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| 1 | Evaluation of Typhoon Waves Simulated by Wave Watch-Ⅲ Model in Shallow Waters Around Zhoushan Islands显示文摘In this study, we simulated typhoon waves in the shallow waters around the Zhoushan Islands using the WaveWatch-Ⅲ(WW3) model version 5.16, the latest version released by the National Oceanic and Atmospheric Administration. Specifically, we used in-situ measurements to evaluate the performance of seven packages of input/dissipation source terms in the WW3 model. We forced the WW3 model by wind fields derived from a combination of the parametric Holland model and high-resolution European Center for Medium-Range Weather Forecasts(ECMWF) wind data in a 0.125? grid, herein called H-E winds. We trained the H-E winds by fitting a shape parameter B to buoy-measured observations, which resulted in a smallest root mean square error(RMSE) of 3 m s^(-1) for B, when treated as a constant 0.4. Then, we applied the seven input/dissipation terms of WW3, labelled ST1, ST2, ST2+STAB2, ST3, ST3+STAB3, ST4, and ST6, to simulate the significant wave height(SWH) up to 5 m during typhoons Fungwong and Chan-hom around the Zhoushan Islands. We then compared the SWHs of the simulated waves with those measured by the in-situ buoys. The results indicate that the simulation using ST2 performs best with an RMSE of 0.79 m for typhoon Fung-wong and an RMSE of 1.12 m for typhoon Chan-hom. Interestingly, we found the simulated SWH results to be relatively higher than those of the observations in the area between Hangzhou Bay and the Zhoushan Islands. This behavior is worthy of further investigation in the future. | SHENG Yexin SHAO Weizeng LI Shuiqing ZHANG Yuming YANG Hongwei ZUO Juncheng | 2019 | Journal of Ocean University of China2019，18，2： | 9 |
| 2 | 基于误差修正和LSTM的海浪高度预测模型显示文摘准确可靠的海浪高度预测是海洋工程和沿海工程应用的一项重要任务,如海洋渔业捕捞和近海勘探工程。提出一种基于误差修正和长短期记忆(LSTM)网络的海浪高度预测模型,采用自适应噪声完备集合经验模态(CEEMDAN)分解误差序列,产生误差模态分量,根据斯皮尔曼(Spearman)产生的相关系数划分每个模态分量的权重,利用长短期记忆网络对误差模态分量进行预测,将权重和预测模态分量相结合,融合到未来对应点位的预测值中,提高预测精度。实验结果表明,在均方根误差(RMSE)、拟合优度(R^(2))等评价指标上,与极限学习机(ELM)、融合注意力机制LSTM(A-LSTM)等模型进行比较,CSLM模型的评价指标较好,验证了CSLM模型的有效性和可行性。 | 卢鹏 孙肖鹤 邹国良 王振华 郑宗生 | 2022 | 海洋测绘2022，42，5： | 0 |
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