0518号台风"达维"风暴潮分析

2005年9月26日凌晨4时,32年来最强的台风“达维”在海南省万宁市山根镇沿海地区登陆。“达维”一路横扫,肆虐全省18市县,重创海南,直接经济总损失达116.47亿元。本文对这次台风的风暴潮特点及其成因进行了较深入的分析,初步探讨灾情分布,为今后该类台风风暴潮预报积累经验及防灾减灾提供基础资料依据。     

厦门港潮汐、风暴潮耦合模型

建立一个适合台湾海峡及小港湾,特别是能够反映小港湾的漫滩及潮汐、风暴潮耦合效应的数值模型,对小港湾风暴潮进行数值预报,有重要的社会和经济意义.文中以厦门港作为小港湾研究区域,在漫滩、风暴潮的耦合、开边界条件等方面对小港湾风暴潮数值计算作了一定的尝试,提出了一种能够模拟小港湾台风增水,潮位和流场变化情况的途径.     

A numerical storm surge forecast model with Kalman filter

ImUcrIONThe deterministic storm stirge nurnrical fOrecast Tnedel has played an imPOrtant role inroutine storm surge real-time fOrecast. But somtimes the error of forecast is still large by usingdeterministic medels (Je1esnianshi et al., l992). The source of these errors mainly comesfrom (1 ) errors of wind stress and medel's open boundary, (2) non--optimized medel param-eter, (3) error of model equations, (4) error of medel's numrical methed, etc. The effec-ti ve methed to solve this probl…     

基于ADCIRC+SWAN耦合模型的风暴潮数值模拟研究——以深圳西部海域为例

本文采用ADCIRC和SWAN模式建立了基于非结构网格的深圳海域高分辨率天文潮-风暴潮-海浪耦合数值模型。基于历史统计资料,以1604号台风"妮妲"作为基础路径、以1319号超强台风"天兔"作为设计强度设计了超强台风,计算了超强台风影响下深圳西部海域的最高潮位,结果显示宝安机场、赤湾和深圳湾顶的最高潮位为5.71 m、4.67 m和5.06 m,分别超过当地红色警戒潮位2.69 m、1.65 m和2.98 m。为验证波浪增水作用设计了敏感性实验,结果显示波浪对风暴潮增水的贡献量值约0.1 m。     

Implementation and application of a nested numerical storm surge forecast model in the East China Sea

A nested numerical storm surge forecast model for the East China Sea is developed. Aone-way relaxing nest method is used to exchange the information between coarse grid and fine grid. In the inner boundary of the fine grid model a transition area is set up to relax the forecast variables. This ensures that the forecast variables of the coarse model may transit to those of fine grid gradually, which enhances the model stability. By using this model, a number of hindcasts and forecast are performed for six severe storm surges caused by tropical cyclones in the East China Sea. The results show good agreement with the observations.     

威马逊台风对上海地区风暴增水的影响

本文首先介绍威马逊台风(0205号)的生成、发展和消亡过程以及该台风的特点，然后介绍了我中心发布上海市沿海风暴潮预报的有关情况，并对威马逊台风引发的特大增水成因作了分析。认为持续的东北大风和低压效应是台风增水的主要动力因素。这次台风过程末对上海市沿海造成超过警戒水位的原因是当台风位置处于有利于产生极大增水时，上海市沿海恰逢天文小潮汛(出现在低潮时)：而当天文高潮出现时，台风位置已稍偏北，不利于增水。最后作了两个推论，认为如果上海市再遇到类似的台风过程，而又恰逢天文大潮汛，或台风北上的路径再靠近大陆1个经距的话，上海市将出现超过600cm的特大潮位。     

Modeling hurricane waves and storm surge using integrally-coupled,scalable computations

The unstructured-mesh SWAN spectral wave model and the ADCIRC shallow-water circulation model have been integrated into a tightly-coupled SWAN + ADCIRC model. The model components are applied to an identical, unstructured mesh; share parallel computing infrastructure; and run sequentially in time. Wind speeds, water levels, currents and radiation stress gradients are vertex-based, and therefore can be passed through memory or cache to each model component. Parallel simulations based on domain decomposition utilize identical sub-meshes, and the communication is highly localized. Inter-model communication is intra-core, while intra-model communication is inter-core but is local and efficient because it is solely on adjacent sub-mesh edges. The resulting integrated SWAN + ADCIRC system is highly scalable and allows for localized increases in resolution without the complexity or cost of nested meshes or global interpolation between heterogeneous meshes. Hurricane waves and storm surge are validated for Hurricanes Katrina and Rita, demonstrating the importance of inclusion of the wave-circulation interactions, and efficient performance is demonstrated to 3062 computational cores.     

Neural network prediction of a storm surge

The occurrence of storm surge does not only destroy the resident's lives, but also cause the severe flooding in coastal areas. Therefore, accurate prediction of storm surge is an important task during the coming typhoon. Conventional numerical methods and experienced methods for storm surge prediction have been developed in the past, but it is still a complex ocean engineering problem which many factors, including the central pressure of typhoon, the speed of the typhoon, the heavy rainfall, coastal topography and local features influence the variation of storm surge. In fact, this problem is still a complex nonlinear relationship that can not solved efficiently by these two methods. Therefore, this paper presents an application of the neural network for forecasting the storm surge. The original data of Jiangjyun station in Taiwan will be used to test the performance of the present model. The results indicate that the neural network can be efficiently forecasted storm surge using the four input factors, including the wind velocity, wind direction, pressure and harmonic analysis tidal level.     

基于多变量LSTM神经网络模型的风暴潮临近预报

台风的风暴潮是台风引发的一种重要次生灾害,对沿海城市带来的威胁是多方面的。及时准确地预报风暴潮,对沿海地区采取合理措施减少人员伤亡和经济损失具有重要意义。本文利用长短期记忆神经网络 （LSTM） 模型,综合考虑风速、 风向、气压等气象因素和前时序的潮位数据,建立了风暴潮的临近预报模型。结果表明,基于 LSTM 的临近预报模型具有相当的预报技巧,利用前时序的风速和风向数据以及潮位数据建立的模型可对风暴潮潮位进行准确地预测。研究还表明,仅考虑前时序潮位的预测模型误差最大,考虑气压后的模型预测能力有一定进步,而考虑风的要素以后,预测的效果提升更为明显。     

浪潮耦合的舟山渔港台风暴潮数值模拟

建立能精确模拟舟山渔港台风暴潮过程的浪潮耦合模型,对渔港防灾减灾具有重要意义。基于Delft3D中的FLOW和WAVE模块,在二重嵌套网格下建立风暴潮和波浪的耦合模型。以9711号台风Winnie为背景,验证耦合模型的可靠性,结果显示,风速、天文潮潮位、风暴潮潮位和有效波高的计算值与实测值吻合良好。利用风暴潮模型与耦合模型分别计算了舟山海域的风暴潮,分析了波浪对风暴潮潮位的抬升影响,定海和镇海站最大波浪增水分别为23 cm和34 cm,耦合模型的模拟精度要高于风暴潮模型。通过模拟9711号台风期间舟山渔港的风暴潮过程,分析了风暴潮的时空分布特征,并给出了浪潮耦合作用对于风暴潮时空分布的影响。     

青岛沿海风暴潮分析

利用“青岛海军验潮站”(青岛大港1号码头)1949~2003年潮汐资料,采用统计风暴高潮位≥510cm出现频数的方法,对青岛沿海风暴潮进行统计分析。结果表明:诱发青岛沿海显著风暴高潮位的天气系统都是台风,集中出现在8月下旬~9月上旬,阴历初三至初五、十五至十八两个天文大潮时段。     

Artificial neural network-based storm surge forecast model: Practical application to Sakai Minato,Japan

The present study describes a novel way of a systematic and objective selection procedure for the development of an Artificial Neural Network-based storm Surge Forecast Model (ANN-SFM) with the 5, 12 and 24 h-lead times and its application to Sakai Minato area on the Tottori coast, Japan. The selection procedure guides how to determine the superiority of the best performing model in terms of the appropriate combination of unit number in the hidden layer and parameter in the input layer. In the application of ANN-SFM to Sakai Minato, it is found that the best 5 and 12 h-forecast ANN-SFMs are established with the most suitable set of 70 units (the number of hidden neurons) and the input components of surge level, sea level pressure, the depression rate of sea level pressure, longitude, latitude, central atmospheric pressure and highest wind speed. The best 24 h-forecast ANN-SFM is determined with 160 units and the input parameters of surge level, sea level pressure, the depression rate of sea level pressure, longitude and latitude. The proposed method of the selection procedure is able to be adaptable to other coastal locations for the development of the artificial neural network-based storm surge forecast model as establishing the superiority of the most relevant set combining unit numbers and input parameters.     

Storm surge simulation along the Meghna estuarine area: an alternative approach

In this study, numerical prediction of surges associated with a storm was made through the method of lines (MOL) in coordination with the newly proposed RKARMS (4, 4) method for the meghna estuarine region, along the coast of Bangladesh. For this purpose, the vertically integrated shallow water equations (SWEs) in Cartesian coordinates were firstly transformed into ordinary differential equations (ODEs) of initial valued, which were then soloved using the new RKARMS (4, 4) method. Nested grid technique was employed for resolving the complexities of the region of interest with minimum cost. Fresh water discharge through the lower Meghna River was taken into account along the north east corner of the innermost child scheme. Numerical experiments were performed with the severe cyclone on April 1991 that crossed the coast over the study area. Simulated results by the study were found to be in good agreement with some reported data and were found to compare well with the results obtained by the MOL in addition with the classical 4th order Runge-Kutta (RK (4, 4)) method and the standard finite difference method (FDM).     

Support vector regression methodology for storm surge predictions

To avoid property loss and reduce risk caused by typhoon surges, accurate prediction of surge deviation is an important task. Many conventional numerical methods and experimental methods for typhoon surge forecasting have been investigated, but it is still a complex ocean engineering problem. In this paper, support vector regression (SVR), an emerging artificial intelligence tool in forecasting storm surges is applied. The original data of Longdong station at Taiwan ‘invaded directly by the Aere typhoon’ are considered to verify the present model. Comparisons with the numerical methods and neural network indicate that storm surges and surge deviations can be efficiently predicted using SVR.     

风暴潮数值模拟中风应力拖曳系数的伴随法反演研究

借助伴随同化方法,利用实测水位资料,对空间分布的风应力拖曳系数做了反演研究.假定风应力拖曳系数具有空间分布特征,即在模拟海区中均匀选取一些独立点,利用这些独立点的风应力拖曳系数线性插值得到全场的风应力拖曳系数.同化实验结果表明,采用空间分布的风应力拖曳系数得到的模拟结果,明显优于将风应力拖曳系数取为常数和依照经验公式计...     

Correcting the errors in the initial conditions and wind stress in storm surge simulation using an adjoint optimal technique

An adjoint data assimilation methodology is applied to the Princeton Ocean Model and is evaluated by obtaining “optimal” initial conditions, sea surface forcing conditions, or both for coastal storm surge modelling. By prescribing different error sources and setting the corresponding control variables, we performed several sets of identical twin experiments by assimilating model-generated water levels. The experiment results show that, when the forecasting errors are caused by the initial (or surface boundary) conditions, adjusting initial (or surface boundary) conditions accordingly can significantly improve the storm surge simulation. However, when the forecasting errors are caused by surface boundary (or initial) conditions, data assimilation targeting improving the initial (or surface boundary) conditions is ineffective. When the forecasting errors are caused by both the initial and surface boundary conditions, adjusting both the initial and surface boundary conditions leads to the best results. In practice, we do not know whether the errors are caused by initial conditions or surface boundary conditions, therefore it is better to adjust both initial and surface boundary conditions in adjoint data assimilation.     

Coupled process-based cyclone surge simulation for the Bay of Bengal

We have developed a wind-wave-surge coupled process-based numerical model for simulating storm surge, consisting of a meso-scale atmospheric model (MM5), a third-generation spectral wave model (WW3) and a coastal ocean model (POM). We introduced an additional sea surface shear stress by wave dissipation into the model to consider the process of energy transfer from winds to currents through whitecap breaking. We demonstrate the importance of this energy transfer path through a hindcast simulation of a cyclone surge in April, 1991 in the Bay of Bengal: it helps generate mean current and has wave effects on wind-induced current fields in extremely shallow water areas.     

0216号台风风暴潮分析

本文着重分析了0216号台风引起的福建全省沿海风暴潮几个显著特点及其成因，初步探讨灾情分布的原因，为今后该类型台风风暴潮预报积累经验及防灾减灾提供基础资料依据。     

Application of artificial neural networks in typhoon surge forecasting

A typhoon-surge forecasting model was developed with a back-propagation neural network (BPN) in the present paper. The typhoon's characteristics, local meteorological conditions and typhoon surges at a considered tidal station at time t−1 and t were used as input data of the model to forecast typhoon surges at the following time. For the selection of a better forecasting model, four models (Models A–D) were tested and compared under the different composition of the above-mentioned input factors. A general evaluation index that is a composition of four performance indexes was proposed to evaluate the model's overall performance. The result of typhoon-surge forecasting was classified into five grades: A (excellent), B (good), C (fair), D (poor) and E (bad), according to the value of the general evaluation index. Sixteen typhoon events and their corresponding typhoon surges and local meteorological conditions at Ken–fang Tidal Station in the coast of north-eastern Taiwan between 1993 and 2000 were collected, 12 of them were used in model's calibration while the other four were used in model's verification. The analysis of typhoon-surge forecasting results at Ken–fang tidal station show that the Model D composing 18 input factors has better performance, and that it is a suitable BPN-based model in typhoon-surge forecasting. The Model D was also applied to typhoon-surge forecasting at Cheng-kung Tidal Station in south-eastern coast of Taiwan and at Tung-shih Tidal Station in the coast of south-western Taiwan. Results show that the application of Model D in typhoon-surge forecasting at Cheng-kung Tidal Station has better performance than that at Tung-shih Tidal Station.     

Wave set-up in the storm surge along open coasts during Typhoon Anita

Wave set-up in storm surges is studied using a numerical model for coasts in Tosa Bay, Japan, open to the Pacific Ocean. Simulation models employing only atmospheric pressures and winds as external forces are unable to properly simulate open coast storm surge heights, such as those due to Typhoon Anita (1970). However, the present study shows that a numerical model incorporating wave-induced radiation stresses, as well as wind stresses and pressure gradients, is able to account for the open coast surge heights. There is a maximum contribution of 40% by the radiation stresses to the peak sea level rises. This study also evaluates the effects of the tides; including the tides improves the agreement between the predicted water surface elevations and the observations. The difference in predictions between one-way coupling from wave to surge models and two-way coupling of the surge and wave models is found to be small.