Wave Spectral Patterns during a Historical Cyclone: A Numerical Model for Cyclone Gonu in the Northern Oman Sea

Allahdadi, Mohammad Nabi; Chaichitehrani, Nazanin; Allahyar, Mohammadreza; McGee, Laura

doi:10.4236/ojfd.2017.72009

Cited by 24 publications

(25 citation statements)

References 26 publications

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“…This approach represents dissipation of spectral energy as a function of mean spectral frequency and steepness. It is an appropriate approach for simulation of wave height as a result of generation and growth by local wind, and the default method for resolving whitecapping dissipation in SWAN and other popular spectral models like WAM and Mike21-SW. To achieve higher simulation accuracies for wave height and wave period, calibrations of the model for the whitecapping parameter and the wave period parameter delta are necessary (Allahdadi et al, 2017;Siadatmousavi et al, 2012;Niroomandi et al, 2018;Allahdadi et al, 2004a). However, wave model applications for different regions (including open-ocean and shelf waters) show that Komen-type methods tend to underestimate both peak and mean wave periods (SWAN, 2015;van Vledder et al, 2016;Siadatmousavi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is imperative to examine these application ranges for different regions. Otherwise, extensive efforts may be required for model sensitivity analysis and calibration (Chaichitehrani, 2018;Allahdadi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The present study was motivated by a recent 31-year simulation that authors implemented for the US east coast to characterize wave energy resources (Allahdadi et al, 2019). We used SWAN for this modeling since due to complex coastal geometry we could benefit from the flexible mesh option made available by SWAN.…”

Section: Introductionmentioning

confidence: 99%

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Predicting ocean waves along the US east coast during energetic winter storms: sensitivity to whitecapping parameterizations

Allahdadi

Neary

2019

Ocean Sci.

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Abstract. The performance of two methods for quantifying whitecapping dissipation incorporated in the Simulating Waves Nearshore (SWAN) wave model is evaluated for waves generated along and off the US east coast under energetic winter storms with a predominantly westerly wind. Parameterizing the whitecapping effect can be done using the Komen-type schemes, which are based on mean spectral parameters, or the saturation-based (SB) approach of van der Westhuysen (2007), which is based on local wave parameters and the saturation level concept of the wave spectrum (we use “Komen” and “Westhuysen” to denote these two approaches). Observations of wave parameters and frequency spectra at four National Data Buoy Center (NDBC) buoys are used to evaluate simulation results. Model–data comparisons show that when using the default parameters in SWAN, both Komen and Westhuysen methods underestimate wave height. Simulations of mean wave period using the Komen method agree with observations, but those using the Westhuysen method are substantially lower. Examination of source terms shows that the Westhuysen method underestimates the total energy transferred into the wave action equations, especially in the lower frequency bands that contain higher spectral energy. Several causes for this underestimation are identified. The primary reason is the difference between the wave growth conditions along the east coast during winter storms and the conditions used for the original whitecapping formula calibration. In addition, some deficiencies in simulation results are caused along the coast by the “slanting fetch” effect that adds low-frequency components to the 2-D wave spectra. These components cannot be simulated partly or entirely by available source terms (wind input, whitecapping, and quadruplet) in models and their interaction. Further, the effect of boundary layer instability that is not considered in the Komen and Westhuysen whitecapping wind input formulas may cause additional underestimation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting ocean waves along the US east coast during energetic winter storms: sensitivity to whitecapping parameterizations

Allahdadi

Neary

2019

Ocean Sci.

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“…However, along the west coast of India, the sea breeze plays a major role in modulating the wave conditions that lead to the dominance of wind seas over the swells at several occasions (Vethamony et al, ; Aboobacker et al, ). Although relatively low in occurrence, the tropical cyclones generate high waves in the Arabian Sea with SWHs up to 5.0–10.0 m (Mashhadi et al, ; Allahdadi et al, ).…”

Section: Introductionmentioning

confidence: 99%

The climatology of shamals in the Arabian Sea—Part 2: Surface waves

Aboobacker¹,

Shanas²

2018

Intl Journal of Climatology

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The wave conditions in the Arabian Sea are mainly controlled by the SW/NE monsoon winds, the swells from the South Indian Ocean, and to some extent by the regional/local wind systems. The shamal winds are one of the important regional wind systems, which contribute to the wave characteristics in the Arabian Sea considerably. Previous studies explored the existence of shamal swells in the Arabian Sea, based on measurements and modelling during selected shamal events. The present study investigates the climatological features of the shamal waves in the Arabian Sea by analysing ERA-Interim significant wave heights (SWH) for the period 1987-2016. The ERA-Interim SWHs were validated against altimeter observations and available measurements in the Arabian Sea. Monthly and annual climatology of shamal and total waves were derived, the trends were estimated and discussed. The results indicate that the annual and monthly shamal SWHs (mean and 99th percentile) are significantly decreasing in most part of the Arabian Sea, while they are increasing in the Gulf of Oman. Significant increase in the 99th percentile total SWHs were estimated in the central Arabian Sea. The makran windinduced waves were identified in the northern Arabian Sea for the first time.

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“…This approach represents dissipation of spectral energy as a function of mean 45 spectral frequency and steepness. It is an appropriate approach for simulation of wave height as a result of generation and growth by local wind, and the default method for resolving whitecapping dissipation in SWAN and other popular spectral models like WAM and Mike21-SW. To achieve higher simulation accuracies for wave height and wave period, calibrations of the model for the whitecapping parameter and the wave period parameter delta are necessary (Allahdadi et al, 2017;Siadatmousavi et al, 2011 and2012;Kamranzad et al, 2016;Niroomandi et al, 2018; 50 . However, wave model applications for different regions show that Komen-type methods tend to underestimate both peak and mean wave periods.…”

mentioning

confidence: 99%

Predicting Ocean Waves along the U.S. East Coast During Energetic Winter Storms: sensitivity to Whitecapping parameterizations

Allahdadi¹,

He²,

Neary³

2018

Preprint

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The performance of two methods for quantifying whitecapping dissipation incorporated in the SWAN wave model is evaluated for waves generated along and off the U.S. East Coast under energetic winter storms with a predominantly westerly wind. Parameterizing the whitecapping effect can be done using the Komen-type schemes, which are based on mean spectral parameters, or the saturation-based (SB) approach of van der Westhuysen (2007), which is based on local wave parameters and the saturation level concept of the wave spectrum (we use "Komen" and 15 "Westhuysen" to denote these two approaches). Observations of wave parameters and frequency spectra at four NDBC buoys are used to evaluate simulation results. Model-data comparisons show that when using the default parameters in SWAN, both Komen and Westhuysen methods underestimate wave height. Simulations of mean wave period using the Komen method agree with observations, but those using the Westhuysen method are substantially lower.Examination of source terms shows that the Westhuysen method underestimates the total energy transferred into the 20 wave action equations, especially in the lower frequency bands that contain higher spectral energy. Several causes for this underestimation are identified. The primary reason is the difference between the wave growth conditions along the East Coast during winter storms and the conditions used for the original whitecapping formula calibration. In addition, some deficiencies in simulation results are caused along the coast by the "slanting fetch" effect that adds low-frequency components to the 2-D wave spectra. These components cannot be simulated partly or entirely by 25 available wind input formulations. Further, the effect of boundary layer instability that is not considered in the Komen and Westhuysen whitecapping wind input formulas may cause additional underestimation. 65parameters as incorporated in SWAN performed slightly better in the simulation of wave height, period, and frequency spectra than the SB method. This conclusion contradicts the results of previous studies. Although van Velder et al. Ocean Sci. Discuss., https://doi.

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Wave Spectral Patterns during a Historical Cyclone: A Numerical Model for Cyclone Gonu in the Northern Oman Sea

Cited by 24 publications

References 26 publications

Predicting ocean waves along the US east coast during energetic winter storms: sensitivity to whitecapping parameterizations

Predicting ocean waves along the US east coast during energetic winter storms: sensitivity to whitecapping parameterizations

The climatology of shamals in the Arabian Sea—Part 2: Surface waves

Predicting Ocean Waves along the U.S. East Coast During Energetic Winter Storms: sensitivity to Whitecapping parameterizations

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