Adjusting of Wind Input Source Term in WAVEWATCH III Model for the Middle-Sized Water Body on the Basis of the Field Experiment

Kuznetsova, Alexandra; Baydakov, Georgy; Papko, V. V.; Kandaurov, Alexander; Vdovin, Maxim; Sergeev, D. A.; Troitskaya, Yuliya

doi:10.1155/2016/8539127

Cited by 15 publications

(4 citation statements)

References 24 publications

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“…Comparison with the measurements at Gorky Reservoir showed that the energy transfer over the wave spectrum in the model was too slow, resulting in a less steep waves than those observed [47]. It is the nonlinear four-wave interactions parametrization DIA that determines the rate of the energy transfer over the wave spectrum.…”

Section: Experiments With Different Wind Input Parameterizationsmentioning

confidence: 92%

“…Fetch-limited waves are characterized by more intense wind pumping and stronger non-linearity than is the case in the standard parameterization ST1, which is configured for modeling in the open sea. Therefore, in the second experiment (hereafter-ST1_mod) using the same wind input parameterization, the aerodynamic drag coefficient was changed in accordance with the dependence obtained from field data at Gorky Reservoir [46]:…”

Section: Experiments With Different Wind Input Parameterizationsmentioning

confidence: 99%

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Influence of Novaya Zemlya Bora on Sea Waves: Satellite Measurements and Numerical Modeling

2020

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This paper investigates for the first time sea waves during Novaya Zemlya bora—a downslope windstorm on the western coast of the archipelago during eastern winds—using a statistical and case-study approach. Statistical analysis of altimeter data off the western coast of Novaya Zemlya during bora shows that, despite strong wind forcing, the frequency of hazard wave heights was low due to the limited fetch. This result was confirmed by the high-resolution numerical simulations of two severe bora episodes. However, the influence of bora on sea waves in some cases was significant: bora increased wave height at a distance from shore greater than 200 km and wave height anomaly was up to 2–3 m. The influence of the wind input parametrization choice during bora is great in the coastal region; however, parametrizations with fetch-limited modifications and strong-wind adopted aerodynamic drag coefficient do not improve the modeling results in the open sea where altimeter data are available.

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Section: Experiments With Different Wind Input Parameterizationsmentioning

confidence: 92%

Section: Experiments With Different Wind Input Parameterizationsmentioning

confidence: 99%

Influence of Novaya Zemlya Bora on Sea Waves: Satellite Measurements and Numerical Modeling

2020

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“…In addition, numerical simulations on sensitivity are needed because their forecasting performance and accuracy are affected by the spatial and temporal resolution of the model and the wind-wave physics mechanism. In particular, sensitivity numerical simulations for the parameters included in the physics package are performed to optimize the wave model by considering the state of offshore wind input data, topographical characteristics, and grid structure [34][35][36][37]. A wave model's physics package consists of many physical parameters related to wave development parameters, propagation and dissipation, and nonlinear effects by offshore wind.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Forecasting Performance for ST6 Parameterization in High-Resolution Wave Model Based on WAVEWATCH III

Roh

Chang

et al. 2023

JMSE

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A regional wave forecasting system in East Asia, including the Korean Peninsula, was built based on WAVEWATCH III using offshore wind forecast data from the Global Data Assimilation Prediction System. The numerical simulations were performed on the sensitivity of the interaction between input wind and wave development. The forecasts for each condition were compared and verified with the observational data of marine meteorological buoys from 1 August to 30 September 2020. The sensitivity conditions were configured to have a specific range of variables related to the directional distribution of input winds (SINA0) and variables indicating the development of input wind–wave (CDFAC) in the ST6. The results were presented by calculating the mean error and root mean square error for all observation points. Overall, as the CDFAC increased, the mean error tended to decrease according to the forecast time and the root mean square error increased. Although the effect of SINA0 at the same CDFAC was insignificant, when SINA0 increased in sections where the significant wave height decreased rapidly, the significant wave height tended to decrease. In addition, the main variables that affect the physical process of wind–wave interaction should be considered to improve wave model forecasting performance and accuracy.

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“…Studies in different areas of ocean physics rely on statistics of long-term observations. Different methods and instruments are used to do such measurements and collected data might be used, for example, to validate models and to assimilate data in real-time forecasting [1,2].…”

Section: Introductionmentioning

confidence: 99%

Surface waves prediction based on long-range acoustic backscattering in a mid-frequency range

Ermoshkin,

Kosteev,

Ponomarenko

et al. 2022

Preprint

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New data was obtained for a frequency band that had not been so well-studied for sea surface probing applications before. During the described 2-weeks sea experiment 1-3 kHz tonal pulses were emitted from a platform, located on the northern Black Sea shelf, and Doppler spectrum of reverberation was studied. We believe that this band is worth further studying due the sound propagation range is large enough to meet practical needs in coastal zone while the angle-distance resolution is quite moderate. However it is quite difficult to interpret the obtained data since backscattering spectrum shape is influenced by a series of effects and has a complicated link to wind waves and currents parameters. Backscattering of acoustical signals was received for distances around 2 nautical miles. Significant wave height, dominant wave frequency were estimated as the result of such signals processing with the use of machine learning tools. A decision-tree-based mathematical regression model was trained to solve the inverse problem. Wind waves prediction is in a good agreement with direct measurements, made on the platform, and machine learning results allow physical interpretation.

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Adjusting of Wind Input Source Term in WAVEWATCH III Model for the Middle-Sized Water Body on the Basis of the Field Experiment

Cited by 15 publications

References 24 publications

Influence of Novaya Zemlya Bora on Sea Waves: Satellite Measurements and Numerical Modeling

Influence of Novaya Zemlya Bora on Sea Waves: Satellite Measurements and Numerical Modeling

Sensitivity Analysis of Forecasting Performance for ST6 Parameterization in High-Resolution Wave Model Based on WAVEWATCH III

Surface waves prediction based on long-range acoustic backscattering in a mid-frequency range

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