Articles | Volume 14, issue 3
https://doi.org/10.5194/os-14-453-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-14-453-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Numerical modeling of surface wave development under the action of wind
Dmitry Chalikov
CORRESPONDING AUTHOR
Shirshov Institute of Oceanology, Saint Petersburg 199053, Russia
Russian State Hydrometeorological University, Saint Petersburg 195196, Russia
University of Melbourne, Victoria 3010, Australia
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Cited
18 citations as recorded by crossref.
- Numerical investigation of energy exchange between wind and waves D. Chalikov 10.1016/j.oceaneng.2025.121341
- Dispersion of tracer particles by wave turbulence C. Kirezci et al. 10.1016/j.physd.2023.133725
- Directional Wave Scattering Distribution Modes Analysis and Synthesis of Random Ocean Media Roughness for SAR Electromagnetic Interactions Using Feature Fusion in Dynamic Sea States: A Survey I. Shahrezaei & H. Kim 10.1109/ACCESS.2024.3380201
- Intermittency of gravity wave turbulence on the surface of an infinitely deep fluid: Numerical experiment A. Skvortsov et al. 10.1016/j.physleta.2022.128337
- Statistical Properties of 3-D Waves Simulated with 2-D Phase-Resolving Model D. Chalikov 10.3103/S1541308X23020048
- Numerical simulation of soliton gas within the Korteweg-de Vries type equations . Диденкулова et al. 10.25743/ICT.2019.24.2.005
- Predicting Surface Stokes Drift with Deep Learning X. Yu et al. 10.3390/w17070983
- A 2D Model for 3D Periodic Deep-Water Waves D. Chalikov 10.3390/jmse10030410
- Interpretation of the spectral wave forecast model results using the phase-resolving model D. Chalikov et al. 10.59887/2073-6673.2023.16(2)-2
- A Novel SAR Fractal Roughness Modeling of Complex Random Polar Media and Textural Synthesis Based on a Numerical Scattering Distribution Function Processing I. Shahrezaei & H. Kim 10.1109/JSTARS.2021.3084822
- Transfer function for adaptive methods of estimating directional spectra using a fully nonlinear wave model and laboratory measurements Z. Torres et al. 10.1007/s10236-025-01739-7
- Kinetic equations in a third-generation spectral wave model Q. Liu et al. 10.1017/jfm.2020.1036
- Accelerated reproduction of 2-D periodic waves D. Chalikov 10.1007/s10236-020-01435-8
- Spatial evolution of young wind waves: numerical modelling verified by experiments L. Shemer et al. 10.1017/jfm.2020.549
- Two-Dimensional Modeling of Three-Dimensional Waves D. Chalikov 10.1134/S0001437021060047
- Impact of the Gulf Stream on ocean waves M. Allahdadi et al. 10.1016/j.dsr2.2022.105239
- Wave hindcast under tropical cyclone conditions in the South China Sea: sensitivity to wind fields L. Jia et al. 10.1007/s13131-023-2227-1
- Numerical Modeling of Sea Waves D. Chalikov 10.1134/S0001433820030032
18 citations as recorded by crossref.
- Numerical investigation of energy exchange between wind and waves D. Chalikov 10.1016/j.oceaneng.2025.121341
- Dispersion of tracer particles by wave turbulence C. Kirezci et al. 10.1016/j.physd.2023.133725
- Directional Wave Scattering Distribution Modes Analysis and Synthesis of Random Ocean Media Roughness for SAR Electromagnetic Interactions Using Feature Fusion in Dynamic Sea States: A Survey I. Shahrezaei & H. Kim 10.1109/ACCESS.2024.3380201
- Intermittency of gravity wave turbulence on the surface of an infinitely deep fluid: Numerical experiment A. Skvortsov et al. 10.1016/j.physleta.2022.128337
- Statistical Properties of 3-D Waves Simulated with 2-D Phase-Resolving Model D. Chalikov 10.3103/S1541308X23020048
- Numerical simulation of soliton gas within the Korteweg-de Vries type equations . Диденкулова et al. 10.25743/ICT.2019.24.2.005
- Predicting Surface Stokes Drift with Deep Learning X. Yu et al. 10.3390/w17070983
- A 2D Model for 3D Periodic Deep-Water Waves D. Chalikov 10.3390/jmse10030410
- Interpretation of the spectral wave forecast model results using the phase-resolving model D. Chalikov et al. 10.59887/2073-6673.2023.16(2)-2
- A Novel SAR Fractal Roughness Modeling of Complex Random Polar Media and Textural Synthesis Based on a Numerical Scattering Distribution Function Processing I. Shahrezaei & H. Kim 10.1109/JSTARS.2021.3084822
- Transfer function for adaptive methods of estimating directional spectra using a fully nonlinear wave model and laboratory measurements Z. Torres et al. 10.1007/s10236-025-01739-7
- Kinetic equations in a third-generation spectral wave model Q. Liu et al. 10.1017/jfm.2020.1036
- Accelerated reproduction of 2-D periodic waves D. Chalikov 10.1007/s10236-020-01435-8
- Spatial evolution of young wind waves: numerical modelling verified by experiments L. Shemer et al. 10.1017/jfm.2020.549
- Two-Dimensional Modeling of Three-Dimensional Waves D. Chalikov 10.1134/S0001437021060047
- Impact of the Gulf Stream on ocean waves M. Allahdadi et al. 10.1016/j.dsr2.2022.105239
- Wave hindcast under tropical cyclone conditions in the South China Sea: sensitivity to wind fields L. Jia et al. 10.1007/s13131-023-2227-1
- Numerical Modeling of Sea Waves D. Chalikov 10.1134/S0001433820030032
Latest update: 03 Nov 2025
Short summary
Waves obtain energy from wind; they grow and increase in size and speed of propagation. The structure of wave fields becomes complicated due to appearance of new wave components. Finally, the sea surface looks like a poorly organized motion consisting of quickly running large hills and hollows covered with smaller waves. This process can be successfully simulated on computers. Such investigations allow us to understand the physics of sea waves, which is important for practice.
Waves obtain energy from wind; they grow and increase in size and speed of propagation. The...