Ocean Modelling最新文献

Assessing basin scale modelling for projecting storm surge extremes under climate change scenarios in northwest Ireland 评估在爱尔兰西北部气候变化情景下预测极端风暴潮的流域尺度模型

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

Ocean Modelling

Pub Date : 2025-12-03 DOI: 10.1016/j.ocemod.2025.102660

Tasneem Ahmed , Andrea Cucco , Giovanni Quattrocchi , Leo Creedon , Iulia Anton , Michele Bendoni , Stefano Taddei , Carlo Brandini , Salem S Gharbia

This study evaluates the performance of the SHYFEM (System of HydrodYnamic Finite Element Modules) ocean model in simulating storm surges within Donegal Bay (northwest Ireland) for climate projection applications. A high-resolution Basin Scale Model (BSM) configuration of SHYFEM, spanning the North Atlantic is employed in barotropic mode accounting exclusively for atmospheric forcing with no tidal contribution included. To evaluate its accuracy, the BSM is compared against a Limited Area Model (LAM) configuration of SHYFEM implemented at the same study site.

The LAM includes tidal constituents through the downscaling of sea surface height (SSH) from a calibrated deep-water ocean model provided by the Copernicus Marine Environment Monitoring Service (CMEMS). Comparison is performed to quantify the impact of non-linear tide-surge interaction on residual water levels computation.

On average the LAM achieves 3 cm greater accuracy than the BSM in reproducing the time series of residual water levels measured by four tide gauges within the bay. Nevertheless, although both models tend to underestimate the extreme values, the BSM better captures the climatological statistics of storm surge events, closely matching the observed return levels associated with 5, 10, 25, and 50 year return periods.

Further improvements in return level estimates and residual water level error metrics are obtained through iterative calibration of main model parameters, validating the BSM’s effectiveness in simulating storm surges despite the absence of tide-surge interaction.

A Chi-squared significance test applied to tide gauge observations confirms that tide-surge interaction is statistically non-significant within Donegal Bay for surge thresholds at the 99th, 99.95th, and 99.99th percentiles. These findings support the use of BSM, driven exclusively with atmospheric fields (without including tides), for reliable simulation of storm surges and their climatological statistics in this region.

本研究评估了SHYFEM（水动力有限元模块系统）海洋模式在模拟多尼戈尔湾（爱尔兰西北部）风暴潮中的气候预测应用的性能。采用横跨北大西洋的高分辨率盆地尺度模式（BSM）配置，在正压模式下只考虑大气强迫，不包括潮汐贡献。为了评估其准确性，将BSM与在同一研究地点实施的SHYFEM的有限区域模型（LAM）配置进行了比较。通过哥白尼海洋环境监测服务（CMEMS）提供的校准深水海洋模型，通过降低海面高度（SSH）的比例，LAM包括潮汐成分。通过比较来量化非线性潮涌相互作用对剩余水位计算的影响。在重现湾内四个潮汐计所测得的剩余水位时间序列时，LAM的准确度比BSM平均高3厘米。然而，尽管两种模式都倾向于低估极端值，但BSM更好地捕捉了风暴潮事件的气候统计数据，与观测到的与5年、10年、25年和50年的回归期相关的回归水平密切匹配。通过对主要模型参数的迭代校准，进一步改进了回归水位估计和剩余水位误差指标，验证了BSM在没有潮涌相互作用的情况下模拟风暴潮的有效性。应用于验潮仪观测的卡方显著性检验证实，在第99、99.95和99.99百分位的潮涌阈值上，多尼戈尔湾内的潮涌相互作用在统计上不显著。这些发现支持使用仅由大气场（不包括潮汐）驱动的BSM来可靠地模拟该地区的风暴潮及其气候统计。

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引用次数: 0

Evolution of wind-generated shallow water waves in a Benney–Luke equation Benney-Luke方程中风力产生的浅水波浪的演化

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

Ocean Modelling

Pub Date : 2025-12-02 DOI: 10.1016/j.ocemod.2025.102659

Montri Maleewong , Roger Grimshaw

In our recent papers Maleewong and Grimshaw (2024b, 2025), we used the Korteweg–de Vries (KdV) equation and its two-dimensional extension, the Kadomtsev–Petviashvili (KP) equation to describe the evolution of wind-driven water wave packets in shallow water. Both equations were modified to include the effect of wind forcing, modelled using the Miles critical level instability theory. In this paper that is extended to a Benney–Luke (BL) equation, similarly modified for wind forcing. The motivation is that the BL equation is isotropic in the horizontal space variables, unlike the KP model, and noting that the KdV model is one-dimensional. The modified BL equation is studied using wave modulation theory as in our previous work on the forced KdV and KP equations, and with comprehensive numerical simulations. Despite the very different spatial structure the results show that under the right initial conditions and parameter settings, solitary wave trains again emerge.

在我们最近的论文Maleewong和Grimshaw （2024b, 2025）中，我们使用了Korteweg-de Vries （KdV）方程及其二维扩展Kadomtsev-Petviashvili （KP）方程来描述浅水中风力水波包的演化。两个方程都进行了修改，以包括风强迫的影响，使用迈尔斯临界水平不稳定理论建模。本文将其推广为对风强迫进行类似修正的Benney-Luke （BL）方程。动机是BL方程在水平空间变量中是各向同性的，不像KP模型，并且注意到KdV模型是一维的。与之前研究强制KdV和KP方程一样，本文采用波调制理论对修正后的BL方程进行研究，并进行了全面的数值模拟。结果表明，在适当的初始条件和参数设置下，孤波列再次出现。

引用次数: 0

DB-SICNet: A dual-branch model for predicting Arctic sea ice concentration DB-SICNet：预测北极海冰浓度的双分支模式

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

Ocean Modelling

Pub Date : 2025-12-01 DOI: 10.1016/j.ocemod.2025.102658

Ling Tan , Jinlong Xu , Wei Zhang , Wenjia Chen , Jingming Xia

In the context of global climate warming, the changes in Arctic sea ice have garnered significant attention. Traditional models face challenges in predicting sea ice concentration (SIC) due to the complexity and interdependence of meteorological factors, which make it difficult to quantify their impacts on sea ice variability. Temporal dynamics of sea ice concentration changes are underutilized, while the limitations of single-model predictions worsen the issue. To address these challenges, this paper proposes a novel dual-branch Arctic sea ice concentration forecasting method, called DB-SICNet. This method integrates OSI-SAF sea ice data and ERA5 meteorological data, employing a multi-scale feature fusion module to extract key features from the meteorological factors. A dynamic temporal weighting mechanism captures periodic variation patterns by assigning weights to data points over time, and the model combines ConvLSTM and UNet in a dual-branch integrate to improve prediction accuracy. Comprehensive experimental evaluations demonstrate that, compared to popular models such as CMIP6 and IceNet, DB-SICNet provides more accurate forecasts of Arctic sea ice coverage for the upcoming month. The study also employs DeepLIFT attribution analysis to identify the critical role of sea surface temperature in the prediction of SIC. The findings of this research can offer robust support for navigation planning and sea ice-related applications in the Arctic region.

在全球气候变暖的背景下，北极海冰的变化引起了人们的极大关注。由于气象因子的复杂性和相互依赖性，传统模式在预测海冰浓度方面面临挑战，难以量化其对海冰变率的影响。海冰浓度变化的时间动态未得到充分利用，而单一模式预测的局限性使这一问题更加严重。为了解决这些挑战，本文提出了一种新的双分支北极海冰浓度预测方法，称为DB-SICNet。该方法将OSI-SAF海冰数据与ERA5气象数据相结合，采用多尺度特征融合模块从气象要素中提取关键特征。该模型采用动态时间加权机制，通过为数据点分配权重来捕获周期性变化模式，并将ConvLSTM和UNet结合在双分支集成中，以提高预测精度。综合实验评估表明，与CMIP6和IceNet等流行模式相比，DB-SICNet对未来一个月的北极海冰覆盖率提供了更准确的预测。利用DeepLIFT归因分析，确定了海表温度在SIC预测中的关键作用。本研究结果可为北极地区的导航规划和海冰相关应用提供强有力的支持。

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引用次数: 0

Improving Kuroshio forecasts with an eddy-resolving AI prediction system 利用涡流解析人工智能预测系统改进黑潮预测

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

Ocean Modelling

Pub Date : 2025-11-28 DOI: 10.1016/j.ocemod.2025.102657

Junkai Qian , Qiang Wang , Wuhong Guo , Huier Mo , Hui Zhang

The Kuroshio, a powerful western boundary current in the North Pacific, exhibits multi-scale variability that profoundly affects regional weather, climate, marine ecosystems, and fisheries, rendering its accurate prediction indispensable. However, this variability is driven by complex multi-scale physical processes, necessitating high-resolution numerical models that are computationally expensive and often constrained by limited timeliness. In recent years, the emergence of data-driven models has opened new avenues for ocean forecasting, and the global ocean intelligent prediction systems are now approaching or even surpassing traditional numerical models across various metrics. Despite these advances, their performance in the Kuroshio region remains limited. To address this challenge, this study develops an eddy-resolving (1/12°) Kuroshio Intelligent Prediction System (KIPS) based on the Swin Transformer architecture. Specifically designed to capture Kuroshio dynamics, KIPS uses an autoregressive strategy to generate daily forecasts of three-dimensional temperature, salinity, current, and sea surface height, with a lead time of up to 10 days. KIPS achieves higher accuracy compared to existing numerical and AI-based prediction systems, while significantly reducing computational costs. In operational forecasts, KIPS successfully captures several recent eddy shedding and merging events in the southern Kuroshio region of Japan, demonstrating agreement with near-real-time satellite observations. These results underscore the value of integrating prior physical knowledge into region-specific forecast systems to improve fine-scale ocean prediction.

黑潮是北太平洋强大的西边界流，其多尺度变化深刻影响着区域天气、气候、海洋生态系统和渔业，因此对黑潮的准确预测必不可少。然而，这种可变性是由复杂的多尺度物理过程驱动的，需要高分辨率的数值模型，这些模型计算成本很高，而且往往受限于有限的时效性。近年来，数据驱动模式的出现为海洋预报开辟了新的途径，全球海洋智能预报系统在各种指标上正在接近甚至超越传统的数值模式。尽管取得了这些进展，但它们在黑潮地区的表现仍然有限。为了解决这一挑战，本研究开发了一种基于Swin变压器架构的涡流分辨力（1/12°）黑潮智能预测系统（KIPS）。KIPS专为捕捉黑潮动态而设计，使用自回归策略生成三维温度、盐度、洋流和海面高度的每日预报，提前期长达10天。与现有的数值和基于人工智能的预测系统相比，KIPS实现了更高的精度，同时显著降低了计算成本。在业务预报中，KIPS成功捕获了日本黑潮南部地区最近发生的几次涡旋脱落和合并事件，与近实时卫星观测结果一致。这些结果强调了将先验物理知识整合到特定区域的预测系统中以改善精细尺度海洋预测的价值。

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引用次数: 0

Multi-model physics informed neural networks to the shallow water equations for cosine bell advection 多模型物理将神经网络引入余弦钟状平流的浅水方程

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

Ocean Modelling

Pub Date : 2025-11-22 DOI: 10.1016/j.ocemod.2025.102656

Susmita Saha , Satyasaran Changdar , Soumen De

Solving the shallow water equations is essential in science and engineering for understanding and predicting geophysical phenomena such as atmospheric and oceanic flows. Physics-informed machine learning has emerged as a powerful alternative to traditional numerical methods, avoiding the complexities of grid generation and enabling mesh-free solutions to partial differential equations. In this study, we apply a sequential multi-model approach within a time-decomposed framework to solve the shallow water equations on a rotating sphere, in the context of meteorological applications. We employed advanced physics-informed neural networks integrated with deep learning, using diverse network architectures to conduct a detailed analysis of cosine bell advection across multiple orientations on the Earth. The results demonstrate high predictive accuracy, underscoring the method’s transformative potential for geophysical fluid dynamics. We also implemented a finite difference upwind scheme and a fully data-driven deep neural network to supplement the validation process and comparative analysis. Additionally, we perform a sensitivity analysis to examine the influence of physics-informed error terms on the training dynamics of the networks.

在科学和工程中，求解浅水方程对于理解和预测大气和海洋流动等地球物理现象至关重要。物理知识的机器学习已经成为传统数值方法的强大替代方案，避免了网格生成的复杂性，并使偏微分方程的无网格解成为可能。在这项研究中，我们在时间分解框架内应用序列多模型方法来求解气象应用背景下旋转球体上的浅水方程。我们采用了先进的物理信息神经网络与深度学习相结合，使用不同的网络架构对地球上多个方向的余弦钟平流进行了详细分析。结果显示了较高的预测精度，强调了该方法在地球物理流体动力学方面的变革潜力。我们还实现了一个有限差分迎风方案和一个完全数据驱动的深度神经网络，以补充验证过程和比较分析。此外，我们进行了敏感性分析，以检查物理通知误差项对网络训练动态的影响。

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引用次数: 0

Implementation and evaluation of a new parameterization of submesoscale vertical flux in a mesoscale-resolving model in the North Pacific 北太平洋中尺度解析模式中亚中尺度垂直通量新参数化的实现与评价

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

Ocean Modelling

Pub Date : 2025-11-20 DOI: 10.1016/j.ocemod.2025.102655

Zhe Feng , Zhiwei Zhang , Jinchao Zhang , Wenda Zhang , Man Yuan , Zhao Jing , Wei Zhao , Jiwei Tian

Submesoscale processes play a key role in re-stratifying the upper ocean through inducing strong vertical buoyancy flux (VBF). Because the prevailing climate and global ocean models are unable to resolve submesoscale processes, submesoscale VBF needs to be parameterized in models to reduce the associated simulation bias. Recently, Zhang et al. (2023) proposed a new VBF parameterization which simultaneously considers submesoscale baroclinic instability and strain-induced frontogenesis (Zhang23 parameterization hereafter). In this study, we implement the Zhang23 parameterization in a mesoscale-resolving (9-km) configuration of Regional Ocean Modeling System (ROMS) for the North Pacific, and assess its impact by comparing results with observations and a submesoscale-resolving (1-km) simulation. The parameterized VBFs have similar magnitudes and spatial patterns with those derived from the 1-km simulation, demonstrating the effectiveness of Zhang23 parameterization. Additionally, the Zhang23 parameterization yields significantly reduced mixed-layer depth (MLD) and strengthened upper-ocean stratification in winter compared with those in the control run without this parameterization. In the Kuroshio Extension region, the sensitivity run including the Zhang23 parameterization reduces the deep MLD bias by 94 % and yields an upper-ocean stratification in better agreement with a submesoscale-resolving simulation. These results show that the Zhang23 parameterization has a good potential to improve the simulation of upper-ocean processes in mesoscale-resolving models.

亚中尺度过程通过诱导强垂直浮力通量（VBF）在上层海洋重新分层中起关键作用。由于主流气候和全球海洋模式无法解析亚中尺度过程，因此需要在模式中参数化亚中尺度VBF以减少相关的模拟偏差。最近，Zhang等（2023）提出了一种同时考虑亚中尺度斜压不稳定和应变诱导锋生的VBF参数化方法（Zhang23参数化后见下文）。在本研究中，我们在北太平洋区域海洋模拟系统（ROMS）的9 km中尺度分辨率配置中实现了Zhang23参数化，并通过与观测结果和亚中尺度分辨率（1 km）模拟的比较来评估其影响。参数化的vfs与1 km模拟的vfs具有相似的大小和空间格局，证明了Zhang23参数化的有效性。此外，与未进行参数化的对照组相比，Zhang23参数化显著降低了冬季混合层深度（MLD），强化了上层海洋分层。在黑潮扩展区，包括Zhang23参数化在内的敏感性运行将深层MLD偏差降低了94%，并产生了与亚中尺度分辨模拟更一致的上层海洋分层。这些结果表明，Zhang23参数化在中尺度解析模式中具有改善上层海洋过程模拟的良好潜力。

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引用次数: 0

Significant wave height prediction using a novel hybrid model of group method of data handling 用一种新的混合模型分组数据处理方法进行显著波高预测

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

Ocean Modelling

Pub Date : 2025-11-15 DOI: 10.1016/j.ocemod.2025.102654

Naiwen Mei , Zhonglian Jiang , Bingchang Weng , Zhen Yu , Shijun Chen

Significant wave height (WVHT) has been identified as a key influencing factor in the research fields of coastal engineering, naval architecture and ocean engineering, maritime management, and other related disciplines. The wave height sequences are always featured as nonlinear and non-stationary, thus seriously concerned in ship voyage planning and route selection. The refined WVHT prediction will support the ship speed optimization and energy efficiency management. A novel hybrid model based on Variational Mode Decomposition (VMD) and Group Method of Data Handling (GMDH) has been proposed. Intrinsic mode functions (IMFs) of WVHT sequence were obtained by VMD, which were subsequently adopted as model inputs of GMDH. The contribution of various input variables was explored through sensitivity analysis. The hybrid VMD-GMDH model was validated through field dataset of National Data Buoy Center, and evaluated with different metrics. Its performance was further compared with four other models, namely GMDH, EMD-GMDH, GRU and VMD-LSTM. The results highlight the importance of data preprocessing through VMD and the prediction accuracy is greatly improved. Specifically, the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) decrease by 29.1%, 15.8%, 18.6% and 15.8%, respectively. The correlation coefficient (R²) is improved by 3.32%. The novel hybrid VMD-GMDH model provides an effective tool for WVHT prediction and would support the intelligent oceanographic studies.

有效波高（Significant wave height， WVHT）在海岸工程、船舶与海洋工程、海事管理等相关学科的研究中已被确定为一个关键的影响因素。波浪高度序列具有非线性和非平稳性，在船舶航次规划和航路选择中具有重要意义。精细化的WVHT预测将支持航速优化和能效管理。提出了一种基于变分模态分解（VMD）和数据处理成组方法（GMDH）的混合模型。通过VMD获得WVHT序列的内禀模态函数（IMFs），并将其作为GMDH的模型输入。通过敏感性分析探讨了各输入变量的贡献。通过国家数据浮标中心的野外数据集对VMD-GMDH混合模型进行了验证，并用不同的指标进行了评价。进一步与GMDH、EMD-GMDH、GRU、VMD-LSTM四种模型进行性能比较。结果表明，通过VMD对数据进行预处理的重要性，预测精度得到了很大的提高。其中，均方误差（MSE）、均方根误差（RMSE）、平均绝对百分比误差（MAPE）和平均绝对误差（MAE）分别下降了29.1%、15.8%、18.6%和15.8%。相关系数（R2）提高3.32%。该混合模式为WVHT预报提供了有效的工具，为智能海洋研究提供了支持。

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引用次数: 0

Assessing the effects of sea level rise on ocean waves and surge events along the victorian coast 评估海平面上升对维多利亚海岸的海浪和浪涌事件的影响

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

Ocean Modelling

Pub Date : 2025-11-14 DOI: 10.1016/j.ocemod.2025.102653

Rui Li , Huy Quang Tran , Jak McCarroll , Alexander V. Babanin

This study investigates nonlinear surges and extreme wind-wave patterns off the coast of Victoria by simulating sea level rise (SLR) scenarios of 0.5, 0.8, 1.0 and 1.4 meters alongside a 31-year hindcast (1990–2020) using the validated SCHISM-WWMIII coupled wave-circulation model. Model simulations were compared with observational data, confirming the accuracy of the results. Our findings indicate that sea levels along the Victorian coast have been rising at a rate of 1.46 × 10^⁻² cm/year, while wave heights in the Southern Ocean have also increased over time. However, the rate of wave height increase is lower along the Victorian coast compared to the Southern Ocean. Due to island blocking, mean wave heights in Bass Strait remain lower than those in the Southern Ocean, yet extreme water levels in the strait exceed those in the open ocean. The impact of SLR is most pronounced in the waters south of Tasmania, where maximum elevations exceed 1.2 meters under the 1.0-meter SLR scenario. SLR contributes to higher mean water levels and increased wave heights off the coast of Victoria, underscoring the complex interactions between rising sea levels and coastal wave dynamics. Wave direction and peak period were also examined, but their changes under SLR scenarios were found to be minimal. These findings highlight the importance of incorporating both SLR and wave dynamics into coastal hazard assessments to better understand future risks.

本研究利用经验证的schistic - wwmiii波浪环流耦合模式，模拟了1990-2020年31年的海平面上升（SLR）情景（0.5、0.8、1.0和1.4米），研究了维多利亚海岸的非线性涌浪和极端风浪模式。将模型模拟结果与观测数据进行了比较，证实了结果的准确性。我们的研究结果表明，维多利亚海岸的海平面一直在以每年1.46 × 10⁻2厘米的速度上升，而南大洋的海浪高度也随着时间的推移而增加。然而，与南大洋相比，维多利亚海岸的浪高增长率较低。由于岛屿阻塞，巴斯海峡的平均波高仍然低于南大洋，但海峡的极端水位超过了公海。单反的影响在塔斯马尼亚岛以南的水域最为明显，在1.0米单反的情况下，那里的最大海拔超过1.2米。SLR导致了维多利亚海岸平均水位的上升和浪高的增加，强调了海平面上升和海岸波浪动力学之间复杂的相互作用。波浪方向和峰值周期也进行了研究，但发现它们在单反情景下的变化很小。这些发现强调了将SLR和波浪动力学结合到海岸危害评估中的重要性，以更好地了解未来的风险。

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引用次数: 0

Equations for modelling contaminant impacts throughout a marine ecosystem 模拟整个海洋生态系统中污染物影响的方程

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

Ocean Modelling

Pub Date : 2025-11-07 DOI: 10.1016/j.ocemod.2025.102646

Raisha Lovindeer , Elizabeth A. Fulton , Susan E. Allen , Javier Porobic , Douglas J. Latornell , Hem Nalini Morzaria-Luna , Alaia Morell

Biological risk assessment modelling for oil spills using whole-of-ecosystem models has the benefit of assessing species-specific toxicology and the chronic impact of oil spills by layering these impacts on top of the already-built ecosystem within the model. In deterministic models this approach requires tracking contaminants as they move throughout the biology of the ecosystem, from uptake to loss. Here we consolidate, modify, and add to existing equations to produce a synergistic set that can be used to define the impact of contaminants on biological groups throughout the food web. We demonstrate how these equations work, individually as well as in tandem, for oil-based contaminants by implementing them in a three-dimensional marine ecosystem model. We assess the sensitivity of parameters within these equations, showing the impact on the model outcome. Although we focus on oil-based contaminants in our examples, the equations presented can be applied to any contaminants in the aquatic or marine environment.

利用整个生态系统模型对石油泄漏进行生物风险评估建模，通过将这些影响叠加在模型中已经建立的生态系统之上，可以评估特定物种的毒理学和石油泄漏的慢性影响。在确定性模型中，这种方法需要跟踪污染物在生态系统中从吸收到流失的整个生物过程。在这里，我们整合、修改并添加到现有的方程中，以产生一个协同集，可用于定义污染物对整个食物网生物群体的影响。通过在三维海洋生态系统模型中实现这些方程，我们演示了这些方程如何单独或串联地对油基污染物起作用。我们评估了这些方程中参数的敏感性，显示了对模型结果的影响。虽然我们在例子中关注的是油基污染物，但所提出的方程可以应用于水生或海洋环境中的任何污染物。

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引用次数: 0

Tsunami data assimilation and forecast in the Kii Channel using high-frequency radar: Bathymetry effects on the propagation of measurement errors 利用高频雷达同化和预报Kii海峡的海啸资料：测深对测量误差传播的影响

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

Ocean Modelling

Pub Date : 2025-11-05 DOI: 10.1016/j.ocemod.2025.102651

Muhammad Irham Sahana , Ryotaro Fuji , Hirofumi Hinata

High-frequency (HF) radar has become a promising tool for tsunami forecasting based on assimilation of surface current data. However, the accuracy of HF radar-derived velocity vectors is affected by multiple error sources, including sea surface conditions, ionospheric disturbances, human activities, and inherent measurement errors associated with the beam-crossing angles. If not properly accounted for, these errors can degrade the tsunami forecast accuracy. This study explored the influence of realistic bathymetry on the propagation and amplification of noise-induced (measurement error-induced) tsunamis. These tsunamis caused localized variations in the assimilated and forecasted tsunami heights, particularly through refraction and shoaling. Measurement error assimilation with energy ray tracing has significant implications for tsunami early warning systems: it helps identify regions likely to undergo noise-induced tsunamis originating from radar coverage. By incorporating beam-angle-dependent measurement errors into the optimal interpolation method and considering actual bathymetry, we achieved stable and accurate tsunami forecasts for the Mw 9.0 Nankai Trough earthquake scenario. The method predicted maximum coastal tsunami heights 23–78 min before they arrived at Osaka Bay, with 92 % forecast accuracy and 0.8 % standard deviation across 15 experiments. In addition, careful tuning of the optimal characteristic length (L) in relation to tsunami velocities and observation errors was found to be crucial for balancing the suppression of noise-induced tsunamis and retention of tsunami signals. Both excessively small and large values of L degraded the performance, underscoring the importance of dynamic tuning for operational systems. Future research should focus on optimizing the assimilation parameters by monitoring the measurement error status.

高频（HF）雷达已成为一种很有前途的基于表面流资料同化的海啸预报工具。然而，高频雷达速度矢量的精度受到多种误差源的影响，包括海面条件、电离层干扰、人类活动以及与波束穿越角相关的固有测量误差。如果不加以适当的考虑，这些误差会降低海啸预报的准确性。本研究探讨了现实测深对噪声（测量误差）海啸传播和放大的影响。这些海啸引起了同化和预测海啸高度的局部变化，特别是通过折射和浅滩作用。利用能量射线追踪的测量误差同化对海啸预警系统具有重要意义：它有助于确定雷达覆盖范围内可能发生由噪声引起的海啸的区域。通过将波束角相关测量误差纳入最优插值方法，并结合实际测深，实现了Mw 9.0南开海槽地震情景下稳定、准确的海啸预报。该方法在海啸到达大阪湾前23-78分钟预测了最大海岸海啸高度，15次实验的预测精度为92%，标准偏差为0.8%。此外，仔细调整与海啸速度和观测误差相关的最佳特征长度(L)对于平衡噪声引起的海啸的抑制和海啸信号的保留至关重要。L的值过小或过大都会降低性能，这强调了动态调优对操作系统的重要性。未来的研究应着眼于通过监测测量误差状态来优化同化参数。

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引用次数: 0