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…     

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。     

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.     

Deterministic and ensemble storm surge prediction for Atlantic Canada with lead times of hours to ten days

Regional deterministic and ensemble surge prediction systems (RDSPS and RESPS respectively) are used to forecast sea levels off the east of Canada and northeast US. The surge models for the RDSPS and RESPS have grid spacings of 1/30° and 1/12° respectively. The models are driven by surface air pressure and 10 m winds generated by operational global deterministic and ensemble prediction systems that are run operationally by the Canadian Meteorological Centre. Surge forecasts are evaluated for the period 1 March, 2013 to 31 March 2014. Based on traditional statistics (e.g., standard deviation of the difference between observations and predictions) both systems are shown to have skill in forecasting surges six days into the future. It is shown however that skill exists beyond six days if allowance is made for errors in the timing of large surges. The usefulness of the RESPS is demonstrated for two positive surges (important for coastal flooding and erosion) and a negative surge (important for safe navigation in shallow water). It is shown that the RESPS can identify events not forecast by the RDSPS, and can also add useful additional information on the timing of the surge, an important consideration in tidally dominated waters. Several new types of display are used to illustrate the sort of information that can be generated by the RESPS to support the issuers of warnings of unusually high and low total water levels.     

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。     

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.     

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.     

基于高低潮的风暴增水人工神经网络预报模型

针对只有高低潮数据的情况,利用人工神经网络建立起一种预报当前台风时刻后第一个高潮时增水的模型。该模型选取台风在当前时刻、前6 h、前12 h、前18 h的中心经度、纬度、最大风速、中心气压以及当前时刻前第一个高潮时刻的风暴增水为输入单元。台风当前时刻后第一个高潮时刻风暴增水为模型输出单元。利用历史资料形成的规范化后的模式对,对模型进行训练,训练成功后,结合台风因子预报模型,即可用于风暴增水的预报。经过长江口高桥站高低潮实测资料的检验,结果表明该模型提取到了风暴增水效应,说明该模型可用于风暴增水的预报。     

Adaptive mesh refinement for storm surge

An approach to utilizing adaptive mesh refinement algorithms for storm surge modeling is proposed. Currently numerical models exist that can resolve the details of coastal regions but are often too costly to be run in an ensemble forecasting framework without significant computing resources. The application of adaptive mesh refinement algorithms substantially lowers the computational cost of a storm surge model run while retaining much of the desired coastal resolution. The approach presented is implemented in the GeoClaw framework and compared to ADCIRC for Hurricane Ike along with observed tide gauge data and the computational cost of each model run.     

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.     

基于改进的神经网络方法的风暴潮灾害经济损失预测

风暴潮灾害一直以来对中国东南沿海地区的社会经济发展具有较为严重的负面影响, 是对中国造成危害最为严重的海洋灾害之一, 建立一个准确有效的损失评估模型进行风暴潮灾害损失预测, 对风暴潮灾害的预防具有重要的意义。本文在现有研究的基础上收集了2000—2018年中国东南沿海的琼、粤、闽、浙等省份记录较为完整的风暴潮灾害相关数据, 在综合考虑危险性、承灾体脆弱性、孕灾环境和防灾减灾能力的基础上, 建立起更为完整的风暴潮灾害损失的指标体系。相较于单一的BP神经网络, 本文在借鉴机器学习相关理论的基础上搭建了差分进化灰狼算法(DEGWO)优化的BP神经网络, 对样本进行训练和仿真测试。结果表明, 通过DEGWO算法优化后的模型误差更小, 数据的拟合程度更高, 对比而言, 提高了风暴潮灾害损失预测的精确性, 能够为风暴潮灾害损失预测的研究提供新的思路, 同时也为风暴潮灾害的防灾减灾管理提供了指导。     

基于多种神经网络的风暴潮增水预测方法的比较分析

简要介绍了利用BP神经网络、小波神经网络、递归神经网络进行风暴潮增水值预测的原理。选取广东省珠江口以南的阳江站2017年风暴潮增水数据进行测试。结果表明,三种神经网络方法针对阳江地区风暴潮增水的预测均具有可靠性和实用性。以当前增水值为输入量的单因子模型更能反映真实风暴潮增水趋势,而从增水极值预测的准确性来看,以台风风力、气压、风向等相关参数为输入量的多因子模型优于单因子模型。BP神经网络更适用于多因子长时间预测,小波神经网络在单因子短时间预测上准确性更高,递归神经网络预测值与实测值相关性更强。在工程运用中,需根据地域时空特点、数据资料的丰富度与预测值评估指标选择合适的方法。     

Influence of bathymetric fluctuations on coastal storm surge

Natural events constantly alter nearshore bathymetric properties. Hurricanes particularly affect bathymetry as they pass over a body of water. To compute an accurate forecast or recreate a hurricane's effects through hindcasting techniques, an operational bathymetry data set must be known in advance. However, obtaining and maintaining current and accurate bathymetric data can be costly and difficult to manage. In this paper we examine the extent to which variations in nearshore bathymetry affect the storm surge at the coast. A common question for wave and surge modeling is, “how good is the bathymetric data?” If we can allow for a range of fluctuations in the bathymetry without significantly adjusting the results of the surge predictions, we can potentially save months of field work and millions of dollars. A one-dimensional (1D) analytical solution for waves and water level is developed for initial testing. In the 1D case we find that as long as the amplitudes of the bathymetric fluctuations are less than 60% of the original depth, the surge at the coast is within ± 10% of the surge generated on the initial bottom slope. If the fluctuation produces a hole, a deepening of the local bathymetry, within 80% of the local water depth, the coastal storm surge calculated is still within 10% of the unperturbed value computed for bottom slopes shallower than 1:20. In addition, we find there is an optimum distance offshore for each sloped profile that corresponds to a depth between 25 and 40 m, beyond which the effects of bathymetric fluctuations begin to decrease. A coupled 2D modeling system is implemented to test our hypothesis along a realistic coastline. After selecting three study sites, we vary the bathymetry at the selected locations by ± 20%. Consistent with the 1D tests, the storm surge at the shoreline varies by less than 5%.     

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.     

连云港温带风暴潮及可能最大温带风暴潮的计算

用46a资料首次对连云港温带风暴潮进行了统计分析,计算了不同重现期的温带风暴潮(增、减水)值,并划分引起温带风暴潮的天气类型;进而首次构造引起连云港可能最大温带风暴潮(增、减水)的天气系统;最后,采用经过典型温带风暴潮过程数值模拟检验的风暴潮数学模型,计算了连云港可能最大温带风暴潮,计算结果已被江苏田湾(连云港)核电站厂址设计部门采用.     

神经网络在珠江口风暴潮预报中的应用

当风暴潮沿河道上溯时,处于珠江口地区的灯笼山测站和黄埔测站的水位之间存在着非线性响应关系。文章利用BP人工神经网络,建立了两测站台风暴潮和天文潮的综合增水效应预报模型,对9903、9908和9910号台风期间黄埔站的综合增水进行了预报,并针对不同预报时段对计算结果和潮位极值的准确程度进行了相应的讨论。结果表明,该方法的预报精度较高。最后通过与纯风暴增水模型的对比,说明了综合增水模型的优越性。     

The effect of tidal inlets on open coast storm surge hydrographs

Bridge scour modeling requires storm surge hydrographs as open ocean boundary conditions for coastal waters surrounding tidal inlets. These open coast storm surge hydrographs are used to accurately determine both horizontal and vertical circulation patterns, and thus scour, within the inlet and bay for an extreme event. At present, very little information is available on the effect that tidal inlets have on these open coast storm surge hydrographs. Furthermore, current modeling practice enforces a single design hydrograph along the open coast boundary for bridge scour models. This study expands on these concepts and provides a more fundamental understanding on both of these modeling areas.     

河口挡潮闸对三角洲潮汐不对称时空变化的影响

三角洲内部潮汐不对称性与三角洲地貌演变方向有重要关系。目前诸多研究关注海洋、陆地边界的改变对三角洲内的潮汐不对称性演变规律的影响。实际上, 大规模的河口工程也会对三角洲内的潮汐不对称性产生影响。在众多河口工程中, 河口挡潮闸由于直接削弱口门处海洋潮动力, 影响最为直接。荷兰三角洲是潮动力主导型三角洲, 受洪潮灾害影响较为严重, 为此在荷兰西南部修建了世界上著名的三角洲挡潮闸工程。文章以荷兰莱茵河-默兹河三角洲为研究对象, 分析以挡潮闸为主的河口工程对三角洲内河网潮汐不对称性演变特征的影响。选取莱茵河-默兹河三角洲13个潮位站点的50~60年的水文资料, 利用非平稳调和分析方法计算分析三角洲内部潮汐传播特性, 并进一步研究潮汐涨落潮历时不对称性演变特征, 揭示河口挡潮闸工程对潮汐河网中潮汐动力和潮汐不对称性的影响。研究表明, 莱茵河-默兹河三角洲为显著的涨潮占优型河口, 潮汐不对称现象总体向上游沿程增强。河口挡潮闸修建后受河网径流量和潮动力剧烈变化的影响, 封闭的南部通道内的潮波大幅度削弱, 潮汐不对称现象在下游增强在上游减弱。北部、中部通道其他站点则因为通道径流增大, 潮汐不对称现象增强, 中部站点变化更为显著。     

A real-time, event-triggered storm surge forecasting system for the state of North Carolina

A new real-time, event-triggered storm surge prediction system has been developed for the State of North Carolina to assist emergency managers, policy-makers and other government officials with evacuation planning, decision-making and resource deployment during tropical storm landfall and flood inundation events. The North Carolina Forecast System (NCFS) was designed and built to provide a rapid response assessment of hurricane threat, accomplished by driving a high-resolution, two-dimensional, depth-integrated version of the ADCIRC (Advanced Circulation) coastal ocean model with winds from a synthetic asymmetric gradient wind vortex. These parametric winds, calculated at exact finite-element mesh node locations and directly coupled to the ocean model at every time step, are generated from National Hurricane Center (NHC) forecast advisories the moment they are inserted into the real-time weather data stream, maximizing the number of hours of forecast utility. Tidal harmonic constituents are prescribed at the open water boundaries and applied as tidal potentials in the interior of the ocean model domain. A directional surface roughness parameterization that modulates the wind speed at a given location based on the types of land cover encountered upwind, a forest canopy sheltering effect, and a spatially varying distribution of Manning’s–n friction coefficient used for computing the bottom/channel bed friction are also included in the storm surge model. Comparisons of the simulated wind speeds and phases against their real meteorological counterparts, of model elevations against actual sea surface elevations measured by NOAA tide gauges along the NC coast, and of simulated depth-averaged current velocities against Acoustic Doppler Current Profiler (ADCP) data, indicate that this new system produces remarkably realistic predictions of winds and storm surge.     

Reconstruction of Hurricane Katrina's wind fields for storm surge and wave hindcasting

As the most costly US natural disaster in history, Hurricane Katrina fostered the IPET forensic study to better understand the event. All available observations from several hundred space-, land-, sea-, and aircraft-based measurement platforms were gathered and processed to a common framework for height, exposure, and averaging time, to produce a series of wind field snapshots at 3 h intervals to depict the wind structure of Katrina when in the Gulf of Mexico. The stepped-frequency microwave radiometer was calibrated against GPS sondes to establish the upper range of the instrument and then used to determine the wind field in the storm's core region in concert with airborne Doppler radar winds adjusted to the surface from near the top of the PBL (500 m). The SFMR data were used to develop a method to estimate surface winds from 3 km level reconnaissance aircraft observations, taking into consideration the observed azimuthal variation of the reduction factor. The “SFMR method” was used to adjust reconnaissance flight-level measurements to the surface in the core region when SFMR and Doppler winds were not available. A variety of coastal and inland mesonet data were employed, including portable towers deployed by Texas Tech University, University of Louisiana at Monroe, and the Florida Coastal Monitoring Program, as well as fixed mesonet stations from Louisiana State Universities Marine Consortium, University of Southern Mississippi, and Agricultural Networks from Louisiana, Mississippi, and Alabama, and the Coastal Estuarine Network of Alabama and Mississippi. Also included were land- (WSR-88D VAD and GBVTD, ASOS, Metar, LLWAS, HANDAR), space- (QuikScat, GOES cloud drift winds, WindSat), and marine- (GPS sondes, Buoys, C-MAN, ships) platforms. The wind fields serve as an analysis of record and were used to provide forcing for wave and storm surge models to produce hindcasts of water levels in the vicinity of flood control structures.