Wave measurement and modeling in Chesapeake Bay

Lin, Weiqi; Sanford, Lawrence P.; Suttles, Steven E.

doi:10.1016/s0278-4343(02)00120-6

Cited by 80 publications

(52 citation statements)

References 18 publications

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“…Furthermore, the SWAN simulations overestimate the H s and underestimate the T 01 where wave heights are less than 1 m (Figure 2a,b). A similar conclusion is found in Lin et al's paper [44], and it is commonly acknowledged that the third generation wave models are generally weak in predicting wave periods [45]. In addition, a similar deviation was exposed in pioneer simulations [35].…”

Section: Validations In Time Domainsupporting

confidence: 83%

Wind Profiles and Wave Spectra for Potential Wind Farms in South China Sea. Part II: Wave Spectrum Model

Liu

Qian

et al. 2017

Energies

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Abstract:Along with the commercialization of offshore wind energy in China, the South China Sea has been identified as ideal for constructing offshore wind farms, especially for farms consisting of floating wind turbines over deep waters. Since the wind profiles and wave spectra are somewhat primitive for the design of an offshore wind turbine, engineering models describing the wind and wave characteristics in the South China Sea area are necessary for the offshore wind energy exploitation given the meteorological, hydrological, and geographical differences between the South China Sea and the North/Norwegian Sea, where the commonly used wind profile and wave spectrum models were designated. In the present study; a series of numerical simulations were conducted to reveal the wave characteristics in the South China Sea under both typhoon and non-typhoon conditions. By analyzing the simulation results; the applicability of the Joint North Sea Wave Project (JONSWAP) spectrum model; in terms of characterizing the wind-induced wave fields in the South China Sea; was discussed. In detail; the key parameters of the JONSWAP spectrum model; such as the Phillips constant; spectral width parameter; peak-enhancement factor, and high frequency tail decay; were investigated in the context of finding suitable values.

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Section: Validations In Time Domainsupporting

confidence: 83%

Wind Profiles and Wave Spectra for Potential Wind Farms in South China Sea. Part II: Wave Spectrum Model

Liu

Qian

et al. 2017

Energies

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“…In addition, it is also to be observed that the SWAN model slightly underestimates periods which present relatively large bias and E MRS . This result is consistent with the conclusions obtained by Lin et al (2002) in the Cheasapeake Bay and by Dragani et al (2008) …”

Section: Resultssupporting

confidence: 94%

Numerical simulation of wind waves on the Río de la Plata: evaluation of four global atmospheric databases

et al. 2012

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The performance of NCEP/NCAR I, NCEP/DOE II, JRA-25 and ERA-Interim global databases, implemented as atmospheric forcings of the SWAN model in the Río de la Plata region, was quantitatively tested by calculating the bias, the mean square root error, the determination coefficient and the slope of the line fitted between observed and simulated wave parameters (significant wave height, mean period and direction). Even though statistical estimators showed no evident differences for wave periods and directions some noticeable differences were observed for simulated significant wave heights depending on the forcing used. The lowest bias (0.22 m) was obtained when the SWAN model was forced by ERA-Interim. With regard to the mean square root errors, the lowest values were obtained when NCEP/NCAR I (0.16 m) and NCEP/DOE II (0.19 m) were used as forcing. In addition, the best slope for simulated heights (0.79) was obtained using NCEP/DOE II. Computed determination coefficients for heights, periods and directions were very similar (0.89-0.93) for all the simulations carried out in this study. Energetic and severe wave events were given special consideration. The most energetic wave episode recorded in the Río de la Plata mouth (24 August, 2005) was analyzed and discussed in particular. It was concluded that during energetic atmospheric conditions the best agreement is achieved by implementing NCEP/DOE II as forcing. In the light of these results it is concluded that NCEP/DOE II is the most suitable atmospheric forcing to simulate wave heights with the SWAN model in the Río de la Plata region.

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“…This formulation yields an approximately linear relationship between the drag coefficient and wind speed when the Charnock parameter is taken as constant. Numerous studies have accounted for sea state within this parameter by using a wave age (C p /u * or C p /U 10 ) formulation of the drag coefficient or the Charnock parameter (Geernaert et al 1986;Lin et al 2002;Edson et al 2013;Fisher et al 2015). In coastal environments, fetch limitation can result in high degrees of spatial variability in surface wind stress due to a combination of variable surface winds and waves, which can result in significant spatial and temporal variations in the drag coefficient .…”

Section: A Wind-wave Effects In the Atmospheric Surface Boundary Layermentioning

confidence: 99%

“…However, the upper Chesapeake Bay is characterized as a pure wind-sea environment, such that wave energy in the upper bay is entirely generated by local winds, with any incoming ocean swell dissipating to negligible energy levels by the time it reaches the midbay (Lin et al 2002). Phillips (1985) hypothesized that a portion of the wind-sea spectrum would be in equilibrium with wind forcing, such that the source terms in Eq.…”

Section: August 2017 F I S H E R E T a Lmentioning

confidence: 99%

Surface Wave Effects on the Translation of Wind Stress across the Air–Sea Interface in a Fetch-Limited, Coastal Embayment

Fisher

Sanford

Scully

et al. 2017

Journal of Physical Oceanography

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The role of surface gravity waves in structuring the air-sea momentum flux is examined in the middle reaches of Chesapeake Bay. Observed wave spectra showed that wave direction in Chesapeake Bay is strongly correlated with basin geometry. Waves preferentially developed in the direction of maximum fetch, suggesting that dominant wave frequencies may be commonly and persistently misaligned with local wind forcing. Direct observations from an ultrasonic anemometer and vertical array of ADVs show that the magnitude and direction of stress changed across the air-sea interface, suggesting that a stress divergence occurred at or near the water surface. Using a numerical wave model in combination with direct flux measurements, the air-sea momentum flux was partitioned between the surface wave field and the mean flow. Results indicate that the surface wave field can store or release a significant fraction of the total momentum flux depending on the direction of the wind. When wind blew across dominant fetch axes, the generation of short gravity waves stored as much as 40% of the total wind stress. Accounting for the storage of momentum in the surface wave field closed the air-sea momentum budget. Agreement between the direction of Lagrangian shear and the direction of the stress vector in the mixed surface layer suggests that the observed directional difference was due to the combined effect of breaking waves producing downward sweeps of momentum in the direction of wave propagation and the straining of that vorticity field in a manner similar to Langmuir turbulence.

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Wave measurement and modeling in Chesapeake Bay

Cited by 80 publications

References 18 publications

Wind Profiles and Wave Spectra for Potential Wind Farms in South China Sea. Part II: Wave Spectrum Model

Wind Profiles and Wave Spectra for Potential Wind Farms in South China Sea. Part II: Wave Spectrum Model

Numerical simulation of wind waves on the Río de la Plata: evaluation of four global atmospheric databases

Surface Wave Effects on the Translation of Wind Stress across the Air–Sea Interface in a Fetch-Limited, Coastal Embayment

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