NOVEMBER 2012 Z H A N G A N D U H L H O R N 3587 Hurricane Sea Surface Inflow Angle and an Observation-Based Parametric Model JUN A. ZHANG Rosenstiel School of Marine and Atmospheric Science, University of Miami, and NOAA/AOML/Hurricane Research Division, Miami, Florida ERIC W. UHLHORN NOAA/AOML/Hurricane Research Division, Miami, Florida (Manuscript received 22 November 2011, in final form 2 May 2012) ABSTRACT This study presents an analysis of near-surface (10 m) inflow angles using wind vector data from over 1600 quality-controlled global positioning system dropwindsondes deployed by aircraft on 187 flights into 18 hurricanes. The mean inflow angle in hurricanes is found to be 222.6862.28 (95% confidence). Composite analysis results indicate little dependence of storm-relative axisymmetric inflow angle on local surface wind speed, and a weak but statistically significant dependence on the radial distance from the storm center. A small, but statistically significant dependence of the axisymmetric inflow angle on storm intensity is also found, especially well outside the eyewall. By compositing observations according to radial and azimuthal location relative to storm motion direction, significant inflow angle asymmetries are found to depend on storm motion speed, although a large amount of unexplained variability remains. Generally, the largest storm- 2 relative inflow angles (,2508) are found in the fastest-moving storms (.8ms 1) at large radii (.8 times the radius of maximum wind) in the right-front storm quadrant, while the smallest inflow angles (.2108) are found in the fastest-moving storms in the left-rear quadrant. Based on these observations, a parametric model of low-wavenumber inflow angle variability as a function of radius, azimuth, storm intensity, and motion speed is developed. This model can be applied for purposes of ocean surface remote sensing studies when wind direction is either unknown or ambiguous, for forcing storm surge, surface wave, and ocean circulation models that require a parametric surface wind vector field, and evaluating surface wind field structure in numerical models of tropical cyclones. 1. Introduction In situ near-surface wind vector observations in tropical cyclones are available from global positioning system Estimating hurricane surface wind distributions and (GPS) dropwindsondes deployed by research and re- maxima is an operational requirement of the National connaissance aircraft (Hock and Franklin 1999). Glob- Hurricane Center (NHC), as coastal watches and warn- ally, however, direct measurements of sea surface winds ings are issued based on storm impacts at landfall, in- in tropical cyclones are still highly infrequent, so methods cluding storm surge. Fairly recent development of have been developed to estimate surface winds from wind reliable instrumentation has resulted in more accurate data observed at higher altitudes by research aircraft estimates of tropical cyclone surface wind speed and (e.g., Franklin et al. 2003; Powell et al. 2009), from sat- direction. Currently, remotely sensed surface wind ellite imagery (Velden et al. 2006), and from surface speed observations in tropical cyclones are provided pressure observations (Knaff and Zehr 2007). In com- by spaceborne microwave sensors (Katsaros 2010) and parison to surface wind speed data, wind direction in- airborne stepped-frequency microwave radiometers formation is exceedingly sparse. (SFMR; Uhlhorn and Black 2003; Uhlhorn et al. 2007). Mapping the two-dimensional surface wind vector field in tropical cyclones has several important applica- tions. First, storm surge models are generally forced by Corresponding author address: Dr. Jun Zhang, NOAA/AOML/ Hurricane Research Division, Universtiy of Miami/CIMAS, 4301 surface winds, which not only require the wind magni- Rickenbacker Causeway, Miami, FL 33149. tude but also the wind direction. It has traditionally been E-mail: [email protected] a standard practice to use axisymmetric parametric wind DOI: 10.1175/MWR-D-11-00339.1 Ó 2012 American Meteorological Society Unauthenticated | Downloaded 09/24/21 12:31 AM UTC 3588 MONTHLY WEATHER REVIEW VOLUME 140 models to force storm surge models (e.g., Peng et al. of the ocean response to hurricanes when a parametric 2006; Rego and Li 2009). These parametric wind models, wind model is used to force an ocean model (e.g., Price such as the Sea, Lake and Overland Surges from Hur- 1983; Yablonsky and Ginis 2009; Halliwell et al. 2011). ricanes (SLOSH) wind model (Phadke et al. 2003), Because of the ubiquitous cyclonic flow near the sur- Holland’s model (Holland 1980; Holland et al. 2010), face in tropical cyclones, documentation of observed and Willoughby’s model (Willoughby et al. 2006) esti- surface wind directions is typically described in terms of mate the radial profile of axisymmetric wind speed or surface inflow angles, although such studies are rela- tangential wind component without wind direction in- tively sparse. Numerical studies (e.g., Kepert 2010a,b; formation. The wind direction is then arrived at by Bryan 2012) often cite the observational result pre- applying a constant inflow angle, and an asymmetry in sented by Powell (1982, hereafter P82) from data ob- wind speed is simply assumed due to storm forward tained in Hurricane Frederic (1979). Earth-relative motion. Some storm-surge studies (e.g., Westerink et al. inflow angles over the open ocean were found by P82 to 2008) have utilized the National Oceanic and Atmo- vary from outflow of 1128 to inflow of 2558, with greater spheric Administration (NOAA)/Hurricane Research inflow in the right-rear (RR) quadrant and weaker in- Division (HRD) real-time Hurricane WIND analysis flow in the left-front (LF) quadrant, and with a mean value system (H*WIND) product (Powell et al. 1998), which of 2228. Powell et al. (2009) examined a large sample of estimates surface wind direction applied to SFMR wind dropwindsonde data and found a mean inflow angle of speeds as simply a constant angle subtracted from the 2238, although details regarding asymmetric structure flight-level wind direction (M. Powell 2005, personal were not investigated. Note that the original analytical communication). treatment of tropical cyclone inflow was presented by Second, remotely sensed wind direction accuracy in Malkus and Riehl (1960), who suggested an axisymmetric tropical cyclones, particularly in the high-wind inner- average inflow angle of 2208 to 2258 outside of the eye- core region, is often highly degraded as a result of sev- wall, but decreasing to less than 258 at the radius of eral physical factors. Nadir-viewing passive microwave maximum wind (Rmax), was consistent with boundary radiometers (e.g., SFMR) are insensitive to wind di- layer energy constraints. This conclusion also depended on rection and spaceborne wide-swath imagers suffer from knowledge of the surface exchange coefficients of mo- resolution and rain absorption artifacts (e.g., Connor mentum and moist enthalpy, and boundary layer depth, and Chang 2000). Active microwave sensors such as which were not very well known at the time (e.g., French scatterometers may saturate, are attenuated in heavy et al. 2007; Zhang et al. 2008, 2009; Zhang 2010; Haus precipitation, and are also limited by spatial resolution et al. 2010; Kepert 2010a; Smith and Montgomery 2010). for tropical cyclone applications, particularly in the The purpose of this paper is to investigate the mean inner-core region (Brennan et al. 2009). The resolution and asymmetric structure of near-surface inflow angle limitations can be overcome by synthetic aperture radar (at an altitude of 10 m) over a broad range of tropical (SAR); however, SARs typically provide only a single cyclone characteristics, including storm motion, in- view and therefore determining the wind direction is an tensity, and size, utilizing the extensive database of GPS ambiguous problem (Shen et al. 2009). In addition, it is dropwindsonde wind vector observations. Based on the often assumed that the surface roughness elements that data analysis results, a simple parametric model of the provide the radar backscatter mechanism are aligned mean plus wavenumber-1 asymmetric inflow angle field with the wind direction, which may not always be ac- is developed and tested. Section 2 describes the data curate (e.g., Donelan et al. 1997; Drennan et al. 1999; sources, quality control, and analysis methodology. In Grachev et al. 2003; Drennan et al. 2003). section 3, analysis results are presented for both mean Third, predicting tropical cyclone intensity is viewed and asymmetric fields. Section 4 describes the para- as a coupled atmosphere–ocean problem, thus under- metric model development, evaluation, and case-study standing the air–sea interaction and ocean feedbacks comparisons and section 5 summarizes the results and to hurricanes is of paramount importance (e.g., Black discusses the applications of the parametric model. et al. 2007; Shay et al. 1989; Jacob et al. 2000; Shay and Uhlhorn 2008; Jaimes and Shay 2010; Uhlhorn and Shay 2. Data and quality control 2012). Accurately specifying the surface wind direction may benefit wave forecast models, which have been in- GPS dropwindsonde data used in this study were creasingly inserted into the air–sea interface in coupled collected on 187 hurricane research and reconnaissance model applications (e.g., Moon et al. 2007; Zhao and flights in 18 hurricanes (Table 1) between 1998 and 2010. Hong 2011). Accurate representation of the surface Detailed description of dropwindsonde instrumentation wind