Drag Coefficients with Fetch-Limited Wind Waves*

Lin, Weiqi; Sanford, Lawrence P.; Suttles, Steven E.; Valigura, Richard A.

doi:10.1175/1520-0485(2002)032<3058:dcwflw>2.0.co;2

Cited by 22 publications

(24 citation statements)

References 44 publications

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“…This is certainly the case for Lake Valkea-Kotinen due to its small size. In order to test the hypothesis that this physical effect plays a significant role, a momentum flux partitioning parameterisation (Appendix 2) was introduced in LAKE model following parameterisations suggested in Lin et al (2002) and Kitaigorodskii and Volkov (1965). This parameterisation includes wind fetch that is set to a constant value of 100 m. The resulting temperature profiles may be seen in Fig.…”

Section: Temperature Profilesmentioning

confidence: 99%

“…Following (Kitaigorodskii and Volkov, 1965;Lin et al, 2002), one can write: where r a Á the air density, C Á the mean wave phase speed, C wave,10 Á the surface drag coefficient for 10 m height, u 10 Á the wind speed at 10 m over the lake. The difference of formulation used here from that by (Lin et al, 2002) is that C wave,10 is calculated by surface flux scheme of LAKE model, i.e. including effects of stratification, whereas in (Lin et al, 2002) it is given by a logarithmic law.…”

Section: Appendix 2 Momentum Flux Partitioningmentioning

confidence: 99%

“…The difference of formulation used here from that by (Lin et al, 2002) is that C wave,10 is calculated by surface flux scheme of LAKE model, i.e. including effects of stratification, whereas in (Lin et al, 2002) it is given by a logarithmic law. For the mean wave speed we use the relation with dominant wave speed (C p ), C 00.83C p (e.g.…”

Section: Appendix 2 Momentum Flux Partitioningmentioning

confidence: 99%

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Simulation of surface energy fluxes and stratification of a small boreal lake by a set of one-dimensional models

Stepanenko¹,

Joehnk²,

Machulskaya³

et al. 2014

Tellus A: Dynamic Meteorology and Oceanography

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Simulation of surface energy fluxes and stratification of a small boreal lake by a set of one-dimensional models A B S T R A C T Five one-dimensional (1D) lake models were run for the open water season in 2006 for Lake Valkea-Kotinen (Finland) using on-lake measured meteorological forcing. The model results were validated using measurements of water temperature and of eddy covariance (EC) fluxes. The surface temperature is satisfactorily simulated by all models showing slight overestimation (by 0.1Á1.18C). Both sensible and latent heat fluxes are positively biased in respect to EC data, consistent with earlier studies. However, correlation coefficients between EC-fluxes and those simulated are relatively high ranging from 0.55 to 0.74. The skill to simulate vertical temperature profiles by different models is assessed as well. It is found that the lake models underestimate the EC-derived surface drag coefficient, however providing realistic temperature profiles. It is argued that the real momentum flux from the atmosphere is larger than simulated, however it is split up between the wave development and the acceleration of lake currents. Adopting the simple parameterisation for momentum flux partitioning in one of the models showed that this mechanism can be significant. Finally, the effect of including the lake bathymetry data in k-o models was the drastic overheating of water below the thermocline. This is likely to be caused by omitting the heat flux at the lake margins. Thus, the parameterisation of heat flux at the lake's margins should be included in the models; otherwise it is recommended to neglect bathymetry effects for such small water bodies as the Lake Valkea-Kotinen.

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Section: Temperature Profilesmentioning

confidence: 99%

Section: Appendix 2 Momentum Flux Partitioningmentioning

confidence: 99%

Section: Appendix 2 Momentum Flux Partitioningmentioning

confidence: 99%

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Simulation of surface energy fluxes and stratification of a small boreal lake by a set of one-dimensional models

Stepanenko¹,

Joehnk²,

Machulskaya³

et al. 2014

Tellus A: Dynamic Meteorology and Oceanography

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show abstract

“…Lin et al () studied the air‐sea momentum flux exchange from the standpoint of waveform drag theory, defining the waveform drag as

τ_{p} = ρ_{a} C_{D} ((), u_{10, n} - 0.83 C_{p}) ∣ u_{10, n} - 0.83 C_{p} ∣,

where C D = [ κ / ln (10/ z 0, n )] 2 (the same as in equation , assuming that the logarithmic wind profile holds), C p is the peak wave phase velocity, and | · | indicates the absolute value for a quantity. It should be stressed that the form drag coefficient C D defined by Lin et al () is related to the roughness length z 0, n instead of the shape of sea waves. García‐Nava et al () and Romero et al () used a similar concept, but a more complicated expression for the form drag coefficient.…”

Section: Discussionmentioning

confidence: 99%

Effects of Wind Direction on Variations in Friction Velocity With Wind Speed Under Conditions of Strong Onshore Wind

et al. 2018

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The effects of wind direction on variations in friction velocity with wind speed are studied under moderate (≥9 m/s) to strong (≥22 m/s) onshore wind conditions using 20‐Hz ultrasonic wind data from a coastal tower at three different heights. The effects of different averaging time intervals of 20, 10, 2, and 1 min on the variations are also investigated. Three typhoons passed by the tower during the 150 hr of observations. Regardless of wind direction, friction velocity increases with increasing wind speed, and linear regression shows that the rate of increase is ~50% less than previously reported. However, a leveling‐off or decrease in friction velocity with increasing wind speed is found under strong wind conditions using a bin‐averaged method. Wind direction affects the variations in friction velocity with wind speed. Friction velocity increases with increasing wind speed at a similar growth rate to that of previously published results, with a leveling‐off or decrease at wind speeds higher than 22 m/s when the wind blows roughly normal to the shoreline. Form drag induced by sea waves with longer wavelengths is suggested to be responsible for the effects of wind direction on the variations in friction velocity with wind speed. An averaging time interval of 1 min yields representative variations in friction velocity with wind speed using the observations described in this study.

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“…However, these functions show major discrepancies. Lin et al (2002) pointed out that the variability of C d not explained by U 10 is substantial. Weaver (2004) used the advanced circulation hurricane storm surge model (ADCIRC) to test the sensitivity of storm surges on seven different C d .…”

Section: Introductionmentioning

confidence: 99%

Adjoint free four-dimensional variational data assimilation for a storm surge model of the German North Sea

et al. 2016

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In this paper, a data assimilation scheme based on the adjoint free Four-Dimensional Variational(4DVar) method is applied to an existing storm surge model of the German North Sea. To avoid the need of an adjoint model, an ensemble-like method to explicitly represent the linear tangent equation is adopted. Results of twin experiments have shown that the method is able to recover the contaminated low dimension model parameters to their true values. The data assimilation scheme was applied to a severe storm surge event which occurred in the North Sea in December 5, 2013. By adjusting wind drag coefficient, the predictive ability of the model increased significantly. Preliminary experiments have shown that an increase in the predictive ability is attained by narrowing the data assimilation time window.

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Drag Coefficients with Fetch-Limited Wind Waves*

Cited by 22 publications

References 44 publications

Simulation of surface energy fluxes and stratification of a small boreal lake by a set of one-dimensional models

Simulation of surface energy fluxes and stratification of a small boreal lake by a set of one-dimensional models

Effects of Wind Direction on Variations in Friction Velocity With Wind Speed Under Conditions of Strong Onshore Wind

Adjoint free four-dimensional variational data assimilation for a storm surge model of the German North Sea

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