Search Results (44)

Extreme surface winds and wave heights of tropical cyclones (TCs)—pose serious threats to coastal community, infrastructure and environments. In recent decades, progress in numerical wave modeling has significantly enhanced the ability to reconstruct and predict wave behavior. This review offers an in-depth overview of TC-related wave modeling utilizing different computational schemes, with a special attention to WAVEWATCH III (WW3) and Simulating Waves Nearshore (SWAN). Due to the complex air–sea interactions during TCs, it is challenging to obtain accurate wind input data and optimize the parameterizations. Substantial spatial and temporal variations in water levels and current patterns occurs when coastal circulation is modulated by varying underwater topography. To explore their influence on waves, this study employs a coupled SWAN and Finite-Volume Community Ocean Model (FVCOM) modeling approach. Additionally, the interplay between wave and sea surface temperature (SST) is investigated by incorporating four key wave-induced forcing through breaking and non-breaking waves, radiation stress, and Stokes drift from WW3 into the Stony Brook Parallel Ocean Model (sbPOM). 20 TC events were analyzed to evaluate the performance of the selected parameterizations of external forcings in WW3 and SWAN. Among different nonlinear wave interaction schemes, Generalized Multiple Discrete Interaction Approximation (GMD) Discrete Interaction Approximation (DIA) and the computationally expensive Wave-Ray Tracing (WRT) A refined drag coefficient (C_d) equation, applied within an upgraded ST6 configuration, reduce significant wave height (SWH) prediction errors and the root mean square error (RMSE) for both SWAN and WW3 wave models. Surface currents and sea level variations notably altered the wave energy and wave height distributions, especially in the area with strong TC-induced oceanic current. Finally, coupling four wave-induced forcings into sbPOM enhanced SST simulation by refining heat flux estimates and promoting vertical mixing. Validation against Argo data showed that the updated sbPOM model achieved an RMSE as low as 1.39 m, with correlation coefficients nearing 0.9881. Full article

The main goal of this study is to develop a method for finding the joint probability distribution of the state of the characteristics of the NEMO (Nucleus for European Modeling of the Ocean) ocean dynamics model with data assimilation using the Generalized Kalman filter (GKF) method developed earlier by the authors. The method for finding the joint distribution is based on the Karhunen–Loeve decomposition of the covariance function of the joint characteristics of the ocean. Numerical calculations of the dynamics of ocean currents, surface and subsurface ocean temperatures, and water salinity were carried out, both with and without assimilation of observational data from the Argo project drifters. The joint probability distributions of temperature and salinity at individual points in the world ocean at different depths were obtained and analyzed. The Atlantic Meridional Overturning Circulation (AMOC) system was also simulated using the NEMO model with and without data assimilation, and these results were compared to each other and analyzed. Full article

The sound speed in the ocean has a considerable impact on the characteristics of underwater acoustic propagation. The swift gathering of the underwater three-dimensional (3D) sound speed field is essential for target detection, underwater acoustic communication, and navigation. Currently, the reconstruction of the underwater sound speed utilizing satellite remote sensing data of the sea surface has emerged as a significant area of research. However, dynamic activities within the ocean result in varying degrees of perturbation in the sound speed structure. Relying solely on sea surface information will restrict the accuracy of sound speed reconstruction. In response to this issue, by utilizing multi-source satellite remote sensing data alongside Argo profiles, we first implemented the random forest (RF) algorithm to establish the statistical mapping relationship from the sea surface temperature (SST), sea level anomaly (SLA), and absolute dynamic topography (ADT) to the density, and thus, reconstructed a 3D density field. Subsequently, based on the sea surface environmental information, we introduced the underwater vertical density as a novel input for sound speed calculations and proposed a new model for 3D sound speed field reconstruction (RF-SDR). The experimental results indicate that utilizing both the sea surface environmental variables and underwater density as inputs yielded an average root-mean-square error (RMSE) of 1.51 m/s for the reconstructed sound speed, along with an average mean absolute error (MAE) of 0.85 m/s. Following the incorporation of density into the reconstruction inputs, the two error metrics exhibited reductions of 31% and 35%, respectively. And the proposed RF-SDR model demonstrated a reduction in the RMSE by 36% and in the MAE by 43% when compared with the commonly utilized single Empirical Orthogonal Function regression (sEOF-r) method. Furthermore, simulations of the sound propagation with both the reconstructed sound speed and Argo sound speed demonstrated a high degree of consistency in the computed acoustic propagation losses. The correlation coefficients consistently exceeded 0.7, thereby reinforcing the validity of the reconstructed sound speed. Full article

The evolution of the three-dimensional thermohaline structure of mesoscale eddies is crucial for assessing energy and mass transfer during their long-distance propagation in the ocean. However, the understanding and quantitative evaluation of the role that mesoscale eddies play in driving variations of thermohaline in the deep sea remains constrained due to the scarcity of in situ observations, particularly in marginal seas such as the South China Sea (SCS). In this study, we propose an artificial intelligence (AI)–physics-based deep learning model that integrates satellite measurements and Argo data from 2003 to 2021 to reconstruct the three-dimensional thermohaline structure of mesoscale eddies in the SCS. Besides utilizing basic sea surface hydrodynamic parameters obtained from satellite data for model training, an additional branch incorporating eddy physical parameters was introduced to optimize the model. The results demonstrate that the model effectively reconstructs thermohaline properties within mesoscale eddies in the SCS. Compared to Argo observations, the average root mean square error (RMSE) for temperature (salinity) within anticyclonic eddies was 0.34 °C (0.036 PSU), while it was 0.36 °C (0.032 PSU) within cyclonic eddies in the upper 1500 m. Further validation using high-resolution glider observations tracking an anticyclonic eddy originating in the SCS confirms the model’s efficiency, achieving an RMSE of 0.2962 °C (0.0138 PSU) for temperature (salinity). The accuracy of our proposed model significantly outperforms that of HYCOM and GLORYS simulations, with the RMSE reduced by 40% to 60%. The distinctive capabilities provide valuable insights into understanding the fine-scale structures of mesoscale eddies, especially in regions with limited in situ data. Full article

The study of upper ocean mixing processes, including their dynamics and thermodynamics, has been a primary focus for oceanographers and meteorologists. Wave breaking in deep water is believed to play a significant role in these processes, affecting air–sea interactions and contributing to the energy dissipation of surface waves. This, in turn, enhances the transfer of gas, heat, and mass at the ocean surface. In this paper, we use the FVCOM-SWAVE coupled wave and current model, which is based on the MY-2.5 turbulent closure model, to examine the response of upper ocean turbulent kinetic energy (TKE) and temperature to various wave breaking parametric schemes. We propose a new parametric scheme for wave breaking energy at the sea surface, which is based on the correlation between breaking wave parameter $R_B$ and whitecap coverage. The impact of this new wave breaking parametric scheme on the upper ocean under typhoon conditions is analyzed by comparing it with the original parametric scheme that is primarily influenced by wave age. The wave field simulated by SWAVE was verified using Jason-3 satellite altimeter data, confirming the effectiveness of the simulation. The simulation results for upper ocean temperature were also validated using OISST data and Argo float observational data. Our findings indicate that, under the influence of Typhoon Nanmadol, both parametric schemes can transfer the energy of sea surface wave breaking into the seawater. The new wave breaking parameter $R_B$ scheme effectively enhances turbulent mixing at the ocean surface, leading to a decrease in sea surface temperature (SST) and an increase in mixed layer depth (MLD). This further improves upon the issue of uneven mixing of seawater at the air–sea interface in the MY-2.5 turbulent closure model. However, it is important to note that wave breaking under typhoon conditions is only one aspect of wave impact on ocean disturbances. Therefore, further research is needed to fully understand the impact of waves on upper ocean mixing, including the consideration of other wave mechanisms. Full article

In order to study the impact of acoustic propagation characteristics in the northeastern South China Sea, GEBCO08 global terrain grid data and Argo data were used to numerically simulate the acoustic transmission characteristics of two stations in the northeast South China Sea affected by the Kuroshio. The impact of different marine environments on acoustic transmission characteristics was analyzed. The results show that increasing the deployment depth of a sound source within a certain range will reduce the transmission loss; deploying a sound source near the axis of the surface acoustic channel or the deep-sea acoustic channel will also greatly increase the propagation distance of sound signals; and the presence of topography such as undersea mountains will increase the transmission loss. Full article

Wave-induced mixing can enhance vertical mixing in the upper ocean, facilitating the exchange of heat and momentum between the surface and deeper layers, thereby influencing ocean circulation and climate patterns. Building on previous research, this study proposes a wave-induced mixing parameterization scheme (referred to as EXP3) specifically designed for typhoon periods. This scheme was integrated into the fully coupled ocean–wave–atmosphere model COAWST and applied to analyze Typhoon In-Fa (2021) as a case study. The simulation results were validated against publicly available data, demonstrating a good overall match with observed phenomena. Subsequently, a comparative analysis was conducted between the EXP3 scheme, the previous scheme (EXP2) and the original model scheme (EXP1). Validation against Argo and Drifter buoy data revealed that both EXP2 and EXP3, which include wave-induced mixing effects, resulted in a decrease in the simulated mixed layer depth (MLD) and mixed layer temperature (MLT), with EXP3 showing closer alignment with the observed data. Compared to the other two experiments, EXP3 enhanced vertical motion in the ocean due to intensified wave-induced mixing, leading to increased upper-layer water divergence and upwelling, a decrease in sea surface temperature and accelerated rightward deflection of surface currents. This phenomenon not only altered the temperature structure of the ocean surface layer but also significantly impacted the regional ocean dynamics. Full article

Wave-induced radiation stress (RS), as a primary driver of ocean currents influenced by waves, plays an important role in the response of upper ocean temperatures under typhoons. Previous studies have mainly focused on wave-generated currents and coastal currents in nearshore areas. This paper incorporates the geostrophic effect into the wave-induced radiation stress of wave-current interaction, and the effect of waves on the changes in upper ocean temperature (including sea surface temperature (SST) and mixed layer temperature) under typhoon Nanmadol (2022) is studied. The FVCOM-SWAVE model is used to conduct a preliminary numerical study in the western Pacific Ocean. The RS with the geostrophic effect increased the horizontal and vertical components, leading to an enhancement in turbulent mixing and a decrease in SST by up to 1.0 °C to 1.4 °C, which is closer to the SST obtained by OISST remote sensing fusion observation data. In the strong divergence domain, the direction of the vortex flow exhibits a more pronounced turn to the right, accompanied by an increase in water velocity. The vertical temperature profile of the ocean shows that the water below is perturbed by the RS component of the geostrophic effect, and the depth of the mixed layer increases by about 2 m, which is closer to the depth of the mixed layer observed by the Argo floats, indirectly enhancing the vertical mass transport of the ocean. In general, this shows that RS, which takes into account geostrophic effects, enhances the effect of waves on the water below, indirectly leading to lower temperatures in the upper ocean, and the simulated results align more closely with the observed data, offering valuable insights for enhancing marine numerical forecasting accuracy. Full article

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs. Full article

With the inception of the Argo program, the global ocean observation network is undergoing continuous advancement, with profiling buoys emerging as pivotal components of this network, thus garnering increased attention in research. In efforts to enhance the efficiency of profiling buoys and curtail energy consumption, a teardrop-shaped buoy design is proposed in this study. Moreover, an optimization methodology leveraging neural networks and genetic algorithms has been devised to attain an optimal profile curve. This curve seeks to minimize drag and drag coefficient while maximizing drainage, thereby improving hydrodynamic performance. Simulation-based validation and analysis are conducted to assess the efficacy of the optimized buoy design. Results indicate that the drag of the teardrop-shaped buoy with a deflector decreased by 9.2% compared to pre-optimized configurations and by 22% compared to buoys lacking deflectors. The hydrodynamic profile devised in this study effectively enhances buoy performance, laying a solid foundation for ocean thermal energy generation and buoyancy regulation control. Additionally, the optimized scheme serves as a valuable blueprint for the design of ocean exploration devices. Full article

Efficiently searching for multiple targets in complex environments with limited perception and computational capabilities is challenging for multiple robots, which can coordinate their actions indirectly through their environment. In this context, swarm intelligence has been a source of inspiration for addressing multi-target search problems in the literature. So far, several algorithms have been proposed for solving such a problem, and in this study, we propose two novel multi-target search algorithms inspired by the Firefly algorithm. Unlike the conventional Firefly algorithm, where light is an attractor, light represents a negative effect in our proposed algorithms. Upon discovering targets, robots emit light to repel other robots from that region. This repulsive behavior is intended to achieve several objectives: (1) partitioning the search space among different robots, (2) expanding the search region by avoiding areas already explored, and (3) preventing congestion among robots. The proposed algorithms, named Global Lawnmower Firefly Algorithm (GLFA) and Random Bounce Firefly Algorithm (RBFA), integrate inverse light-based behavior with two random walks: random bounce and global lawnmower. These algorithms were implemented and evaluated using the ArGOS simulator, demonstrating promising performance compared to existing approaches. Full article

Marine monitoring equipment such as Argo profiling buoys and underwater gliders are important devices for oceanographic research and marine resource exploration. In this study, a novel mobile buoy capable of vertical profiling motion like Argo profiling buoys and sawtooth gliding motion like underwater gliders is proposed. The proposed mobile buoy can switch between the two motion modes with controllable wings. To verify the feasibility of the proposed mobile buoy, a fluid–multibody coupling model considering multibody dynamics and hydrodynamics was developed to investigate the dynamic response. A scaled-down buoy prototype was fabricated and the feasibility of the two motion modes was experimentally investigated in a laboratory tank. The experimental results agree well with the results of numerical simulation. This work can be helpful for the design and analysis of this kind of mobile buoy. Full article

The strong wind-induced current and sea level have influences on the wave distribution in a tropical cyclone (TC). In particular, the wave–current interaction is significant in the period in which the TC passed the mesoscale eddy. In this study, the wave fields of Typhoon Chan-hom (2015) are hindcastly simulated using a coupled oceanic model that utilizes a nested triangle grid, i.e., the finite-volume community ocean model-simulating waves nearshore (FVCOM-SWAVE) model. The forcing wind field is composited from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data and the simulation using a parametric Holland model, denoted as H-E. The open boundary fields include tide data from TPOX.5 and the hybrid coordinate ocean model (HYCOM) global datasets, including sea surface temperature (SST), sea surface salinity, sea surface current, and sea level data. The simulated oceanic parameters (e.g., the significant wave height, SWH) are validated against the measurements from the Jason-2 altimeter, yielding a root mean square error (RMSE) of 0.58 m for the SWH, a correlation (COR) coefficient of 0.94, and a scatter index (SI) of 0.23. Similarly, the simulated SSTs are compared with the remote sensing products of the remote sensing system (REMSS) and the measurements from Argos, yielding an RMSE of <0.8 °C, a COR of >0.95, and an SI of <0.04. The significant zonal asymmetry of the wave distribution along the typhoon track is observed. The Stokes drift is calculated from the FVCOM-SWAVE simulation results, and then the contribution of the Stokes transport is estimated using the Ekman–Stokes numbers. It is found that the ratio of the Stokes transport to the total net transport can reach >80% near the typhoon center, and the ratio is reduced to approximately <20% away from the typhoon center, indicating that Stokes transport is an essential aspect in the water mixing during a TC. The mesoscale eddies are detected by the sea level anomalies (SLA) fusion data from AVISO. It is found that the significant wave heights, Stokes drift, and Stokes transport inside the eddy area were higher than those outside the eddy area. These parameters inside the cold mesoscale eddies were higher than t inside the warm mesoscale eddies. Otherwise, the SST mainly increased within the cold mesoscale eddies area, while decreased within the warm mesoscale eddies area. The influence of mesoscale eddies on the SST was in proportion to the eddy radius and eddy EKE. Full article

Optimising a swarm of many robots can be computationally demanding, especially when accurate simulations are required to evaluate the proposed robot configurations. Consequentially, the size of the instances and swarms must be limited, reducing the number of problems that can be addressed. In this article, we study the viability of using surrogate models based on Gaussian processes and artificial neural networks as predictors of the robots’ behaviour when arranged in formations surrounding a central point of interest. We have trained the surrogate models and tested them in terms of accuracy and execution time on five different case studies comprising three, five, ten, fifteen, and thirty robots. Then, the best performing predictors combined with ARGoS simulations have been used to obtain optimal configurations for the robot swarm by using our proposed hybrid evolutionary algorithm, based on a genetic algorithm and a local search. Finally, the best swarm configurations obtained have been tested on a number of unseen scenarios comprising different initial robot positions to evaluate the robustness and stability of the achieved robot formations. The best performing predictors exhibited speed increases of up to 3604 with respect to the ARGoS simulations. The optimisation algorithm converged in 91% of runs and stable robot formations were achieved in 79% of the unseen testing scenarios. Full article

Radiation stress is defined as the excess momentum caused by ocean waves, which exerts an indispensable impact on the upper-layer ocean conditions as waves pass by. Previous research concentrated on sea surface cooling caused by typhoons. In this paper, we investigated the effect of wave-induced radiation stress on upper-layer ocean temperature (including sea surface temperature (SST) and mixed-layer temperature) under typhoon conditions, as well as the effect of radiation stress on the surface current field. The FVCOM-SWAVE model, which is based on the SWAN model, is used to simulate the response of upper-layer ocean temperature to radiation stress. The simulated results, when validated with Jason-3 satellite and ARGO data, could reproduce the observed phenomenon well in general. Compared to simulations without radiation stress, the bias in the SST results is reduced by about 1 °C if the radiation stress term is taken into account. The mixed-layer depth temperature is expected to be simulated more accurately, with a root mean square error (RMSE) of less than 1.63 °C and a correlation coefficient (COR) of about 0.94. Results show that wave-induced radiation stress enhances the surface current and causes certain deviations to the right so that the upper water diverges and upwelling increases, resulting in a decrease in SST. When the influence of double typhoons is considered, the airflow of LEKIMA(L) rotates from the northwest toward KROSA (R), limiting the development of significant wave height (SWH) and reducing the cooling range. As a result, the present study is of tremendous importance in precisely forecasting the ocean state of the western North Pacific (WNP). Full article