Zhongjie He

Drifter displacements are commonly converted into interval-mean velocity observations for Eulerian data assimilation (EUDA), but this temporal compression can lose information about flow evolution when observations are widely separated in time. We propose temporal cross-covariance Lagrangian data assimilation (Tc-LaDA), which represents velocity states at multiple times within an observation interval and couples terminal-position innovations with intermediate-time velocity states through ensemble-estimated temporal cross-covariances. Tc-LaDA requires neither explicit tangent-linear nor adjoint models, reducing model-specific implementation requirements. Identical-twin double-gyre experiments show that Tc-LaDA generally yields lower velocity-vector RMSE and mean angular error than EUDA at 2-, 5-, and 10-day intervals, whereas EUDA remains competitive at the 1-day interval and performs better in some dynamical regimes depending on the verification metric. Ablation experiments indicate that within the tested cases, off-diagonal temporal cross-covariances provide additional benefits beyond the multi-time control-vector formulation. These results demonstrate the potential of Tc-LaDA for assimilating sparsely sampled drifter positions.

Subsurface ocean observations remain severely limited in spatial coverage due to the high cost and operational difficulty of in-situ deployment. Although moored buoys and profiling floats enable continuous, minute-level sampling at fixed locations, the temporal evolution information they record is largely underutilized in existing frameworks—where point measurements are typically reduced to daily means or treated as static interpolation nodes. This study investigates a fundamental question: can single-point time series encode sufficient spatial information to reconstruct the surrounding temperature field? To address this, we propose a metric learning-based framework that learns to align temporal features with spatial patterns in a shared latent space, enabling spatial inversion from single-point time series. The framework is validated using the 100 m depth temperature field in the northern South China Sea, derived from a high-resolution reanalysis product. Results show that temporal and spatial features exhibit no significant correlation when optimized solely for reconstruction; after metric learning-based joint adjustment, they form clear cluster structures in the latent space. The reconstructed spatial fields capture the dominant modes of oceanic features, primarily internal wave-related spatial patterns, though fine-scale details remain limited. Quantitative evaluation yields an RMSE of 0.12 °C and an R² of 0.21, providing the first quantified evidence for the feasibility of the “time-depth for spatial-breadth” strategy. These findings suggest that single-point temporal dynamics encode meaningful spatial information, offering a new perspective for sparse-observation ocean state estimation.

Accurate and timely detection of emergency event holds significant practical value for mitigating potential risks and losses. However, such emergency event often exhibits continuous, gradual evolutionary patterns in the temporal dimension, and this non-stepwise transition introduces inherent ambiguity to the detection task. To address the ambiguity issues in emergency event detection tasks within complex systems, this paper proposes a Multi-Scale Fuzzy Learning method with Weak Supervision (MS-FuLW). The proposed method first constructs a dynamic label mapping mechanism based on fuzzy set theory, extending traditional binary labels into fuzzy labels to characterize the gradual transition process of emergency events. Next, a Multi Scale Convolutional Network (MSC) is designed to extract both local abrupt features and global trend features from time series data in parallel, thereby enhancing the model ability to capture complex patterns, along with a specialized fuzzy loss function to improve compatibility with fuzzy labels. Finally, a weakly super vised learning mechanism is introduced to optimize the fuzzy label modeling process and reduce dependency on precise anno tations. The experimental results show that compared to baseline methods, MS-FuLW steadily improves detection real-time performance by 5%–25% in real environments, providing an effective solution for real-time risk warning in complex systems.

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.

Previous studies have pointed out that spectral nudging is still insufficient in improving the long-term simulation ability of numerical models. In response to this problem, this study started with the Luzon Strait and its adjacent areas and discussed the influence of the selection of correction targets on its long-term spectral nudging. We established two sets of numerical experiments with the same parameter configuration except for the correction target: one was the monthly climatological target, and the other was the monthly real-time. The results showed that, compared with the climatology, the real-time target improved the consistency with the observations in large-scale variability on the premise of ensuring the correction of the climatological bias of the model. Further verification of the real-time scheme better simulated the meso- and small-scale characteristics, especially more accurately reproducing the position, intensity, and movement trend of eddies when the Kuroshio intrusion event occurred. Multi-scale energy analysis revealed the significance of adjusting large-scale potential energy to improve the overall simulation ability. The premise is that the correction target needs to fully contain these effective large-scale signals and non-stationary features, and then introduce them into the numerical integration of the regional model through appropriate band-pass filter parameter settings, driving a more reasonable large-scale background state thereby.

Motivated by the need for a simple and effective assimilation scheme that could be used in a relocatable ocean model, a new assimilation algorithm called ensemble optimal smoothing (EnOS) was developed. This scheme was a straightforward extension of the ensemble optimal interpolation (EnOI) by involving time correlation information in the Kalman gain. The main advantage of this scheme was the ability to estimate the present state from the time history of observation. We first examined the new scheme in an ideal ocean model using simulated observations. Further applying these two assimilation schemes to the Chinese offshore and adjacent waters, the root-mean-square error (RMSE) of the EnOS scheme was reduced by 6.4% relative to EnOI. The results showed that the EnOS was more efficient and effective in eliminating model errors when compared to the EnOI scheme.

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).