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Public goods for the global ocean sustainable development (GOSD) are key to promoting the sustainable development of oceans globally. To understand the utilization and protection status of global ocean resources as well as the international cooperation and trend regarding GOSD, this study summarizes the current status and development trends of the GOSD public goods in China and abroad, analyzes the major scientific and technological problems and challenges faced by China in related fields, and proposes the development priorities of the GOSD public goods in terms of engineering technologies. Furthermore, it proposes that China should boost its comprehensive strength in ocean science and technology by establishing an effective mechanism for ocean observation and data sharing and enhance international cooperation and strategic planning to promote its international influence in the field of GOSD public goods.

Significant Wave Height (SWH) is crucial for many human activities, such as marine navigation, offshore operations, and coastal management. Traditionally, SWH is modeled using numerical wave models, which, while accurate, are computationally intensive and constrained by incomplete physical representations of wave spectral evolution. This study introduces a simple global deep learning-based model for SWH, which uses the current SWH field and the wind field at the next time step as inputs to predict the SWH field at the next time step. This approach mirrors the rolling prediction strategy of numerical wave models. After training on a re-analysis dataset, the errors of the model diverge lightly with time when given a good initial field because no spectral information is used. However, after diverging for ~200 hours, the errors stabilize, remaining comparable to those of state-of-the-art numerical wave models. Additionally, the error divergence can be mitigated through the assimilation of altimeter measurements. This deep learning model can not only serve as an efficient surrogate for traditional numerical wave models but also provide a baseline for statistical modeling of global SWH due to its simplicity in inputs and outputs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, we propose a method for wind speed retrieval using a random forest (RF) algorithm for Cyclone Global Navigation Satellite System (CYGNSS) data. We first compared CYGNSS data with soil moisture active passive (SMAP) data and found a certain deviation in the CYGNSS “young sea, limited fetch” (YSLF) data product for high winds. Then, we used SMAP as the “ground truth” to train an RF model and applied it to the wind speed retrieval of CYGNSS data. The experimental results show that using the RF algorithm for wind speed retrieval can eliminate noise in the CYGNSS YSLF wind speed data and improve retrieval accuracy. In addition, we explored the impact of different input parameter combinations on model performance and found that using an 11-parameter model in CYGNSS wind speed retrieval can achieve optimal performance. This can provide a valuable reference for rapid near-real-time retrieval of tropical cyclones using CYGNSS. [ABSTRACT FROM AUTHOR]