每年由台风诱发暴雨引起洪水、滑坡等灾害在我国沿海地区造成巨大损失。作者采用多维复合极值分布理论(Multivariate compound extreme value distribution),以受台风暴雨危害较严重的浙江温州地区为例,对台风诱发暴雨的降水量进行了概...每年由台风诱发暴雨引起洪水、滑坡等灾害在我国沿海地区造成巨大损失。作者采用多维复合极值分布理论(Multivariate compound extreme value distribution),以受台风暴雨危害较严重的浙江温州地区为例,对台风诱发暴雨的降水量进行了概率预测。该理论以影响暴雨强度的台风中心气压差和降水量为研究对象,并考虑导致极端降水的台风过程的出现频次的概率特性,预测结果较传统方法更为合理和安全。展开更多
本文基于卫星遥感的观测海表面温度(Sea Surface Temperature,SST)数据和自然资源部第一海洋研究所全球0.1°分辨率海浪-潮流-环流耦合数值预报模式(The surface wave-tide-circulation coupled ocean model developed by First Ins...本文基于卫星遥感的观测海表面温度(Sea Surface Temperature,SST)数据和自然资源部第一海洋研究所全球0.1°分辨率海浪-潮流-环流耦合数值预报模式(The surface wave-tide-circulation coupled ocean model developed by First Institute of Oceanography,MNR,China,FIO-COM)的预报结果,采用线性回归模型和长短期记忆神经网络(Long Short-Term Memory,LSTM)对SST预报结果进行误差校正。利用2016—2021年的数据开展了一系列对比试验,线性回归模型基于局部线性的假设实现对下一时刻误差的预报,LSTM利用2016—2020年共56个月的历史偏差数据训练模型,使用2021年的数据进行检验。结果表明,线性回归模型和LSTM神经网络都可以改善预报结果,LSTM神经网络相对于线性回归模型的效果更好,SST误差降低70%左右;与线性回归模型相比,经LSTM校正后的各点的偏差的概率密度分布集中在0附近。LSTM方法在统计意义上优于线性拟合且结果更稳定,可进一步推广到海洋数值预报多要素偏差校正。展开更多
Wave slamming is an important phenomenon due to its destructive power,and with the rapid development of offshore wind turbines,wave slamming on vertical cylinders has garnered lots of attention.However,the phenomenon ...Wave slamming is an important phenomenon due to its destructive power,and with the rapid development of offshore wind turbines,wave slamming on vertical cylinders has garnered lots of attention.However,the phenomenon of wave slamming on vertical cylinders is very complicated due to both the intrinsic complexity of breaking waves and that of slamming forces.The objective of this paper is to provide a general review of research related to this problem,including theoretical methods,experimental studies,numerical simulations,and full-scale measurements.Based on these approaches,the momentum theory/pressure impulse theory,spatial distribution characteristics of impacts to various breaking waves,wave generation methods,analysis methods for measured forces under structure response,scale effects in experiments,and in-situ measurements have been introduced and discussed.Results show that simplifications in existing models for wave impacting such as wave characteristics and structural response reduce its applicability and should be studied further both in theoretical,experimental and numerical researches.展开更多
Extreme value analysis is an indispensable method to predict the probability of marine disasters and calculate the design conditions of marine engineering.The rationality of extreme value analysis can be easily affect...Extreme value analysis is an indispensable method to predict the probability of marine disasters and calculate the design conditions of marine engineering.The rationality of extreme value analysis can be easily affected by the lack of sample data.The peaks over threshold(POT)method and compound extreme value distribution(CEVD)theory are effective methods to expand samples,but they still rely on long-term sea state data.To construct a probabilistic model using shortterm sea state data instead of the traditional annual maximum series(AMS),the binomial-bivariate log-normal CEVD(BBLCED)model is established in this thesis.The model not only considers the frequency of the extreme sea state,but it also reflects the correlation between different sea state elements(wave height and wave period)and reduces the requirement for the length of the data series.The model is applied to the calculation of design wave elements in a certain area of the Yellow Sea.The results indicate that the BBLCED model has good stability and fitting effect,which is close to the probability prediction results obtained from the long-term data,and reasonably reflects the probability distribution characteristics of the extreme sea state.The model can provide a reliable basis for coastal engineering design under the condition of a lack of marine data.Hence,it is suitable for extreme value prediction and calculation in the field of disaster prevention and reduction.展开更多
文摘每年由台风诱发暴雨引起洪水、滑坡等灾害在我国沿海地区造成巨大损失。作者采用多维复合极值分布理论(Multivariate compound extreme value distribution),以受台风暴雨危害较严重的浙江温州地区为例,对台风诱发暴雨的降水量进行了概率预测。该理论以影响暴雨强度的台风中心气压差和降水量为研究对象,并考虑导致极端降水的台风过程的出现频次的概率特性,预测结果较传统方法更为合理和安全。
基金supported by the National Natural Foundation of China(No.50679076)the Office of State Flood Control and Drought Relief Headquarters P.R.of China(No.20060120)~~
文摘本文基于卫星遥感的观测海表面温度(Sea Surface Temperature,SST)数据和自然资源部第一海洋研究所全球0.1°分辨率海浪-潮流-环流耦合数值预报模式(The surface wave-tide-circulation coupled ocean model developed by First Institute of Oceanography,MNR,China,FIO-COM)的预报结果,采用线性回归模型和长短期记忆神经网络(Long Short-Term Memory,LSTM)对SST预报结果进行误差校正。利用2016—2021年的数据开展了一系列对比试验,线性回归模型基于局部线性的假设实现对下一时刻误差的预报,LSTM利用2016—2020年共56个月的历史偏差数据训练模型,使用2021年的数据进行检验。结果表明,线性回归模型和LSTM神经网络都可以改善预报结果,LSTM神经网络相对于线性回归模型的效果更好,SST误差降低70%左右;与线性回归模型相比,经LSTM校正后的各点的偏差的概率密度分布集中在0附近。LSTM方法在统计意义上优于线性拟合且结果更稳定,可进一步推广到海洋数值预报多要素偏差校正。
基金the National Natural Science Foundation of China(Grant Nos.51720105010,51979029)the Major Scientific and Technological Project of CNOOC(KJGG2022-0202)Innovative Research Foundation of Ship General Performance(Grant No.31422119).
文摘Wave slamming is an important phenomenon due to its destructive power,and with the rapid development of offshore wind turbines,wave slamming on vertical cylinders has garnered lots of attention.However,the phenomenon of wave slamming on vertical cylinders is very complicated due to both the intrinsic complexity of breaking waves and that of slamming forces.The objective of this paper is to provide a general review of research related to this problem,including theoretical methods,experimental studies,numerical simulations,and full-scale measurements.Based on these approaches,the momentum theory/pressure impulse theory,spatial distribution characteristics of impacts to various breaking waves,wave generation methods,analysis methods for measured forces under structure response,scale effects in experiments,and in-situ measurements have been introduced and discussed.Results show that simplifications in existing models for wave impacting such as wave characteristics and structural response reduce its applicability and should be studied further both in theoretical,experimental and numerical researches.
文摘Extreme value analysis is an indispensable method to predict the probability of marine disasters and calculate the design conditions of marine engineering.The rationality of extreme value analysis can be easily affected by the lack of sample data.The peaks over threshold(POT)method and compound extreme value distribution(CEVD)theory are effective methods to expand samples,but they still rely on long-term sea state data.To construct a probabilistic model using shortterm sea state data instead of the traditional annual maximum series(AMS),the binomial-bivariate log-normal CEVD(BBLCED)model is established in this thesis.The model not only considers the frequency of the extreme sea state,but it also reflects the correlation between different sea state elements(wave height and wave period)and reduces the requirement for the length of the data series.The model is applied to the calculation of design wave elements in a certain area of the Yellow Sea.The results indicate that the BBLCED model has good stability and fitting effect,which is close to the probability prediction results obtained from the long-term data,and reasonably reflects the probability distribution characteristics of the extreme sea state.The model can provide a reliable basis for coastal engineering design under the condition of a lack of marine data.Hence,it is suitable for extreme value prediction and calculation in the field of disaster prevention and reduction.
