Effects of tidal currents on winter wind waves in the Qiongzhou Strait: a numerical study

Bai, Peng; Zheng, Lianyuan; Liu, Cong; Wu, Junshan; Xie, Lingling

doi:10.1007/s13131-020-1673-2

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…The root mean square error (RMSE) of wave height from COAWST was 0.93 m, with a correlation coefficient of 0.97. In addition, the COAWST model also performed well in the hydro-meteorological simulations over China [23].…”

Section: Coawst Modelmentioning

confidence: 83%

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

Zang,

Zou,

et al. 2024

Atmosphere

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In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases.

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Section: Coawst Modelmentioning

confidence: 83%

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

Zang,

Zou,

et al. 2024

Atmosphere

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“…COAWST modeling system is an open-source tool that includes an ocean model (ROMS); an atmospheric model (WRF); one hydrology model (WRF-Hydro); two wave models (SWAN and WAVEWATCHIII), one Sediment Models for sediment transport; and a sea-ice model. The COAWST system can be very useful for offshore studies because it can consider/model the complex interactions between the ocean, waves, and the atmosphere [e.g., see 35,36,37,38].…”

Section: The Coawst Modelmentioning

confidence: 99%

Considering the effects of sea waves on offshore wind simulations in the Weather Research and Forecasting model

Penchah,

Bakhoday-Paskyabi,

Bui

2024

Preprint

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One of key factors in simulating offshore wind field is con- sidering the effects of waves on momentum and energy ex- changes between the atmosphere and ocean. However, most NWP models traditionally overlook the effects of waves, due to the lack of suitable roughness length parameterization. To address this issue, coupling atmospheric and wave mod- els has emerged which handles the wind-wave interaction through the exchange of a few quantities between two mod- els. On the other hand, integrating atmospheric and wave models requires substantial computational resources and as- sociated expenses. In this study, we developed an offline wave coupling method in the WRF to account for wave ef- fects on wind field simulations. With this method, the WRF is modified to read wave parameters and bathymetry data, and to parameterize roughness length using these data. Our study aims to evaluate the offline method by comparing it to an online coupling system that uses the coupled atmosphere and wave models, as well as observation data. We found that the offline method utilizes almost 17% fewer computational resources and 19% less computational time compared to the online method, while maintaining identical forecasting accu- racy. Evaluation of the results during dominant swell con- dition suggested that both methods are able to reduce the forecast uncertainty compared to LiDAR measurement spe- cially in the lower part of atmosphere.

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Seasonal amplitude of principal tidal constituents in shallow shelf regions

Huang,

2024

Estuarine, Coastal and Shelf Science

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Effects of tidal currents on winter wind waves in the Qiongzhou Strait: a numerical study

Cited by 5 publications

References 33 publications

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

Considering the effects of sea waves on offshore wind simulations in the Weather Research and Forecasting model

Seasonal amplitude of principal tidal constituents in shallow shelf regions

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