The Kuroshio Extension region is well known for its strong eddy activity. In this paper, using satellite altimetry‐measured sea surface height anomaly data from 1993 to 2012 in an extended Kuroshio Extension region (140–180°E, 25–45°N), we analyze eddy characteristics: eddy size, polarity, lifetime, intensity, trajectory, and spatial and temporal distributions. Using temperature and salinity vertical profiles measured by Argo floats, we examine the eddy impact on vertical stratification. During the 20‐year period, 7,574 eddies are identified (based on following complete eddy trajectories) with a lifetime equal to or longer than 4 weeks. The numbers of cyclonic and anticyclonic eddies are found to be approximately the same. The distribution of eddy sizes peaks at a radius of about 40 km. The radius at the peak is at the same order as the first baroclinic deformation radius or the horizontal shear scale of the Kuroshio flow. The normalized eddy statistical characteristics show that eddies have different characteristics at different stages of their lifetimes. Among eddies with lifetimes longer than 50 weeks, more anticyclonic (cyclonic) eddies are found north (south) of 35°N. In contrast, among eddies with lifetimes shorter than 20 weeks, more cyclonic (anticyclonic) eddies are found north (south) of 35°N. The asymmetric distribution of eddies suggests two different eddy generation mechanisms: (1) the development of meanders in the Kuroshio path leading to the pinch off of eddies with longer lifetime (larger size) and (2) horizontal shear instability (barotropic instability) leading to eddies of shorter life (smaller size). We further apply an eddy‐resolved numerical product to quantitatively investigate the eddy generation mechanisms.

Sea surface temperature (SST) is an important physical factor in the interaction between the ocean and the atmosphere. Accurate monitoring and prediction of the temporal and spatial distribution of SST are of great significance in dealing with climate change, disaster prevention, disaster reduction, and marine ecological protection. This study establishes a prediction model of sea surface temperature for the next five days in the East China Sea using long-term and short-term memory neural networks (LSTM). It investigates the influence of different parameters on prediction accuracy. The sensitivity experiment results show that, based on the same training data, the length of the input data of the LSTM model can improve the model’s prediction performance to a certain extent. However, no obvious positive correlation is observed between the increase in the input data length and the improvement of the model’s prediction accuracy. On the contrary, the LSTM model’s performance decreases with the prediction length increase. Furthermore, the single-point prediction results of the LSTM model for the estuary of the Yangtze River, Kuroshio, and the Pacific Ocean are accurate. In particular, the prediction results of the point in the Pacific Ocean are the most accurate at the selected four points, with an RMSE of 0.0698 °C and an $R^2$ of 99.95%. At the same time, the model in the Pacific region is migrated to the East China Sea. The model was found to have good mobility and can well represent the long-term and seasonal trends of SST in the East China Sea.

Mesoscale eddies are important to ocean circulation due to their roles in the transport of mass, energy, and heat. This study employs a combination of data sources to initiate a statistical analysis of eddy spatiotemporal characteristics in the Bay of Bengal (BOB) to elucidate the sea surface and vertical structures of the eddies and their impacts on sea surface chlorophyll (Chl) distributions. The results suggest that 1237 cyclonic eddies (CEs) and 1121 anticyclonic eddies (AEs) were detected in 26 years. The number of two eddy polarities was almost the same, and most of them spread to the west or southwest direction. The vertical change of temperature (T) and salinity (S) caused by the eddies is studied and the anomalous eddies, i.e., a CE (AE) eddy with warm (cold) water at the center, are mainly distributed on the northeast side of the Island of Sri Lanka. Furthermore, CEs are found to increase Chl concentration in the surrounding sea by approximately 11.15%, while AEs decrease concentrations also by approximately 11.25%. Changes in Chl concentrations occur most rapidly during the mature and intensification eddy phases. Observations also indicate that the strong local current and wind fields are the primary mechanisms in eddy generation.

The ocean chlorophyll-a (Chl-a) concentration is an important variable in the marine environment, the abnormal distribution of which is closely related to the hazards of red tides. Thus, the accurate prediction of its concentration in the East China Sea (ECS) is greatly important for preventing water eutrophication and protecting the coastal ecological environment. Processed by two different pre-processing methods, 10-year (2011–2020) satellite-observed chlorophyll-a data and logarithmic data were used as the long short-term memory (LSTM) neural network training datasets in this study. The 2021 data were used for comparison to prediction results. The past 15 days’ data were used to predict the concentration of chlorophyll-a for the five following days. Results showed that the predictions obtained by both pre-processing methods could simulate the seasonal distribution of the Chl-a concentration in the ECS effectively. Moreover, the prediction performance of the model driven by the original values was better in the medium- and low-concentration regions. However, in the high-concentration region, the prediction of extreme concentrations by the two data-driven LSTM models showed underestimation, considering that the prediction performance of the model driven by the original values was better. Results of sensitivity experiments showed that the prediction accuracy of the model decreased considerably when the backward prediction time step increased. In this study, the neural network was driven only by chlorophyll-a, whose concentration in the ECS was forecasted, and the effect of other relevant marine elements on Chl-a was not considered, which is the current weakness of this study.

Deep learning methods have excellent prospects for application in wave forecasting research. This study employed the convolutional LSTM (ConvLSTM) algorithm to predict the South China Sea (SCS) significant wave height (SWH). Three prediction models were established to investigate the influences of setting different parameters and using multiple training data on the forecasting effects. Compared with the SWH data from the China–France Ocean Satellite (CFOSAT), the SWH of WAVEWATCH III (WWIII) from the pacific islands ocean observing system are accurate enough to be used as training data for the ConvLSTM-based SWH prediction model. Model A was preliminarily established by only using the SWH from WWIII as the training data, and 20 sensitivity experiments were carried out to investigate the influences of different parameter settings on the forecasting effect of Model A. The experimental results showed that Model A has the best forecasting effect when using three years of training data and three hourly input data. With the same parameter settings as the best prediction performance Model A, Model B and C were also established by using more different training data. Model B used the wind shear velocity and SWH as training and input data. When making a 24-h SWH forecast, compared with Model A, the root mean square error (RMSE) of Model B is decreased by 17.6%, the correlation coefficient (CC) is increased by 2.90%, and the mean absolute percentage error (MAPE) is reduced by 12.2%. Model C used the SWH, wind shear velocity, wind and wave direction as training and input data. When making a 24-h SWH forecast, compared with Model A, the RMSE of Model C decreased by 19.0%, the CC increased by 2.65%, and the MAPE decreased by 14.8%. As the performance of the ConvLSTM-based prediction model mainly rely on the SWH training data. All the ConvLSTM-based prediction models show a greater RMSE in the nearshore area than that in the deep area of SCS and also show a greater RMSE during the period of typhoon transit than that without typhoon. Considering the wind shear velocity, wind, and wave direction also used as training data will improve the performance of SWH prediction.