Estimating Overwater Friction Velocity and Exponent of Power-Law Wind Profile from Gust Factor during Storms

Hsu, S. A.

doi:10.1061/(asce)0733-950x(2003)129:4(174)

Cited by 49 publications

(36 citation statements)

References 6 publications

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“…4. Another novelty related to the previous one in our validation method is the computation of the instantaneous roughness of the sea according to the wavelength and wave period given by the buoy [46]. In this way we can obtain the wind speed at 10 m in a given instant, because the buoys provides wavelength, wave period and wind speed at 3 m in that instant.…”

Section: Discussionmentioning

confidence: 99%

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Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

et al. 2017

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In this article, offshore wind energy potential is measured around the West Mediterranean using the WRF meteorological model without 3DVAR data assimilation (the N simulation) and with 3DVAR data assimilation (the D simulation). Both simulations have been checked against the observations of six buoys and a spatially distributed analysis of wind based on satellite data (second version of Cross-Calibrated Multi-Platform, CCMPv2), and compared with ERA-Interim (ERAI). Three statistical indicators have been used: Pearson's correlation, root mean square error and the ratio of standard deviations. The simulation with data assimilation provides the best fit, and it is as good as ERAI, in many cases at a 95% confidence level. Although ERAI is the best model, in the spatially distributed evaluation versus CCMPv2 the D simulation has more consistent indicators than ERAI near the buoys. Additionally, our simulation's spatial resolution is five times higher than ERAI. Finally, regarding the estimation of wind energy potential, we have represented the annual and seasonal capacity factor maps over the study area, and our results have identified two areas of high potential to the north of Menorca and at Cabo Begur, where the wind energy potential has been estimated for three turbines at different heights according to the simulation with data assimilation.

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Section: Discussionmentioning

confidence: 99%

“…All the buoys have their anemometer at a height of 3 m (z = 3m), and it was necessary to estimate wind speed at 10 meters to compare with the two WRF simulations. Instead of assuming a constant roughness [23], Hsu's [46] equation was used to calculate the instantaneous roughness of the sea (z 0 ):…”

Section: Evaluation Of Wrf Runsmentioning

confidence: 99%

Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

et al. 2017

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“…ψ m (z/L) is the Monin-Obukhov similarity function for momentum flux, which is neglected under neutral stability conditions (simulations S4 and S5 in Table 2). Under non-neutral stability conditions (simulation S3), this function is defined according to Hsu [56]:…”

Section: Wind Stress Formulationsmentioning

confidence: 99%

“…for unstable conditions (z/L < 0) (10) According to Donelan [57], the stability parameter (z/L) is related to the bulk Richardson number Ri B , such that for unstable conditions (i.e., T w > T a ) (11) and for unstable conditions (i.e., T a > T w ) (12) T a and T w being the air and water temperatures, respectively, and [54,56] (3), (7) and (8). Furthermore, there is experimental evidence of the increase of C D over water surfaces for weak winds under 5 m/s [58][59][60].…”

Section: Wind Stress Formulationsmentioning

confidence: 99%

Envisat/ASAR Images for the Calibration of Wind Drag Action in the Doñana Wetlands 2D Hydrodynamic Model

et al. 2013

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Doñana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of Doñana's topography, the wind drag action can induce the flooding or emergence of extensive areas, detectable in remote sensing images. Envisat/ASAR scenes acquired before and during strong and persistent wind episodes enabled the spatial delineation of the wind-induced water displacement. A two-dimensional hydrodynamic model of Doñana wetlands was built in 2006 with the aim to predict the effect of proposed hydrologic restoration actions within Doñana's basin. In this work, on-site wind records and concurrent ASAR scenes are used for the calibration of the wind-drag modeling by assessing different formulations. Results show a good adjustment between the modeled and observed wind drag effect. Displacements of up to 2 km in the wind direction are satisfactorily reproduced by the hydrodynamic model, while including an atmospheric stability parameter led to no significant improvement of the results. Such evidence will contribute to a more accurate simulation of hypothetic or design scenarios, when no information is available for the atmospheric stability assessment.

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“…• -77 overwater wind speed measurements, a preliminary estimation of the gust factor G (which is defined as the ratio of the maximum gust speed to the sustained wind speed) could be done via the following empirical formulation (Hsu 2001(Hsu , 2003a(Hsu , 2003b:…”

Section: Seawatch Buoymentioning

confidence: 99%

Offshore wind speed and wind power characteristics for ten locations in Aegean and Ionian Seas

et al. 2012

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This paper utilizes wind speed data measured at 3 and 10 m above water surface level using buoys at 10 stations in Ionian and Aegean Seas to understand the behaviour of wind and thereafter energy yield at these stations using 5 MW rated power offshore wind turbine. With wind power densities of 971 and 693 W/m 2 at 50 m above water surface level, Mykonos and Lesvos were found to be superb and outstanding windy sites with wind class of 7 and 6, respectively. Other locations like Athos, Santorini and Skyros with wind power density of more than 530 W/m 2 and wind class of 5 were found to be the excellent sites. Around 15-16% higher winds were observed at 10 m compared to that at 3 m. Lower values of wind speed were found during summer months and higher during winter time in most of the cases reported in the present work. Slightly decreasing (∼2% per year) linear trends were observed in annual mean wind speed at Lesvos and Santorini. These trends need to be verified with more data from buoys or from nearby onshore meteorological stations. At Athos and Mykonos, increasing linear trends were estimated. At all the stations the chosen wind turbine could produce energy for more than 70% of the time. The wind speed distribution was found to be well represented by Weibull parameters obtained using Maximum likelihood method compared to WAsP and Method of Moments.

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Estimating Overwater Friction Velocity and Exponent of Power-Law Wind Profile from Gust Factor during Storms

Cited by 49 publications

References 6 publications

Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

Envisat/ASAR Images for the Calibration of Wind Drag Action in the Doñana Wetlands 2D Hydrodynamic Model

Offshore wind speed and wind power characteristics for ten locations in Aegean and Ionian Seas

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