Eddies can be identified and tracked based on satellite altimeter data. However, few studies have focused on nowcasting the evolution of eddies using remote sensing data. In this paper, an improved Convolutional Long Short-Term Memory (Conv-LSTM) network named Prednet is used for eddy nowcasting. Prednet, which uses a deep, recurrent convolutional network with both bottom-up and top-down connects, has the ability to learn the temporal and spatial relationships associated with time series data. The network can effectively simulate and reconstruct the spatiotemporal characteristics of the future sea level anomaly (SLA) data. Based on the SLA data products provided by Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) from 1993 to 2018, combined with an SLA-based eddy detection algorithm, seven-day eddy nowcasting experiments are conducted on the eddies in South China Sea. The matching ratio is defined as the percentage of true eddies that can be successfully predicted by Conv-LSTM network. On the first day of the nowcasting, matching ratio for eddies with diameters greater than 100 km is 95%, and the average matching ratio of the seven-day nowcasting is approximately 60%. In order to verify the performance of nowcasting method, two experiments were set up. A typical anticyclonic eddy shedding from Kuroshio in January 2017 was used to verify this nowcasting algorithm’s performance on single eddy, with the mean eddy center error is 11.2 km. Moreover, compared with the eddies detected in the Hybrid Coordinate Ocean Model data set (HYCOM), the eddies predicted with Conv-LSTM networks are closer to the eddies detected in the AVISO SLA data set, indicating that deep learning method can effectively nowcast eddies.

As the waters of marine primary production, the euphotic zone is the primary living environment for aquatic organisms. Eddies account for 90% of the ocean’s kinetic energy and they affect marine organisms’ habitats by the excitation of vertical velocities and the horizontal advection of nutrients and ecosystems. Satellite observations indicate that anticyclones mainly deepen the euphotic zone depth, while cyclones do the opposite. The anomalies reach 5 m on average in the region of high eddy amplitude and frequent eddy occurrence. In addition, we found that the anomalies have an extreme value in each of the 5°–23° and 23°–55° and reach a maximum at around 38 degrees with the increase in latitude. In the eddy-center coordinate system, the minus gradient direction of the negative anomaly is consistent with the background flow field and the direction of the eddy movement. Meanwhile, the anomaly increases along the radial direction to about 0.2r and then decreases. Finally, there is a significant linear correlation between the anomaly magnitude and the eddy amplitude. The conclusion of this research and related mechanism explanation contributes to marine biology research and conservation, the estimates of marine primary productivity, and the understanding of the biogeochemical properties of eddy modulation in the upper water column.

A substantial portion of ocean eddies, especially small ones, may be missed due to insufficient spatial or temporal sampling by satellite altimetry. In order to illustrate the influence of spatial resolution on eddy detection, this study provides a comparison of eddy identification, tracking, and analysis between two sets of merged altimeter data with spatial resolutions of 1/4° and 1/8°. One main study area (the Mediterranean Sea), and three confirmatory areas (the South-China Sea, the North-West Pacific, and the South-East Pacific) are chosen. The results show that the number and density of eddies captured by the 1/8° data are about twice as much as those captured by the 1/4° data, while the ratios of corresponding eddy parameters, i.e., radius, amplitude, (eddy kinetic energy (EKE)) are about 0.6–0.8 (1.3) for the two datasets (1/8° ÷ 1/4°). Long-term eddy tracking (1993–2018) is conducted in the Mediterranean Sea, indicating that the improvement in spatial resolution will increase the observed values of both the lifetime and the propagation distance of robust eddies. The number of eddies identified using the 1/4° data only accounts for ~30% to 60% of those identified using the 1/8° data. However, for eddies that can be detected using the two datasets, ~5% to 10% present errors (i.e., confusion). In comparison between the four regions, we find that for the enclosed seas with complex conditions, the increase in spatial resolution may lead to more significant improvements in the efficiency and accuracy of eddy detection.

Subsurface chlorophyll maxima is an important ecological feature of planktonic ecosystems. Although the vertical profiles can be determined through the implementation of Biogeochemical-Argo buoy, this method is not compatible with the ocean observation requirements of high-resolution spatiotemporal measurements. Here we demonstrate that deep learning can proficiently fill in these observational gaps when combined with sea surface data from Ocean Color Radiometry. First, the sparse vertical profile data of Biogeochemical-Argo is fused with sea surface data to construct the benchmark dataset for deep learning; Second, encouraged by the idea of dense numerical representations, the comprehensive model combined with coupled embedding and bidirectional Gated Recurrent Unit is proposed to inverse the vertical profile with BGC-Argo and satellite data; Then the in-depth spatiotemporal analysis of the subsurface chlorophyll maxima phenomenon is performed by the parametric equation method and deep learning method as well; Finally, extensive experiments in the Northwest Pacific were conducted to demonstrate the effectiveness of the proposed methodology. The impressive results indicate that the proposed method can compensate for the lack of sparse in situ observations of chlorophyll concentration, the determination coefficient is increased by more than 20%. This study is of great significance to marine ecology and provides important insight into artificial intelligence in the study of subsurface oceanic phenomena.

Oceans at a depth ranging from ~100 to ~1000-m (defined as the intermediate water here), though poorly understood compared to the sea surface, is a critical layer of the Earth system where many important oceanographic processes take place. Advances in ocean observation and computer technology have allowed ocean science to enter the era of big data (to be precise, big data for the surface layer, small data for the bottom layer, and the intermediate layer sits in between) and greatly promoted our understanding of near-surface ocean phenomena. During the past few decades, however, the intermediate ocean is also undergoing profound changes because of global warming, the research and prediction of which are of intensive concern. Due to the lack of three-dimensional ocean theories and field observations, how to remotely sense the intermediate ocean from space becomes a very attractive but challenging scientific issue. With the rapid development of the next generation of information technology, artificial intelligence (AI) has built a new bridge from data science to marine science (called Deep Blue AI, DBAI), which acts as a powerful weapon to extend the paradigm of modern oceanography in the era of the metaverse. This review first introduces the basic prior knowledge of water movement in the ~100 m ocean and vertical stratification within the ~1000-m depths as well as the data resources provided by satellite remote sensing, field observation, and model reanalysis for DBAI. Then, three universal DBAI methodologies, namely, associative statistical, physically informed, and mathematically driven neural networks, are elucidated in the context of intermediate ocean remote sensing. Finally, the unique advantages and potentials of DBAI in data mining and knowledge discovery are demonstrated in a top-down way of “surface-to-interior”viaseveral typical examples in physical and biological oceanography.

This paper presents a dataset on the identification of submesoscale eddies, derived from high-resolution chlorophyll a data captured by GOCI I in the northwest Pacific Ocean. Our methodology involves a combination of digital image processing, filtering, and object detection techniques, along with a specific chlorophyll a image enhancement procedure to extract essential information about submesoscale eddies. This information includes their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score. The dataset spans eight time intervals, ranging from 00:00 to 08:00 (UTC) daily, covering the period from 1 April 2011 to 31 March 2021. A total of 19 136 anticyclonic eddies and 93 897 cyclonic eddies were identified, with a minimum confidence threshold of 0.2. The mean radius of anticyclonic eddies is 24.44 km (range 2.5 to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 to 44 km). This unprecedented hourly resolution dataset on submesoscale eddies offers valuable insights into their distribution, morphology, and energy dissipation. It significantly contributes to our understanding of marine environments, ecosystems, and the improvement of climate model predictions. The dataset is available at https://doi.org/10.5281/zenodo.13989785 (Wang and Yang, 2023).