Improving the accuracy of hurricane wave modeling in Gulf of Mexico with dynamically-coupled SWAN and ADCIRC

Vijayan, Linoj; Huang, Wenrui; Ma, Mengdi; Özgüven, Eren Erman; Ghorbanzadeh, Mahyar; Yang, Jieya; Yang, Zhaoqing

doi:10.1016/j.oceaneng.2023.114044

Cited by 19 publications

(8 citation statements)

References 29 publications

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“…Figure 6 also indicates that simulated maximum water levels are lower than those observed at most of the gauge locations. One possible explanation is that the simulation does not include wave heights, which [84] indicates were 0.5 m or greater for Michael at several of the observation locations examined here. Another is that the atmospheric forcing used here only includes the simulated TC wind fields, not the large-scale easterly flow that observation time series indicate increased water levels prior to the storm's arrival.…”

Section: Best Track Simulation and Comparison With Observationsmentioning

confidence: 92%

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Morss,

Ahijevych,

Fossell

et al. 2024

Water

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Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.

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Section: Best Track Simulation and Comparison With Observationsmentioning

confidence: 92%

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Morss,

Ahijevych,

Fossell

et al. 2024

Water

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show abstract

“…Red triangles denote highwater marks (HWMs) from the USGS survey (https://stn.wim.usgs.gov/FEV/#MichaelOct2018), utilized for hydrological validation within the numerical model. In light of this limitation, the time series of storm surge and significant wave height from our previous large-scale SWAN-ADCIRC modeling (Vijayan et al 2023) The model predicts storm surge and significant wave heights across the Gulf of Mexico during Hurricane Michael (Fig. 2b).…”

Section: Data Sourcesmentioning

confidence: 99%

Numerical modeling of wave-surge effects on barrier-island breaching in St. Joseph Peninsula during Hurricane Michael

Ma,

Huang,

Vijayan

et al. 2023

Preprint

Self Cite

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Better understanding the effects of hurricane wave and storm surge on barrier-island breaching is important for both scientific research and coastal hazards mitigations. In this study, the 2D non-hydrodynamic Xbeach model has been applied to investigate interactions of hurricane wave, storm surge, and morphological processes in the case study of St. Joseph Peninsula during Category 5 Hurricane Michael. Model validations show a 2.45% average error and the 0.88 skill score between modeled and observed high water marks and bed elevations, respectively. Analysis of spatial distributions of currents and water levels indicates that a narrow area was overtopped at peak storm surge and wave. The gap was then quickly enlarged as the breaching area by wave-surge actions. By investigating foredune and peak dune along the central axis of breaching area, it shows that the foredune erosion on the sea side by wave-surge-current indirectly lead to the breach of the peak dune area in the barrier island. The Froude number shows a strong correlation with quick erosion of the barrier, indicating wave-surge supercritical flow is one of the major factors causing the barrier breaching. Results of cross sections of bed elevations and instantaneous surge-wave profiles at different storm surge stages reveal the evolution of the barrier-island breach. Results from this study provide valuable references for coastal hazard mitigation and resilience communities.

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“…Amunugama et al (2020) successfully reproduced typhoon phenomena, storm surges, and wave height by employing the COAWST model to simulate several violent typhoons in Japan. Hurricane Michael was simulated by Vijayan et al (2023) using the dynamic coupling of SWAN and ADCIRC. They observed that the dynamic coupling model of SWAN and ADCIRC significantly improved the accuracy of the simulation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of significant wave height using a VMD-LSTM-rolling model in the South Sea of China

Ding,

Wu,

Shen

et al. 2024

Front. Mar. Sci.

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Accurate prediction of significant wave height is crucial for ocean engineering. Traditional time series prediction models fail to achieve satisfactory results due to the non-stationarity of significant wave height. Decomposition algorithms are adopted to address the problem of non-stationarity, but the traditional direct decomposition method exists information leakage. In this study, a hybrid VMD-LSTM-rolling model is proposed for non-stationary wave height prediction. In this model, time series are generated by a rolling method, after which each time series is decomposed, trained and predicted, then the predictions of each time series are combined to generate the final prediction of significant wave height. The performance of the LSTM model, the VMD-LSTM-direct model and the VMD-LSTM-rolling model are compared in terms of multi-step prediction. It is found that the error of the VMD-LSTM-direct model and the VMD-LSTM-rolling model is lower than that of the LSTM model. Due to the decomposition of the testing set, the VMD-LSTM-direct model has a slightly higher accuracy than the VMD-LSTM-rolling model. However, given the issue of information leakage, the accuracy of the VMD-LSTM-direct model is considered false. Thus, it has been proved that the VMD-LSTM-rolling model exhibits superiority in predicting significant wave height and can be applied in practice.

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Improving the accuracy of hurricane wave modeling in Gulf of Mexico with dynamically-coupled SWAN and ADCIRC

Cited by 19 publications

References 29 publications

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Numerical modeling of wave-surge effects on barrier-island breaching in St. Joseph Peninsula during Hurricane Michael

Prediction of significant wave height using a VMD-LSTM-rolling model in the South Sea of China

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