The primary clientele of a harbor is vessels, and vessels are primarily influenced by external forces such as wind (on the water surface), currents (underwater), and waves (affecting vessel stability). Therefore, it is necessary to comprehensively consider safety factors such as marine environmental forces and port characteristics. As ship sailing falls under applied science, acquiring marine meteorological information regarding ship routes can enhance port navigational safety. However, in the face of changes in the environmental conditions of harbor waters, it is essential to fully consider the impact of the external environment on ship maneuvering. One can effectively navigate complex operating environments by devising reasonable ship-handling plans. In the context of sea level rise caused by extreme climatic events, long-term variations, trends, and random factors are at play. Previous assessments of sea level rise have often relied on linear regression and the least squares method to determine coefficients. However, these methods fail to accurately capture the actual trend of sea level rise. Additionally, traditional harmonic analysis methods are unable to analyze sea level rise as well. Therefore, in this study, the techniques of simple moving average (SMA), empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD) were applied to analyze sea level rise. The obtained results of sea level rise under different analysis conditions were integrated with a hydrodynamic model that incorporates both wave and tidal characteristics to calculate the overall coastal dynamics parameters, which are crucial for ship navigation. The research findings contribute to the study of ship navigational safety issues by examining the distribution characteristics of port meteorology under climate change conditions. They offer valuable insights for mariners to assess navigational safety and devise maneuvering strategies based on the actual water flow conditions. Furthermore, the findings help identify and address potential risks and issues, ultimately ensuring the safety of navigation.

In this study, a hydrodynamic model was used that includes the effects of wave–current interactions to simulate the wave and current patterns before and after offshore wind turbine installation in western Taiwan. By simulating the waves and currents after the offshore wind turbine was established, the waves and currents caused by the wind turbine were seen to have a limited range of influence, which is probably within an area about four to five times the size of the diameter (12–15 m) of the foundation structure. Overall, the analysis of the simulation results of the wave and current patterns after the offshore wind turbines were established shows that the underwater foundation only affected the local area near the pile structure. The wind farm (code E) of the research case can be equipped with about 720 cage cultures; if this is extended to other wind farms in the western sea area, it should be possible to produce economic-scale farming operations such as offshore wind power and fisheries. However, this study did not consider the future operation of the entire offshore wind farm. If the operation and maintenance of offshore wind farms are not affected, and if the consent of the developer is obtained, it should be possible to use this method to provide economically large-scale farming areas as a mutually beneficial method for offshore wind power generation and fisheries.

Due to the rapid development of computers, researchers have made efforts since the 1990s to develop typhoon forecasting models and stochastic typhoon simulation models to assess typhoon disasters and risks. Typhoon forecasting models are primarily used to predict and track the movement of typhoons and provide warning information to the general public before landfall. Stochastic typhoon simulation models can assess extreme wind speeds and compensate for the limitations of current observations and simulation data length. Taiwan experiences approximately three to four typhoons yearly, of varying intensities and paths. Whether the marine meteorological data includes events of strong typhoon centers passing through will affect the results of frequency analysis. The development of offshore wind power in Taiwan is closely related to the unique marine meteorological conditions throughout the lifecycle stages, including wind farm site selection, feasibility studies, planning and design, construction and installation, operation and maintenance, and decommissioning. This study references relevant research and analyzes sixty-three scenarios using nine types of maximum storm wind speed radii and seven Holland-B parameters. The data from Japan Meteorological Agency Best Track Data (JMA BTD) is utilized, explicitly selecting 20 typhoon events after 2000 for wind speed simulation using a typhoon wind speed model. After validating the typhoon wind speeds with observation data from the Central Weather Bureau (CWB) in Hsinchu and the Longdong buoy, the technique of Monte Carlo simulation is utilized to generate synthetic typhoons randomly. The average of the relative absolute errors for the simulated maximum wind speeds is calculated, and through comprehensive evaluation, optimal parameter combinations ($R_m$, $B$) are obtained.