The generation and propagation mechanism of strong nonlinear waves in the South China Sea is an essential research area. In this study, the third-generation wave model WAVEWATCH Ⅲ is employed to simulate wave fields ...The generation and propagation mechanism of strong nonlinear waves in the South China Sea is an essential research area. In this study, the third-generation wave model WAVEWATCH Ⅲ is employed to simulate wave fields under extreme sea states. The model, integrating the ST6 source term, is validated against observed data, demonstrating its credibility. The spatial distribution of the occurrence probability of strong nonlinear waves during typhoons is shown, and the waves in the straits and the northeastern part of the South China Sea show strong nonlinear characteristics. The high-order spectral model HOS-ocean is employed to simulate the random wave surface series beneath five different platform areas. The waves during the typhoon exhibit strong nonlinear characteristics, and freak waves exist. The space-varying probability model is established to describe the short-term probability distribution of nonlinear wave series. The exceedance probability distributions of the wave surface beneath different platform areas are compared and analyzed. The results show that with an increase in the platform area, the probability of a strong nonlinear wave beneath the platform increases.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
基金financially supported by the National Key R&D Program of China(No.2022YFC3104205)the National Natural Science Foundation of China(No.42377457).
文摘The generation and propagation mechanism of strong nonlinear waves in the South China Sea is an essential research area. In this study, the third-generation wave model WAVEWATCH Ⅲ is employed to simulate wave fields under extreme sea states. The model, integrating the ST6 source term, is validated against observed data, demonstrating its credibility. The spatial distribution of the occurrence probability of strong nonlinear waves during typhoons is shown, and the waves in the straits and the northeastern part of the South China Sea show strong nonlinear characteristics. The high-order spectral model HOS-ocean is employed to simulate the random wave surface series beneath five different platform areas. The waves during the typhoon exhibit strong nonlinear characteristics, and freak waves exist. The space-varying probability model is established to describe the short-term probability distribution of nonlinear wave series. The exceedance probability distributions of the wave surface beneath different platform areas are compared and analyzed. The results show that with an increase in the platform area, the probability of a strong nonlinear wave beneath the platform increases.
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。
