The marine ecosystem is one of the most extensive and abundant ecosystems on Earth. Marine plankton is an important component, and its abundance, number of species, and dominant species are regarded as important monitoring indicators. Aiming at the problems of low accuracy and high complexity in identifying plankton based on convolutional neural networks, this study proposes a lightweight identification algorithm for plankton images based on the improved MobileNetV2. Firstly, the network layer structure is extracted by redesigning features to balance the depth and width of the network to reduce the model parameters; secondly, the lightweight coordinate attention (CA) mechanism is introduced to strengthen the attention and extraction ability of key areas; in addition, the structure of the network classifier is optimized to improve the utilization efficiency of the model parameters. The results show that the model achieves a 95.46% accuracy and 94.48% recall in 12 kinds of images. Compared with the initial MobileNetV2, the parameters and calculation amount are reduced by 72.47% and 52.09%, respectively, and the reasoning time for a single image is 6.15 ms. The model realizes the accurate identification of plankton in situ under the premise of ensuring it is lightweight. Combining time information and depth data, it is of great significance for marine ecological environment monitoring and prediction to obtain the abundance of various plankton.

The lack of dimensionality of ocean observations makes it difficult to utilize multi-scale data assimilation to correct model errors with limited observations. Since satellite observations can provide high-resolution and time-continuous sea surface information, this study utilizes sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) anomalies to invert the vertical temperature and salinity fields and thus realize multi-scale data assimilation in the three-dimensional space. We propose a temperature and salinity inversion network (TSI-Net) for reconstructing the mapping of the sea surface’s spatial distribution features to vertical structural features to obtain pseudo-observed fields. In this study, measured satellite remote-sensing data and temperature and salinity profiles are used to correct the model errors in the waters around the China Sea. The sensitivity analysis shows that the multi-component inversion can better fit the temperature field relationship, with a correlation coefficient of about 0.87. The results of the assimilation experiments show that the analytical field obtained by assimilating the pseudo-observed field is more consistent with the target field in terms of the spatial distribution characteristics.

In this letter, we propose and investigate a refractive index (RI) sensor with sectional sensitivity based on cladding-regenerated long period fiber grating (CR-LPFG). The CR-LPFG is fabricated by using CO2 laser to polish on the composite structure of single-mode fibers(SMFs) spliced with multi-mode fibers(MMFs). The operating mechanism of the CO2 laser results in the removal of the original cladding of the MMFs and the creation of a new refractive index layer on the core. This technique, initially proposed by us, aims to enhance sensitivity to the external environment by facilitating the exposure of evanescent field and increasing the core diameter mismatch. The experimental results demonstrate that the refractive index sensitivity of the sensor is 396.39 nm/RIU, 1878.60 nm/RIU, and 663.32 nm/RIU in the intervals of 1.3321-1.3605 RIU, 1.3605-1.3858 RIU, and 1.3858-1.4190 RIU, respectively. The mode transition results in a phenomenon of sensitivity segmentation, offering various options to meet different needs in practical applications. Therefore, we propose a cost-effective refractive index sensor with high application potential.

Using the local multiscale energy and vorticity analysis (MS-EVA) and based on the global high-resolution ocean reanalysis product GLORYS12V1 for 20 years, this study investigates the energy transfers and conversions of Kuroshio in the Luzon Strait and its adjacent regions through three scales, namely, the climatological scale, the seasonal scale, and the eddy scale. The results show that the inverse cascades of kinetic energy dominate the energy transfer east of Luzon (at both the eddy and seasonal scales). Kuroshio transfers the climatological kinetic energy to the eddy scale through a forward energy cascade in Luzon Strait and east of Taiwan. Because the topography of Luzon Strait and Kuroshio jointly block and limit the westward propagation of non-local eddies, the eddy energy in the South China Sea west of Luzon Strait tends to depend on local forward potential energy cascades. In these subregions, potential energy drives the accumulation of kinetic energy under the action of buoyancy conversion: interannual (seasonal) potential energy as the source of multiscale energy in the Luzon Strait (the east of Taiwan).

A spatiotemporal empirical orthogonal function (STEOF) forecast method is proposed and used in medium- to long-term sea surface height anomaly (SSHA) forecast. This method embeds temporal information in empirical orthogonal function spatial patterns, effectively capturing the evolving spatial distribution of variables and avoiding the typical rapid accumulation of forecast errors. The forecast experiments are carried out for SSHA in the South China Sea to evaluate the proposed model. Experimental results demonstrate that the STEOF forecast method consistently outperforms the autoregressive integrated moving average (ARIMA), optimal climatic normal (OCN), and persistence prediction. The model accurately forecasts the intensity and location of ocean eddies, indicating its great potential for practical applications in medium- to long-term ocean forecasts.