Observation and numerical experiments for drag coefficient under typhoon wind forcing

Cao, Huiqiu; Zhou, Liangming; Li, Shuiqing; Wang, Zhifeng

doi:10.1007/s11802-017-3091-4

Cited by 3 publications

(6 citation statements)

References 24 publications

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“…In a storm surge model, the drag coefficient C d is generally used to calculate the wind stress. The parameterization of C d has been widely studied through laboratory experiments, model simulations, and oceanic measurements [7][8][9][11][12][13]15,18,32,33]. Most previous studies exhibited a linear increase of the drag coefficient with wind speed U 10 at 10 m above the sea surface.…”

Section: Wind-stress Drag Coefficientmentioning

confidence: 99%

“…When the wind speed is larger than 6 m/s, the piecewise function C d of Yelland and Taylor [32] also supports a linear increase with wind speed U 10 . Analogously, the latter two parts from piecewise Function (15) are in linear forms, and the only distinction is the unequal coefficients. These five drag coefficients calculated from Equations ( 11)-( 15) are shown in Figure 2.…”

Section: Wind-stress Drag Coefficientmentioning

confidence: 99%

“…In order to achieve the goals of the present study, two sets of numerical experiments were designed. In the first experiment, five drag coefficient formulas from Equations ( 11)- (15) were used for the storm surge model (denoted by E1, E2, E3, E4, and E5, respectively) in order to evaluate the influence of the drag coefficient on the storm surge level (Table 2). In the second experiment, we estimated the influence of the initial values of a and b in the C d expression on the storm surge level in the process of data assimilation.…”

Section: Experimental Designs and Model Setting 241 Experimental Designsmentioning

confidence: 99%

“…In early studies, the drag coefficient appeared in the form of linear expression [7][8][9][10]. In recent years, many scholars have been devoted to optimizing and improving the drag coefficient through different methods [11][12][13][14][15][16][17][18][19][20]. By combining observed data, Guan and Xie extended the drag coefficient to a near-linear function that depended on wave age [11].…”

Section: Introductionmentioning

confidence: 99%

“…The formulation of the drag coefficient was also applied to a typhoon forecast model, which suggested that predictions of surface wind speed and typhoon track were promoted [14]. Based on numerical experiments and observations in the South China Sea, Cao et al formulated the drag coefficient related to wind speed in the form of a piecewise function [15]. This parameterization of the drag coefficient raised the model accuracy compared to that from Wu [2] used in SWAN.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assimilation Research of Wind Stress Drag Coefficient Based on the Linear Expression

Nie

et al. 2021

JMSE

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The wind stress drag coefficient plays an important role in storm surge models. This study reveals the influences of wind stress drag coefficients, which are given in form of formulas and inverted by the data assimilation method, on the storm surge levels in the Bohai Sea, Yellow Sea, and East China Sea during Typhoon 7008. In the process of data assimilation, the drag coefficient is based on the linear expression Cd = (a + b × U10) × 10−3 (generally speaking, a and b are empirical parameters determined by observed data). The results showed that the performance of the data assimilation method was far superior to those of drag coefficient formulas. Additionally, the simulated storm surge levels obviously changed in the neighborhood of typhoon eye. Furthermore, the effect of initial values of a and b in the Cd expression on the storm surge levels was also investigated when employing the data assimilation method. The results indicated that the simulation of storm surge level was the closest to the observation when a and b were simultaneously equal to zero, whereas the simulations had slight differences when the initial values of a and b were separately equal to the drag coefficients from the work of Smith, Wu, and Geernaert et al. Therefore, we should choose appropriate initial values for a and b by using the data assimilation method. As a whole, the data assimilation method is much better than drag coefficient parameterization formulas in the simulation of storm surges.

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Section: Wind-stress Drag Coefficientmentioning

confidence: 99%

Section: Wind-stress Drag Coefficientmentioning

confidence: 99%

Section: Experimental Designs and Model Setting 241 Experimental Designsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assimilation Research of Wind Stress Drag Coefficient Based on the Linear Expression

Nie

et al. 2021

JMSE

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Analysis of the spatial and temporal sensitivities of key parameters in the SWAN model: An example using Chan-hom typhoon waves

Zhang

et al. 2020

Estuarine, Coastal and Shelf Science

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Numerical Study on Storm Surge Level Including Astronomical Tide Effect Using Data Assimilation Method

Nie

et al. 2022

Atmosphere

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In the storm surge model, the wind drag coefficient Cd is a critical parameter that has a great influence on the forecast of the storm surge level. In the present study, the effect of various wind drag coefficient parameterizations on the storm surge level is investigated in the Bohai Sea, Yellow Sea and East China Sea for Typhoons 7203 and 7303. Firstly, the impacts of initial values of a and b in the linear expression Cd = (a + b × U10) × 10−3 on the pure storm surge model are evaluated based on the data assimilation method. Results indicate that when a and b (i.e., the wind drag coefficients given by Smith, Wu, Geernaert et al. and Mel et al.) are non-zeros, the performance of the model has little difference, and the result from Wu is slightly better. However, they are superior to the performance of the model adopting zero initial values. Then, we discuss the influences of diverse ways of calculating wind drag coefficients, which are inverted by data assimilation method (including both linear and constant Cd) and given in the form of linear formulas, on simulating pure storm surge level. They show that the data assimilation-based coefficients greatly exceed those of the ordinary coefficient formulas. Moreover, the wind drag coefficient in the linear form is a little better than that in constant form when the data assimilation method is used. Finally, the assessment of the impact of astronomical tides on the storm surge level is conducted, and the simulation demonstrates that the storm surge model, which has the combination of four constituents (M2, S2, K1, O1) and wind drag coefficient inverted by the data assimilation method with the linear Cd, exhibits the best performance.

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Observation and numerical experiments for drag coefficient under typhoon wind forcing

Cited by 3 publications

References 24 publications

Assimilation Research of Wind Stress Drag Coefficient Based on the Linear Expression

Assimilation Research of Wind Stress Drag Coefficient Based on the Linear Expression

Analysis of the spatial and temporal sensitivities of key parameters in the SWAN model: An example using Chan-hom typhoon waves

Numerical Study on Storm Surge Level Including Astronomical Tide Effect Using Data Assimilation Method

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