Abstract. In recent years, deep learning-based ocean forecasting has become a prominent research focus. However, recent studies often rely on operational ocean forecast systems to provide initial conditions. Operational ocean forecast systems typically use Ensemble Data Assimilation (EDA) to generate these initial conditions. Nonetheless, the high computational cost of numerical models limits the ensemble sizes in EDA, resulting in rank deficiencies in the background error covariance matrix and introducing spurious correlations. To address these challenges and advance the operationalization of deep learning-based ocean forecasting, we propose Deepcov-EnKF, a deep learning-enhanced background error covariance method for massive Ensemble Kalman Filter (EnKF) applications in Sea Surface Temperature (SST) forecasting. The proposed method incorporates a deep learning-based SST forecasting model to generate approximately 5,000 ensemble members. It then directly maps this high-dimensional perturbation set into a background error covariance matrix using a deep neural network. This approach reduces the computational cost of covariance estimation by more than 200-fold compared to conventional techniques. Experimental results demonstrate that the forecasting model can initialize reliable 60-day SST predictions with a Root Mean Square Error (RMSE) of approximately 0.6 °C. Furthermore, assimilation diagnostics reveal that Deepcov-EnKF effectively resolves the spurious correlations in the covariance matrix. The method also exhibits robust stability and outperforms advanced numerical assimilation methods during a 360-day cycling forecasting experiment. This study confirms that Deepcov-EnKF overcomes key limitations of traditional EDA frameworks, significantly enhances the accuracy of SST assimilation, and lays the foundation for high-precision marine forecasting systems.

With the rise of scalable vector extension instruction sets in ARM processor architectures, large Fortran-based scientific codes face challenges in performance portability. This paper focuses on CLOUDSC, a computationally intensive and data-access complex cloud microphysics parameterization scheme from the Integrated Forecasting System of ECMWF. We propose Auto-CLOUDSC, an auto-generation framework that optimizes CLOUDSC by the following methods, including (1) An auto-generator that contains three modules: function interface generator, code structure analyzer, and expression parser to convert Fortran to vectorization instruction sets. (2) A physics-combine algorithm that applies loop fusion to reduce redundant memory access. (3) A cache-aware algorithm that uses cache tiling and data layout optimization to improve data reuse. Experiments demonstrate that the auto-generated code achieves a speedup of 1.3 to 2.1 times over the original Fortran baseline on the Phytium FT2000+ ARMv8 processor.

Accurately calculating the momentum exchange between the atmosphere and the ocean is essential for global weather forecasting. However, current widely used parameterization schemes face challenges in accurately estimating sea surface roughness z0 $\left({z}_{0}\right)$. Deep learning (DL) technology offers new opportunities to address this problem. Here, we present a DL-based parameterization scheme for sea surface roughness, which we have integrated into a global forecasting system. The DL-based scheme demonstrates advantages over four widely used traditional schemes in offline and online tests. In the offline tests based on observational data, the DL-based scheme significantly improved root mean square errors (RMSEs) for the calculated z0 ${z}_{0}$, with a decrease of at least 50.00% $50.00\,\%$ compared to the four traditional schemes. In the online tests based on the coupling of the DL-based scheme with a global forecasting system, the DL-based scheme also shows advantages over four traditional schemes, especially in forecasting the geopotential height and the wind field for the Southern Hemisphere.

Numerical weather prediction (NWP) models are pivotal for weather forecasting and climate research. As high performance computing (HPC) enters the exascale era, these models are advancing towards unprecedented high-resolution, generating explosive data volumes. In this case, Input/Output (I/O) operations have become a major performance bottleneck, hindering scalability and efficiency. In this paper, we focus on optimizing the I/O performance for the Yin-He Global Spectral Model (YHGSM). To alleviate these bottleneck, we implement a solution centered on a decoupled, asynchronous I/O paradigm. By integrating the XIOS framework, our method externalizes I/O tasks to dedicated server processes, which we enhance with a custom data transfer algorithm that strategically buffers data to maximize the overlap between computation and communication. The resulting integrated system is referred to as YHGSM-XIOS. The experimental results demonstrate substantial performance gains, showing that at a scale of 5,120 processes, the YHGSM-XIOS scheme reduces I/O overhead from 43.08 % to a mere 5.27 % and achieves an overall speedup of 2.31 times compared to the baseline system.

Spectral models, due to their stability and efficiency, have become one of the most popular approaches for implementing numerical weather prediction systems. Given the complexity of these models, they often require the use of multi-node computing resources for parallel processing to meet the stringent real-time requirements. However, as the number of nodes increases, the efficiency of inter-node communication becomes a critical bottleneck. In the case of the Yin-He Global Spectral Model (YHGSM), developed by the National University of Defense Technology, communication overhead is very high during the Fourier transform section, which consists of the transform itself and the subsequent transposition from the z-μ decomposition to the z-m decomposition. To address this challenge, we introduce an optimized scheme that overlaps communication with computation. By grouping corresponding communication and computation tasks, this approach leverages non-blocking communication techniques within MPI, combined with the use of asynchronous communication progress threads. Our experimental results demonstrate that this scheme can reduce execution time by up to 30% compared to the non-overlapped version, thereby significantly hiding communication overhead and enhancing the efficiency of YHGSM.

The Advanced Radiative Transfer Modeling System(ARMS),a computationally efficient satellite observation op-erator,has been successfully integrated into the YinHe four-dimensional variational data assimilation(YH4DVAR)system.This study investigates the impacts of assimilating Advanced Microwave Sounding Unit-A(AMSU-A)ob-servations from the Meteorological Operational Satellite-C(MetOp-C)on the performance of YH4DVAR.Through a month-long global statistical analysis and a case study of Typhoon Hinnamnor,we evaluate the benefits of AMSU-A data assimilation under clear sky conditions.Key findings are as follows.(1)ARMS achieves simulation accuracy comparable to RTTOV(Radiative Transfer for the Television and InfraRed Observation Satellite Operational Verti-cal sounder)version 11.2,demonstrating only a 0.5％discrepancy in data retention after quality control.(2)Imple-mentation of ARMS as an operator in YH4DVAR enhances forecast accuracy for the 850-hPa temperature and 500-hPa geopotential height in the tropical region.(3)Compared to RTTOV,ARMS has improved the intensity forecast of Typhoon Hinnamnor and reduced mean wind speed errors by approximately 2％and central pressure errors by ap-proximately 1％.ARMS has now been operationally adopted as an alternative observational operator wi-thin YH4DVAR,demonstrating exceptional numerical stability,computational efficiency,and promising potential for future satellite data assimilation applications.

Meteorological satellite remote sensing is important for numerical weather forecasts, but its accuracy is affected by many things during observation and retrieval, showing that it can be improved. As a standard way to measure wind from space, atmospheric motion vectors (AMVs) are used. They are separate pieces of information spread out in the troposphere, which gives them more depth than regular surface or sea surface wind measurements. This makes rectifying problems more difficult. For error correction, this research builds a deep-learning model that is specific to AMVs. The outcomes show that AMV observational errors are greatly reduced after correction. The root mean square error (RMSE) drops by almost 40% compared to ERA5 true values. Among these, the optimization of solar observation errors exceeds 40%; the discrepancies at varying atmospheric pressure altitudes are notably improved; the degree of optimization for data with low QI coefficients is substantial; and there remains potential for enhancement in data with high QI coefficients. Furthermore, there has been a significant enhancement in the consistency coefficient of the wind’s physical properties. In the assimilation forecasting experiments, the corrected AMV data demonstrated superior forecasting performance. With more training, the model can fix things better, and the changes it makes last for a long time. The results show that it is possible and useful to use deep learning to fix errors in meteorological remote-sensing data.

In a typical non-hydrostatic spectral dynamic numerical weather prediction (NWP) kernel, all forecast variables are transformed between grid point and spectral spaces to compute their gradients and solve the implicit problem. This kernel requires numerous spectral transformations, which depend heavily on extensive global communication and significantly hinder parallel computing efficiency. This paper introduces an innovative non-hydrostatic spectral kernel that incorporates a finite-volume method within the spectral framework. We have developed a horizontal divergence (D)-based structure equation, allowing direct computation of most prognostic variables and their horizontal gradients at grid point space. By doing so, the need for spectral transformations is substantially decreased. Our experiments demonstrate that this new approach reduces the cost of spectral transformations by up to 40%, enhancing the overall model efficiency by 15%-22%. Additionally, a series of tests confirmed the accuracy and stability of this new solver. This paper proposes a new non-hydrostatic spectral solver by applying a finite-volume method (FVM) into the spectral framework, where a horizontal divergence (D)- based structure equation is constructed and most of the prognostic variables as well as their horizontal gradients are calculated in grid point space directly. The number of spectral transforms is dramatically reduced. In practice, the cost of spectral transform with the proposed solver is reduced up to 40%. image

The Yin‐He Global Spectral Model (YHGSM) is a dry‐mass conserving hydrostatic global spectral model, relying on spectral transforms to compute horizontal derivatives. We present an extension of YHGSM core named YHGSM‐FVM which uses a second‐order finite‐volume method (FVM) to compute the horizontal derivatives in grid‐point space instead of the spectral approach. With this approach, the computational efficiency of the spectral model is improved since part of the spectral transforms is superseded by FVM which only needs local data and the computational demand is lower. More importantly, YHGSM‐FVM is still a spectral model solving the Helmholtz equation directly in spectral space with a highly efficient semi‐implicit semi‐Lagrangian advection scheme. The comparisons between YHGSM‐FVM and YHGSM are conducted, and the results show that both models have comparable prediction skill, but YHGSM‐FVM outperforms YHGSM in computational efficiency.

In this article, we describe an implementation of two‐way coupling between the Yin–He Global Spectral Model (YHGSM) and the Wave Model (WAM). The YHGSM atmosphere model gives the surface air water density and 10‐m wind for the WAM model, while the WAM model provides the Charnock coefficient as a response. The effects on the meteorological aptitude of the two‐way coupled system are assessed statistically. The coupled model exhibits a 75% significant positive impact on both the northern (NthH) and the southern hemisphere (SthH) for all metrics at almost all pressure levels. Typhoon track assessments of the coupled model will be implemented later. The results show that the coupling of the WAM model benefits typhoon landfall prediction. Finally, we make a comprehensive comparison of the significant wave height (SWH) between the YHGSM–WAM coupled model and the European Centre for Medium‐Range Weather Forecasts (ECMWF) model with buoy data from the Chinese Ocean Monitoring Network. The comparison shows that the YHGSM–WAM coupled model exhibits more accuracy and predictive stability of SWH forecasts against buoy observations than the ECMWF forecasts. The above results indicate that the YHGSM–WAM coupled model possesses superior performance to uncoupled models.

The newly developed nonhydrostatic（NH） global spectral dynamical core is evaluated by using three-dimensional（3D） benchmark tests with/without moisture. This new dynamical core differs from the original Aladin-NH like one in the combined use of a dry-mass vertical coordinate and a new temperature variable, and thus, it inherently conserves the dry air mass and includes the mass sink effect associated with precipitation flux. Some 3D dry benchmark tests are first conducted, including steady state, dry baroclinic waves, mountain waves in non-sheared and sheared background flows, and a dry Held–Suarez test. The results from these test cases demonstrate that the present dynamical core is accurate and robust in applications on the sphere, especially for addressing the nonhydrostatic effects.Then, three additional moist test cases are conducted to further explore the improvement of the new dynamical core.Importantly, in contrast to the original Aladin-NH like one, the new dynamical core prefers to obtain simulated tropical cyclone with lower pressure, stronger wind speeds, and faster northward movement, which is much closer to the results from the Model for Prediction Across Scales（MPAS）, and it also enhances the updrafts and provides enhanced precipitation rate in the tropics, which partially compensates the inefficient vertical transport due to the absence of the deep convection parameterization in the moist Held–Suarez test, thus demonstrating its potential value for full-physics global NH numerical weather prediction application.

As the number of electric vehicles (EVs) increases rapidly, the problem of electric vehicle charging has widely become a concern. Therefore, considering the fact that charging time for one EV cannot be shortened quickly and the number of charging stations will not expand rapidly, how to schedule charging operations of electric vehicles in urban areas becomes a very important issue, since it can improve charging efficiency and relieve charging anxiety of EV users. Up to now, there is no scheduling software tool for practical use in this field. Based on the analysis of electric vehicle charging behavior characteristics, this paper investigates the EV charging problem at the scheduling level. First, a mathematical model for coordinated charging of EVs is proposed to minimize the total charging time for a given number of vehicles. Second, an earliest finish charging scheduling algorithm is presented to solve the charging problem. Then, by considering the combinatorial nature and practical applications with large number of EVs, two practical swarm-optimization-based EV charging scheduling algorithms are proposed. A real-life case study is presented to illustrate the proposed approaches.

As a new kind of 2D carbon allotrope, graphdiyne (GDY) has the characteristics of uniform pores and large specific surface area due to its unique electronic space arrangement structure. Therefore, we fabricated a novel gold nanoparticles (Au NPs)/GDY/carbon cloth (CC) SERS substrate by a simple electrodeposition process using GDY nanowall wrapping the CC as support. The effects of electrodeposition conditions on the microstructure and SERS activity of Au NPs/GDY/CC substrate were studied. Under the condition of optimizing deposition time and deposition voltage, the prepared Au NPs/GDY/CC substrate can detect 10-13 M with rhodamine 6G (R6G) as a probe molecule. The maximum enhancement factor (EF) is 2.2 x 1012, and the concentration of R6G is loga-rithmically correlated with the intensity of the SERS band with a linear correlation coefficient (R2) of 0.97036. These indicate that the Au NPs/GDY/CC substrate has good sensitivity. The prepared Au NPs/GDY/CC SERS substrate was applied to the detection of organic dyes of malachite green (MG) and methyl violet (MV) in water, and the detection limits of both can reach 10-9 M. This highly sensitive and high-performance SERS substrate has broad application prospects in the detection of organic dyes.

In Recirculating Aquaculture System (RAS), feeding frequency is an important factor affecting fish growth, and precise feeding according to the state of fish is the key to improving the aquaculture efficiency. The current problems in detecting the feeding frequency of fish include low efficiency, high technical requirements, and the influenced by the aquaculture environment. In this paper, we adopt a computer vision method to calculate feeding frequency to achieve objective with high accuracy. Firstly, we process the video of the fish according to the inter-frame difference method to obtain the image of the feeding state of the fish. Then we propose a modified VGG16 model to determine the feeding state of the fish, transform it into a 0–1 classification problem and calculate the feeding frequency of the fish. The feeding frequency and the growth status of the fish are then used to develop an intelligent feeding strategy to improve the growth rate of the fish and the conversion rate of the bait. Tests have shown that the accuracy of identifying feeding state by the modified VGG16 model can reach 92.4%. The method has a positive effect on the development of recirculating aquaculture system.

Compared with the conventional igniters, a liquid hydrogen/liquid oxygen (LH2/LOX) torch igniter has significant advantages of ignition energy, reliability, and reusability for LH2/LOX rocket motors. However, the mechanisms that underpin the LH2/LOX torch igniter are not fully understood yet. In this study, the effects of the injection distance and droplet size on the igniter, typically on the torch power (i.e., ignition energy) and thermal protection, were numerically investigated. Meanwhile, two fire tests with different conditions were conducted to validate the numerical model prior to the simulation. The simulation results show that increasing the injection distance is beneficial to improve the torch power but not conducive to thermal protection under our conditions. Moreover, LOX droplet size marginally affects the torch power and thermal protection because of the fuel-rich combustion in our study. Nevertheless, the increase of LH2 droplet size results in a higher inner wall temperature, which contradicts the purpose of thermal protection. (C) 2022 Elsevier Masson SAS. All rights reserved.

The prediction of chaotic time series systems has remained a challenging problem in recent decades. A hybrid method using Hankel Alternative View Of Koopman (HAVOK) analysis and machine learning (HAVOK-ML) is developed to predict chaotic time series. HAVOK-ML simulates the time series by reconstructing a closed linear model so as to achieve the purpose of prediction. It decomposes chaotic dynamics into intermittently forced linear systems by HAVOK analysis and estimates the external intermittently forcing term using machine learning. The prediction performance evaluations confirm that the proposed method has superior forecasting skills compared with existing prediction methods.

Biomass-derived porous carbon (PC) loaded with precious metals have a synergistic enhancement effect on surface-enhanced Raman scattering (SERS). In this paper, PC containing various functional groups and large specific surface area, good stability and certain biocompatibility were prepared using tomato skins and introduced into the preparation of Ag nanoflowers (NFs) SERS substrates. Rhodamine 6G (R6G) was used as the Raman probe molecule to evaluate the sensing performance of Ag NFs@PC. The results showed that the stable dispersion and protective effect of PC on Ag NPs significantly improved the sensitivity and long-term stability of traditional Ag NFs. In practical application, Ag NFs@PC was further used to successfully achieve the quantitative analysis of trace methylene blue (10 ppt) and malachite green (10 ppt) in a lake water system. Therefore, SERS sensor Ag NFs@PC is expected to become a promising candidate sensor for detecting in the field of environment and food monitoring. Graphical abstract

In the process of Recirculating Aquaculture System (RAS), artificial feeding method has many problems, such as inappropriate feeding rhythm and waste of bait, which seriously affect the growth of fish. In order to tackle this problem, we designed an intelligent feeding algorithm applied to the RAS. First, the feeding frequency of fish in the culture pools and the amount of residual bait are acquired by processing the feeding image and video data captured by the camera on top of the culture pools by computer vision method. Then, the subsequent feeding amount are determined by the heuristic algorithm based on the amount of residual bait, the feeding frequency of fish and the other key parameters. The experimental results show that the intelligent feeding algorithm can save about 15% bait than the traditional manual feeding method without affecting the growth of fish.

As online spamming has posed serious security threat to cyberspaces, relevant detection technologies based on artificial intelligence is being widely studied. Existing related research literatures can be divided into two classes: methods based on behavior patterns and methods based on semantic patterns. In order to better solve this challenge, we clearly proposed a semantics and behaviors-collaboratively driven spammer detection Method (Co-Sdm) in social networks. In particular, long-term behavior and semantic pattern of multi-source information fusion and collaborative coding is introduced. Therefore, a more comprehensive feature space representation can be captured to further detect spammers and improve the ability to deal with spam. In the experiment, we carried out a series of experiments under different scenarios and basic parameters based on two real datasets. Compare the high efficiency of Co-Sdm clearly proposed with the three baselines of multiple evaluation index values. The test results show that, compared with the baseline, the average characteristic of Co-Sdm has improved by about 5%.

This study investigated the indoor air quality (IAQ) during humid season in an old badminton hall, to explore the IAQ characteristics of natural ventilated sports buildings for public use. The indoor air parameters (temperature, relative humidity and air velocity) and indoor air pollutants (CO2, TVOC, PM2.5 and PM10) were measured. A subjective approach was carried out through questionnaire survey. 185 valid questionnaires were recovered, and 68.7% of the participants had exercised. Results show that the indoor air qualities obtained through objective and subjective approaches were obviously different. Indoor PM, TVOC and CO2 concentrations were normal, but 37.3% of the participants complained about the building materials' smell and 73.5% of the participants reported obvious sweaty odor. Physical activity might reduce a person's sensitivity to the environment. The participants generally felt warm and hot because of the high relative humidity. Post-exercise participants felt significantly hotter than those who did not exercise, and were generally more receptive to IAQ. The method of Fanger was employed to narrow the gap between subjective and objective approaches with a modified parameter, and to furtherly estimate the ventilation. The present study demonstrates the necessity to combine two approaches together to assess the IAQ in sports buildings.