Pub Date : 2024-10-10DOI: 10.1016/j.ocemod.2024.102454
Jianbin Xie , Xingru Feng , Guandong Gao
Suspended sediment plays an important role in coastal topography evolution and ecological environment change. To obtain a clear picture of the underlying mechanisms, we studied the response of suspended sediment dynamics to tidal current and wave-current interactions using the wave-current-sediment model of SCHISM. The results revealed evident tidal asymmetry in the study area, and showed that the suspended sediment concentration (SSC) markedly changes within a tidal cycle. We also disassembled the wave–current interactions to determine the contribution of each physical mechanism of the wave and hydrodynamic models. Regarding the importance of various effects of wave-current interactions on SSC, the wave-induced bottom shear stress and wave-induced radiation stress should be considered. The importance of advection in horizontal space is comparable to that of wave-induced bottom shear stress and wave-induced radiation stress, and is greater than that of the other types of wave energy advection. This study successfully explained all the mechanisms that influence the variation of SSC to the southwest of Hainan Island, which is helpful for coastal management and could provide a reference for other coastal areas.
{"title":"Variation of suspended-sediment caused by tidal asymmetry and wave effects","authors":"Jianbin Xie , Xingru Feng , Guandong Gao","doi":"10.1016/j.ocemod.2024.102454","DOIUrl":"10.1016/j.ocemod.2024.102454","url":null,"abstract":"<div><div>Suspended sediment plays an important role in coastal topography evolution and ecological environment change. To obtain a clear picture of the underlying mechanisms, we studied the response of suspended sediment dynamics to tidal current and wave-current interactions using the wave-current-sediment model of SCHISM. The results revealed evident tidal asymmetry in the study area, and showed that the suspended sediment concentration (SSC) markedly changes within a tidal cycle. We also disassembled the wave–current interactions to determine the contribution of each physical mechanism of the wave and hydrodynamic models. Regarding the importance of various effects of wave-current interactions on SSC, the wave-induced bottom shear stress and wave-induced radiation stress should be considered. The importance of advection in horizontal space is comparable to that of wave-induced bottom shear stress and wave-induced radiation stress, and is greater than that of the other types of wave energy advection. This study successfully explained all the mechanisms that influence the variation of SSC to the southwest of Hainan Island, which is helpful for coastal management and could provide a reference for other coastal areas.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102454"},"PeriodicalIF":3.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441822","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}
Pub Date : 2024-10-09DOI: 10.1016/j.ocemod.2024.102453
Fangrui Xiu , Zengan Deng
Langmuir circulations and turbulence (LT) are crucial in the upper ocean mixed layer, significantly affecting the air-sea exchange of momentum, heat, and mass. The development of an appropriate LT parameterization scheme is vital for ocean modeling. This study employed the Large-eddy Simulation (LES) and the Physics-informed Neural Network (PINN) to optimize the KC04 Langmuir turbulence scheme by dynamically adjusting E6 as a key parameter determined by winds and waves. The LES simulations under different wind wave states indicated the PINN-inferred values for E6. Modelling results from GOTM in OCSPapa station demonstrated that the optimized scheme outperformed the original KC04 scheme in simulating the vertical eddy diffusivity and temperature, with an ∼6.24% annual reduction in the root mean square error (RMSE) for the temperature and an ∼8.23% reduction in the RMSE during autumn. Furthermore, the optimized scheme resulted in a thicker mixed layer, reaching 4.9 m. This enhanced LT parameterization scheme exhibited the improved robustness for variable spatiotemporal resolutions, significantly improving the modeling accuracy.
{"title":"A dynamically adaptive Langmuir turbulence parameterization scheme for variable wind wave conditions: Model application","authors":"Fangrui Xiu , Zengan Deng","doi":"10.1016/j.ocemod.2024.102453","DOIUrl":"10.1016/j.ocemod.2024.102453","url":null,"abstract":"<div><div>Langmuir circulations and turbulence (LT) are crucial in the upper ocean mixed layer, significantly affecting the air-sea exchange of momentum, heat, and mass. The development of an appropriate LT parameterization scheme is vital for ocean modeling. This study employed the Large-eddy Simulation (LES) and the Physics-informed Neural Network (PINN) to optimize the KC04 Langmuir turbulence scheme by dynamically adjusting E<sub>6</sub> as a key parameter determined by winds and waves. The LES simulations under different wind wave states indicated the PINN-inferred values for E<sub>6</sub>. Modelling results from GOTM in OCSPapa station demonstrated that the optimized scheme outperformed the original KC04 scheme in simulating the vertical eddy diffusivity and temperature, with an ∼6.24% annual reduction in the root mean square error (RMSE) for the temperature and an ∼8.23% reduction in the RMSE during autumn. Furthermore, the optimized scheme resulted in a thicker mixed layer, reaching 4.9 m. This enhanced LT parameterization scheme exhibited the improved robustness for variable spatiotemporal resolutions, significantly improving the modeling accuracy.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102453"},"PeriodicalIF":3.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421567","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}
Pub Date : 2024-10-09DOI: 10.1016/j.ocemod.2024.102450
Shuyi Zhou , Jiuke Wang , Yuhan Cao , Brandon J. Bethel , Wenhong Xie , Guangjun Xu , Wenjin Sun , Yang Yu , Hongchun Zhang , Changming Dong
Significant wave height (SWH) stands as one of the most crucial parameters for maritime activities. However, even the SWH data from the widely utilized European Centre for Medium-Range Weather Forecast Integrated Forecasting System (ECMWF-IFS) carries errors and uncertainties. In this study, the Light Gradient Boosting Machine (LightGBM) is used to inference the global ECMWF-IFS SWH forecast biases. The results demonstrate that globally, the LightGBM reduces the root mean square error by 10–20 %. Particularly noteworthy is the enhanced forecast accuracy observed in the western Pacific during late summers. Furthermore, the corrected forecast results during Super Typhoon Lekima in 2019 showcase the capability of model to effectively enhance the forecast accuracy of typhoon-induced wind waves, even when four typhoons occur concurrently. This study establishes the feasibility of LightGBM in inferencing single-step SWH forecast bias and presents a cost-effective model for enhancing global wave forecasts.
{"title":"Improving the accuracy of global ECMWF wave height forecasts with machine learning","authors":"Shuyi Zhou , Jiuke Wang , Yuhan Cao , Brandon J. Bethel , Wenhong Xie , Guangjun Xu , Wenjin Sun , Yang Yu , Hongchun Zhang , Changming Dong","doi":"10.1016/j.ocemod.2024.102450","DOIUrl":"10.1016/j.ocemod.2024.102450","url":null,"abstract":"<div><div>Significant wave height (SWH) stands as one of the most crucial parameters for maritime activities. However, even the SWH data from the widely utilized European Centre for Medium-Range Weather Forecast Integrated Forecasting System (ECMWF-IFS) carries errors and uncertainties. In this study, the Light Gradient Boosting Machine (LightGBM) is used to inference the global ECMWF-IFS SWH forecast biases. The results demonstrate that globally, the LightGBM reduces the root mean square error by 10–20 %. Particularly noteworthy is the enhanced forecast accuracy observed in the western Pacific during late summers. Furthermore, the corrected forecast results during Super Typhoon Lekima in 2019 showcase the capability of model to effectively enhance the forecast accuracy of typhoon-induced wind waves, even when four typhoons occur concurrently. This study establishes the feasibility of LightGBM in inferencing single-step SWH forecast bias and presents a cost-effective model for enhancing global wave forecasts.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102450"},"PeriodicalIF":3.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441821","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}
Pub Date : 2024-10-08DOI: 10.1016/j.ocemod.2024.102452
Shukui Cheng , Anzhou Cao , Jinbao Song , Xinyu Guo
Sea surface temperature cooling (SSTC) is an important indicator of the ocean response to typhoons and is a factor in the evolution of typhoons. Understanding the intricate mechanisms underlying the SSTC induced by different typhoons is important. Based on the numerical simulation, we investigated the SSTC induced by typhoons Megi (2010), Linfa (2015), and Sarika (2011), which had relatively similar tracks in the South China Sea. As the strongest (weakest) typhoon, Megi (Sarika) induced the largest (smallest) SSTC, which is consistent with the traditional understanding that stronger typhoons usually induce larger SSTC than weaker typhoons. However, the SSTC induced by the moderate typhoon Linfa was nearly comparable to that induced by Megi, while Linfa had a wind power input an order of magnitude smaller. A comparison of near-inertial waves (NIWs) induced by Linfa and Megi showed that the former contained a larger proportion of high modes, substantially contributing to vertical shear. Consequently, the vertical mixing coefficient during Linfa reached one third of that during Megi. Because the SSTC is primarily influenced by vertical mixing, which is dominated by vertical diffusion at the mixed layer depth, the relatively strong vertical mixing coefficient and large temperature gradient during Linfa ultimately resulted in the SSTC nearly comparable to that induced by Megi. The results of this study enhance the understanding of typhoon-induced SSTC.
{"title":"Contribution of high-mode near-inertial waves to enhanced typhoon-induced sea surface temperature cooling in the South China Sea","authors":"Shukui Cheng , Anzhou Cao , Jinbao Song , Xinyu Guo","doi":"10.1016/j.ocemod.2024.102452","DOIUrl":"10.1016/j.ocemod.2024.102452","url":null,"abstract":"<div><div>Sea surface temperature cooling (SSTC) is an important indicator of the ocean response to typhoons and is a factor in the evolution of typhoons. Understanding the intricate mechanisms underlying the SSTC induced by different typhoons is important. Based on the numerical simulation, we investigated the SSTC induced by typhoons Megi (2010), Linfa (2015), and Sarika (2011), which had relatively similar tracks in the South China Sea. As the strongest (weakest) typhoon, Megi (Sarika) induced the largest (smallest) SSTC, which is consistent with the traditional understanding that stronger typhoons usually induce larger SSTC than weaker typhoons. However, the SSTC induced by the moderate typhoon Linfa was nearly comparable to that induced by Megi, while Linfa had a wind power input an order of magnitude smaller. A comparison of near-inertial waves (NIWs) induced by Linfa and Megi showed that the former contained a larger proportion of high modes, substantially contributing to vertical shear. Consequently, the vertical mixing coefficient during Linfa reached one third of that during Megi. Because the SSTC is primarily influenced by vertical mixing, which is dominated by vertical diffusion at the mixed layer depth, the relatively strong vertical mixing coefficient and large temperature gradient during Linfa ultimately resulted in the SSTC nearly comparable to that induced by Megi. The results of this study enhance the understanding of typhoon-induced SSTC.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102452"},"PeriodicalIF":3.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421565","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}
Pub Date : 2024-10-05DOI: 10.1016/j.ocemod.2024.102445
E. Guerrero Fernández , M.J. Castro Díaz , Y. Wei , C. Moore
This work presents a morpho-hydrodynamic model and a numerical approximation designed for the fast and accurate simulation of sediment movement associated with extreme events, such as tsunamis. The model integrates the well-established hydrostatic shallow-water equations with a transport equation for the moving bathymetry that relies on a bedload transport function. Subsequently, this model is discretized using the path-conservative finite volume framework to yield a numerical scheme that is not only fast but also second-order accurate and well-balanced for the lake-at-rest solution. The numerical discretization separates the hydrodynamic and morphodynamic components of the model but leverages the eigenstructure information to evolve the morphologic part in an upwind fashion, preventing spurious oscillations. The study includes various numerical experiments, incorporating comparisons with laboratory experimental data and field surveys.
{"title":"Modeling sediment movement in the shallow-water framework: A morpho-hydrodynamic approach with numerical simulations and experimental validation","authors":"E. Guerrero Fernández , M.J. Castro Díaz , Y. Wei , C. Moore","doi":"10.1016/j.ocemod.2024.102445","DOIUrl":"10.1016/j.ocemod.2024.102445","url":null,"abstract":"<div><div>This work presents a morpho-hydrodynamic model and a numerical approximation designed for the fast and accurate simulation of sediment movement associated with extreme events, such as tsunamis. The model integrates the well-established hydrostatic shallow-water equations with a transport equation for the moving bathymetry that relies on a bedload transport function. Subsequently, this model is discretized using the path-conservative finite volume framework to yield a numerical scheme that is not only fast but also second-order accurate and well-balanced for the lake-at-rest solution. The numerical discretization separates the hydrodynamic and morphodynamic components of the model but leverages the eigenstructure information to evolve the morphologic part in an upwind fashion, preventing spurious oscillations. The study includes various numerical experiments, incorporating comparisons with laboratory experimental data and field surveys.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102445"},"PeriodicalIF":3.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421566","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}
Pub Date : 2024-10-05DOI: 10.1016/j.ocemod.2024.102446
Shuo Li , Alexander V. Babanin , Qingxiang Liu , Changlong Guan
The parameterization of air–sea CO2 transfer velocity employed in the estimation of bulk fluxes over global ocean is typically established on wind speed but could suffer from the deviations induced by sea states. In this study, the effectiveness of wave-based formulations are substantiated by reproducing climatological air–sea CO2 flux and gas transfer velocity. Sea states play a significant role in facilitating CO2 transfer, particularly in mid to high latitude regions with high wind speeds. The variability in transfer velocity induced by sea states is estimated up to 19% at the wind speed of 15 m/s. The two wave-based formulations used in this study are combined using a critical value of the Reynolds number. The combined formulation further improves estimates of the CO2 gas transfer velocity.
{"title":"Evaluation of wave-based parameterizations of air–sea CO2 gas transfer over global oceans","authors":"Shuo Li , Alexander V. Babanin , Qingxiang Liu , Changlong Guan","doi":"10.1016/j.ocemod.2024.102446","DOIUrl":"10.1016/j.ocemod.2024.102446","url":null,"abstract":"<div><div>The parameterization of air–sea CO<sub>2</sub> transfer velocity employed in the estimation of bulk fluxes over global ocean is typically established on wind speed but could suffer from the deviations induced by sea states. In this study, the effectiveness of wave-based formulations are substantiated by reproducing climatological air–sea CO<sub>2</sub> flux and gas transfer velocity. Sea states play a significant role in facilitating CO<sub>2</sub> transfer, particularly in mid to high latitude regions with high wind speeds. The variability in transfer velocity induced by sea states is estimated up to 19% at the wind speed of 15 m/s. The two wave-based formulations used in this study are combined using a critical value of the Reynolds number. The combined formulation further improves estimates of the CO<sub>2</sub> gas transfer velocity.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102446"},"PeriodicalIF":3.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536989","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}
Pub Date : 2024-10-05DOI: 10.1016/j.ocemod.2024.102447
A.G. Filippini , L. Arpaia , V. Perrier , R. Pedreros , P. Bonneton , D. Lannes , F. Marche , S. De Brye , S. Delmas , S. Lecacheux , F. Boulahya , M. Ricchiuto
Hydrodynamic modeling for coastal flooding risk assessment is a highly relevant topic. Many operational tools available for this purpose use numerical techniques and implementation paradigms that reach their limits when confronted with modern requirements in terms of resolution and performances. In this work, we present a novel operational tool for coastal hazards predictions, currently employed by the BRGM agency (the French Geological Survey) to carry out its flooding hazard exposure studies and coastal risk prevention plans on International and French territories. The model, called UHAINA (wave in the Basque language), is based on an arbitrary high-order discontinuous Galerkin discretization of the nonlinear shallow water equations with SSP Runge–Kutta time stepping on unstructured triangular grids. It is built upon the finite element library AeroSol, which provides a modern C++ software architecture and high scalability, making it suitable for HPC applications. The paper provides a detailed development of the mathematical and numerical framework of the model, focusing on two key-ingredients : (i) a pragmatic treatment of the solution in partially dry cells which guarantees efficiently well-balancedness, positivity and mass conservation at any polynomial order; (ii) an artificial viscosity method based on the physical dissipation of the system of equations providing nonlinear stability for non-smooth solutions. A set of numerical validations on academic benchmarks is performed to highlight the efficiency of these approaches. Finally, UHAINA is applied on a real operational case of study, demonstrating very satisfactory results.
{"title":"An operational discontinuous Galerkin shallow water model for coastal flood assessment","authors":"A.G. Filippini , L. Arpaia , V. Perrier , R. Pedreros , P. Bonneton , D. Lannes , F. Marche , S. De Brye , S. Delmas , S. Lecacheux , F. Boulahya , M. Ricchiuto","doi":"10.1016/j.ocemod.2024.102447","DOIUrl":"10.1016/j.ocemod.2024.102447","url":null,"abstract":"<div><div>Hydrodynamic modeling for coastal flooding risk assessment is a highly relevant topic. Many operational tools available for this purpose use numerical techniques and implementation paradigms that reach their limits when confronted with modern requirements in terms of resolution and performances. In this work, we present a novel operational tool for coastal hazards predictions, currently employed by the BRGM agency (the French Geological Survey) to carry out its flooding hazard exposure studies and coastal risk prevention plans on International and French territories. The model, called UHAINA (wave in the Basque language), is based on an arbitrary high-order discontinuous Galerkin discretization of the nonlinear shallow water equations with SSP Runge–Kutta time stepping on unstructured triangular grids. It is built upon the finite element library AeroSol, which provides a modern C++ software architecture and high scalability, making it suitable for HPC applications. The paper provides a detailed development of the mathematical and numerical framework of the model, focusing on two key-ingredients : (i) a pragmatic <span><math><msup><mrow><mi>P</mi></mrow><mrow><mn>0</mn></mrow></msup></math></span> treatment of the solution in partially dry cells which guarantees efficiently well-balancedness, positivity and mass conservation at any polynomial order; (ii) an artificial viscosity method based on the physical dissipation of the system of equations providing nonlinear stability for non-smooth solutions. A set of numerical validations on academic benchmarks is performed to highlight the efficiency of these approaches. Finally, UHAINA is applied on a real operational case of study, demonstrating very satisfactory results.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102447"},"PeriodicalIF":3.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441823","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}
Pub Date : 2024-09-25DOI: 10.1016/j.ocemod.2024.102441
Yiran Wang, Kai Yin, Sudong Xu, Shangpeng Gong, Mingxuan Li
Vegetation communities distributed in coastal zones and offshore wetlands are important compositions for sand stabilization and stability of the ecosystem. This paper studies the impact of flexible vegetation on beach profile evolution by constructing an XBeach numerical model. Firstly, the mathematical model of flexible vegetation beach is established based on the generalized vegetation parameters. The XBeach numerical model is validated by the wave flume experiment to prove that a semi-empirical equation of flexible vegetation drag coefficient is valid in beach profile evolution. Then, the numerical model is used to study the beach profile evolution with flexible vegetation under different wave parameters and summarize the corresponding laws. Finally, the differences between flexible and rigid vegetation on beach evolution are compared. Results show that the beach profile evolution roughly increased with the increase of wave parameters. The Starting Point of Evolution in beach shifts offshore and the evolution range gradually broadens as the wave height or period increases. In addition, the flexible vegetation beach shows greater evolution than rigid vegetation beach and the Starting Point of Evolution also tends to be more offshore, particularly under conditions of long periods and large wave heights. This study can provide references for beach protection and ecological restoration in coastal areas.
{"title":"Numerical investigation of coastal profile evolution under effect of submerged flexible vegetation by XBeach wave model","authors":"Yiran Wang, Kai Yin, Sudong Xu, Shangpeng Gong, Mingxuan Li","doi":"10.1016/j.ocemod.2024.102441","DOIUrl":"10.1016/j.ocemod.2024.102441","url":null,"abstract":"<div><div>Vegetation communities distributed in coastal zones and offshore wetlands are important compositions for sand stabilization and stability of the ecosystem. This paper studies the impact of flexible vegetation on beach profile evolution by constructing an XBeach numerical model. Firstly, the mathematical model of flexible vegetation beach is established based on the generalized vegetation parameters. The XBeach numerical model is validated by the wave flume experiment to prove that a semi-empirical equation of flexible vegetation drag coefficient is valid in beach profile evolution. Then, the numerical model is used to study the beach profile evolution with flexible vegetation under different wave parameters and summarize the corresponding laws. Finally, the differences between flexible and rigid vegetation on beach evolution are compared. Results show that the beach profile evolution roughly increased with the increase of wave parameters. The Starting Point of Evolution in beach shifts offshore and the evolution range gradually broadens as the wave height or period increases. In addition, the flexible vegetation beach shows greater evolution than rigid vegetation beach and the Starting Point of Evolution also tends to be more offshore, particularly under conditions of long periods and large wave heights. This study can provide references for beach protection and ecological restoration in coastal areas.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102441"},"PeriodicalIF":3.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359134","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}
Pub Date : 2024-09-18DOI: 10.1016/j.ocemod.2024.102430
Mercè Casas-Prat , Leah Cicon , Benoit Pouliot , Natacha B. Bernier , Alex J. Cannon , Rodney Chan
This study presents the first set of CanESM5-driven wave projections for two emission scenarios (SSP5-8.5 and SSP2-4.5) and two time periods for mid- and end-century. While coarse resolution climate models, like CanESM5, might be less attractive for development of ocean wave projections, their results are needed to explore the full range of inter-model uncertainty in CMIP6 projections. Considering the coarse resolution limitation, wave simulations were obtained with a proposed computationally efficient 2-step bias-correction approach that consists of (i) calibrating the wind-to-wave energy transfer in the ocean wave model to reduce the underestimation of extremes resulting from coarse resolution, and (ii) bias-correcting the surface winds with a multivariate bias-correction to reduce remaining systematic biases. Results showed overall good performance in comparison with state of the art reanalysis and satellite data. Resulting projections provide increased understanding of future changes in wave conditions, confirming previously reported global-scale changes, such as higher waves in the eastern tropical Pacific and lower waves in the North Atlantic. They also provide more detailed information for areas affected by sea ice conditions in comparison to the latest CMIP5-based wave ensembles, which is critical for the Arctic region, a hotspot for ocean wave changes. Moreover, while the largest changes are typically seen by the end-century under SSP5-8.5, this study reveals that for some variables and areas, such as the mean wave period, larger changes occur for lower warming levels as a result of competing driving factors. Finally, the presented projections can contribute to ongoing efforts to generate a large multi-model ensemble of wave projections based on CMIP6.
{"title":"CanESM5-derived ocean wave projections — Considerations for coarse resolution climate models","authors":"Mercè Casas-Prat , Leah Cicon , Benoit Pouliot , Natacha B. Bernier , Alex J. Cannon , Rodney Chan","doi":"10.1016/j.ocemod.2024.102430","DOIUrl":"10.1016/j.ocemod.2024.102430","url":null,"abstract":"<div><div>This study presents the first set of CanESM5-driven wave projections for two emission scenarios (SSP5-8.5 and SSP2-4.5) and two time periods for mid- and end-century. While coarse resolution climate models, like CanESM5, might be less attractive for development of ocean wave projections, their results are needed to explore the full range of inter-model uncertainty in CMIP6 projections. Considering the coarse resolution limitation, wave simulations were obtained with a proposed computationally efficient 2-step bias-correction approach that consists of (i) calibrating the wind-to-wave energy transfer in the ocean wave model to reduce the underestimation of extremes resulting from coarse resolution, and (ii) bias-correcting the surface winds with a multivariate bias-correction to reduce remaining systematic biases. Results showed overall good performance in comparison with state of the art reanalysis and satellite data. Resulting projections provide increased understanding of future changes in wave conditions, confirming previously reported global-scale changes, such as higher waves in the eastern tropical Pacific and lower waves in the North Atlantic. They also provide more detailed information for areas affected by sea ice conditions in comparison to the latest CMIP5-based wave ensembles, which is critical for the Arctic region, a hotspot for ocean wave changes. Moreover, while the largest changes are typically seen by the end-century under SSP5-8.5, this study reveals that for some variables and areas, such as the mean wave period, larger changes occur for lower warming levels as a result of competing driving factors. Finally, the presented projections can contribute to ongoing efforts to generate a large multi-model ensemble of wave projections based on CMIP6.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102430"},"PeriodicalIF":3.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314190","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}
In this study, we conducted a comprehensive and integrated test of tropical cyclone track prediction using deep learning technologies, aiming to enhance the efficiency and accuracy of the prediction methods. We employed the Best Track dataset from the China Meteorological Administration's Tropical Cyclone Data Center, which covers the Northwest Pacific region from 1949 to 2023. This dataset provides comprehensive coverage, encompassing critical tropical cyclone details like time, latitude, longitude, and wind speed. Our focus was on evaluating and comparing different deep learning models, including Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Gated Recurrent Units (GRU), for their effectiveness in handling complex time series data. Through detailed analysis of various model configurations, including factors such as input-output lengths, hidden size, the number of layers, the implementation of bi-directional networks, and attention mechanisms, we discovered that LSTM and GRU models significantly outperform traditional RNN models in dealing with long-term dependencies and enhancing prediction accuracy. Moreover, the LSTM model, used to forecast key tropical cyclones during the 2023 Pacific tropical cyclone season, achieved mean errors of 21.84 km, 37.56 km, and 26.12 km for Typhoons Mawar, Doksuri, and Saola, respectively. This method also demonstrated high efficiency in rapid response to extreme weather changes, processing each tropical cyclone's forecast in just about 8 s. The results not only illustrate the practical utility of deep learning in tropical cyclone track prediction but also provide new, effective tools for disaster prevention and mitigation efforts.
{"title":"Deep learning approaches in predicting tropical cyclone tracks: An analysis focused on the Northwest Pacific Region","authors":"Peng Hao, Yaqi Zhao, Shuang Li, Jinbao Song, Yu Gao","doi":"10.1016/j.ocemod.2024.102444","DOIUrl":"10.1016/j.ocemod.2024.102444","url":null,"abstract":"<div><div>In this study, we conducted a comprehensive and integrated test of tropical cyclone track prediction using deep learning technologies, aiming to enhance the efficiency and accuracy of the prediction methods. We employed the Best Track dataset from the China Meteorological Administration's Tropical Cyclone Data Center, which covers the Northwest Pacific region from 1949 to 2023. This dataset provides comprehensive coverage, encompassing critical tropical cyclone details like time, latitude, longitude, and wind speed. Our focus was on evaluating and comparing different deep learning models, including Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Gated Recurrent Units (GRU), for their effectiveness in handling complex time series data. Through detailed analysis of various model configurations, including factors such as input-output lengths, hidden size, the number of layers, the implementation of bi-directional networks, and attention mechanisms, we discovered that LSTM and GRU models significantly outperform traditional RNN models in dealing with long-term dependencies and enhancing prediction accuracy. Moreover, the LSTM model, used to forecast key tropical cyclones during the 2023 Pacific tropical cyclone season, achieved mean errors of 21.84 km, 37.56 km, and 26.12 km for Typhoons Mawar, Doksuri, and Saola, respectively. This method also demonstrated high efficiency in rapid response to extreme weather changes, processing each tropical cyclone's forecast in just about 8 s. The results not only illustrate the practical utility of deep learning in tropical cyclone track prediction but also provide new, effective tools for disaster prevention and mitigation efforts.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102444"},"PeriodicalIF":3.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434459","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}
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