Ocean Modelling最新文献_第9页

Parameterization of surface roller evolution in wave-induced current modeling 波浪感应电流建模中表面滚子演化的参数化

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-21 DOI: 10.1016/j.ocemod.2025.102522

Chao Ji , Qi Jiang , Dianguang Ma , Yuefeng Wu , Guoquan Ran , Xianwei Kong , Qinghe Zhang

{"title":"Parameterization of surface roller evolution in wave-induced current modeling","authors":"Chao Ji , Qi Jiang , Dianguang Ma , Yuefeng Wu , Guoquan Ran , Xianwei Kong , Qinghe Zhang","doi":"10.1016/j.ocemod.2025.102522","DOIUrl":"10.1016/j.ocemod.2025.102522","url":null,"abstract":"<div><div>Surface rollers, which are onshore-traveling bores of broken waves, store dissipated wave energy and delay the transfer of energy to the mean flow. Traditional roller evolution models typically rely on two key parameters: the roller slope and the energy transfer fraction, both of which are often treated as empirical constants. However, this approach can result in unrealistic or inaccurate simulations of wave-induced currents.</div><div>In this study, we propose parameterizations for the roller slope and energy transfer fraction in the roller evolution equation for wave-induced current modeling. Three roller slope parameterization methods were compared, and on the basis of their performance in simulating wave-induced currents under various conditions, one method was selected and modified to ensure both physical consistency and computational flexibility. Building on this framework, we further refined the energy transfer fraction by identifying optimal values for different locations. These values and local wave and bathymetric parameters were subsequently used to perform least-squares fitting, yielding an effective parameterization of the energy transfer fraction.</div><div>Model evaluations demonstrate that our roller slope and energy transfer fraction parameterizations provide a robust theoretical foundation for wave-induced current modeling, significantly enhancing its accuracy and applicability, compared with previous methods.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102522"},"PeriodicalIF":3.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulating ice–wave interactions in the Laurentian Great Lakes using a fully coupled hydrodynamic–ice–wave model 利用完全耦合的水动力-冰波模型模拟劳伦森五大湖的冰波相互作用

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-21 DOI: 10.1016/j.ocemod.2025.102513

M. Javad Javaherian , David Cannon , Jia Wang , Ayumi Fujisaki-Manome , Peng Bai , Lei Zuo

{"title":"Simulating ice–wave interactions in the Laurentian Great Lakes using a fully coupled hydrodynamic–ice–wave model","authors":"M. Javad Javaherian , David Cannon , Jia Wang , Ayumi Fujisaki-Manome , Peng Bai , Lei Zuo","doi":"10.1016/j.ocemod.2025.102513","DOIUrl":"10.1016/j.ocemod.2025.102513","url":null,"abstract":"<div><div>Hydrodynamic modeling in cold climate regions necessitates more sophisticated approaches that accurately simulate ice–wave interactions. Traditional models often overlook the complex coupling mechanisms between ice and ocean waves, especially the two-way processes where ice attenuates wave energy and waves break ice floes. This oversight can also intensify modeling challenges in coastal areas, including large lakes, where ice–wave interactions influence storm surges, high waves, and coastal erosion. To address this gap, this paper introduces an enhanced modeling approach that integrates both ice-induced wave attenuation and wave-induced ice breakage. To implement these processes, the Finite-Volume Community Ocean Model (FVCOM) is coupled with an unstructured-grid wave model (SWAN) and the unstructured-grid version of the Los Alamos Sea Ice Model (UG-CICE) to form the FVCOM–SWAVE–UG-CICE framework. Using this fully coupled model, simulations were conducted for the Great Lakes. Results of the modeled ice concentration, ice thickness, and significant wave heights were reported and validated against observational data from the U.S. National Ice Center (NIC) and in-situ under-ice measurements. To further study the coupling effects, results of the proposed model were also compared with those from no coupling and one-way coupling (focusing only on ice-induced wave attenuation) models. Comparative analyses demonstrated significant improvements in the predicted ice concentration with the proposed fully coupled model. These findings reveal the importance of incorporating ice–wave interactions in accurately predicting ice cover dynamics in freshwater systems.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"195 ","pages":"Article 102513"},"PeriodicalIF":3.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of offshore wind farm monopiles on hydrodynamics interacting with wind-driven waves 海上风电场单桩与风浪相互作用对水动力的影响

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-19 DOI: 10.1016/j.ocemod.2025.102521

Seyed Taleb Hosseini , Johannes Pein , Joanna Staneva , Y. Joseph Zhang , Emil Stanev

{"title":"Impact of offshore wind farm monopiles on hydrodynamics interacting with wind-driven waves","authors":"Seyed Taleb Hosseini , Johannes Pein , Joanna Staneva , Y. Joseph Zhang , Emil Stanev","doi":"10.1016/j.ocemod.2025.102521","DOIUrl":"10.1016/j.ocemod.2025.102521","url":null,"abstract":"<div><div>This paper investigates the local and regional impact of offshore wind farm (OWF) foundations on hydrodynamics in interaction with wind-induced waves at the Meerwind-OWF site (German Bight, North Sea) on tidal and monthly time scales. For this purpose, a 3D high-resolution coupled circulation-wave model based on unstructured grids is employed, which enables an effective transition in resolution from ∼2 km in marine open boundaries to ∼2 m near the foundations. The OWF monopiles induce different local and regional changes of the monthly mean velocity at mid-depth: a decrease of ∼5 % near the piles and an increase of ∼1 % in a wider region surrounding the OWF. The latter can be attributed to the relevant regional reduction in water density of ∼0.02 %. Consequently, the monthly potential energy anomaly increases by ∼5 % outside the OWF, while it decreases by 40 % inside it. The monopiles reduce the monthly significant wave height (Hs) from ∼5 % within the OWF to <1 % over distances of ∼20 km. The prevailing westerly waves can affect the tidal asymmetry, particularly on the eastern side of the piles. This results in an asymmetry in the intensity of turbulent wakes on either side of the piles, in both monthly and tidal timescales. However, wave intensification can disrupt the periodic tidal pattern of the wake. An extreme event with Hs>4 m creates a peak wake during the slack water that is higher than those at times of maximum tidal currents during spring tides.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"195 ","pages":"Article 102521"},"PeriodicalIF":3.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural emulator based on physical fields for accelerating the simulation of surface chlorophyll in an Earth System Model 基于物理场的加速模拟地球系统模型中地表叶绿素的神经仿真器

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-12 DOI: 10.1016/j.ocemod.2024.102491

Bizhi Wu , Shiyao Zheng , Shasha Li, Shanlin Wang

{"title":"Neural emulator based on physical fields for accelerating the simulation of surface chlorophyll in an Earth System Model","authors":"Bizhi Wu , Shiyao Zheng , Shasha Li, Shanlin Wang","doi":"10.1016/j.ocemod.2024.102491","DOIUrl":"10.1016/j.ocemod.2024.102491","url":null,"abstract":"<div><div>Simulating the ocean biogeochemical module (BGC-enabled) in the Community Earth System Model (CESM) is computationally expensive, often requiring significantly more resources than the physical climate component. In this study, we propose an alternative approach to generate biogeochemical data using a neural network emulator, BGC-UNet, which predicts ocean surface chlorophyll concentrations based on physical fields from CESM, such as solar short-wave heat flux (SHF-QSW), potential temperature (TEMP), and zonal and meridional velocity (UVEL, VVEL). BGC-UNet is designed as a UNet-like architecture and employs a patch-based methodology with dilated sampling to efficiently reconstruct biogeochemical data from physical inputs. This framework potentially enables high-resolution chlorophyll predictions without running full BGC-enabled simulations. Our evaluation demonstrates that BGC-UNet’s outputs closely align with CESM’s simulated surface chlorophyll, supported by both quantitative metrics and visual analysis. Additionally, the emulator achieves a simulation speed approximately 248 times faster than traditional BGC-enabled CESM simulations. Although the current focus is on surface chlorophyll, the model shows potential for future extension to other biogeochemical variables. By leveraging only 40 years of simulated data for training, BGC-UNet replicates the trends observed in CESM, making it a promising tool for accelerating Earth system modeling.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"195 ","pages":"Article 102491"},"PeriodicalIF":3.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid model based on chaos particle swarm optimization for significant wave height prediction 基于混沌粒子群优化的混合模型有效波高预测

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-11 DOI: 10.1016/j.ocemod.2025.102511

Can Yang , Qingchen Kong , Zuohang Su , Hailong Chen , Lars Johanning

{"title":"A hybrid model based on chaos particle swarm optimization for significant wave height prediction","authors":"Can Yang , Qingchen Kong , Zuohang Su , Hailong Chen , Lars Johanning","doi":"10.1016/j.ocemod.2025.102511","DOIUrl":"10.1016/j.ocemod.2025.102511","url":null,"abstract":"<div><div>Short-term prediction of significant wave height (SWH) has crucial impacts on operation safety of offshore structures and marine navigations. However, conventional intelligent models have limitations in predicting non-linear situations. This paper introduces a hybrid algorithm combining chaos particle swarm optimization (CPSO) with a support vector regression (SVR) model to enhance the generalization and nonlinear handling capabilities for SWH prediction. Additionally, Principal Component Analysis (PCA) is incorporated to reduce information redundancy. To validate the proposed model's predictive performance, several alternatives are tested, including the single SVR model, PCA-SVR, and PCA-GA (Genetic Algorithm)-SVR models. Additionally, the PCA-GWO (Grey Wolf Optimizer)-SVR and PCA-CPSO-SVR models are compared to assess the effects of GWO and CPSO techniques. Significant improvements were observed when comparing CPSO-SVR with other algorithms. Prediction efficiency was evaluated using mean absolute error (MAE), root mean square error (RMSE), and the correlation coefficient (R). Across different test set lengths, the PCA-CPSO-SVR model reduced RMSE by 54.12 % to 74.88 % compared to the benchmark. These results demonstrate the hybrid PCA-CPSO-SVR model's strong generalization ability and superior predictive capacity for non-stationary waves.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"195 ","pages":"Article 102511"},"PeriodicalIF":3.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correcting physics-based global tide and storm water level forecasts with the temporal fusion transformer 用时间融合变压器校正基于物理的全球潮汐和暴雨水位预报

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-11 DOI: 10.1016/j.ocemod.2025.102509

A.R. Cerrone , L.G. Westerink , G. Ling , C.P. Blakely , D. Wirasaet , C. Dawson , J.J. Westerink

{"title":"Correcting physics-based global tide and storm water level forecasts with the temporal fusion transformer","authors":"A.R. Cerrone , L.G. Westerink , G. Ling , C.P. Blakely , D. Wirasaet , C. Dawson , J.J. Westerink","doi":"10.1016/j.ocemod.2025.102509","DOIUrl":"10.1016/j.ocemod.2025.102509","url":null,"abstract":"<div><div>Global and coastal ocean surface water elevation prediction skill has advanced considerably with improved algorithms, more refined discretizations, and high-performance parallel computing. Model skill is tied to mesh resolution, the accuracy of specified bathymetry/topography, dissipation parameterizations, air-sea drag formulations, and the fidelity of forcing functions. Wind forcing skill can be particularly prone to errors, especially at the land-ocean interface. The resulting biases and errors can be addressed holistically with a machine-learning (ML) approach. Herein, we weakly couple the Temporal Fusion Transformer to the National Oceanic and Atmospheric Administration’s (NOAA) Storm and Tide Operational Forecast System (STOFS-2D-Global) to improve its forecasting skill throughout a 7-day horizon. We demonstrate the transformer’s ability to enrich the hydrodynamic model’s output at 228 observed water level stations operated by NOAA’s National Ocean Service. We conclude that the transformer is a rapid way to correct STOFS-2D-Global forecasted water levels provided that sufficient covariates are supplied. For stations in wind-dominant areas, we demonstrate that including past and future wind-speed covariates makes for a more skillful forecast. In general, while the transformer renders consistent corrections at both tidally and wind-dominant stations, it does so most aggressively at tidally-dominant stations. We show notable improvements in Alaska and the Atlantic and Pacific seaboards of the United States. We evaluate several transformers instantiated with different hyperparameters, covariates, and training data to provide guidance on how to enhance performance.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"195 ","pages":"Article 102509"},"PeriodicalIF":3.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-scale prediction for the Laurentian Great Lakes 劳伦森五大湖的跨尺度预报

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-10 DOI: 10.1016/j.ocemod.2025.102512

Y. Joseph Zhang , Joshua Anderson , Chin H. Wu , Dmitry Beletsky , Yuli Liu , Wei Huang , Eric J. Anderson , Saeed Moghimi , Edward Myers

{"title":"Cross-scale prediction for the Laurentian Great Lakes","authors":"Y. Joseph Zhang , Joshua Anderson , Chin H. Wu , Dmitry Beletsky , Yuli Liu , Wei Huang , Eric J. Anderson , Saeed Moghimi , Edward Myers","doi":"10.1016/j.ocemod.2025.102512","DOIUrl":"10.1016/j.ocemod.2025.102512","url":null,"abstract":"<div><div>In this paper, for the first time, all five Great Lakes are simulated using a 3D baroclinic model using a single, seamless unstructured mesh without nesting, including adjacent flood plains and watershed inflows to better connect the hydrodynamic model to the hydrologic model. The hydraulic controls at Sault St Marie and Niagara Falls are simulated using an internal flow boundary approach with the observed flow. The model is shown to exhibit good skills for total water level (TWL) and temperature, with RMSE of 9.5 cm for TWL and ∼1.6 °C for surface temperature and temperature profiles from a 60–day simulation. Sensitivity results reveal the importance of hydrologic forcing even for this short-term simulation. Results from a 210-day simulation indicate that the model is capable of capturing major lake-wide circulation patterns discussed in previous studies and providing further details in those patterns. The new model can potentially serve as a base to unify Great Lakes modeling while simultaneously providing flexibility for site specific studies in any areas of interest.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102512"},"PeriodicalIF":3.1,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating appropriate artificial intelligence approaches to reliably predict coastal wave overtopping and identify process contributions 研究适当的人工智能方法来可靠地预测海岸波浪过顶并确定过程贡献

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-06 DOI: 10.1016/j.ocemod.2025.102510

Michael McGlade , Nieves G. Valiente , Jennifer Brown , Christopher Stokes , Timothy Poate

{"title":"Investigating appropriate artificial intelligence approaches to reliably predict coastal wave overtopping and identify process contributions","authors":"Michael McGlade , Nieves G. Valiente , Jennifer Brown , Christopher Stokes , Timothy Poate","doi":"10.1016/j.ocemod.2025.102510","DOIUrl":"10.1016/j.ocemod.2025.102510","url":null,"abstract":"<div><div>Predicting coastal wave overtopping is a significant challenge, exacerbated by climate change, increasing the frequency of severe flooding and rising sea levels. Digital twin technologies, which utilise artificial intelligence to mimic coastal processes and dynamics, may offer new opportunities to predict coastal wave overtopping and flooding reliably and computationally efficiently. This study investigates the effectiveness of training various artificial intelligence models using wave buoy, meteorological, and recorded coastal wave overtopping observations to predict the occurrence and frequency of overtopping at 10-minute intervals. These models have the potential for future large-scale global applications in estimating wave overtopping and flood forecasting, particularly in response to climate warming. The model types selected include machine-learning random forests, extreme gradient boosting, support vector machines, and deep-learning neural networks. These models were trained and tested using recorded observational overtopping events, to estimate wave overtopping and flood forecasting in Dawlish and Penzance (Southwest England). The random forests performed exceptionally well by accurately and precisely estimating coastal wave overtopping and non-overtopping 97 % of the time within both locations, outperforming the other models. Moreover, the random forest model outperforms existing process-based and EurOtop-based models. This research has profound implications for increasing preparedness and resilience to future coastal wave overtopping and flooding events by using these random forest models to predict overtopping and flood forecasting on wider global and climate scales. These trained random forests are significantly less computationally demanding than existing process-based models and can incorporate the important effect of wind on overtopping, which was neglected in existing empirical approaches.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102510"},"PeriodicalIF":3.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantify the compound effects caused by the interactions between inland river system and coastal processes in hurricane coastal flooding through controlled hydrodynamic modeling experiments 通过控制水动力模拟实验，量化飓风海岸洪水中内陆河系与海岸过程相互作用的复合效应

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-03 DOI: 10.1016/j.ocemod.2025.102506

Hongyuan Zhang , Dongliang Shen , Len Pietrafesa , Paul Gayes , Shaowu Bao

{"title":"Quantify the compound effects caused by the interactions between inland river system and coastal processes in hurricane coastal flooding through controlled hydrodynamic modeling experiments","authors":"Hongyuan Zhang , Dongliang Shen , Len Pietrafesa , Paul Gayes , Shaowu Bao","doi":"10.1016/j.ocemod.2025.102506","DOIUrl":"10.1016/j.ocemod.2025.102506","url":null,"abstract":"<div><div>Coastal flooding during hurricanes is a complex phenomenon involving the interaction of multiple drivers operating across different spatial scales, such as storm surge, rainfall, river discharge, and tides. Accurately assessing and predicting compound flooding requires considering the cross-scale nature of these processes and their interdependencies. This study investigates the compound effects and cross-scale interactions of flood drivers during Hurricane Matthew (2016) along the South Carolina coast using a coupled hydrology-hydrodynamic model. The model domain encompasses the land-ocean continuum, from rivers to the coastal ocean, allowing for the examination of compound flooding across the entire system. Controlled numerical experiments are conducted to quantify the individual, combined, and compound impacts of flood drivers across scales by selectively including or excluding riverine, storm surge, and tidal forcings. The coupled modeling approach reveals distinct zones of positive and negative compound effects, depending on the alignment of coastal water levels with river flood timing. River-surge interactions alter flooding, causing increases upstream and decreases downstream compared to isolated effects. Tide-surge and tide-river interactions induce oscillatory compound effects. The study demonstrates that the compound effect significantly influences hurricane coastal flooding beyond the linear superposition of flooding caused by individual drivers. The cross-scale modeling framework and analysis approach presented here can inform multi-hazard analysis, coastal flood risk management, and future studies of complex, multi-scale hydrologic systems.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102506"},"PeriodicalIF":3.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Bayesian backtracking problem in oceanic drift modelling 海洋漂移模型中的贝叶斯回溯问题

IF 3.1 3区地球科学

Ocean Modelling Pub Date : 2025-02-03 DOI: 10.1016/j.ocemod.2025.102505

Øyvind Breivik , Bente Moerman , Knut-Frode Dagestad , Tor Nordam , Gaute Hope , Lars Robert Hole , Arthur A. Allen , Lawrence D. Stone

{"title":"The Bayesian backtracking problem in oceanic drift modelling","authors":"Øyvind Breivik , Bente Moerman , Knut-Frode Dagestad , Tor Nordam , Gaute Hope , Lars Robert Hole , Arthur A. Allen , Lawrence D. Stone","doi":"10.1016/j.ocemod.2025.102505","DOIUrl":"10.1016/j.ocemod.2025.102505","url":null,"abstract":"<div><div>Backtracking the drift of particles and substances is central to a range of studies in oceanography as well as in law enforcement, search and rescue and the mapping and investigation of marine pollution. Here we demonstrate how a Lagrangian particle model can be used in a forward mode with a Bayesian prior estimate on the release location of the object of interest. We show that for well-behaved drifters, forward and backward (reverse modelling) yield similar results over short periods, if the currents are only weakly divergent. However, for drifters undergoing discontinuous state changes, such as stranding, or objects abruptly and irreversibly changing their drift properties, or for buoyant drifters in strongly convergent flows, backward drift can yield wrongful search areas. We demonstrate this for a case where a liferaft is assigned a wind-speed dependent probability of capsizing, leading to an instantaneous change in drift properties. We also demonstrate the forward and backward methods for a drifter release experiment in the Agulhas current where we also assess the challenges of biases in the current fields. Finally, a method for incorporating multiple observations of debris with a forward model in the Bayesian posterior estimate of the initial location is outlined.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102505"},"PeriodicalIF":3.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0