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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。 相似文献
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基于海气耦合模型的渤海两种类型风暴潮数值模拟与对比分析 总被引：1，自引：0，他引：1
渤海一年四季都易受到由温带风暴和热带气旋所致风暴潮的影响。为了缓解风暴潮灾害对海岸地区人员生命财产的影响，十分有必要了解大型风暴潮的发生过程和机制。目前大部分研究主要局限于单一的温带风暴潮或台风风暴潮。本文利用所构建的海气耦合数值模型研究了发生于渤海的两种类型的风暴潮，对发生在渤海的2次典型强风暴潮过程进行了模拟。由WRF模型模拟得到的风场强度和最低海平面气压与实测数据吻合较好，由ROMS模型模拟得到的风暴潮期间水位变化过程与潮位站观测结果也吻合较好。对两种类型风暴潮期间的风场结钩、海面风应力、海洋表面平均流场以及水位分布进行了分析对比，并将耦合模型结果与非耦合模型结果进行了对比。研究表明，渤海两种类型风暴潮期间的风场结钩、海面风应力、海洋表面平均流场以及水位分布等均存在巨大差异。渤海风暴潮的强度主要由海洋表面的驱动力所决定，但同时也受海岸地形地貌的影响。 相似文献
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《Coastal Engineering》2004,51(4):277-296
A cyclone induced storm surge and flood forecasting system that has been developed for the northern Bay of Bengal is presented. The developed system includes a cyclone forecasting model that uses statistical models for forecasting of the cyclone track and maximum wind speed, and an analytical cyclone model for generation of cyclone wind and pressure fields. A data assimilation system has been developed that allows updating of the cyclone parameters based on air pressure and wind speed observations from surface meteorological stations. The forecasted air pressure and wind fields are used as input in a 2D hydrodynamic model for forecasting storm surge levels and associated flooding. An efficient uncertainty prediction procedure based on Harr's point estimation method has been implemented as part of the forecasting system for prediction of the uncertainties of the forecasted storm surge levels and inundation areas caused by the uncertainties in the cyclone track and wind speed forecasts. The developed system is applied on a severe cyclone that hit Bangladesh in April 1991. The simulated storm surge and associated flooding are highly sensitive to the cyclone data. The cyclone data assimilation system provides a more accurate cyclone track when the cyclone approaches the coastline, which results in a significant improvement of the storm surge and flood predictions. Application of the uncertainty prediction procedure shows that the large uncertainties of the cyclone track and intensity forecasts result in large uncertainties of the forecasted storm surge levels and flood extend. The forecasting system shows very good forecasting capabilities up to 24 h before the actual landfall. 相似文献
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Neural network prediction of a storm surge 总被引：4，自引：0，他引：4
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. 相似文献
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Storm surges are abnormal rises in sea level along coastal areas and are mainly formed by strong wind and atmospheric depressions.When storm surges coincide with high tide,coastal flooding can occur.Creating storm surge prediction systems has been an important and operational task worldwide.This study developed a coupled tide and storm surge numerical model of the seas around Taiwan for operational purposes at the Central Weather Bureau.The model was calibrated and verified by using tidal records from seas around Taiwan.Model skill was assessed based on measured records,and the results are presented in details.At 3-minute resolution,tides were generally well predicted,with the root mean-square errors of less than 0.11 m and an overall correlation of more than 0.9.Storms(winds and depressions) were introduced into the model forcing by using the parameter typhoon model.Five typical typhoons that threatened Taiwan were simulated for assessment.The surges were well predicted compared with the records. 相似文献
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本文建立一个温带风暴潮模式，包括海上边界层风场模式和风暴潮数值模式。利用建立的温带风暴潮模式，模拟了影响连云港的几次显著温带风暴潮过程，结果表明，本模式所采用的海上边界层风场模式和风暴潮数值模式是匹配的，能够满足海洋工程中的风暴潮数值计算的需要，甚至可以成为日常温带风暴潮数值预报的有用手段。 相似文献
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渤海沿岸是风暴潮多发区域。研究者多关心渤海局地风引起风暴潮变化,而忽略黄、东海天气系统对渤海风暴潮的影响。为研究外围天气系统对局地风暴潮的影响,本文采用实测资料对比和设计理想数值试验等方法,对黄、东海天气系统影响的渤海风暴潮进行了研究。结果表明:1、TY1814"摩羯"和TY1818"温比亚"台风风暴潮的实测资料呈现当黄、东海风力较大,而渤海风力较小时,渤海沿岸也会出现较大风暴潮现象; 2、从FVCOM(Finite-Volume Coastal Ocean Model)模拟的理想数值试验中发现,黄、东海风向是东南风时,引起渤海沿岸风暴增水极值最大;3、以入海气旋和登陆北上台风两种类型天气系统风向变化设计理想数值试验,发现黄、东海的东南风持续时间对渤海沿岸风暴潮极值大小和出现时间影响较大。理想试验获得的结论不仅能为渤海风暴潮预测和防灾减灾提供理论依据,还能够有效减少预警应急中漏报的现象,降低沿海经济损失。 相似文献
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东海风暴潮与天文潮的非线性相互作用 总被引：1，自引：0，他引：1
中国东海的风暴潮具有明显的周期性波动。凤暴潮除了决定于风应力和长波效应外,还受到天文潮与风暴潮相互作用的影响。本文利用一个二维数值模式对天文潮与风暴潮相互作用的水位进行了模拟。我们选取了8114号台风加以计算。计算结果与实测资料基本相符,由此说明水位曲线中的潮周期波动主要是由于天文潮与风暴潮之间的非线性相互作用所致。数值实验还表明,如果考虑到天文潮与风暴潮的相互作用可以显著改善水位的预报精度。 相似文献
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天津沿海风暴潮实时监测预报系统 总被引：3，自引：1，他引：3
本文简要介绍了天津沿海风暴潮实时监测预报系统建设的必要性和系统的结构、功能、组成等方面的概况。该系统将实时监测技术、计算机网络技术与信息处理技术有效地结合起来，实现了潮汐、风速、风向等监测参数在无人值守情况下的连续、自动观测和计算机网络系统的实时接入与发布。它的建立一方面可为管理部门、预报部门和生产部门提供实时的监测信息，另一方面可在风暴潮来临时为领导决策提供历史资料和预报信息。它的应用对减少海洋灾害的损失，促进风暴潮减灾防灾体系的建设起到了积极的作用。 相似文献
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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. 相似文献
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海口港风暴潮分析与预报 总被引：2，自引：1，他引：1
本文根据海口港１９７１～１９８９年的实测潮位资料和有关文献,利用差值法分离出台风增减水的过程曲线,对海口港风暴潮的特性和引起增减水的物理机制进行了初步分析。采用经验方法确定了导致本站增水的主导风向及最大的区域,建立了增水极值与本站风力、气压的相互关系,并通过逐步回归分析,给出了海口港风暴潮过程预报方程。最后利用１９９０、１９９１两年的实测资料对预报方程进行了后报检验,结果表明,预报与实测值的吻合程度较好。 相似文献
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The storm surge associated with severe tropical cyclones (TCs) in the Bay of Bengal (BoB) is a serious concern along the coastal regions of India, Bangladesh, Myanmar, and Sri Lanka. It is one of the most hazardous elements associated with landfalling TCs other than strong winds and heavy precipitation and about 75% of the casualities in this region are attributed to storm surges. Therefore, it is highly essential to predict the storm surges with greater accuracy at least 2 days in advance for effective evacuation. In the present study, an attempt is made to simulate the storm surges associated with severe TCs in the BoB using one-way coupling of the Non-hydrostatic Mesoscale Model core of Weather Research and Forecasting (NMM-WRF) system with the two-dimensional finite-difference storm surge model developed at the Indian Institute of Technology Delhi (IITD). The NMM-WRF model simulated track, pressure drop, and radius of maximum wind are used to calculate the wind-stress through Jelesnianski wind formulation. The results are compared with the observed/estimated values as provided by the operational/meteorological agencies of India, Bangladesh, and Myanmar. This study suggests that using simulated surface meteorological fields of a high-resolution mesoscale model, the storm surge can be predicted at least 2 days in advance of the actual landfall of TCs with reasonable accuracy. This approach will be helpful in providing disastrous storm warning well in advance in a coastal region, which will help with rapid evacuation from the vulnerable coastal region, relocation as well as protection of valuables, disaster mitigation, and coastal zone management. 相似文献
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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. 相似文献
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台风预报的准确性在风暴潮预报中起着重要作用。台风强度和路径的不确定性意味着使用集合模式来预报风暴潮。本文利用中央气象台的最优路径台风参数驱动国家海洋环境预报中心业务化的水动力学模型，开展华南沿海的风暴潮模拟，模式模拟结果与实测吻合较好。为了改进计算效率，采用CUDA Fortran 语言对模型进行了改造，改造后的模型在计算结果与原模型基本一致的基础上，计算时间缩短了99%以上。通过融合欧洲中期天气预报中心（ECWMF）的50条路径与3种可能台风强度构造出了150个台风事件，并用150个台风事件驱动改进的风暴潮数值模型，计算结果可以提供集合预报产品和概率预报产品。通过“山竹”台风风暴潮过程可以发现集合平均预报结果和概率预报结果与实测吻合较好。改进的数值模型可以运行普通工作站上，非常适合风暴潮集合预报，并且可以提供更好的决策产品。 相似文献
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基于观测和再分析数据的LSTM深度神经网络沿海风速预报应用研究 总被引：3，自引：1，他引：2
基于海洋气象历史观测资料和再分析数据等，利用LSTM深度神经网络方法，开展在有监督学习情况下的海面风场短时预报应用研究。以中国近海5个代表站为研究区域，通过气象台站观测数据和ERA-Interim 6 h再分析数据构建数据集。选取21个变量作为预报因子，分别构建两个LSTM深度神经网络框架(OBS_LSTM和ALL_LSTM)。经与2017年WRF模式6 h预报结果对比分析，得出如下结论:构建的两个LSTM风速预报模型可以大幅降低风速预报误差，RMSE分别降低了41.3%和38.8%，MAE平均降低了43.0%和40.0%；风速误差统计和极端大风分析发现，LSTM模型能够抓住地形、短时大风和台风等敏感信息，对于大风过程预报结果明显优于WRF模式；两种LSTM模型对比发现，ALL_LSTM模型风速预报误差最小，具有很好的稳定性和鲁棒性，OBS_LSTM模型应用范围更广泛。 相似文献
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刘安国 《中国海洋大学学报(自然科学版)》1989,(3)
作者根据地方史志资料进行分析与统计,提出山东沿岸风暴潮的特点:存在风潮和台风暴潮两种型式。在风潮中,春季风潮占绝对优势,秋季风潮是次要的。三者的关系是:台风暴潮:春季风潮:秋季风潮=50％:37％:13％。此外,还分析了在风暴潮记载中的有关气象记录问题以及历代王朝有关风暴潮记录的特点。提出历史风暴潮灾情的个例,举出1985年山东沿岸发生的风暴潮灾情,以说明风暴潮灾害的严重性。 相似文献