Wave hindcast studies using SWAN nested in WAVEWATCH III - comparison with measured nearshore buoy data off Karwar, eastern Arabian Sea

Amrutha, M. M.; Kumar, V. Sanil; Sandhya, K. G.; Nair, T. M. Balakrishnan; Rathod, J.L.

doi:10.1016/j.oceaneng.2016.04.032

Cited by 75 publications

(30 citation statements)

References 35 publications

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“…Also, a comparison of simulated and measured bivariate distributions of the maximum occurrence of sea state indicated that the ST4 physics package performed better than the ST2 physics package [34], although ST4 and ST2 showed an increased ability to predict significant wave height and wave energy period, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 9 shows the comparison of monthly averaged significant wave height distributions for no wind and with wind UnSWAN (L) simulations in November 2009. As one can see in Figure 9a, if the wave model does not consider wind effects, the spatial distribution of the significant wave height corresponds to the bathymetry contour shown in Figure 2b, indicating that wave model results are sensitive to bathymetry [34]. The wind forcing significantly changes the wave height spatial distribution (Figure 9b).…”

Section: Wind Effectsmentioning

confidence: 93%

“…However, UnSWAN-ST4 predicted the maximum occurrence sea state at the same significant wave height as the measurements, while UnSWAN-ST2 predicted it for the same energy period. This explains why UnSWAN-ST4 has a better modeling ability to predict significant wave height [34], while UnSWAN-ST2 performs slightly better in simulating energy period.…”

Section: Bivariate Distributionsmentioning

confidence: 96%

See 2 more Smart Citations

Wave Resource Characterization Using an Unstructured Grid Modeling Approach

Yang

Wang

2018

Energies

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Notably, spectral analysis indicates that the ST4 physics package improves upon the ST2 physics package's ability to predict wave power density for large waves, which is important for wave resource assessment, load calculation of devices, and risk management. In addition, bivariate distributions show that the simulated sea state of maximum occurrence with the ST4 physics package matched the observed data better than with the ST2 physics package. This study demonstrated that the unstructured grid wave modeling approach, driven by regional nested grid WWIII outputs along with the ST4 physics package, can efficiently provide accurate wave hindcasts to support wave resource characterization. Our study also suggests that wind effects need to be considered if the dimension of the model domain is greater than approximately 100 km, or O (10 2 km).

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Section: Discussionmentioning

confidence: 99%

Section: Wind Effectsmentioning

confidence: 93%

Section: Bivariate Distributionsmentioning

confidence: 96%

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Wave Resource Characterization Using an Unstructured Grid Modeling Approach

Yang

Wang

2018

Energies

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“…The quality of wave prediction and analysis is also being continuously improved mainly due to the availability of high quality input wind fields for sea-state prediction with the advancement of satellite measurements/oceanography. In the Indian context, there are many reported important research contributions on wave prediction/ hindcasting using third generation wave models [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Wave Hindcasting Using WAM and WAVEWATCH III: A Comparison Study Utilizing Oceansat-2 (OSCAT) Winds

Swain¹,

Pa²,

Balch³

et al. 2017

J Oceanogr Mar Res

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“…There has been considerable advancement in the field of wave modelling and prediction all over the world [7][8][9][10][11][12]. In the past a number of studies are carried in the North Indian Ocean using deep water wave models WAM and WAVEWATCH III [13][14][15][16]. In India, with the launch of OCEANSAT-I (IRS-P4) in 1999, extensive validations were performed with the third generation wave model WAM C4 for the Indian Ocean region using the analysed wind fields provided by NCMRWF [17].…”

Section: Introductionmentioning

confidence: 99%