1. Introduction
a. Surface wind stress and drag coefficient
Tropical cyclones with strong winds, heavy rain, and storm surges produce severe damage to coastal and inland regions annually. To improve the prediction of tropical cyclones’ intensity, and the associated storm surges and precipitation, extensive studies have been devoted to typhoon–ocean interactions. The surface wind stress τ generated by tropical cyclone winds extracts energy and momentum from the storm, limiting its intensity, but also forces ocean currents (Emanuel 1995). It is often parameterized by a drag coefficient Cd, expressed as |τ| = ρairCd|U10|2, where ρair is the air density, and |U10| is the wind velocity at 10 m above the sea surface. Previous studies suggest various empirical forms of Cd as a function of |U10|, atmospheric stability, surface roughness, surface wave height, and wave age (e.g., Charnock 1955; Dyer 1974; Johnson et al. 1998; Drennan et al. 2003). Better understanding of the surface wind stress and its parameterization Cd is thus crucial for forecasting tropical cyclones and improving the prediction of the oceanic response to them.
b. Methods to estimate surface wind stress in tropical cyclones
Most previous studies have computed Cd under tropical cyclones from atmospheric measurements. The wind speed taken by the anemometers on buoys can be used to compute the momentum flux from tropical cyclones to the ocean (e.g., Potter et al. 2015). Powell et al. (2003) and Holthuijsen et al. (2012) estimated the surface roughness length and Cd using the profiles of tropical cyclones’ wind speed taken by GPS dropsondes, assuming the wind speed increases logarithmically with the height above the sea surface. Bell et al. (2012) studied the Cd using the atmospheric angular momentum budget and measurements of tropical cyclones’ wind speed taken by a stepped frequency microwave radiometer (SFMR) mounted on the aircraft.
Alternatively, Cd can be estimated from the momentum flux to ocean currents by measuring velocity under tropical cyclones. Jarosz et al. (2007) and Sanford et al. (2011) analyzed velocity measurements taken by the ADCP on moorings and three electromagnetic autonomous profiling explorer (EM-APEX) floats under tropical cyclones, respectively. Using the depth-integrated linear momentum equation (hereinafter, the linear momentum budget method), they estimated the magnitude of surface wind stress and parameterized Cd as a function of wind speed. The present study uses a similar approach to compute Cd under Typhoon Megi. Note that the Cd estimated using the oceanic velocity measurements as a bottom-up approach may be inconsistent with that using the atmospheric wind speed measurements.
c. Previous drag coefficient estimation methods
The Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) has made observations of the air–sea flux from low to moderate wind speeds (|U10| < 20 m s−1) using ships and buoys since the 1990s (Edson et al. 2013). Employing the TOGA COARE bulk algorithm 3.5 (Edson et al. 2013), the drag coefficient depends more strongly on wind speed than atmospheric stability. It increases from 1 × 10−3 to 2.4 × 10−3, >100%, as the wind speed |U10| increases from 5 to 20 m s−1, whereas the drag coefficient changes only about 5% when the atmospheric stability (z/L, where z is the height above the sea surface and L is the Monin–Obukhov length) varies from −1 to −0.2. Large and Pond (1981) report that Cd is a constant for |U10| = 4–11 m s−1 and increases linearly with wind speed for |U10| = 11–25 m s−1 (Fig. 1a). The latest TOGA COARE bulk algorithm 3.5 also proposes that Cd increases linearly with wind speed for |U10| = 6–25 m s−1 (Edson et al. 2013), with a value slightly greater than that reported by Large and Pond (1981).
(a) The drag coefficient Cd as a function of wind speed at 10 m above the sea surface |U10| from our analysis (thick red lines) and as proposed by previous investigators (other colors). (b) Angle between the surface wind and stress vectors from our analysis (thick red) and from Drennan et al. (1999, their Fig. 6), Zhang et al. (2009, their Figs. 1 and 3), and Potter et al. (2015, their Figs. 1 and 4). Measured wind speed from these investigators is extrapolated to 10 m above the sea surface assuming a logarithmic wind profile. The horizontal and vertical bars describe the ranges of their data. The positive angle implies a stress vector that points clockwise from the wind vector.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
The parameterization of Cd in tropical cyclone wind conditions has been studied extensively using atmospheric and oceanic measurements, laboratory experiments, and model simulations (e.g., Powell et al. 2003; Donelan et al. 2004; Jarosz et al. 2007; Black et al. 2007; Sanford et al. 2011; Holthuijsen et al. 2012; Chen et al. 2013). These studies generally support the linear increase of Cd with wind speed for |U10| < 25 m s−1. For stronger winds, |U10| > 30 m s−1, Cd is “saturated,” either remaining at a constant value or decreasing with wind speed (Fig. 1a). Bell et al. (2012) quantified Cd at wind speeds greater than 52 m s−1 in two hurricanes and report that Cd scattered for extremely high wind speeds |U10| = 52–72 m s−1, with a mean of 2.4 × 10−3 and a standard deviation of 1.1 × 10−3.
Recent studies suggest that the parameterization of Cd by |U10| varies in different sectors of the tropical cyclone. Holthuijsen et al. (2012) report that a maximum Cd (~4.6 × 10−3) is located at the front-left quadrant of the tropical cyclone, and a minimum (~1.7 × 10−3) is located on the right side for |U10| = 30–40 m s−1. Chen et al. (2013) used an atmosphere–wave–ocean coupled model to investigate Cd under Hurricane Frances. In contrast to results reported by Holthuijsen et al. (2012), they conclude that Cd is generally greater at the front-right quadrant of tropical cyclones than at the left side. They suggest that the variation in different quadrants is due to the spatial variability of surface waves forced by the rapid change of tropical cyclones’ wind.
Most previous studies assume that the crosswind stress is insignificant compared to the downwind stress (e.g., Large and Pond 1981). Recent field experiments investigating the effect of surface waves on surface wind stress report significant crosswind stress (Geernaert 1988; Drennan et al. 1999; Grachev et al. 2003; Zhang et al. 2009; Potter et al. 2015). Zhang et al. (2009) report that the direction difference between the surface wind and stress vectors varies from −40° to 60° for wind speeds 5–20 m s−1 (Fig. 1b). Under tropical cyclones, extremely complex surface waves can be generated. The effect of surface waves on the crosswind stress has been studied using numerical models coupled with the surface wave field in tropical cyclones (Moon et al. 2004; Chen et al. 2013; Reichl et al. 2014). Chen et al. (2013) report that the direction difference between the surface wind and the stress vectors is more than 20° within the eyewall of a tropical cyclone, again suggesting a significant crosswind stress.
Studies report a large variability in Cd at wind speeds greater than 25 m s−1 (Fig. 1a) and contradictory results for Cd distribution in different sectors of tropical cyclones (Holthuijsen et al. 2012; Chen et al. 2013). Numerical model studies show significant crosswind stress under tropical cyclones, but field observations are meager and vary greatly.
d. Drag coefficient study of Typhoon Megi 2010
In 2010 an international joint experiment, the Impact of Typhoons on the Ocean in the Pacific (ITOP), was conducted in the western Pacific to study the oceanic response to and recovery from tropical cyclones (D’Asaro et al. 2014). One of the primary scientific goals was to investigate surface wind stress under extreme wind conditions. During ITOP, seven EM-APEX floats were deployed to the right of Typhoon Megi’s track, a category 5 typhoon in October 2010. Following Sanford et al. (2011), we estimate downwind and crosswind stress using velocity measurements taken by these floats via the linear momentum budget method.
Typhoon Megi and EM-APEX float measurements are described in section 2, and the linear momentum budget method is discussed in section 3. The downwind and crosswind drag coefficients are defined. The tidal and low-frequency current velocities may introduce uncertainties to wind stress estimates and are discussed in section 4. The apparent drag coefficients are estimated in section 5. The Price–Pinkel–Weller model (PWP3D) has been used to study the ocean momentum response to tropical cyclones (Price et al. 1994; Sanford et al. 2011). Here, the PWP3D is used to assess the assumed linear momentum budget to estimate surface wind stress (section 6). A correction to the derived wind stress estimates is made to yield the adjusted wind stress (section 7), which is investigated using the PWP3D model. Our drag coefficient estimates and the direction difference between the surface wind and stress vectors are discussed and compared with previous studies (section 8).
2. Experiment and measurements
ITOP targeted Typhoons Fanapi and Megi using measurements taken from various atmospheric and oceanic platforms (D’Asaro et al. 2014). In this analysis, we focus on the drag coefficient estimated using measurements taken during Super Typhoon Megi only (Fig. 2a). Megi formed in the western Pacific on 12 October 2010 and intensified rapidly becoming a category 5 typhoon on 17 October (Wang and Wang 2014). Typhoon Megi moved primarily westward in the western Pacific, passed the northern Philippines on 18 October, turned northwestward into the South China Sea, and dissipated on 23 October after making landfall in China (D’Asaro et al. 2014; Wang and Wang 2014). This study focuses on measurements of Megi in the western Pacific only.
(a) Typhoon Megi’s track (black curve with dots), deployment positions of EM-APEX floats (blue and magenta dots), and position of mooring SA1 (red dot) and (b) the wind map of wind speed at 10 m above the sea surface (color shading) at 2030 UTC 16 Oct at the arrival time of Typhoon Megi at the float array, AVISO surface geostrophic current velocity (black arrows) on 17 Oct, EM-APEX float positions and trajectories (blue and magenta dots and curves), and mooring SA1 position (red dot). Typhoon track is labeled with time as month/day/hour UTC. Float deployment details are listed in Table 1.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
Between 12 and 18 October, 221 GPS dropsondes were deployed from the C130 aircraft to measure vertical profiles of wind speed and direction (Hock and Franklin 1999), and SFMR mounted on the bottom of the C130s measured the microwave brightness temperature. The measurements of microwave brightness temperature were processed to estimate the wind speed at 10 m above the sea surface (|U10|) as described in Uhlhorn and Black (2003). These were cross calibrated and combined to construct a map of the surface winds (Fig. 2b), as described in appendix A. During the measurement period, the radius of maximum wind speed was 15 km, smaller than the average size of tropical cyclones in the western Pacific (~40 km), and the westward translation speed was ~7 m s−1, faster than typical tropical cyclones at the same latitude (4–5 m s−1).
Seven EM-APEX floats were deployed by a C130 aircraft, at a horizontal separation of ~25 km, along 128.3°E between 18.7° and 21°N on 16 October 2010, 1 day before the arrival of the eye of Typhoon Megi (Fig. 2a; Table 1). Floats were recovered by the R/V Roger Revelle on 19 October, 3 days after the deployment. Three EM-APEX floats measured the oceanic response to winds greater than 25 m s−1. One float (em3763c) passed directly under the eye of Megi; the other two floats (em4913a and em3766c) passed at ~42 km and ~73 km north of Megi’s eye on the right side of the storm track. Data obtained from these three floats are used to compute the surface wind stress in this study.
EM-APEX float deployment locations at the time they began profiling during the ITOP experiment.
EM-APEX floats measure the electric and magnetic fields in the ocean (Sanford et al. 2005). The oceanic current velocity, relative to a conductivity-weighted average current
Before the arrival of Megi, floats profiled vertically from near the surface to 230-m depth at a profiling speed of 0.1–0.12 m s−1. Between 1000 UTC 16 October and 2100 UTC 18 October when Megi passed the float array, EM-APEX floats profiled between 30- and 230-m depth to prevent damage by storm-induced surface waves. In the following analysis, the current velocity in the upper 30 m is assumed constant and extrapolated to the surface using the shallowest velocity measurement below 30-m depth. The floats’ positions during this period are estimated using the time integration of current velocity measured by the floats.
Strong near-inertial waves were generated on the right side of Megi due to the inertial resonance of the wind pattern. At 42 km to the right of the storm track (em4913a; Fig. 2a), the near-inertial current was greater than 1 m s−1 (Figs. 3d,e). The surface mixed layer, defined as the shallowest depth where the density gradient is greater than 0.03 kg m−4 and the density is greater than the surface values by more than 0.3 kg m−3, deepened by more than 20 m, from ~40- to 70-m depth, within one-half day after Megi’s eye arrived at the float array, ~2030 UTC 16 October. The base of the surface mixed layer oscillated by ~10 m near the inertial period due to the convergence and divergence of near-inertial waves, in agreement with observations reported by Gill (1984). The surface mixed layer cooled from 29.3° to 28.2°C in 1 day, presumably due to vertical mixing (Sanford et al. 2011).
(a),(d),(g) Zonal velocity, (b),(e),(h) meridional velocity, and (c),(f),(i) temperature measured by three EM-APEX floats. The dashed red lines mark the shallowest depth of float measurements. The distance of each float from Megi’s track is labeled to the right of the right column. The black arrows mark the closest approach of Typhoon Megi to the float array, 2030 UTC 16 Oct. The black dashed curves represent the depth of the surface mixed layer.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
The background oceanic current measured by the floats from the north to Megi’s track varied from 0.4 (em3766c) to 0.1 m s−1 (em3763c) at 12 h before Megi’s arrival (Fig. 3), consistent with the surface geostrophic current estimated from AVISO (Fig. 2b). Tidal currents were also present, though at velocities less than inertial waves, especially in the surface mixed layer. Detailed analysis of the tides is given in appendix B.
Several moorings were deployed during ITOP on the prevailing path of tropical cyclones (D’Asaro et al. 2014). One of the moorings, SA1, was located about 200 km north of Typhoon Megi’s track. The mooring was equipped with a 75-kHz upward-looking ADCP to measure current velocity between 50- and 550-m depth. In the following analysis, mooring velocity measurements will be used to quantify the tidal current during the observational period.
3. Estimates of surface wind stress and drag coefficients
a. Linear momentum budget method
















b. Downwind and crosswind drag coefficients









4. Storm-induced current velocity under Typhoon Megi
Only the wind-driven ocean current velocity vwind should be used to estimate surface wind stress in Eq. (4). The background currents, such as tides vtide and low-frequency currents vlow, which are not directly forced by Megi, will cause the linear momentum budget to unbalance. For example, the depth-integrated linear momentum of a diurnal barotropic tide K1 with amplitude 0.1 m s−1 from the ocean surface to 150-m depth is ~0.7 N m−2, leading to an error of ~20% in surface wind stress estimates if Cd = 4 × 10−3 at |U10| = 30 m s−1. The magnitude of low-frequency currents can also influence the Coriolis rotation term in the linear momentum budget. The integrated momentum of randomly distributed ϵ in the linear momentum budget was investigated but is negligible compared to the contributions of vwind, vtide, and vlow.
To estimate the apparent surface wind stress
Simulations of 40 000 normally distributed (vtide + vlow) are generated in the stochastic simulation (appendix B, section d) and removed from each of the observed EM-APEX float velocity profiles so as to generate 40 000 realizations of apparent wind-forced velocity profiles
5. Estimates of apparent drag coefficients
The |U10| for the computation of drag coefficients is simulated in the normal distribution using the measured wind speed and root-mean-square (RMS) error; RMS is ~4 m s−1 outside Megi’s eyewall, and ~5 m s−1 within the eyewall (appendix A, section a). Using the
(a) Apparent downwind drag coefficient
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
Estimates of apparent drag coefficient are presented as the function of wind speed at 10 m above the sea surface |U10| (small dots and thin vertical lines in Fig. 4). The standard deviations of estimates of apparent drag coefficients
To summarize the effect of wind speed on apparent drag coefficients, we further average drag coefficients in different bins of wind speed (±2 m s−1 at |U10| = 27 m s−1, ±3 m s−1 at |U10| = 31 m s−1, ±3 m s−1 at |U10| = 37 m s−1, and ±9 m s−1 at |U10| = 56 m s−1). We use the mean and standard deviation of apparent drag coefficients from each profile of EM-APEX floats to generate 1000 simulations assuming a normal distribution. Within each bin of wind speed, the apparent drag coefficient averages are computed using the generated simulations from at least three different profiles, and then the mean and the standard deviation of the apparent drag coefficient averages are computed. The estimates of
6. PWP3D model
The apparent drag coefficient under Typhoon Megi is estimated assuming balance of the linear momentum budget. Simulations of oceanic response under Typhoon Megi in the PWP3D model are used to validate the assumption of linear momentum balance here and to correct the apparent drag coefficient due to the neglect of nonlinear and pressure gradient terms (section 7).
a. Model description
Price et al. (1994) developed the PWP3D numerical model using momentum, continuity, temperature, and salinity equations to study oceanic responses to moving tropical cyclones. The initial temperature and salinity fields are assumed horizontally homogeneous. In the following model simulations, horizontally and temporally averaged vertical profiles of EM-APEX float measurements of temperature and salinity taken within 18–19 h before the arrival of Megi’s eye are used as the initial conditions. The horizontal spatial resolution is 3 km, and the temporal resolution is 180 s. The spatial domain of the ocean is ±375 km in the zonal direction and ±300 km in the meridional direction. The vertical resolution is 5 m from the ocean surface to 300-m depth.
In the model, vertical turbulent mixing is parameterized using the bulk Richardson number
For Typhoon Megi’s wind forcing in the model, the storm’s wind speed and direction are interpolated linearly in time, moving in the real translation track and passing the grid point at the center of the spatial domain at 2030 UTC 18 October. The wind speed at the floats’ positions in the model simulations is the same as the observations (Figs. 7g,h,i). The surface wind stress for |U10| < 25 m s−1 in all simulations presented in this study is computed using the drag coefficient parameterization proposed by Large and Pond (1981) and mapped |U10|, assuming no crosswind stress.
b. Model simulations and momentum budget
In the first model simulation presented in this study (hereafter referred to as simulation A), the surface wind stress τ in the model for |U10| > 25 m s−1 is computed using the mapped |U10| advected over the ocean and our estimates of apparent downwind drag coefficient but assuming no crosswind stress. Results of PWP3D model simulations of oceanic currents at three float positions as Megi approached the float array show the surface wind stress increasing and strong currents generated in the surface mixed layer (Fig. 5). At the arrival time of Megi, the simulated currents of ~1.5 m s−1 are similar to those observed by floats (Fig. 3), and the modeled mixed layer deepening, ~30 m, is consistent with the observed deepening at the float on the track of Megi (em3763c).
(a)–(c) PWP3D model simulations of zonal current velocity, (d)–(f) meridional current velocity, (g)–(i) estimated surface wind stress via momentum budget and linear momentum budget at three float positions. Distances of floats from Megi’s track are labeled to the right of the right column. Time is relative to the arrival of Typhoon Megi’s eye at the float array. Black dashed curves in (a)–(f) represent the base of the surface mixed layer. Black lines in (g)–(i) show the surface wind stress computed using observed winds in Typhoon Megi and the parameterization of apparent downwind drag coefficient. Green circles in (g)–(i) are the estimates of surface wind stress computed using the depth-integrated momentum budget [Eq. (3)]. Red curves are the estimates of surface wind stress computed using the depth-integrated linear momentum budget [Eq. (4)]. Purple curves are the pressure gradient term (1/ρ0)∇hp in the depth-integrated momentum budget [Eq. (3)], and cyan curves are the sum of nonlinear advection terms
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
The estimates of surface wind stress computed from Eqs. (3) and (4) are compared with the input surface wind stress (Fig. 5). The estimates of surface wind stress from Eq. (3) agree very well with the input surface wind stress, indicating that the momentum equation is implemented correctly and supporting the choice to neglect turbulent fluxes at 150-m depth in the model. Furthermore, the estimates of surface wind stress from Eq. (4) (the depth-integrated linear momentum budget) agree with the input surface wind stress with an uncertainty of <10% only before the arrival of the storm’s eye. The agreement within Megi’s eyewall and after the passage of Megi’s eye is poor because the pressure gradient and nonlinear advection terms induced by Typhoon Megi are important, as suggested by Sanford et al. (2011). Based on PWP3D model results, drag coefficient estimates using Eq. (4) are reliable only before the arrival of Typhoon Megi, and some corrections near the eyewall of Megi are required. Note that variations in air temperature or solar radiation have little effect on the momentum budget balance. The estimate of surface wind stress using the linear momentum equation varies less than 1%, even with a change of ±3°C in air temperature or an insolation peak increase from 500 to 1000 W m−2.
Because the nonlinear and pressure gradient terms at two floats near Megi’s track (em4913a and em3763c) led to an uncertainty of <15% before the passage of Megi’s eye, another PWP3D model run at the float positions of em3763c (9 km from Megi’s eye) was performed to investigate the corrections to the assumed linear momentum balance (section 7).
7. Estimates of adjusted drag coefficients
In simulation A (section 6), the downwind component of surface wind stress is computed using the mapped |U10| and apparent downwind drag coefficient for |U10| > 25 m s−1 but assuming no crosswind stress. The nonlinear and pressure gradient terms, which are excluded from the estimates of apparent drag coefficients in the linear momentum budget [Eq. (4)],
Correction to the apparent drag coefficients due to nonlinear and pressure gradient terms in the momentum budget. (left) The results from the PWP3D model employing the apparent downwind drag coefficient obtained in this analysis: (a) the sum of the nonlinear and pressure gradient terms on the downwind (green) and crosswind (purple) directions, (b) the apparent downwind drag coefficient (black) and the drag coefficient derived assuming a linear momentum balance (green dots), and (c) the zero crosswind drag coefficient used in the PWP3D model (horizontal black line) and the crosswind drag coefficient derived assuming a linear momentum balance (purple dots). (right) The results from the PWP3D model employing the adjusted downwind and crosswind drag coefficients: (d) the sum of the nonlinear and pressure gradient terms on the downwind (green) and crosswind (purple) directions; (e) the adjusted downwind drag coefficient (black), the downwind drag coefficient computed from the linear momentum budget (green dots), and the observed apparent downwind drag coefficient (red dots and vertical bars); and (f) the adjusted downwind drag coefficient (black), the crosswind drag coefficient computed from the linear momentum budget (purple dots), and the observed apparent crosswind drag coefficient (red dots and vertical bars). The blue dots in (f) show the angle between the wind and stress computed in simulation B. These final results are replotted in Fig. 1.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
Another simulation (simulation B) computes the surface wind stress using the mapped |U10| and adjusted downwind and crosswind drag coefficients for |U10| > 25 m s−1 (black curves in Figs. 6e,f). Drag coefficients computed using Eq. (4) (green dots and purple dots in Figs. 6e,f) agree well with the apparent drag coefficients discussed in section 5 (red dots with vertical bars in Figs. 6e,f), suggesting that the adjusted drag coefficients (black curves in Figs. 6e,f) are the better estimates of the true drag coefficients.
Table 2 summarizes the estimated adjusted drag coefficients in Typhoon Megi. The
The mean and standard errors of adjusted downwind and crosswind drag coefficients at different wind speed intervals during Typhoon Megi. Same as the values shown in Fig. 1.
To further validate our estimates of adjusted
Comparisons of float observations (dots with vertical error bars ± one standard deviation) and PWP3D model simulations of the depth-integrated (0–150 m) storm-induced (a)–(c) zonal linear momentum terms and (d)–(f) meridional linear momentum terms. Simulation B is performed using adjusted drag coefficients derived in the present analysis [red and blue curves in (a)–(f)]. Simulation C is performed using the Cd reported by Holthuijsen et al. (2012) [green and purple curves in (a)–(f)]. The observed wind speed at 10 m above the sea surface |U10| at the floats’ positions (black line with dots) is shown in the bottom panels. The abscissa is the time relative to the arrival time of Typhoon Megi’s eye at the float array. The vertical dashed line represents the time when the measured wind speed at the float positions reaches 25 m s−1.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
Comparisons of float observations and model results of the depth-integrated (0–150 m), storm-induced, linear momentum terms [∂u/∂t (circle), ∂υ/∂t (cross), fu (square), and fυ (triangle)] in simulation B (red) and simulation C (blue) before the passage of Typhoon Megi and at |U10| > 25 m s−1.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
8. Discussion
Many prior studies use wind speed profiles observed in the atmospheric boundary layer to investigate the neutral drag coefficient under tropical cyclones (Powell et al. 2003; Holthuijsen et al. 2012). In this study we instead use oceanic momentum response to study tropical cyclone surface wind stress (Jarosz et al. 2007; Sanford et al. 2011). The estimates of surface wind stress are parameterized by wind speed as drag coefficients.
For |U10| < 30 m s−1, the drag coefficient increases with wind speed, in agreement with previous studies (Fig. 1a). For |U10| = 30–40 m s−1, the magnitude of
For the crosswind component of surface wind stress for |U10| < 30 m s−1, the angle between wind and stress is nearly zero, indicating that the assumed alignment between wind and stress in Monin–Obukhov similarity theory is valid under Typhoon Megi at low wind speeds. For |U10| > 30 m s−1, the angle between wind and stress vectors is >15° (Fig. 1b), slightly greater than a numerical model result from the front-right quadrant of tropical cyclones (Chen et al. 2013). Previous studies suggest swell traveling under the tropical cyclone may yield significant crosswind stress (Chen et al. 2013; Reichl et al. 2014; Potter et al. 2015). The
9. Summary
Velocity, temperature, and salinity measurements were taken by seven EM-APEX floats air deployed on the right side of Typhoon Megi, a small and fast-moving category 5 typhoon, during the ITOP experiment in 2010. Downwind and crosswind drag coefficients were computed from three floats closest to the eye using the depth-integrated linear momentum equation.
Extensive efforts are devoted to estimate the uncertainty of the derived drag coefficients due to the uncertainty in velocity measurements and imperfections in removing non-wind-driven currents. Estimates of the “apparent” drag coefficients are made assuming a linear momentum equation. PWP3D model simulations show that the momentum balance is approximately linear before the arrival of the eye but with significant components of nonlinear and pressure gradient force after the passage of Megi. The effects of nonlinear and pressure gradient terms before the arrival of the typhoon are corrected using the model to obtain the adjusted downwind and crosswind drag coefficients (Fig. 1; Table 2).
At |U10| = 27 m s−1, our estimates of the downwind drag coefficient
We present, for the first time, measurements of the crosswind drag coefficient
This paper focuses on the drag coefficients under Typhoon Megi and on the details of the analysis method. Similar data are available for four other tropical cyclones (Hurricane Frances 2004, Hurricane Gustav 2008, Hurricane Ike 2008, and Typhoon Fanapi 2010). Drag coefficient analyses using the same method will be reported in a subsequent publication.
Acknowledgments
The authors appreciate the Office of Naval Research Physical Oceanography Program (N00014-08-1-0560, N00014-08-1-0577, N00014-10-1-0313, N00014-11-1-0375, and N00014-14-1-0360) for their support, the 53rd Weather Reconnaissance Squadron for their deployments of the EM-APEX floats, and J. F. Price for use of the PWP3D model. J. Carlson and J. Dunlap designed and built the EM sensor systems on the EM-APEX float. Constructive comments from J. F. Price and one anonymous reviewer are appreciated and greatly improved the presentation of this paper.
APPENDIX A
Typhoon Megi Wind Field
a. Wind speed processing
Continuous wind estimates at 10-m height above the sea surface U10 at each float were computed using data from aircraft penetrations of Typhoon Megi. Data from Vaisala RS92 dropsondes deployed by two WC-130J and one Taiwanese DOTSTAR aircraft (Wu et al. 2005) and from a nadir-looking SFMR (Uhlhorn and Black 2003) on the WC-130Js were used. These data can be obtained online (at http://catalog.eol.ucar.edu/itop_2010/). The positions of all measurements are determined by GPS data with an accuracy of better than 100 m.
The dropsondes measure horizontal wind velocity, temperature, and humidity from flight level to within a few meters above the sea surface. Wind speed accuracy is ±0.5 m s−1 (UCAR/NCAR 1993). The U10 was computed directly from the RS92 dropsonde profiles.
The SFMR measures the brightness temperature, which is used to estimate surface wind speed continuously along the aircraft track with a nominal error of 4.0 m s−1 (Uhlhorn and Black 2003) for winds greater than 20 m s−1. The SFMR data were calibrated to the dropsonde 10-m winds using all SFMR and dropsonde data collected during ITOP. The SFMR measurement with only the highest quality data (Q2 = 4) was used to calibrate each nearby dropsonde measurement of U10. Measurement pairs separated by more than 18 s were rejected. A total of 343 pairs remained. Significant bias errors were found after comparing dropsonde and SFMR measurements at the same wind speed and were corrected using a cubic polynomial in log10(|U10|). Corrections are small, 2 m s−1 or less for |U10| = 20–50 m s−1, but rise rapidly below this. The SFMR winds below 20 m s−1 were discarded in this study. The corrected SFMR data matched the dropsonde data with an RMS error of 3.5 m s−1, less than the nominal SFMR error of 4.0 m s−1 (Uhlhorn and Black 2003) and much more than the dropsonde error of 0.5 m s−1. The differences showed no trends in time, with aircraft number, with the distance between the plane and the dropsonde, or with the logged quality of the data.
The corrected SFMR winds, regardless of logged quality, and the 10-m dropsonde winds were used to create maps of the |U10| of Megi. Data from three aircraft surveys were used. Flight 530W deployed 16 dropsondes during two passes through the storm from ~0000 to 0400 UTC 16 October, immediately after deploying the EM-APEX floats. Flight 630W deployed 44 dropsondes during three passes through the storm from ~2100 UTC 16 October to ~0300 UTC 17 October. This was coordinated with a DOTSTAR flight that deployed 10 additional dropsondes around the periphery of the storm. The 744 data points (13 dropsondes) during this survey were within 100 km of the storm center at six different azimuths. This survey coincided with the passage of the storm’s eye through the float array and is the primary source of data for the drag coefficient calculations. Flight 830W deployed 27 dropsondes during four passes through the storm from ~1100 to 1600 UTC 17 October.
For each survey, a continuous wind field was created in two steps. First, a radially symmetric wind field was fit to the observations. For radii r > Rmax the form V = (VmaxRmax/r)n was used. For r < Rmax, the form V = Vmax(r/Rmax) was used. Within the eye, r < Reye, and V = Veye. Figure A1 shows this function for the 17 October survey (n = 0.5, Rmax = 12 km, Vmax = 70 m s−1, Reye = 6 km, and Veye = 10 m s−1). Although this captures much of the wind variations, azimuthal deviations of up to ±10 m s−1 are apparent. Both true azimuthal variations in the storm structure and errors in the location of the storm center used to make the radial map can cause the deviations. These deviations were then mapped (red regions in Fig. A2 representing a higher wind speed than the radial model and blue regions in Fig. A2 representing a lower wind speed) by smoothing the observations in polar coordinates using a Gaussian smoother with scales of 0.1 in log10(r) and π/4 in azimuth and added to the radially symmetric model to form a wind field for the storm at the time of the survey. The standard deviation of the maps from the data is 1.8 m s−1 for Typhoon Megi, well within the expected uncertainty in the SFMR data. The deviations at the peak winds are <1 m s−1. Two maps of the surface wind speed between the surveys, one earlier and one later, were constructed by moving the survey fields along the storm track, for example, the wind map of 17 October in Fig. A3. The |U10| on the floats is computed by linearly interpolating these two maps in time.
Fit of radial model (black line) to all 10-m wind speed measurements from WC130 (630W) and DOTSTAR flights centered on 0000 UTC 17 Oct 2010. Color indicates the azimuthal angle of each measurement, clockwise from north.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
Ratio of wind speed data (colored dots) to the radial model in Fig. 8 as a function of radius from the eye and azimuth from north. This is mapped to form a continuous function (colored).
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
Map of wind speed at 10-m height above the sea surface |U10| (m s−1) for 17 Oct 2010 aircraft surveys (630W) shown by background color with contours. Note logarithmic wind speed scale. Colored dots with black markers show measured wind speed. These are nearly invisible because they closely match the map. Map is shown at two different resolutions in the two panels.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
The float positions are computed by linearly interpolating between roughly hourly GPS fixes, when these are available. Between approximately yeardays 289.4 and 291.5, the floats stopped surfacing to avoid damage and no GPS fixes are available. During these times, float positions were estimated by integrating the depth-averaged velocity measured by the floats, starting from the last GPS fix and adjusting this trajectory with a linear trend to hit the next GPS fix. The error is estimated from the size of this adjustment, typically a few centimeters per second, resulting in a position error of a few kilometers. The wind speeds computed at these floats’ positions are used to calculate drag coefficient in this study (Fig. A4).
Wind speed at three EM-APEX floats. Black bars show the times of the three storm surveys interpolated to the locations of each float. The inset shows contours of the mapped wind field at the time of peak wind at the innermost float. Heavy lines show the trajectories of the three floats each starting at a yellow circle and with the position of the yellow star at the time of the inset map. Dashed line shows the storm track moving from east to west.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
For the outer two floats, the lateral gradients are small. The uncertainty in the estimated wind speed is dominated by measurement and mapping errors. The former uncertainty is taken as the RMS SFMR error ~3.5 m s−1, and the latter is conservatively taken as the RMS deviation of the data from the map ~1.8 m s−1. Combining these yields 4.0 m s−1 RMS. This error is conservative because much of the estimated SFMR error is undoubtedly due to variability in the dropsonde velocity estimates due to boundary layer turbulence.
The central float, under the greatest wind speed, passed under the storm eyewall north of the eye but inside the maximum wind region, one of high spatial gradients. The resulting time series of wind at the float is highly sensitive to the exact float track and the details of the wind map. In particular, the double wind peak results from the float passing under the comma-shaped wind maximum (Fig. A4). Neither the float track nor the wind map are sufficiently accurate to capture the details of this feature correctly. This large uncertainty only occurs for about 1 h while the float was in Megi’s eyewall, with a plausible estimate of the RMS wind speed uncertainty at any given time ±10 m s−1. The uncertainty in the peak hourly average wind speed is less because the float position is known to about 1 km from a recent GPS fix and the small advective velocities. Because the storm moved nearly westward, the float certainly went through the eyewall north of the eye in a region of 50–80 m s−1 winds. The average wind during this time is estimated at 71 ± 5 m s−1 computed by fitting a smoothing spline to the nearby data points and resampling them randomly.
b. Inflow angle of Typhoon Megi
Following Zhang and Uhlhorn (2012), we use data taken from GPS dropsondes deployed in Typhoon Megi between 0100 UTC 16 October and 1200 UTC 17 October to compute the inflow angle α. The mean of α (
The mean and standard deviation of storm-relative inflow angle averaged between 10 and 50 m above the sea surface
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
We average
APPENDIX B
Estimates of Tides and Low-Frequency Ocean Currents on EM-APEX Floats
Tides and low-frequency currents can result in the surface wind stress induced by Typhoon Megi to fall out of balance with the integrated wind-driven momentum in the linear momentum budget. The following analysis focuses on the methods to estimate tidal and low-frequency currents and their corresponding uncertainties to isolate the current velocity for surface wind stress estimates. The subsurface mooring SA1, with an upward-looking 75-kHz ADCP mounted at 550-m depth, was located about 90 km west of the float array (Fig. 2b). The estimates of tides using the velocity measurements taken by the ADCP on the subsurface mooring SA1 are used to estimate tidal amplitude and phase at the EM-APEX floats positions.
a. Estimates of tidal amplitude and phase
We use harmonic fitting to estimate the phase and amplitude of the tides, assuming that the observed velocity is a linear superposition of mean current, inertial waves, and K1 and M2 tides. Velocity measurements on mooring SA1 show an amplitude of K1 greater than O1 and M2 greater than S2 (not shown in this study). The short time interval of velocity measurements on EM-APEX floats, ~3 days, is also not capable of separating the similar frequency signals for K1 from O1 and S2 from M2.
The harmonic analysis is applied to EM-APEX float velocity measurements between 0200 UTC 16 October and 0100 UTC 19 October, with an inertial period at 18°–22°N of 32–38 h and a K1 tidal period of 24 h and M2 of 12.42 h. Note that the periods of inertial waves and diurnal tides are not far apart and therefore estimates of diurnal tides in the linear harmonic analysis could be biased by the presence of inertial waves induced by Megi. To minimize these contamination effects on the estimates of tidal currents, we apply the harmonic analysis to the velocity data between 200- and 220-m depth, which is much deeper than the penetration depth of the storm-induced inertial waves.
Table B1 summarizes tidal current amplitudes and phases estimated from velocity measurements taken from EM-APEX floats and on mooring SA1. The K1 on SA1 leads the K1 on the float (em4390d) at about 20.2°N by about 71° ± 9°. Assuming the K1 tide propagates eastward from the Luzon Strait (Zhao 2014), we estimate a zonal wavelength of 310–400 km and a phase speed of 3.6–4.6 m s−1. Our estimate of the phase speed agrees with the climatology K1 phase speed from AVISO satellite observations of 4–5 m s−1 (Zhao 2014). Estimates of the M2 tidal phase at the float array vary greatly, presumably due to multiple M2 tide sources arriving on the experimental site, as suggested by Zhao (2014). The M2 amplitude estimates from the floats are 0.04–0.14 m s−1 and are generally weaker than the K1 tide.
The estimates of amplitude and phase of K1 and M2 tides from EM-APEX float and mooring SA1 observations. The tidal function is assumed as A cos(ωt + θ), where A is the amplitude, ω is the tidal frequency, and θ is the phase at t = 0, which is the arrival time of Typhoon Megi at the float array, about 2030 UTC 16 Oct. The subscripts x and y are the zonal and meridional directions, respectively. The harmonic fitting is applied to the velocity measurements between 200- and 220-m depth from 0200 UTC 16 Oct to 0100 UTC 19 Oct.
b. Estimation of tides assuming first-mode baroclinic tide
We extrapolate tidal amplitude and phase estimated using EM-APEX float velocity measurements at 200–220-m depth to the layer above 200-m depth. The extrapolation depends on the vertical phase propagation and vertical structure of amplitude, which are not available from limited float measurements. The vertical structure of tidal amplitudes and vertical phase propagation observed on mooring SA1 before Typhoon Megi (Fig. B1) are used to guide the vertical extrapolation of tidal amplitudes and phases on EM-APEX float positions.
Estimates of (top) amplitudes and (bottom) phases of (left) K1 and (right) M2 tidal zonal velocity at mooring SA1. A moving window of ±2 days is used in the harmonic analysis. The white dashed line shows the arrival time of the center of Typhoon Megi on the EM-APEX float array.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
We divide the velocity measurements on SA1 into three layers: (A) 60–150-m depth, (B) 150–200-m depth, and (C) 200–220-m depth. The phases of diurnal and semidiurnal tides estimated on SA1 do not show significant differences between layers A and B, with a mean phase difference of ~0 and a standard deviation of ~30°, and are independent of tidal amplitudes (Figs. B2a,b), suggesting a vertical standing feature of diurnal and semidiurnal tides.
Estimates of the vertical phase propagation and the error on the extrapolation of K1 and M2 amplitude from the deeper layer (layer C) on mooring SA1 to the upper layer using ADCP velocity from April to October 2010. (a),(b) The mean and one standard deviation of the difference of the estimated tidal phases between layers A and B, plotted as a function of tidal amplitude at 0.02 m s−1 interval. (c),(d) The mean and standard deviation of the difference of the estimated tidal amplitude in layer A from that extrapolated from layer C using the first baroclinic modal structure. The range of tidal amplitude estimated in layer C on EM-APEX floats is indicated as the thick black horizontal bars at the bottom of (a)–(d). (e) Example of the correction of tidal amplitude extrapolation on 0000 UTC 17 Oct at mooring SA1. The amplitude of the zonal velocity of the K1 tide on the mooring (black thick line) in layer C is extrapolated to layer A based on the first-mode baroclinic tide vertical structure (black dashed line). The mean bias of amplitude between extrapolated and estimated tides within layer A is −0.03 m s−1 with the standard deviation 0.02 m s−1.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
We extrapolate the amplitude of tides on SA1 from layer C to layer A based on the vertical structure of the first-mode baroclinic tide. The eigenmode structure is obtained using the density profiles taken by Argo floats between the surface and 1600-m depth near SA1 and EM-APEX floats during 14–18 October. The difference between the tidal amplitude estimated directly from velocity in layer A and that estimated from the extrapolation from layer C is computed. Their mean values and one standard deviation of difference are computed as a function of the tidal amplitude in layer C. They vary from −0.08 to 0.05 m s−1 with one standard deviation about 0.03 m s−1 (Figs. B2c,d). The standard deviation of amplitude within layer A is 0.02 m s−1. The amplitude difference due to extrapolation (Figs. B2c,d) is applied to the extrapolation of tidal amplitudes on EM-APEX float measurements.
c. Estimation of low-frequency current
Because the surface geostrophic current and the barotropic adjustment velocity
d. Tides and low-frequency current in the stochastic simulation
The velocity of tides on the EM-APEX floats is extrapolated to the ocean surface assuming the vertical structure of the first-mode baroclinic tide; 10 000 simulations of normally distributed tides (K1 + M2) are generated in a stochastic simulation, using the mean and standard deviation of vertical phase propagation and amplitude difference according to the analysis of tides on mooring SA1 (appendix B, section b). The average velocity of tides near the ocean surface at the arrival of Typhoon Megi is ~0.1–0.15 m s−1 (Fig. B3). The low-frequency current is estimated in four different selected averaging periods (appendix B, section c) after removing the 10 000 simulations of tides. The average low-frequency current speed in the surface mixed layer is less than 0.1 m s−1 at the float em3763c on Typhoon Megi’s track (Fig. B3). The 40 000 simulations of background current velocity (tides + low-frequency current) are removed from the EM-APEX velocity profiles before estimating surface wind stress.
(a)–(c) Average of tidal zonal current velocity, (d)–(f) tidal meridional current velocity, and (g)–(i) low-frequency zonal (blue lines) and meridional current (red lines) velocity in the stochastic simulation at three float positions (rows). Distances of floats from Megi’s track are labeled to the right of the right column. Time is relative to the arrival of Typhoon Megi’s eye at the float array.
Citation: Journal of Physical Oceanography 47, 3; 10.1175/JPO-D-16-0069.1
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