Ocean Modelling最新文献_第7页

The predictability study of oceanic deep learning models: Taking Kuroshio intrusion into South China Sea as an example 海洋深度学习模型的可预测性研究——以黑潮入侵南海为例

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-08-19 DOI: 10.1016/j.ocemod.2025.102622

Qiang Wang , Junkai Qian , Mu Mu , Peng Liang , Bo Qin

Many previous studies have delved into the predictability of atmosphere and ocean in numerical models, which are crucial for guiding and improving predictions. Currently, deep learning prediction models have developed rapidly, yet their predictability remains largely unexplored. This study endeavors to probe the predictability of deep learning models by focusing on the Kuroshio intrusion (KI) into the South China Sea, utilizing the Conditional Nonlinear Optimal Perturbation (CNOP) approach. We first construct a deep learning model for the KI prediction based on the Unet, which can well predict the KI with a lead time of 14 days. By integrating this model with a nonlinear optimization algorithm, we calculate two types of CNOPs: one with a positive sea surface height anomaly (SSHA) error, denoted as CNOP1, and another with a negative error, labeled as CNOP2. These CNOP errors can grow quickly and exert significant effects on the KI prediction: CNOP1 tends to yield an anticyclonic SSHA error, which aligns with the loop path of the Kuroshio, thereby amplifying the intrusion, while CNOP2 has an almost opposite effect. Furthermore, the sensitive area is identified by the spatial structure of the CNOP error, which is mainly located around Luzon strait. Reducing the input data errors in the CNOP sensitive area will more remarkably improve the KI prediction with a relative improvement rate surpassing 20%, compared to the sensitive area identified by occlusion method and other artificially determined areas. Such findings have the potential to elevate the KI prediction skills of deep learning models.

许多先前的研究已经深入研究了数值模式中大气和海洋的可预测性，这对于指导和改进预测至关重要。目前，深度学习预测模型发展迅速，但其可预测性在很大程度上仍未被探索。本文以南海黑潮入侵（KI）为研究对象，利用条件非线性最优摄动（CNOP）方法探讨深度学习模型的可预测性。我们首先构建了基于Unet的KI预测深度学习模型，该模型可以很好地预测KI的提前期为14天。通过将该模型与非线性优化算法相结合，我们计算了两种类型的海面高度异常：一种海面高度异常（SSHA）误差为正，记为CNOP1，另一种海面高度异常为负，记为CNOP2。这些CNOP误差可以快速增长并对KI预测产生显著影响：cno1倾向于产生反气旋SSHA误差，该误差与黑潮的环路路径一致，从而放大了入侵，而cno2的作用几乎相反。利用CNOP误差的空间结构识别出敏感区，主要分布在吕宋海峡附近。减少CNOP敏感区域的输入数据误差，相对于遮挡法识别的敏感区域和其他人为确定的敏感区域，能更显著地提高KI预测，相对改进率超过20%。这些发现有可能提升深度学习模型的KI预测技能。

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引用次数: 0

Integrating machine learning into a fully coupled current-wave-sediment model: Characterizing particle size in the settling process in estuaries of the great barrier reef, Australia 将机器学习集成到一个完全耦合的电流-波浪-沉积物模型中：表征澳大利亚大堡礁河口沉降过程中的粒度

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-08-13 DOI: 10.1016/j.ocemod.2025.102621

Ziyu Xiao , Daniel N. Livsey , Thomas Schroeder , David Blondeau-Patissier , Rodrigo Santa Cruz , Jiasheng Su , Dehai Song , Xiao Hua Wang , Geoffrey Carlin , Andrew D.L. Steven , Joseph R. Crosswell

Accurate prediction of sediment settling is critical for management of coastal ecosystems, but complex estuarine processes that influence sediment deposition and erosion present a major modelling challenge. This study introduces a proof-of-concept framework that integrates machine learning (ML) into environmental simulations to improve accuracy and efficiency by modelling dynamic sediment flocculation processes and their influence on particle size, enabling a more precise determination of settling velocity. Environmental factors influencing in-situ sediment particle size were used to train a regression model based on coeval measurements of three key parameters: salinity, shear rate and suspended sediment concentration (SSC). This regression model was developed using ML and integrated into a fully coupled current-wave-sediment model to simulate the flocculation response to these three parameters. The integrated model framework demonstrates its reliability and accuracy when evaluated against the in-situ measurements, satellite-derived SSC for the Fitzroy Estuary (Great Barrier Reef), and a parametric flocculation model that only relates settling velocity to SSC. We present an example of the ML-based approach outperforming a parametric model by capturing nonlinear particle-hydrodynamic interactions while maintaining computational efficiency, enabling high-resolution SSC simulations. This work demonstrates an advancement for hybrid modelling using rapidly evolving ML applications, offering a scalable tool for sediment transport and water quality management.

沉积物沉降的准确预测对沿海生态系统的管理至关重要，但影响沉积物沉积和侵蚀的复杂河口过程对建模提出了重大挑战。本研究引入了一个概念验证框架，该框架将机器学习（ML）集成到环境模拟中，通过模拟动态泥沙絮凝过程及其对粒径的影响，提高准确性和效率，从而更精确地确定沉降速度。利用影响原位沉积物粒径的环境因子，基于盐度、剪切速率和悬沙浓度（SSC）三个关键参数的同步估算值，训练了一个回归模型。利用ML建立回归模型，并将其集成到一个完全耦合的电流-波-泥沙模型中，模拟絮凝对这三个参数的响应。综合模型框架在与现场测量、菲茨罗伊河口（大堡礁）卫星导出的SSC和仅将沉降速度与SSC联系起来的参数絮凝模型进行比较时证明了其可靠性和准确性。我们提出了一个基于ml的方法的例子，通过捕获非线性粒子-流体动力相互作用，同时保持计算效率，实现高分辨率的SSC模拟，从而优于参数模型。这项工作展示了使用快速发展的ML应用程序的混合建模的进步，为沉积物运输和水质管理提供了可扩展的工具。

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引用次数: 0

Evaluating effectiveness of round-off error compensation with three methods in shallow-water models 评估三种方法在浅水模型中舍入误差补偿的有效性

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-08-05 DOI: 10.1016/j.ocemod.2025.102617

Jiayi Lai , Lanning Wang , Yizhou Yang , Qizhong Wu , Mengxuan Chen

High-performance computing (HPC) limitations remain a significant bottleneck in the development of numerical models. Mixed-precision techniques, which reduce arithmetic precision to improve speed and memory efficiency, offer a promising solution. However, these methods inevitably introduce increased round-off errors that may destabilize model integrations and require smaller integration steps. This study investigates whether round-off error compensation methods can mitigate such precision-reduced errors. Three widely used methods are evaluated including Gill, Kahan, and Quasi Double-Precision (QDP) within shallow-water models. The suitability of using the double-precision fourth-order Runge-Kutta (RK4-DBL) method as a benchmark is first validated through idealized 1D linear shallow-water model experiments with known analytical solutions. Subsequently, ten perturbed initial-condition experiments are conducted for 2D nonlinear shallow-water model to assess the robustness of each compensation method relative to the RK4-DBL benchmark. When applied to fourth-order Runge-Kutta (RK4) in single precision (RK4-SGL), the Gill, Kahan and QDP methods reduce surface-height root-mean-square (RMSE) errors by approximately one order, four orders, and half an order of magnitude, respectively. In terms of computational cost, runtimes increased by 53%, 4%, and 7% relative to the double-precision reference, respectively. Among these compensation methods, the Kahan method achieves the best performance in both error compensation and computational efficiency, followed by the Gill method. The QDP method, though less effective than the other two, still provides meaningful improvements. Overall, this study demonstrates that these three round-off error compensation methods can improve the accuracy of mixed-precision numerical models while maintaining a reasonable computational cost.

高性能计算（HPC）的限制仍然是数值模型发展的一个重要瓶颈。混合精度技术是一种很有前途的解决方案，它通过降低算术精度来提高速度和存储效率。然而，这些方法不可避免地引入了增加的舍入误差，这可能会破坏模型集成的稳定性，并需要更小的集成步骤。本研究探讨舍入误差补偿方法是否可以减轻这种精度降低的误差。对浅水模型中常用的Gill、Kahan和准双精度（QDP）三种方法进行了评价。首先通过已知解析解的理想一维线性浅水模型实验验证了双精度四阶龙格-库塔（RK4-DBL）方法作为基准的适用性。随后，对二维非线性浅水模型进行了10次摄动初始条件实验，以评估每种补偿方法相对于RK4-DBL基准的鲁棒性。当应用于单精度（RK4- sgl）的四阶龙格-库塔（RK4）时，Gill、Kahan和QDP方法分别将表面高度均方根（RMSE）误差降低了大约一个数量级、四个数量级和半个数量级。在计算成本方面，相对于双精度引用，运行时间分别增加了53%、4%和7%。在这些补偿方法中，Kahan方法在误差补偿和计算效率方面的性能最好，Gill方法次之。QDP方法虽然不如其他两种有效，但仍然提供了有意义的改进。总体而言，本研究表明，这三种舍入误差补偿方法可以在保持合理计算成本的同时提高混合精度数值模型的精度。

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引用次数: 0

Assimilation effect of serial observation data in the East Asian Marginal Seas for long period 东亚边缘海长期连续观测资料的同化效应

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-08-05 DOI: 10.1016/j.ocemod.2025.102605

Jae-Ho Lee , You-Soon Chang , Yong Sun Kim , Yang-Ki Cho

Despite the long-standing importance of the serial observation system since the 1960s in the East Asian Marginal Seas (EAMSs), research on the contribution of this valuable data to ocean analysis remains limited. In this study, an Observing System Simulation Experiment (OSSE) was conducted to assess the data assimilation effects of this serial observation system. The OSSE was applied to 22 serial observation lines, with different assimilation periods.

Results show that the best assimilation performance was achieved with the 2-month cycle, which matches the real observation system's period. In the surface layer, the 10-day and 1-month cycles exhibited poorer performance due to an increase in warm bias in the northern part of the East/Japan Sea. In contrast, for the deep layer below 500 m where no serial observation data is available, the 10-day and 1-month cycles showed better performance in short-term simulations for the first seven years for 2012–2018. This improvement is linked to the downward current generated in the northern East/Japan Sea.

In long-term simulations for 2019∼2041, the 2-month cycle demonstrated superior performance, likely due to signal propagation by the southward deep current, which is part of the meridional overturning circulation. These findings were also supported by results from the reverse bias experiment, although the physical mechanisms for interpreting the data assimilation process differ. This study provides valuable insights for long-term ocean prediction and highlights the significance of the serial observation system in enhancing ocean analysis.

尽管自20世纪60年代以来东亚边缘海的连续观测系统具有长期的重要性，但对这一宝贵数据对海洋分析的贡献的研究仍然有限。本研究通过观测系统模拟实验（OSSE）来评估该系列观测系统的数据同化效果。将OSSE应用于22条不同同化周期的连续观测线。结果表明，同化效果最好的周期为2个月，与实际观测系统的周期吻合。在表层，由于东/日本海北部偏暖增加，10天周期和1个月周期表现较差。相比之下，对于500 m以下没有连续观测数据的深层，10天和1个月的周期在2012-2018年前7年的短期模拟中表现更好。这种改善与东部/日本海北部产生的下行洋流有关。在2019 ~ 2041年的长期模拟中，2个月的周期表现出优越的性能，可能是由于向南的深流传播的信号，这是经向翻转环流的一部分。这些发现也得到了反向偏倚实验结果的支持，尽管解释数据同化过程的物理机制不同。该研究为长期海洋预报提供了有价值的见解，并突出了序列观测系统在加强海洋分析方面的意义。

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引用次数: 0

Explainable artificial intelligence of machine and deep learning algorithms for multi-output prediction of wave characteristics 可解释的机器人工智能和深度学习算法，用于多输出波特性预测

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-07-31 DOI: 10.1016/j.ocemod.2025.102604

Zaid Allal , Hassan N. Noura , Ola Salman , Khaled Chahine

Accurately predicting wave characteristics is essential for efficiently harnessing wave energy and ensuring safe maritime operations. This paper compares thirteen machine and deep learning algorithms to forecast wave characteristics using data from a buoy installation in Mooloolaba, Queensland, Australia. The approach diverges from tradition by making multi-output predictions across six wave characteristics, providing a more comprehensive understanding of wave behavior. In addition, it delves into the inner workings of the most effective models through explainable artificial intelligence, revealing the intricate mechanisms underlying their superior performance. The results showcase excellent model performance with minimal error values when dealing with multi-output regression challenges. The results underscore the remarkable potential of these algorithms to predict upcoming wave data on both short-term (30 min) and near-term (1-hour) horizons, allowing for timely intervention for nearshore device maintenance and activation of alert systems.

准确预测波浪特性对有效利用波浪能和确保海上作业安全至关重要。本文利用澳大利亚昆士兰州Mooloolaba浮标装置的数据，比较了13种机器和深度学习算法来预测波浪特征。该方法与传统方法不同，通过对六种波浪特征进行多输出预测，从而更全面地了解波浪行为。此外，它还通过可解释的人工智能深入研究了最有效模型的内部工作原理，揭示了其卓越性能背后的复杂机制。结果表明，在处理多输出回归挑战时，模型性能优异，误差值最小。研究结果强调了这些算法在预测短期（30分钟）和短期（1小时）内即将到来的波浪数据方面的巨大潜力，允许及时干预近岸设备维护和激活警报系统。

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引用次数: 0

Effectiveness of the Offline Fennel model for biogeochemical simulations in the Mediterranean Sea 离线Fennel模型在地中海生物地球化学模拟中的有效性

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-07-25 DOI: 10.1016/j.ocemod.2025.102596

Júlia Crespin , Morane Clavel-Henry , Miquel Canals , Kristen M. Thyng , Veronica Ruiz-Xomchuk , Jordi Solé

Modeling the distribution of biogeochemical components in the ocean is essential for further understanding climate change impacts and assess the functioning of marine ecosystems. This requires robust and efficient physical-biological simulations of coupled ocean-ecosystem models, which are often hindered by limited data availability and computational resources. The option of running biological tracer fields offline, independently from the physical ocean simulation, is appealing due to increased computational efficiency. Here, we present an assessment and implementation of an offline biogeochemical model — the Offline Fennel model — within the Regional Ocean Modeling System (ROMS). Our methodology employs ROMS hydrodynamic outputs to run the biogeochemical model offline. This work also includes the first ground-truthing exercise of the referred offline biogeochemical model. We use a variety of skill metrics to compare the simulated surface chlorophyll to an ocean color dataset (Copernicus Marine Service Mediterranean Ocean Color) and BGC-Argo floats for the 2015–2020 period. The model is able to reproduce the temporal and spatial structures of the main chlorophyll fluctuation patterns in the study area, the Northwestern Mediterranean Sea. This area is of particular interest as it is one of the most productive regions in the entire Mediterranean Basin, with open-ocean upwellings and deep winter convection events occurring seasonally. The typical behavior of the region is likewise effectively represented in the implementation, including offshore primary production, nutrient supplies from the Rhone and Ebro rivers, and mesoscale hydrographic structures. This study provides a baseline for ROMS users in need of executing more biogeochemical simulations independently from more computationally demanding physical simulations.

模拟海洋中生物地球化学成分的分布对于进一步了解气候变化的影响和评估海洋生态系统的功能至关重要。这需要对耦合的海洋生态系统模型进行强大而有效的物理-生物模拟，而这往往受到有限的数据可用性和计算资源的阻碍。离线运行生物示踪剂场，独立于物理海洋模拟，由于提高了计算效率，因此具有吸引力。在这里，我们提出了一个离线生物地球化学模型的评估和实现-离线Fennel模型-在区域海洋模拟系统（ROMS）。我们的方法采用ROMS流体动力学输出来离线运行生物地球化学模型。这项工作还包括参考的离线生物地球化学模型的第一次地面实况练习。我们使用各种技能指标将模拟的表面叶绿素与海洋颜色数据集（哥白尼海洋服务地中海海洋颜色）和BGC-Argo浮标在2015-2020年期间进行比较。该模型能够再现研究区西北地中海主要叶绿素波动模式的时空结构。这一地区特别令人感兴趣，因为它是整个地中海盆地中最具生产力的地区之一，开放的海洋上升流和冬季深层对流事件季节性地发生。该地区的典型行为同样有效地体现在实施中，包括海上初级生产、罗纳河和埃布罗河的营养供应以及中尺度水文结构。这项研究为需要执行更多生物地球化学模拟的ROMS用户提供了一个基线，这些模拟独立于计算要求更高的物理模拟。

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引用次数: 0

Quantification of multi-source contributions to volume transport in the Tsushima Strait 对马海峡体积运输多源贡献的量化

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-07-24 DOI: 10.1016/j.ocemod.2025.102597

Ziyin Meng , Qiyan Ji , Hui Chen , Guantong Lv

As the sole channel connecting both the East China Sea and the Yellow Sea to the Sea of Japan, the contribution of specific sources of volume transport in the Tsushima Strait remains unknown. Using the Lagrangian trajectory model TRACMASS, this study identifies the sources of volume transport through the Tsushima Strait and quantifies the contributions from three major pathways: the East Taiwan Channel, the Taiwan Strait, and the Northern Yellow Sea. The model accurately reproduces the persistent quasi-unidirectional current that transports water through the Tsushima Strait into the Sea of Japan throughout the year. The East Taiwan Channel and Taiwan Strait serve as the principal contributors to the Tsushima Strait’s volume transport, accounting for 1.58 Sv (51.8 %) and 1.04 Sv (34.1 %), respectively. However, the Northern Yellow Sea makes a relatively minor contribution of 0.17 Sv (5.7 %). Volume transport through the Tsushima Strait is primarily sourced from the East Taiwan Channel for most of the year, exhibiting bimodal seasonal peaks in April and November driven by intensified Kuroshio shelf intrusion. In contrast, the Taiwan Strait becomes the dominant contributor during August and September, when strengthened transport occurs under the combined influence of the Taiwan Warm Current and monsoon transition. Volume transport from the Northern Yellow Sea to the Tsushima Strait is primarily driven by the Korean Coastal Current. In addition, the runoff and long-resident water masses retained within the East China Sea and the Yellow Sea also serve as supplementary contributors to the transport of Tsushima Strait.

作为连接东海和黄海与日本海的唯一通道，对马海峡的具体运输量来源的贡献尚不清楚。利用拉格朗日轨迹模式TRACMASS，本文确定了对马海峡的体积输送来源，并量化了三个主要途径：台湾海峡东部、台湾海峡和黄海北部。该模型准确地再现了全年将水通过对马海峡输送到日本海的持续准单向洋流。东台湾海峡和台湾海峡是对马海峡运量的主要来源，分别占1.58 Sv（51.8%）和1.04 Sv（34.1%）。然而，北黄海的贡献相对较小，为0.17 Sv（5.7%）。在一年中的大部分时间里，通过对马海峡的运输量主要来自东台湾海峡，在黑潮陆架入侵加剧的驱动下，在4月和11月出现双峰季节性高峰。8月和9月，台湾暖流和季风转变共同影响下，台湾海峡成为主要的输送源。从黄海北部到对马海峡的运输主要是由朝鲜海岸流驱动的。此外，东海和黄海内滞留的径流和长期驻留的水团也对对马海峡的输送起到补充作用。

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引用次数: 0

Real-time wave model error correction via coupled neural networks and WAM under extreme weather 极端天气下基于耦合神经网络和WAM的实时波模型误差校正

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-07-23 DOI: 10.1016/j.ocemod.2025.102600

Aiyue Liu , Xiaofeng Li , Dongliang Shen

Accurate forecasts of wave parameters, especially significant wave height, are essential for maritime operations, yet predicting wave heights during extreme weather remains difficult due to rapid error growth in numerical models. This study presents a real-time error correction framework that couples a spatiotemporal attention-based neural network with the WAM wave model. The correction network is trained using CFOSAT satellite observations and dynamically coupled with WAM via a Fortran–Python interface. Applied to 114 typhoon events in the Northwest Pacific, the system reduces significant wave height (SWH) root mean square error (RMSE) by 24.6 % and increases the structural similarity index (SSIM) by 26.3 %, compared to WAM predictions made with default tuning parameters. Validation across 32 tropical cyclones with diverse intensities in the Gulf of Mexico shows strong generalization, achieving up to a 47 % reduction in RMSE and enhancing wave spectral accuracy by >30 %. These results highlight the robustness and scalability of this hybrid AI-physics framework, demonstrating its practical value for real-time wave forecasting during extreme weather events.

准确预报波浪参数，特别是重要的浪高，对海上作业至关重要，但由于数值模式的误差迅速增长，在极端天气下预测浪高仍然很困难。本研究提出了一种基于时空注意力的神经网络与WAM波模型相结合的实时纠错框架。校正网络使用CFOSAT卫星观测进行训练，并通过Fortran-Python接口与WAM动态耦合。该系统应用于西北太平洋114个台风事件，与使用默认调谐参数的WAM预测相比，有效波高（SWH）均方根误差（RMSE）降低了24.6%，结构相似指数（SSIM）提高了26.3%。对墨西哥湾32个不同强度热带气旋的验证显示出很强的泛化能力，RMSE降低了47%，波谱精度提高了30%。这些结果突出了这种混合ai -物理框架的鲁棒性和可扩展性，展示了其在极端天气事件中实时海浪预报的实用价值。

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引用次数: 0

Pre-trained physics-informed neural networks for one-dimensional wave propagation in coastal engineering 海岸工程中一维波浪传播的预训练物理信息神经网络

IF 3.1 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-07-23 DOI: 10.1016/j.ocemod.2025.102601

Yunlong Yang , Feng Luo , Zhipeng Chen , Aifeng Tao , Hongping Zhao , Yongfu Dong , Peng Tian , Jinhai Zheng

Modeling wave propagation over variable coastal topographies remains challenging due to the interplay of nonlinear shallow‑water dynamics and dispersive effects. Here, we introduce a Pre‑Trained Physics‑Informed Neural Network (PT‑PINN) framework that couples a physics‑guided pre‑training phase with rigorous cross‑validation to solve the Saint‑Venant and Boussinesq equations. During pre‑training, the network generates a physics‑informed initial approximation, constructs auxiliary supervised data, and yields optimized parameter seeds, all of which accelerate and stabilize subsequent formal training. Cross‑validation on the pre‑training–derived dataset then guides hyperparameter selection, ensuring an effective balance between physics‑driven and data‑driven loss components.

We demonstrate the PT‑PINN approach across four benchmark scenarios: (1) dam‑break flow in a wet domain, (2) non‑breaking wave propagation on inclined slopes, (3) periodic tidal waves in an inclined open channel, and (4) shoaling waves over a submerged breakwater. In each case, PT‑PINNs faithfully capture both bulk wave evolution and fine‑scale dispersive details. Comparative studies against analytical and finite‑difference solutions reveal that PT‑PINNs match their accuracy while offering enhanced stability in representing high‑frequency microscale features. These results underscore the promise of pre‑trained, physics‑informed networks as a versatile and robust tool for coastal wave modeling in complex bathymetric settings.

由于非线性浅水动力学和色散效应的相互作用，模拟波浪在可变海岸地形上的传播仍然具有挑战性。在这里，我们引入了一个预训练的物理信息神经网络（PT - PINN）框架，该框架将物理指导的预训练阶段与严格的交叉验证相结合，以解决Saint - Venant和Boussinesq方程。在预训练期间，网络生成一个物理信息的初始近似，构建辅助监督数据，并产生优化的参数种子，所有这些都加速和稳定随后的正式训练。对预训练衍生的数据集进行交叉验证，然后指导超参数选择，确保物理驱动和数据驱动的损失成分之间的有效平衡。我们在四个基准情景中演示了PT - PINN方法：(1)湿域的溃坝流，(2)倾斜斜坡上的非溃坝波传播，(3)倾斜明渠中的周期性潮汐波，以及(4)淹没防波堤上的浅滩波。在每种情况下，PT - pin都忠实地捕获了体波演化和精细尺度色散细节。对分析和有限差分解决方案的比较研究表明，PT - pin匹配它们的精度，同时在代表高频微尺度特征方面提供增强的稳定性。这些结果强调了预先训练的物理信息网络作为复杂水深环境中海岸波浪建模的多功能和强大工具的前景。

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引用次数: 0

Shelf-wide circulation impacts the flushing time of coastal bays 大陆架环流影响海岸海湾的冲刷时间

IF 3.1 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-07-23 DOI: 10.1016/j.ocemod.2025.102603

Junwei Hua , Jiabi Du , Kyeong Park

Once exiting an estuary into the shelf, transport and retention of materials are subject to the shelf dynamics, and sometimes the deep ocean dynamics. However, the impact of the deep ocean is rarely considered in previous coastal modeling studies, as coastal models typically have a fine resolution only for the coastal region and the domain rarely extends beyond the shelf (depth <200 m). This study demonstrates the role of deep and shelf ocean circulation on the flushing of estuarine bays. With a cross-scale and well-calibrated ocean model for the northwestern Gulf of Mexico (Coarse Small Model) and another one for the entire Gulf of Mexico (Refined Large Model), we examine the flushing time for Galveston Bay through Lagrangian particle-tracking simulations. Both models have similar results regarding salinity and currents near the coast, but Coarse Small Model persistently overestimates/underestimates the flushing time during winter/summer, respectively, compared to Refined Large Model. Analysis of sea surface height and geostrophic currents suggests that Coarse Small Model’s inability to capture the deep ocean synoptic circulations leads to the overestimations of estuarine materials’ retention on the inner shelf and unrealistic flushing time for coastal bays during winter. By increasing the resolution in the deep Gulf from 10 to 5 km, Refined Small Model produces results similar to Refined Large Model. This study highlights the role of shelf and deep ocean dynamics on exchange between estuarine bays and coastal ocean and emphasizes the importance of resolving the shelf-wide dynamics in models focusing on estuarine and coastal waters.

一旦从河口进入陆架，物质的运输和滞留就会受到陆架动力学的影响，有时也会受到深海动力学的影响。然而，以往的海岸模式研究很少考虑深海的影响，因为海岸模式通常只对沿海区域具有较好的分辨率，而且区域很少延伸到大陆架（深度<；200 m）之外。本研究论证了深海环流和陆架环流对河口湾冲淤的作用。利用墨西哥湾西北部的跨尺度和校准良好的海洋模型（粗小模型）和整个墨西哥湾的另一个模型（精细大模型），我们通过拉格朗日粒子跟踪模拟研究了加尔维斯顿湾的冲刷时间。关于海岸附近的盐度和水流，两种模式都有相似的结果，但与精细大模式相比，粗小模式分别持续高估/低估了冬季/夏季的冲刷时间。对海面高度和地转流的分析表明，由于粗小模式无法捕捉深海天气环流，导致对河口物质在内陆架上的滞留量估计过高，对沿海海湾冬季的冲刷时间估计不现实。通过将深海湾的分辨率从10公里提高到5公里，精细小模式产生的结果与精细大模式相似。本研究强调了陆架和深海动力学在河口湾和沿海海洋交换中的作用，并强调了在以河口和沿海水域为重点的模式中解决陆架范围动力学的重要性。

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引用次数: 0