对误差值减小0.17~0. 241 m,第6~18 h预报时效内订正前后均方根误差减小幅度为0.103~0. 28 m。这说明应用订正统计模型对海浪模式输出产品进行订正,也是改进海浪模式预报准确率的一种有效途径。     

山东WRF集合概率预报对台风“麦德姆”逐6h精细化预报检验

用山东WRF集合预报72h预报时效内逐6h概率预报产品对2014年7月24—26日台风"麦德姆"影响山东期间产生的强降水和大风进行了检验。结果表明:WRF集合对强降水和大风有较好的预报能力,总体预报效果较好。强降水和大风的概率都是初期预报概率偏小,后期预报概率较高,开始和结束时间比实况滞后6~18h,前期漏报率高,后期空报率高,总体来说23日20时的预报优于08时的预报。台风低压中心强度和移动路径与实况基本一致,但移速偏慢,这是导致强降水和大风前期漏报后期空报的主要原因。     

上海沿岸海域灾害性大风特征研究与预报检验

选取2011-2017年上海沿岸海域5个浮标站点的风场和海浪数据,分析了大风过程的时间和空间特征;对海浪成长过程进行风向分类,运用滑动相关分析统计了8个风向的海浪滞后时间;计算了大风起风时间的预报提前和滞后量,进行了风速风向的误差和准确率检验。结果表明:越往东部海域,大风时数越多,长江口区东部风速较大;大风极值主要出现在8月份台风过程,出现时段都为傍晚到半夜,大浪极值浪向以东北到东南向为主;秋冬季大风时数多,5-6月大风时数最少;大风风向以西北到东北风为主;海浪成长过程风向分布是东南-西北走向,海浪对风的响应滞后时间平均为3~4 h;大风起风时间预报较实况略有滞后,风速预报的准确率总体在70%以上,预报值较实况值偏小,口外浮标偏小最为明显,偏强率都为0;风向预报准确率低,误差大。     

南海海浪业务化数值预报系统检验

为检验南海海浪业务化数值预报系统的预报效果,利用2010年和2011年3-11月的观测资料,通过计算预报值和观测值的绝对误差、相对误差等统计参数和线性回归分析对南海海浪业务化数值预报系统进行检验.统计结果显示有效波高和平均周期的预报误差24 h＜48 h＜72 h,有效波高的24 h、48 h、72 h预报平均绝对误差小于0.5 m,平均周期的24 h、48 h、72 h预报平均绝对误差小于0.8 s;预报误差有明显的季节变化,10月和11月的预报误差显著小于其它各月;回归分析结果显示预报值与观测值存在中度高度线性相关关系,随着预报时效的增长相关度逐渐递减,预报值较观测值偏大.总体来说,该系统的预报误差在可接受的范围之内,满足业务化预报的要求,但与欧洲气象中心等发达国家的预报系统比较来看,该系统还存在较大差距.     

基于SWAN模式的全球有效波高数值预报结果之初步验证

利用国家海洋环境预报中心基于SWAN模式和NCEP预报风场模拟的全球海浪预报场,结合Jason-2卫星高度计和NDBC浮标资料对全球海浪场进行了自2013年7月到2014年6月为期1 a的统计检验。结果表明:预报波高与实测值吻合较好,24 h、48 h、72 h预报的均方根误差均小于0.6 m,偏差绝对值均小于0.1 m,相关系数均大于0.91。有效波高的预报精度随预报时效的增加而降低,预报误差在48 h内变化不大,而在48 h后明显增大。有效波高的预报偏差存在地域性差别,全球西风带和热带地区的偏差较大,而赤道无风带和副热带高压控制地区的偏差较小。     

北印度洋海浪背景误差分析和同化预报初步评估

基于背景误差分析中的观测法,利用Jason-1卫星高度计沿轨有效波高数据并结合Wave Watch Ⅲ海浪模式预报结果,进行北印度洋海域海浪背景误差分析,得到海浪场背景误差方差和各向同性假设下背景误差相关长度的时空分布特征。按经验函数拟合该海域有效波高背景误差协方差时总残差平方和最小的原则给出了更为适用于该海域的描述公式。在上述工作基础上,采用最优插值同化方法将Jason-1和Jason-2卫星高度计有效波高数据连续同化到海浪模式Wave Watch Ⅲ,按业务化标准对2013年1月北印度洋海域的海浪场进行了同化预报试验,经浮标数据检验发现同化可使海浪24 h预报得到明显改进。     

基于WW3的南海北部有效波高时空变化及其极值重现期估算方法分析

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...     

黄渤海海域16个石油平台站风速资料的初步质量检测

对天津建设的16个石油平台站2017年2-10月的2 min平均风速和极大风速的逐时资料进行了质量检测。其方法包括气候学界限值检查、内部一致性检查、持续性检查、时间一致性检查、空间一致性检查以及质量控制综合分析。结果表明:有87.5%的石油平台站风速观测资料质量较好,能够为海洋气象预报、风能资源评估等提供宝贵的基础支撑。与此同时,由于石油平台上油烟较大、海上高湿、高盐等环境因素影响导致了12.5%的平台站风速资料质量较差,造成其大量时间段的资料不能被直接使用。从而,一定程度上说明了与地面观测资料一样,在使用海洋气象资料之前应进行系统的质量控制,只有清楚并解决海洋气象资料中存在的质量问题,才能保证业务应用、研究分析以及资料统计与加工的准确性和可靠性。     

黄海近岸海雾的初始场择优集合预报试验:个例研究

选取2014年4月发生的一次黄海近岸海雾个例,利用WRF(Weather Research and Forecasting)模式开展了集合预报试验研究。依据每个集合成员初始场中海平面气压、2 m温度、2 m水汽混合比与2 m相对湿度(relative humidity, RH)4个变量的均方根误差(root mean square error, RMSE)与RMSE集合平均值的相对大小,以剔除高于者而保留低于者的原则,设计了4种不同的初始场集合体择优方案,实施了一系列数值预报试验,比较了不同择优方案的集合预报效果。研究结果表明:(1)蒙特卡罗方法所生成的集合体中存在不少海雾预报效果较差的成员,这会降低集合预报效果,因此初始场择优十分必要;(2)以RH作为择优变量的择优方案(记为RH-RMSE方案),集合预报效果明显优于其他3种方案;(3)对比不择优集合预报,采用RH-RMSE方案的择优集合预报效果不仅节省了50%左右的计算时间,并且公正预兆评分(equitable threat score,ETS)改进率高达36%左右。本研究提出的RH-RMSE方案具有业务化应用前景。     

Estimation of mean and extreme waves in the East China Seas

Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves.     

CODAR wave measurements from a North Sea semisubmersible

CODAR, a high-frequency (HF) compact radar system, was operated continuously over several weeks aboard the semisubmersible oil platform Treasure Saga for the purpose of wave-height directional measurement and comparison. During North Sea winter storm conditions, the system operated at two different frequencies, depending on the sea state. Wave data are extracted from the second-order backscatter Doppler spectrum produced by nonlinearities in the hydrodynamic wave/wave and electromagnetic wave/scatter interactions. Because the floating oil rig itself moves in response to long waves, a technique has been developed and successfully demonstrated to eliminate to second order the resulting phase-modulation contamination of the echo, using separate accelerometer measurement of the platform's lateral motions. CODAR wave height, mean direction, and period are compared with data from a Norwegian directional wave buoy; in storm seas with wave heights that exceeded 9 m, the two height measurements agreed to within 20 cm RMS, and the mean direction to better than 15° RMS     

Distributions of freak wave heights measured in the North Sea

We present a statistical analysis of some of the largest waves occurring during 793 h of surface elevation measurements collected during 14 severe storms in the North Sea. This data contains 104 freak waves. It is found that the probability of occurrence of freak waves is only weekly dependent on the significant wave height, significant wave steepness and spectral bandwidth. The probability does show a slightly stronger dependency on the skew and kurtosis of the surface elevation data, but on removing the contribution to these measures from the presence of the freakwaves themselves, this dependency largely disappears.Distributions of extreme waves are modelled by fitting Generalised Pareto distributions, and extreme value distributions and return periods are given for freak waves in terms of the empirical fitted parameters. It is shown by comparison with these fits that both the Rayleigh distribution and the fit of Nerzic and Prevosto severely under-predict the probability of occurrence of extreme waves. For the most extreme freak wave in our data, the Rayleigh distribution over-predicts the return period by about 300 times when compared to the fitted model.     

南海及邻近海域大气-海浪-海洋耦合精细化数值预报系统研制

本文主要介绍了南海及邻近海域大气-海浪-海洋耦合精细化数值预报系统的研制概况。预报区域为99°E~135°E，15°S~45°N，包括渤海、黄海、东海和南海及其周边海域。为了给耦合预报模式提供较准确的预报初始场，在预报开始之前，分别进行了海浪模式和海洋模式的前24小时同化后报模拟。海浪模式和海洋模式都采用了集合调整Kalman滤波同化方法，海浪模式同化了Jason-2有效波高数据；海洋模式同化了SST数据、MADT数据和ARGO剖面数据。为了改进海洋温度和盐度的模拟，我们在海洋模式的垂向混合方案中引入波致混合和内波致混合的作用。预报系统的运行主要包括两个阶段，首先海浪模式和海洋模式进行了2014年1月至2015年10月底的同化后报模拟，强迫场源自欧洲气象中心的六小时的再分析数据产品。然后耦合预报系统将同化后报模拟的结果作为初始场进行了14个月的耦合预报。预报产品包括大气产品（气温、风速风向、气压等）、海浪产品（有效波高和波向等）、海流产品（温度、盐度和海流等）。一系列观测资料的检验比较表明该大气-海浪-海洋耦合精细化数值预报系统的预报结果较为可靠，可以为南海及周边海洋资源开发和安全保障提供数据和信息产品服务。     

Extreme value statistics for wave run-up on a natural beach

Statistics of wave run-up maxima have been calculated for 149 35-minutes data runs from a natural beach. During the experiment incident wave height varied from 0.4 to 4.0 m, incident wave period from 6 to 16 s, and beach face slope from 0.07 to 0.20. Four extreme statistics were calculated; the maximum run-up height during each run, the 2% exceedence level of shoreline elevation, the 2% exceedence height for individual run-up peaks, and the 2% exceedence level for swash height as determined by the zero-upcrossing method. These statistics were best parameterized when normalized by the incident significant wave height and plotted against the Iribarren number, ξ = β/(H/L₀)^1/2. The swash data (with set-up removed) showed less scatter than total run-up (with set-up included). For Iribarren number greater than 1.5 the run-up was dominated by the incident frequencies, for lower Iribarren number longer period motions dominated the swash. A reasonable value of wave steepness for a fully developed storm sea is 0.025 so that a storm Iribarren number can be estimated as 6.3 times the beach slope. Using this and an offshore design wave height, the included graphs may provide guidance in determining a design run-up height.     

Evaluation of two spectral wave models for wave hindcasting in the Mackenzie Delta

Climate change, reduced sea ice and increased ice-free waters over extended areas for longer summer periods potentially lead to increased wave energy in the Beaufort Sea (Wang et al., 2015; Khon et al., 2014) [1], [2], which is a major concern for coastal and offshore engineering activities. We compare two spectral wave models SWAN (Simulating WAves Nearshore) and MIKE 21 SW (hereafter MIKE21) in simulations of storm-generated waves in the Mackenzie Delta region of the southern Beaufort Sea. SWAN model simulations are performed using two nested grids system, whereas MIKE21 uses an unstructured grid system. Forcing fields are defined by hourly hindcast winds. Moving ice edge boundaries are incorporated during storm simulations. Modelled wave spectra from four storms are shown to compare well with field observations. Two established whitecapping formulations in SWAN are investigated: one dependent on mean spectral wave steepness, and the other on local spectral steepness. For the Beaufort Sea study area, we suggest that SWAN wave simulations using the latter local spectral steepness formulation are better than those using the former mean spectral steepness formulation. MIKE21 simulations also tend to agree with SWAN results using the latter whitecapping formulation.