In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window ocean (MSWO) model based on a spatiotemporal attention mechanism, to predict changes in the subsurface ocean temperature structure over the next six months using satellite remote sensing data from the past 24 months. Our results indicate that predictions made using historical remote sensing data closely approximate those made using historical in situ data. This finding suggests that satellite remote sensing data can be used to predict future ocean structures without relying on valuable in situ measurements. Compared to future predictive models, real-time three-dimensional structure reconstruction models can learn more accurate inversion features from real-time satellite remote sensing data. This work provides a new perspective for the application of artificial intelligence in oceanography for ocean structure reconstruction.

Marine heatwaves (MHWs) are changing global ecosystems and bearing profound socio-economic impacts, yet our understanding of the spatial features, temporal evolution characteristics, and regional differences in China’s marginal seas remains insufficient. In this study, the spatio-temporal variation characteristics of the frequency, mean intensity, maximum intensity, cumulative intensity, duration and total days of MHWs are systematically analyzed based on daily sea surface temperature data from Operational Sea Surface Temperature and Ice Analysis (OSTIA) for the period of 1983–2020. The results show the following: The annual mean frequency of MHWs in China’s coastal seas is 1.4–4.6 counts per year and increases gradually from north to south. The annual mean of mean intensity, maximum intensity and cumulative intensity are, respectively, in the ranges of 0.3–2.9 °C, 0.4–3.7 °C and 3.9–41.7 °C days, all of which show a significant decreasing trend from north to south. The annual mean of duration and total days of MHWs are in the respective ranges 8.7–19.7 and 18.9–69.1 days. The annual mean of frequency, cumulative intensity, duration and total days all show a clear increasing trend, with respective linear increases of 1.03 counts, 3.57 °C days, and 1.98 and 17.58 days per decade. The annual means of the mean intensity and maximum intensity have no obvious upward trend, with the exceptions of the Bohai and Yellow Seas. Finally, MHWs were divided into the four categories of moderate, strong, severe and extreme, with the results showing that moderate ones account for more than 70% of all heatwave events, while strong, severe and extreme ones, respectively, account for about 25%, 2% and 0.02%.

In this study, a global-ocean-data-assimilation system based on the three-dimensional variational (3DVAR) scheme is built for operational oceanography. The available observations include satellite altimetry; the satellite-measured sea-surface temperature (SST); and T/S profiles from Argo floats, which are assimilated to provide the initial condition of the global-ocean forecasting. The statistical analysis methods are designed to assess the performance of the data-assimilation scheme, and the results show that the analysis SST fields agree well with OSTIA and MGDSST, and the corresponding root-mean-square errors are, respectively, 0.523 and 0.548 °C. Moreover, the analysis sea-surface-height fields are well represented at the middle and low latitudes and have a slightly greater difference in the regions with strong mesoscale eddies. The variations in the vertical distribution of the forecasting temperature profiles resemble those of the GTS buoy observation. The forecasting salinity profiles correspond well to GTS observations, except with a weaker cold bias between the depths 100 and 200 m (about 0.2 PSU) at buoy station 2901494. Overall, our 3DVAR assimilation system plays a significant role in improving the accuracy of analysis and forecasting fields for operational oceanography.

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.

Operating the ocean value chain requires the implementation of steps that must work systematically and automatically to generate ocean predictions and deliver ocean data information in standard format. This task, which represents the backbone of operational forecasting systems, implies the design of robust workflows that organize pre-processing of the upstream data, run the core models, and handle post-processing before the final delivery. Operational chains require dedicated computational resources to supply demanding modeling runs but also processing and analysis of big volumes of data in relation to the specific spatial scale and consistently for the forecast lead times. The monitoring of each step of the workflow through key performance metrics can support not only timely delivery but also identifying problems and troubleshooting. The paper illustrates the main challenges foreseen by operational chains in integrating complex numerical frameworks from the global to coastal scale and discusses existing tools that facilitate orchestration of operational chain components, including examples of existing systems and their consolidated capacity to provide high-quality and timely ocean forecasts.