Sea surface temperature (SST) has important practical value in ocean related fields. Numerical prediction is a common method for forecasting SST at present. However, the forecast results produced by the numerical forecast models often deviate from the actual observation data, so it is necessary to correct the bias of the numerical forecast products. In this paper, an SST correction approach based on the Convolutional Long Short-Term Memory (ConvLSTM) network with multiple attention mechanisms is proposed, which considers the spatio-temporal relations in SST data. The proposed model is appropriate for correcting SST numerical forecast products by using satellite remote sensing data. The approach is tested in the region of the South China Sea and reduces the root mean squared error (RMSE) to 0.35 °C. Experimental results reveal that the proposed approach is significantly better than existing models, including traditional statistical methods, machine learning based methods, and deep learning methods.

To avoid filter collapse, a new localized weighted ensemble Kalman filter (LWEnKF) is presented. This filter is a nonlinear non‐Gaussian filter that combines some of the advantages of the particle filter (PF) and of the ensemble Kalman filter (EnKF). Additionally, the new method can overcome filter degeneracy in high‐dimensional system applications. Based on the weighted ensemble Kalman filter (WEnKF), we extend the scalar weight of each particle to a vector and limit the influence of distant observations through a localization function. According to the results of experiments using the Lorenz ‘96 model with 40 variables, the LWEnKF with only 10 particles can prevent filter degeneracy. In addition, tests of the new filter are also performed using a two‐layer quasi‐geostrophic model to demonstrate the feasibility of using the new method in high‐dimensional numerical weather prediction models. Comparisons among the LWEnKF, the local particle filter (LPF) and the localized perturbed observation EnKF (LEnKF) reveal that the proposed method can combine the advantages of the latter two in certain aspects, even providing better performance in some situations. This characteristic of the LWEnKF indicates its potential for data assimilation of different types of observations. Moreover, the new filter is compared to the block‐local ensemble Kalman particle filter (LEnKPF). Experiments showed that the LWEnKF has an obvious advantage over the LEnKPF when the number of particles is small, which indicates its potential for realistic applications limited by computing resources.

This study successfully developed a Yin-He Global Ocean Data Assimilation and Forecast System (YHGO), which commenced operational trial runs on 2024 August 15. The system innovatively employs a particle filter-based assimilation method—the localized weighted ensemble Kalman filter—overcoming the limitations of traditional approaches that assume linear or weakly nonlinear models and observation operators while effectively handling non-Gaussian background errors. It incorporates a mass conservation ocean model, which better represents real ocean physics than conventional volume-conserving models. Additionally, it integrates an intelligent reconstruction module that derives subsurface temperature and salinity profiles from satellite sea-surface observations, which are then assimilated as pseudo-observations. Validation based on daily 1- to 10-d forecasts from 2024 August 15 to 2024 November 30, following the Global Ocean Data Assimilation Experiment OceanView Intercomparison and Validation Task Team (GOV-IVTT) Class 4 framework, demonstrates that YHGO achieves higher accuracy in sea-surface temperature and sea-level anomaly predictions than the widely used GLO12V4 system. However, systematic biases remain in the temperature and salinity profile forecasts, which represent key areas for future improvement and optimization to enhance the overall performance of the system.

We presented a new local particle filter named the localized weighted ensemble Kalman filter (LWEnKF), which was tested and verified using a simple high-dimensional Lorenz 96 model. A revised LWEnKF, the proposal weights calculation of which is modified through localization to prevent filter degeneracy for real geophysical models, is explored further in this article and shows lots of potential in the implementation of real complex models. For geophysical models, the ocean dynamics changes slowly compared with that of the atmosphere. With a relatively low resolution, it is weakly nonlinear in the surface layers of the ocean model used in this article, which fits the linear and Gaussian assumptions of the EnKF but could be a challenge for particle filters in the data assimilation process. With only 50 particles, the LWEnKF assimilates the sea-surface temperature (SST), sea-surface height (SSH), temperature, and salinity profiles with affordable computational cost, providing a reasonable forecast. Moreover, the LWEnKF is compared with the ensemble Kalman filter (EnKF) and the local particle filter (PF). For observed variables, the LWEnKF performs comparably to the EnKF, as the observation operator is linear. For unobserved variables, the LWEnKF provides more accurate forecasts than the EnKF, since the latter considers only the correlations, while the former considers higher-order moments. The local PF ensemble does not converge to the observed solution in an ample amount of time in this study, which needs further investigation.

In the rapidly evolving landscape of cybersecurity, safeguarding sensitive information and ensuring the integrity of digital systems have become paramount. Physical unclonable functions (PUFs) have emerged as a cutting-edge technology in the realm of hardware-based security, especially in combating the scourge of counterfeit medicines.^1,2 A PUF refers to a physical object with inherent and unique features that can be generated with a stochastic and nondeterministic process and therefore is impervious to replication and also resistant to physical attack.^3,4 In the realm of commercially available electronic PUFs (e.g., arbiter PUFs and static random-access memory PUFs), the inherent physical variations in semiconductor devices generate unique and unpredictable identifiers.⁴ However, due to their deterministic fabrication mode and encoding mechanism, these electronic PUFs might be susceptible to modeling attacks, notably from machine-learning fronts.² Countermeasures, fostering randomness and unpredictability, are essential to thwart such attacks.^5,6 Optical PUFs, fueled by the inherent randomness in optics and optical materials, have been receiving increasing research interest. Such an emerging technology holds promise for addressing issues in current electronic PUFs, such as vulnerability to attack, limits in entropy sources, aging, and degradation.⁴ The first demonstration of optical PUFs reported in 2002⁷ employed laser speckle patterns generated by random scattering from an inhomogeneous optical medium. Over the past two decades, integrable optical materials and structures involving stochastic processes or significant structural disorders,^8,9 such as plasmonic nanostructures,^{10 – 16} Mie resonators,^{17 – 19} colloidal photonic crystals,^{20 – 22} molecular self-assemblies,²³ and bionic structures,²⁴ have been exploited as compact optical PUFs. Despite their promise, expanding the PUF capacity with ample challenge–response pairs (CRPs) demands dedicated spatial^9,25 or spectral encoding strategies²⁶ of the excitation light field to generate highly flexible graphical information. The required bulky and complex equipment (e.g., a spatial light modulator) often impedes their applications.

Under the motivation of the great success of four-dimensional variational (4D-Var) data assimilation methods and the advantages of ensemble methods (e.g., Ensemble Kalman Filters and Particle Filters) in numerical weather prediction systems, we introduce the implicit equal-weights particle filter scheme in the weak constraint 4D-Var framework which avoids the filter degeneracy through implicit sampling in high-dimensional situations. The new variational particle smoother (varPS) method has been tested and explored using the Lorenz96 model with dimensions $N_x=40$ , $N_x=100$ , $N_x=250$ , and $N_x=400$ . The results show that the new varPS method does not suffer from the curse of dimensionality by construction and the root mean square error (RMSE) in the new varPS is comparable with the ensemble 4D-Var method. As a combination of the implicit equal-weights particle filter and weak constraint 4D-Var, the new method improves the RMSE compared with the implicit equal-weights particle filter and LETKF (local ensemble transformed Kalman filter) methods and enlarges the ensemble spread compared with ensemble 4D-Var scheme. To overcome the difficulty of the implicit equal-weights particle filter in real geophysical application, the posterior error covariance matrix is estimated using a limited ensemble and can be calculated in parallel. In general, this new varPS performs slightly better in ensemble quality (the balance between the RMSE and ensemble spread) than the ensemble 4D-Var and has the potential to be applied into real geophysical systems.