Typhoons and typhoon waves can cause disasters in coastal areas around the world. The Taiwan Strait often experiences typhoons, especially in summer. Numerical models have been adopted to predict typhoons and reduce losses. The Weather Research and Forecasting (WRF) model is widely used in typhoon simulations, and the Simulating WAves Nearshore (SWAN) model performs well in wave simulations. However, significant uncertainty remains in terms of choosing suitable WRF physical parameterizations in different situations. To evaluate the effect of WRF physical parameterizations on wind and wave simulations, 27 simulation experiments were designed. Three typhoon events (Goni, Dujuan, and Meranti) with different tracks that influenced the Taiwan Strait were simulated. Three parameters (wind speed, wind direction and significant wave height) were assessed using Taylor diagrams, and it was found that the best simulation experiment changed according to typhoon tracks and physical parameters. In wind speed simulation, the best simulation experiment is 12 for typhoon Dujuan and 19 for typhoon Goni and Meranti. From the perspective of wind direction simulation, experiments 26, 23, and 2 performed best for typhoons Goni, Dujuan, and Meranti. And experiments 19, 1, and 20 had the best performances in significant wave height simulation for typhoons Goni, Dujuan, and Meranti. The WRF-SWAN model using the best simulation experiment reduced the error and exhibited good performance in the wind and wave simulations. Skill scores of three parameters were all over 70 for typhoon Goni and 80 for typhoon Dujuan and Meranti. The applicability of the best simulation experiments was demonstrated in typhoon simulations with similar tracks. The accuracy of the wave simulation depended on wind speed, wind direction, and their interaction. In addition, a scheme’s sensitivity changed with different typhoon tracks. This study provides references for designing physical parameterizations for use with the WRF-SWAN model, which may help to simulate typhoons and typhoon waves in the Taiwan Strait more accurately in the future.

The terrain, such as Taiwan Island, have been shown to have complex effects on typhoons and the associated typhoon waves. Terrain effects change with typhoon tracks. In this study, three types of typhoon tracks (northern, middle and southern) were defined according to the relationship between the typhoon tracks and Taiwan Island. Typhoons on these three tracks and typhoon waves were simulated using the Weather Research and Forecasting–Simulating Waves Nearshore model. In each type of typhoon, a control case without the Taiwan topography was simulated to compare with real cases. The results showed that typhoons on different tracks were affected by the terrain of Taiwan Island in different ways. Taiwan Island had weakening, decelerating and deflective effects on typhoons. The ranking for the weakening effect was middle track (81%) > southern track (69%) > northern track (3%). The decelerating effect was 7% in the northern track and 25% in the southern track. The deflective effect of the terrain makes typhoons on the northern (southern) track deflect toward the south (north). When a typhoon on a middle track passed over Taiwan Island, a new center of low pressure would replace the former center and make the track discontinuous. In addition, the influence of typhoons on regions near Taiwan Island changed with the typhoon’s tracks. The influence ranking of typhoons in Taiwan Island was the middle track > southern track > northern track, which was consistent with that of typhoons in the Taiwan Strait and opposite to that of typhoons in Fujian Province. The influence ranking of typhoons on the Taiwan Strait was the opposite of the typhoon intensity ranking, which suggests that the intensities of wind and waves in the strait were more related to typhoon tracks than typhoon intensity. The variations in the significant wave height were similar to those of the wind speed, but there was a time lag (2 h) between them due to the wave growth process and swells. In addition, the significant wave height distribution sometimes differed from the wind speed distribution under the influence of swells and terrain.

Precipitation patterns are highly valued in the fields of weather forecasting, water resource management, and estuary environment research. In this study, daily and monthly precipitation TRMM data from 1998 to 2019 were selected, and EOF analysis was employed to analyze the precipitation patterns of the Taiwan Strait and its neighboring regions. We obtained the following results: (1) The rainy season (May–June) is the main contributor to precipitation in the study area. The EOF first mode reflected the overall consistency of the precipitation spatial distribution. However, within each river basin, the magnitude of precipitation variation is spatially different. The magnitude of precipitation variation is significant in the northwestern part of the Minjiang River basin, the southwestern part of the Jiulong River basin, and the southwestern corner of the Hanjiang River basin. These areas happen to correspond to the mountain areas, revealing that topographic precipitation plays a role in the spatial distribution of precipitation in the three river basins. (2) The spatial distributions of the EOF first mode and of precipitation during El Niño in the Minjiang River basin are consistent. This reveals that ENSO is probably the dominant factor in precipitation in the Minjiang River basin. The significant increase in precipitation during El Niño compared with a normal year in the Minjiang River basin confirms this point. (3) In all three strong El Niño years, 1998, 2010, and 2016, the Minjiang River basin experienced significant heavy precipitation in the fall and winter, whereas the Jiulong River and Hanjiang River basins did not (except in 2016). In other words, the Minjiang River basin is more affected by ENSO, while the Jiulong River and Hanjiang River basins are only limitedly impacted by ENSO.

FETSWCM-SWAN and DualSPHysics models are used to study the storm flooding process of Dazuo Seawall in Quanzhou city, Fujian province, China. Firstly, a storm over-topping assessment method is proposed. Secondly, the water level and storm wave at the foot of Dazuo Seawall calculated by FETSWCM-SWAN during the process of Typhoon Usagi in 2013 and Typhoon Dujuan in 2015. Finally, the typical wave and storm run-up processes at Dazuo Seawall are modeled by DualSPHysics. The results show that the typical wave run-up of Dazuo Seawall is less than 2 m, but the storm wave run-up can reach 6.85 m under Usagi. The risk is high when high tide is encountered. The risk of storm flooding is classified into three grades. This risk assessment could provide decision-making support to government departments in warning against storm flooding threats.

In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid distribution difference with high resolution at the surface and bottom. Two types are considered in the model, including removing the Coriolis force (type 1) and employing a different Coriolis force (type 2). According to the experiments, the velocity of the current in type 1 yields uncertainty, and wind energy could penetrate the upper ocean and reach the abyss. The surface velocity in type 2 is fundamentally compatible with the empirical relationship constructed by Ekman, and the curved lines of the vertical distribution of horizontal currents nearly match. For type 1, the velocity is very strong from the sea surface to the bottom. When comparing type 1 and type 2 cases, the Coriolis force obstructs the wind energy transfer into the deep ocean. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF)’s global surface wind distribution indicates that the realistic ocean upper wind boundary is similar to the numerical experiment in the Pacific and Atlantic oceans, where the wind direction is along the latitude line at the equator. In order to make the experimental situation as close as possible to the real ocean, validation experiments are conducted in this study to consider the uncertainty in the current profile at the equator. The simulation results of type 1 differ significantly from the data obtained from the real ocean. This uncertainty may transfer the signal to higher latitudes, causing incorrect simulation results, especially in the critical region. Overall, this research not only makes discoveries in physical ocean theory but also guides predictive and forecasting techniques for ocean modeling.