Mechanism of drag coefficient saturation at strong wind speeds

Abstract

Previous studies have demonstrated the saturation of drag coefficients at strong wind speeds. But the mechanism behind this saturation has not yet been fully clarified. In this study, at normal and strong wind speeds, we use a wind‐wave tank for investigating the peak enhancement factor of the wind‐sea spectrum, which is an appropriate wave parameter for representing interfacial flatness. We measured the water‐level fluctuation using wave gauges. At strong wind speeds, the result shows that the peak enhancement factor of the wind‐sea spectrum decreases with decreasing inverse wave age and with increasing wind speed. This suggests that the distinctive wind‐wave breaking occurs at strong wind speeds. It also suggests that this distinctive breaking of wind waves causes the saturation of drag coefficients at strong wind speeds.

Revised Estimates of Ocean Surface Drag in Strong Winds

“…This saturation level corresponds to a 34% increase relative to the original estimates, with saturation occurring at 12% lower wind speed threshold. Laboratory measurements of drag in high winds over short fetch must be cautiously compared to field measurements, due to the limitation of wind‐wave tanks to develop wave states comparable to those in the open ocean. Drag parameterizations derived from such experiments (Donelan et al, 2004; Takagaki et al, 2012, 2016; Troitskaya et al, 2012), as well as this study, should be used mindfully and cautiously when implemented into numerical weather prediction models such as WRF (Powers et al, 2017) or Model for Prediction Across Scales (MPAS, Skamarock et al, 2012; Skamarock et al, 2018). This work illustrates that not only are weather and ocean prediction models vulnerable to programmer errors but that the data sets and analyses from which parameterization schemes are derived may contain errors as well.…”

“…This parameterization was later used by many (Cavallo et al, 2013; Davis et al, 2008, 2010; Gopalakrishnan et al, 2012; Green & Zhang, 2013, 2014; Nolan et al, 2009). Further, the original data informed and influenced subsequent lines of research (Chen et al, 2007; Chen et al, 2013; Takagaki et al, 2012; Takagaki et al, 2016) and was influential enough to be covered by review papers (Black et al, 2007; Sullivan & McWilliams, 2010). It is thus important to reflect back on the literature since the original publication of the drag saturation data, with the new understanding that the absolute magnitudes of drag coefficient have been underestimated.…”

Correlation between foam-bubble size and drag coefficient in hurricane conditions

“…) at hurricane wind speeds. As noted by Takagaki et al, 2016, the difference in ! behavior in field and laboratory experiments has not yet been fully clarified.…”

Nonlinear Impacts of Surface Exchange Coefficient Uncertainty on Tropical Cyclone Intensity and Air‐Sea Interactions

“…Accurate estimates of the surface exchange coefficients at high wind speeds have been challenging to obtain in both the laboratory and nature, because of the dangers of collecting in situ observations within the turbulent TC boundary layer. As a result, the surface exchange coefficients are highly uncertain and recent studies often disagree on the sign of the relationship between wind speed and the exchange coefficients (Black et al, 2007;Bell et al, 2012;Chen et al, 2018;Donelan, 2018;Donelan et al, 2004;Holthuijsen et al, 2012;Hsu et al, 2017;Komori et al, 2018;Powell et al, 2003;Troitskaya et al, 2012Troitskaya et al, , 2016Takagaki et al, 2016). Bell et al (2012) attempted to estimate the surface exchange coefficients and suggested that the current uncertainty may be larger than 40%.…”