一、办刊宗旨: 本刊是我国唯一的海洋环境预报的学术期刊,由国家海洋环境预报中心主办、国内公开发行.本刊聚焦于海洋预报、海洋气象预报和海洋防灾减灾的前沿发展动态和最新研究成果,刊登海洋科学、大气科学及其他相关学术领域的研究成果、发展趋势与动态、信息交流和工作经验总结等方面的文章.
In order to improve the prediction accuracy of annual number of typhoons affecting Guangxi, this paper uses related methods to find high impact factor based on the sample data of typhoons affecting Guangxi from 1951 to 2020 provided by Shanghai Typhoon Institute of China Meteorological Administration, the 88 atmospheric circulation feature quantities and 26 SST index data provided by the National Climate Center. In view of the complexity of physical factors in typhoon number forecasting, in order to obtain more comprehensive factor information, the random forest is used to screen the factors, and a prediction model for annual number of typhoons affecting Guangxi utilizing three machine learning algorithms, i. e. Random Forest, Support Vector Regression and Gate Recurrent Unit （GRU）, is established. The results show that the prediction ability of using factors selected by Random Forest screening is significantly improved, and the prediction ability of using machine learning algorithms is higher than that of Ridge Regression method. Among them, GRU has the best prediction, and the absolute error is reduced by 10.30% compared with Ridge Regression method, followed by Random Forest and Support Vector Regression, with errors reduced by 9.44% and 7.47%, respectively.
Based on the shallow water equation and the third-generation wave model, a coupled storm surgewave model is constructed for the Jiangsu coastal sea. A hybrid wind field model is constructed with parametric model and reanalysis wind fields. Typical typhoons in the Jiangsu coastal sea are classified into four types according to typhoon paths. By simulating the typical typhoon wave processes with different characteristics affecting Jiangsu coastal sea, the spatial and temporal distribution characteristics of typhoon wave heights caused by different types of typical typhoons in Jiangsu coastal sea are studied. The results show that, the "northern passing type" and "northward moving type along the coast of Jiangsu" typhoons have a greater impact on the northern and southern Jiangsu coastal areas, respectively, and the "eastern passing type" typhoons have the widest impact on the Jiangsu offshore areas. The "northern passing type" and "eastern passing type" typhoons cause longer duration of large waves in the Jiangsu offshore areas, in addition, the "northern passing type" and "north moving type along the coast of Jiangsu" typhoons cause continuous large wave heights in the coastal and radiative sand ridge areas of Jiangsu, respectively.
In order to better reveal the quantitative correlation between the formation mechanism of evaporation duct and turbulent flux near the sea surface, the P-RSHMU evaporation duct model is introduced because of its good prediction performance under stable and unstable conditions.The correlation analysis between the height of evaporation duct predicted by P-RSHMU model and turbulent flux is quantitatively given based on the measured hydro-meteorological data collected by the offshore platforms.The results show that: the P-RSHMU model can effectively suppress the evaporation duct height of 40m under stable conditions. For all data (regardless of stability conditions), the correlation coefficient between evaporation duct height and latent heat flux calculated by this model is 0.92, the correlation coefficient with momentum flux is -0.41, and the correlation coefficient with sensible heat flux is -0.12. In the three cases, the height of evaporation duct is significantly correlated with the latent heat flux, and the correlation coefficient is greater than 0.9. These results provide further data support for enriching the formation mechanism and connotation of evaporation duct.
This paper reviews available methods for prediction of wave overtopping and researches on early warning of wave overtopping risk. In general, various empirical formulae based on physical model tests are widely used to predict the mean overtopping discharge. In recent years, numerical modeling and machine learning algorithms such as artificial neural networks have also been developed and used. Besides, quantitative analysis and assessment of overtopping risk have also been paid more attention. Finally, this paper proposes the importance of establishing overtopping forecast and risk early warning system in coastal risk management.
