Ocean Modelling最新文献_第6页

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

Machine learning-based intelligent parameterization of source functions in numerical wave model 数值波模型中基于机器学习的源函数智能参数化

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

Ocean Modelling

Pub Date : 2025-07-22 DOI: 10.1016/j.ocemod.2025.102602

Fuhua Huang , Zeyu Wang , Longyu Jiang , Feng Hua

In recent years, although the application of machine learning in parameterizing complex marine physical processes has gradually become widespread, most of the existing studies rely on statistically correlated parameter selection methods for neural network construction and lack physical support. This study proposed a physics-guided neural network parameterization method combining physical feature selection and data-driven modeling. By integrating source function parameterization equations (wind input, wave breaking dissipation, wave-wave nonlinear interactions) from the MASNUM-WAM physical framework into the feature engineering of a backpropagation neural network (BPNN), a physically guided parameterization model was developed. The experiments show that the three major source functions exhibit excellent prediction performance (R²>0.95, RMSE<0.09, BIAS between -0.02 and 0.05), with stable results across multi-test points. Then, a new directional wave spectra prediction model was developed using the prediction results. Directional wave spectra predictions show strong consistency with MASNUM-WAM (COR>0.92, RMSE<0.09 m²s, |BIAS|≤0.03 m²s). Spectral integration parameters achieve high accuracy: significant wave height (RMSE≤0.477 m), mean wave direction (RMSE≤1.010°), and mean wave period (0.203 s≤RMSE≤0.247 s). Feature importance analysis reveals that wave breaking dissipation contributes most substantially to directional wave spectra prediction accuracy, while initial conditions, wave-wave nonlinear interaction, wind field components exhibit variable influence, and wind input term maintains a minor but consistent role. This physics-guided approach retains data-driven advantages while enhancing model reliability and computational efficiency, offering a new pathway for parametric research in ocean wave simulation.

近年来，虽然机器学习在参数化复杂海洋物理过程中的应用逐渐广泛，但现有的研究大多依赖于统计相关的参数选择方法来构建神经网络，缺乏物理支持。提出了一种结合物理特征选择和数据驱动建模的物理导向神经网络参数化方法。通过将MASNUM-WAM物理框架中的源函数参数化方程（风输入、破波耗散、波-波非线性相互作用）整合到反向传播神经网络（BPNN）的特征工程中，建立了物理导向的参数化模型。实验表明，三个主要的源函数具有良好的预测性能（R²>0.95, RMSE<0.09， BIAS在-0.02 ~ 0.05之间），并且在多个测试点上结果稳定。然后，利用预测结果建立了一种新的定向波谱预测模型。方向波谱预测结果与masnu - wam具有较强的一致性（COR>0.92, RMSE<0.09 m²s, |BIAS|≤0.03 m²s）。光谱积分参数精度较高：有效波高（RMSE≤0.477 m）、平均波向（RMSE≤1.010°）、平均波周期（0.203 s≤RMSE≤0.247 s）。特征重要性分析表明，破波耗散对定向波谱预测精度的影响最大，初始条件、波波非线性相互作用、风场分量的影响是变化的，风输入项的影响较小，但作用一致。该方法在保持数据驱动优势的同时，提高了模型可靠性和计算效率，为海浪模拟参数化研究提供了新的途径。

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引用次数: 0

Evaluation of a 3D unstructured grid model for the New York-New Jersey Harbor under different forcing sources 不同强迫源下纽约-新泽西港三维非结构网格模型评价

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

Ocean Modelling

Pub Date : 2025-07-17 DOI: 10.1016/j.ocemod.2025.102598

Kyungmin Park , Y. Joseph Zhang , Emanuele Di Lorenzo , Gregory Seroka , Ayumi Fujisaki-Manome , Shachak Pe'eri , Saeed Moghimi , John G.W. Kelley

This paper presents an in-depth evaluation of a 3D unstructured grid model under various forcing sources, with a focus on the New York-New Jersey (NY-NJ) harbor. The model is first calibrated and evaluated through control runs, ensuring it accurately captures essential processes around the NY/NJ harbor. The sensitivity experiments highlight the significant roles and contributions of different forcing sources in coastal ocean conditions such as total water level, currents, salinity, and water temperature. Different tidal forcings, including FES2014, TPXO9 v1, and TPXO9 v5, show significant effects on tidal components, total water levels, currents, and water temperature, with minimal impact on salinity. Surface forcings from the HRRR, ERA5, and GFS demonstrate variable influences on water temperature predictions, while total water level, currents, and salinity are less sensitive to the different atmospheric forcing sources. Different open ocean conditions from CMEMS, HYCOM, and GRTOFS exhibited minor impacts on hydrodynamic variables in the inland rivers and estuaries but noticeably affected ocean surface currents and vertical structures of water temperature on the continental shelf. Different river discharges from USGS and NWM show high sensitivities of salinities and upstream water levels while shelf-scale ocean currents and vertical structures of water temperatures are similar across the different river discharges. The findings emphasize the necessity of selecting optimal forcing sources to minimize uncertainties and enhance predictive capabilities, supporting better decision-making in coastal management and hazard mitigation.

本文以纽约-新泽西（NY-NJ）港为例，对不同强迫源下的三维非结构化网格模型进行了深入评估。该模型首先通过控制运行进行校准和评估，确保它准确捕获NY/NJ港口周围的基本过程。敏感性实验强调了不同强迫源在总水位、海流、盐度和水温等沿海海洋条件下的重要作用和贡献。FES2014、TPXO9 v1和TPXO9 v5对潮分量、总水位、水流和水温的影响显著，对盐度的影响最小。来自HRRR、ERA5和GFS的地表强迫对水温预测的影响是可变的，而总水位、海流和盐度对不同大气强迫源的敏感性较低。CMEMS、HYCOM和GRTOFS的不同开放海洋条件对内陆河和河口水动力变量的影响较小，但对海流和大陆架水温垂直结构的影响较大。USGS和NWM的不同河流流量对盐度和上游水位具有很高的敏感性，而不同河流流量的大陆架尺度洋流和水温垂直结构相似。研究结果强调了选择最佳强迫源的必要性，以尽量减少不确定性并增强预测能力，从而支持在沿海管理和减灾方面做出更好的决策。

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引用次数: 0

Revisiting tidal rectification by bottom topography 从底部地形再看潮汐整流

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

Ocean Modelling

Pub Date : 2025-07-15 DOI: 10.1016/j.ocemod.2025.102587

Logueminda Sabaga , Yves Morel , Nadia Ayoub , Patrick Marsaleix , Hoavo Hova , Alexis Chaigneau

Tidal rectification plays a key role in controlling mean transport in coastal areas and coast-basin material exchange. To calculate mean flows, conventional approaches require high-resolution basin-scale numerical simulations which demands substantial computational resources. This study revisits tidal rectification governed by topographic variation and bottom friction, and proposes a new analytical solution.

The first step is to derive solutions in the simplest possible configuration. We thus revisit solutions in one-dimensional (1D) configurations, using a Lagrangian approach from which Eulerian results are derived. Exact solutions are provided for the frictionless case and new approximate solutions are developed for a more realistic quadratic bottom friction.

We then analyze the influence of viscosity on solutions from numerical models. We find that the latter has moderate influence when quadratic bottom friction is considered. However, when the steady rectified current extends over regions deeper than a critical depth, viscosity can lead to spurious effects and alter the accuracy of the numerical results. We show the critical depth can be expressed as a function of friction coefficient, tidal flux and topography variation length-scale.

We finally extend the analytical solutions derived for the 1D case to the two-dimensional (2D) case. The 2D solutions are compared to results from an ocean general circulation model solving the full barotropic equations in an academic configuration with a complex topography and a quadratic bottom friction. Comparison between analytical solutions and numerical simulations shows good agreement for both the magnitude and direction of the steady rectified tidal current. Sensitivity tests to bottom friction and tide amplitude show that the steady rectified current is parallel to the isobaths and independent of the magnitude of the bottom friction coefficient at first order.

潮汐整流在控制沿海地区平均输运和海岸-盆地物质交换中起着关键作用。为了计算平均流量，传统的方法需要高分辨率的流域尺度数值模拟，这需要大量的计算资源。本文重新研究了地形变化和海底摩擦对潮汐整流的影响，并提出了一种新的解析解。第一步是在尽可能简单的配置中推导解。因此，我们重新审视一维（1D）构型的解决方案，使用拉格朗日方法，从欧拉结果推导。给出了无摩擦情况下的精确解，并给出了更为实际的二次底摩擦情况下的近似解。然后通过数值模型分析了粘度对溶液的影响。当考虑二次底摩擦时，后者的影响较小。然而，当稳定整流电流延伸到超过临界深度的区域时，粘度会导致虚假效应并改变数值结果的准确性。结果表明，临界深度可以表示为摩擦系数、潮汐通量和地形变化长度尺度的函数。最后，我们将一维情况下的解析解扩展到二维（2D）情况。将二维解与海洋环流模型在复杂地形和二次底摩擦条件下求解全正压方程的结果进行了比较。解析解与数值模拟结果的比较表明，稳态整流潮流的大小和方向符合较好。对底摩擦和潮汐幅值的敏感性试验表明，稳定整流电流平行于等深线，与一阶底摩擦系数的大小无关。

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