海浪预报知识讲座：第十一讲 海浪预报技术及预报方法（4）—海浪经验统计预报方法（Ⅱ）

国家海洋环境预报中心,应用20多年东亚地面天气图和海浪实况图资料,提出一些适合我国近海和邻近大洋海浪经验统计预报方法。这些方法大致分为三类:一是冷空气影响下海浪场的分析预报方法;二是温带气旋影响下海浪场分析预报方法;三是热带气旋影响下海浪场分析预报方法。     

黄、渤海冷空气海浪场的集合预报试验

利用欧洲集合天气预报系统51个预报风场驱动SWAN海浪模式,对黄、渤海2013年12月-2014年2月期间受冷空气影响的海浪场进行数值模拟试验,并利用浮标观测资料对海浪集合预报结果进行初步检验分析,结果显示:从逐时平均偏差结果可知,24h预报时效内集合平均与控制预报性能相近,48~72h预报时效内,集合平均明显优于控制预报,但均比实况偏小;集合分位值(75、90百分位值和极端值)明显优于集合平均,且预报时效越长,优势越明显,集合预报极端值与实况相当或略偏大;从逐24h平均偏差结果可知,集合分位值(75、90百分位值和极端值)比集合平均和控制预报更接近实况。总的分析表明:集合分位值(75、90百分位值和极端值)对受冷空气影响的海浪场具有较强的分辨能力,可以提高对海浪场的预报水平,且有较好的应用潜力。     

山东半岛东部沿海海浪预报方法初步探讨

本文采用麦夸特法（Levenberg—Marquardt）＋通用全局优化的拟合方法,依据山东半岛不同海区海浪和风的实况资料,确定不同海区拟合方程,拟合方程以WRF风场预报模式预报24小时风速为预报因子,以海浪浪高为预报量。根据拟合方程计算出的每个海区的浪高的值,对每个海区分别进行海浪的浪高预报,产品以GRADES图形的形式输出。     

基于舰船耐波性的中国近海海浪特征统计(英文)

海浪是影响舰船行进和结构设计的最核心的环境要素。海浪参数的统计分析是建立舰船环境适应性长期预测模型的基础。对1993-2011年沿海台站、浮标观测资料,高度计观测资料和船舶报观测资料进行双线性插值,给出了海浪波高的分析场。利用波高分析场对WAVEWATCHIII(WW3)模拟结果进行最优插值同化。给出了海浪统计玫瑰图、极端海浪统计参数和海浪累年二维极值海浪谱等与舰船耐波性有关的海浪统计参数,分析了中国近海的波浪特征。     

2002年12个海水浴场海浪特征分析及预报结果分析

海水浴场海浪预报是海水浴场海洋环境预报中一项十分重要的预报，它不仅影响到游泳舒适度，更重要的是它直接影响游泳者生命安全。近年来，游泳者被海浪卷入深海伤亡事故经常发生，因此海水浴场海浪预报引起有关方面的高度重视。本文利用天气图、海浪图和12个海水浴场实测海浪资料，对12个海水浴场的海浪特征进行初步分析，取得的结果将对今后海水浴场海浪预报有较大帮助。     

弱气压场中南海东北部海浪预报失败典型个案

目前在广东气象界里对海浪预报服务情况分析是比较少的,针对2005年12月底到2006年1月初南海东北部石油平台用户对海浪预报产品服务的意见反馈,本文通过分析该时段的天气背景,中尺度数值预报产品,实况风浪情况,综合我们提供给用户的海浪预报产品进行对比,目的是找出该次预报不成功的事实和原因,给用户有所交代;同时希望通过这类总结分析,对海上风浪预报服务提出警示,避免类似错误再次出现,提高专业预报服务质量。     

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。     

近海近岸高精度海浪业务化数值预报系统

基于第三代海浪模式WaveWatchⅢ和Swan,采用四重网格嵌套建立了黄海、南海近海海浪的高精度数值预报系统,以及青岛第一海水浴场、广西北海银滩浴场、海南三亚亚龙湾海水浴场3个示范区近岸定点海浪的精细化数值预报系统。通过后报和预报试验对所建立的数值预报系统进行了系统的检验,后报波高与实测值吻合较好。准业务化预报试验表明有效波高的预报精度随预报时效的增加而降低,近海海浪大于2 m的平均预报相对误差小于30%。浴场海浪的平均预报绝对误差为0.35 m左右。预报精度可以满足业务化预报的要求。     

基于MASNUM海浪预报系统的北印度洋波浪特征模拟与预报分析

基于MASNUM海浪数值预报系统的全球10 a后报数据库资料,分析了北印度洋区域波浪分布特征.由于该地区受季风控制显著,夏季波浪大于冬季;在空间分布上,西部比东部风大、浪大,在亚丁湾、索马里外海波浪最大.基于Janson-1卫星高度计有效波高观测资料,对MASNUM海浪预报系统的预报性能进行了检验,检验结果表明,预报波高均方根误差在0.5 m左右,短期的24 h预报效果好于48 h和72 h,冬季好于夏季.另外,对预报误差进行了相应的概率分布分析.     

一种海浪预报方法的探讨

一种海浪预报方法的探讨林克式（温州海洋管区）一、问题的提出数值预报，半经验半理论的预报，经验统计预报海浪仍是当今我国海浪预报的三大方法。由于受条件的限制，我们对东海油田的海浪预报，虽然找出了五十多条预报指标，并用微机进行按需挑取，预报效果仍然不好。而...     

Spectral wave data assimilation for the prediction of waves in the North Sea

Spectral observations from pitch-and-roll buoys have been assimilated in a North Sea wave model, in order to study their impact on the wave analysis and forecast. The assimilation is based on Optimal Interpolation (OI) of a limited number of characteristic spectral parameters. In a case study, the propagation of the corrections through the model domain is followed, and it is clarified for which wave conditions the data assimilation has the largest influence on the forecast: this is especially the case for swell waves with long travel times between the assimilation site and the location where validation is carried out. A 1-year test has been carried out in which an analysis and subsequent forecast were produced four times a day. From a statistical analysis of the results a modest but systematic improvement of the 12-h forecast is found. When only swell cases are selected, the impact is more pronounced. It is argued that for shelf seas like the North Sea, more progress is to be expected from extension of the ‘conventional' observations network (buoys and wave radars) than from satellite measurements.     

Sea state estimation using multiple ships simultaneously as sailing wave buoys

The article presents initial ideas towards a network-based approach for sea state estimation used for marine operations and other maritime applications. In principle, all available means, ranging from in situ buoys, fleet of ships to remote sensing by satellite and aircraft, could be considered, emphasising that each means and any combinations among may act simultaneously. This study focuses on just one of the means; the use of ships as sailing wave buoys. The article introduces the wave buoy analogy, i.e. ship-as-a-wave-buoy, and it makes a proposal on how to impose (different) weights to the single ship-specific wave spectrum estimates obtained from multiple ships. Moreover, the work includes a discussion about the importance to associate a measure to reflect the (un)certainty of the wave spectrum estimate. The article presents a numerical case study, where multiple ships act simultaneously as wave spectrum-estimators. The case study relies on numerical motion simulations, as appropriate full-scale data is not yet available. In the analysis, it is shown that the use of simultaneous data from multiple ships leads to more accurate wave spectrum estimations.     

Real time wave forecasting using neural networks

Forecasting of ocean wave heights, with warning time of a few hours or days, is necessary in planning many operation-related activities in the ocean. Such information is currently derived by numerically solving the differential equation representing wave energy balance. The solution procedure involved is extremely complex and calls for very large amounts of meteorological and oceanographic data. This paper presents a complementary and simple method to make a point forecast of waves in real time sense based on the current observation of waves at a site. It incorporates the technique of neural networks. The network involved is first trained by different algorithms and then used to forecast waves with lead times varying from 3 to 24 h. The results of different training algorithms are compared with each other. The neural output is further compared with the statistical AR models.     

Wave profile measurement by wavelet transform

A new technique for measuring wave profiles by wavelet transform using the Mexican Hat wavelet as the mother wavelet is introduced. This technique has the potential to provide low cost, high resolution field measurements of wave profiles in the laboratory. The experiments to capture the video image of the wave profile were carried out in a wave flume. Then, the Mexican Hat wavelet was adopted to trace out the exact profiles of the waves from the captured video images. The series of tests on numerical data and video images show promise as means of detecting two-dimensional profiles of waves.     

Wave field mapping with particle image velocimetry

A number of different methods for the 3D measurement of the water surface in wave fields have been proposed in the literature, based mostly on refraction, photogrammetry and/or reflected light intensity. Although these methods can map the distorted water surface, they appear to be difficult in practical applications in hydraulic engineering laboratories. A novel wave mapping technique using particle image velocimetry to measure surface flow velocities, and linear wave theory to determine wave heights from the measured velocities, has been developed and validated for regular waves. The method allows for the mapping even of complex wave fields with simple means and appears robust enough for application in the laboratory.     

Forecasting ocean wave energy: Tests of time-series models

This paper evaluates the ability of time-series models to predict the energy from ocean waves. Data sets from four Pacific Ocean sites are analyzed. The energy flux is found to exhibit nonlinear variability. The probability distribution has heavy tails, while the fractal dimension is non-integer. This argues for using nonlinear models. The primary technique used here is a time-varying parameter regression in logs. The time-varying regression is estimated using both a Kalman filter and a sliding window, with various window widths. The sliding window method is found to be preferable. A second approach is to combine neural networks with time-varying regressions, in a hybrid model. Both of these methods are tested on the flux itself. Time-varying regressions are also used to forecast the wave height and wave period separately, and combine the forecasts to predict the flux. Forecasting experiments are run at an hourly frequency over horizons of 1-4 h, and at a daily frequency over 1-3 days. All the models are found to improve relative to a random walk. In the hourly data sets, forecasting the components separately achieves the best results in three out of four cases. In daily data sets, the hybrid and regression models yield similar outcomes. Because of the intrinsic variability of the data, the forecast error is fairly high, comparable to the errors found in other forms of alternative energy, such as wind and solar.     

人工神经网络技术在台风浪预报中的应用

利用人工神经网络中的BP算法，结合南海硇洲岛海区近30年的台风及台风浪资料，经预期因子的选择并作对比试验，建立了本海区较为理想的台风浪人工神经网络预报模型。结果表明：人工神经网络方法在台湾浪的预报上，有较好的拟合历史台风浪高的能力，利用该模型对台风浪高的预报也达到了一定的精度。为实际台风浪浪的预报增加了新方法、新思路。     

南海北部波浪特征分析

利用南海北部近海区域(20°36.298′N,110°45.433′E)于2012年至2013年的波浪实测资料,统计分析了其波浪特征,为海洋工程建设和波浪能利用提供基本波浪参数。统计结果表明,南海北部年平均有效波高为1.2m,周年平均十分之一部分大波波高为1.5m,年平均周期4.0s,周年最大平均有效波高为4.97m,周年最大平均十分之一部分大波波高为7.34m,常浪向为E向,次常浪向为ENE向。强浪向为E向,次强浪向为ESE向。