The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal interpolation method using HY-2A SWH data played a positive role in enhancing the accuracy of the global ocean wave simulations and could effectively improve the deviations of SWH in the simulation processes. The root mean square errors of the NDBC buoy inspections were improved by 7 to 44% after the assimilation, and those of China’s offshore buoy inspections were improved by 3 to 11% after the assimilation. It was observed that the farther the buoys were from the shore, the better the effects of the assimilation improvements. The root mean square errors of the Jason-2 satellite data validations were improved by 17% after the assimilation, with monthly improvements of 8–25%. The improvements occurred in most of the global oceans, particularly in the Southern Ocean, the Eastern Pacific Ocean and the Indian Ocean. The results obtained in this research can be used as a reference for the operational applications of China’s ocean satellite data in ocean wave data assimilation and prediction.

Based on the WAVEWATCH III wave model, China’s National Marine Environmental Forecasting Center has developed an operational global ocean wave forecasting system that covers the Arctic region. In this study, in situ buoy observations and satellite remote sensing data were used to perform a detailed evaluation of the system’s forecasting results for 2022, with a focus on China’s offshore and global ocean waters, so as to comprehensively understand the model’s forecasting performance. The study results showed the following: In China’s coastal waters, the model had a high forecasting accuracy for significant wave heights. The model tended to underestimate the significant wave heights in autumn and winter and overestimate them in spring and summer. In addition, the model slightly underestimated low (below 1 m) wave heights, while overestimating them in other ranges. In terms of spatial distribution, negative deviations and high scatter indexes were observed in the forecasting of significant wave heights in semi-enclosed sea areas such as the Bohai Sea, Yellow Sea, and Beibu Gulf, with the largest negative deviation occurring near Liaodong Bay of the Bohai Sea (−0.18 m). There was a slight positive deviation (0.01 m) in the East China Sea, while the South China Sea exhibited a more significant positive deviation (0.17 m). The model showed a trend of underestimation for the forecasting of the mean wave period in China’s coastal waters. In the global oceanic waters, the forecasting results of the model were found to have obvious positive deviations for most regions, with negative deviations mainly occurring on the east coast and in relatively closed basins. There were latitude differences in the forecasting deviations of the model: specifically, the most significant positive deviations occurred in the Southern Ocean, with smaller positive deviations toward the north, while a slight negative deviation was observed in the Arctic waters. Overall, the global wave model has high reliability and can meet the current operational forecasting needs. In the future, the accuracy and performance of ocean wave forecasting can be further improved by adjusting the parameterization scheme, replacing the wind fields with more accurate ones, adopting spherical multiple-cell grids, and data assimilation.

To meet the needs of marine disaster prevention and mitigation, this paper proposes a systematic methodological framework to describe the annual risk situation of Disastrous Sea Waves (DSWs) from four perspectives. Its application is demonstrated for the China Sea in 2023 as a case study. The systematic approach is reflected in the following: (1) a comprehensive description of DSW risks based on three dimensions: occurrence frequency, maximum intensity, and hazard index; (2) an overview of the DSW risk characteristics for the year through spatial and monthly distributions; (3) a comparative analysis of the year’s DSWs, with historical data based on anomalies and return periods used to assess the risk characteristics and extremities; and (4) an analysis of the causes of the year’s characteristics based on monthly anomalies and weather systems. Through its application to the China Sea in 2023, the analysis process is introduced as follows. (1) High-Frequency and Intensity Areas: DSWs frequently occurred in the northeastern South China Sea (SCS) and Taiwan Strait, exceeding 450 h. The maximum significant wave height (H_s), reaching 11.3 m, was recorded in the southern East China Sea (ECS) in August. (2) Extremity in Frequency and Attribution: The occurrence frequency was extremely high, with the cumulative hours exceeding the historical average by 159 h (9.1%). The southwestern SCS showed the most significant excess, up to 168 h (>120%). The reason for this was that DSWs in January caused by prolonged cold air lasted 236 h longer (121%). (3) Extremity in Intensity and Attribution: The maximum H_s in the southern ECS and Taiwan Strait was 2 m (30%) higher than the historical average. The intensified cold air waves caused the higher intensities. (4) Hazard Levels: Higher risk occurred in the southwestern SCS, southern ECS, and Taiwan Strait, while the highest extremity occurred in the Bohai Sea.