WSR – 88D Observations of Tropical Cyclone Low-level Wind Maxima

1. WSR – 88D Observations of Tropical Cyclone Low-level Wind Maxima Texas Tech UniversityWind Science and Engineering Lubbock Severe Weather Conference February, 18 2010 Ian M. Giammanco1, John L. Schroeder2, Mark D. Powell3, Doug A. Smith4 1 PhD Candidate Wind Science and Engineering, Texas Tech University 2 Associate Professor, Atmospheric Science Group, Texas Tech University 3 Research Meteorologist, NOAA COAPS, Florida State University 4 Associate Professor, Department of Civil Engineering, Texas Tech University

2. OVERVIEW • Literature review • VAD Processing technique • Composite profiles • Identified features VAD wind profile example, KMLB Hurricane Frances (2004)

3. Literature Review • Kepert (2001) and Kepert and Wang (2001) provided mechanisms in which “super-gradient” flow could develop in the hurricane boundary layer • Powell et al. (2003) and Franklin et al. (2003) showed a broad “jet-like” feature near 500 m in GPS dropwindsonde composite profiles From Powell et al. (2003)

4. Literature Review • Kepert (2001) and Kepert and Wang (2001) found that the radial advection of momentum and vertical advection of the radial wind contributed to the development of super-gradient flow • Validated model results from snapshots of individual hurricanes using GPS dropwindsonde data • Linear model was able to replicate some of the mean structure observed by GPS dropwindsondes Model results and GPS dropwindsonde observations for Hurricane Georges Kepert (2006a)

5. GPS Dropwindsonde Profiles

6. MOTIVATION • Significant low-level wind speed maxima have been commonly observed in GPS dropwindsonde profiles (11% of all sondes contained a wind maximum below 200 m) • GPS dropwindsonde composite wind profiles exhibited structure similar to a low-level jet • Are features present at landfall? • Do minimum design standards cover this change in wind speed height? • Do the features represent a significant departure from log or power law wind profiles • WSR-88D VAD wind profiles provided opportunity to examine mean vertical wind profiles during hurricane landfalls Images courtesy of gulfstream.net

7. VAD Technique • Assume variation in radial velocity as a function of azimuth follows a Fourier series (Lhermitte and Atlas 1961; Browning and Wexler 1968) • Fit a Fourier series to the radial velocity versus azimuth data • Fourier coefficients used to compute wind speed, direction, deformation, etc… • Volumetric and temporal average

8. VAD Technique • For the current study, area of interest is the lowest 1 km • VAD processing was conducted for velocity data within 3-5 km of radar site, for each tilt angle • Approximate vertical resolution of 75 m for • VCP 11 (resolution a function of VCP)

9. VAD Technique • Assumption: Linear wind field (no curvature) • Assumption: Fall velocity of target is horizontally uniform • Limitations: Non-homogeneous fall speeds • Variability in the reflectivity field

10. VAD Wind Profiles • 330 VAD Wind profiles over 14 events • from 1996-2008 • Error estimated from residuals from the Fourier series fits ~ 3 ms-1

11. VAD Wind Profiles • VAD profiles composited to generate mean profiles • Binned by height • Stratified by radius, quadrant, and Radius/Radius of maximum winds • Data normalized by layer mean wind speed below 500 m (MBL)

12. VAD Wind Profiles

13. VAD Wind Profiles

14. VAD Wind Profiles

15. VAD Wind Profiles – Hurricane Bertha (KLTX)

16. VAD Wind Profiles – Hurricane Bertha (KLTX)

17. VAD Wind Profiles – Hurricane Fran (KLTX)

18. VAD Wind Profiles – Hurricane Fran (KLTX)

19. VAD Wind Profiles – Hurricane Frances (KMLB)

20. VAD Wind Profiles – Hurricane Frances (KMLB)

21. VAD Wind Profiles – Hurricane Frances (KMLB)

22. VAD Wind Profiles – Hurricane Frances (KMLB)

23. VAD Wind Profiles – Hurricane Ike (KHGX)

24. VAD Wind Profiles – Hurricane Ike (KHGX)

25. VAD Wind Profiles

26. VAD Wind Profiles • VAD wind profile observations binned by height • Log and Power Law profiles fit to the ~50 -400 m layer. Pushes the limit of similarity theory • Assumes neutral stability • Mean R2 for logarithmic least squares fits of 0.93 • Mean R2 for Power law least squares fits of 0.86

27. Low-level wind maxima identified in VAD wind profiles Decrease in height of the wind maximum with decreasing radius Lowest maxima found near or slightly within radius of maximum winds Logarithmic wind profile beneath maximum Features also found near rainbands Peak wind speed within entire dataset 55.6 ms-1 VAD Wind Profiles

28. QUESTIONS? COMMENTS… REFERENCES: Browning, K. A., and R. Wexler, 1968: The determination of kinematic properties of a wind field using Doppler radar. J. Appl. Meteor., 7, 105-113. Franklin, J.L., M.L. Black and K. Valde, 2003: GPS dropwindsonde profiles in hurricanes and their operational implications. Wea. Forecasting, 18, 32-44 Kepert, J.D., 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part I: Linear theory. J. Atmos. Sci.58, 2469-2483. Kepert, J.D. and Y. Wang, 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part II: Non-linear enhancement. J. Atmos. Sci.58, 2469-2483. Kepert, J.D., 2006: Observed boundary layer wind structure and balance in the hurricane core. Part I: Hurricane Georges. J. Atmos. Sci. 63, 2169-2193. Lhermitte, R., and D. Atlas, 1961. Precipitation motion by pulse Doppler radar. Proc. Ninth Wea. Radar Conf., Boston, Ma, Am. Meteor. Soc., 218-223. Powell, M.D., P.J. Vickery, and T.A. Reinhold, 2003: Reduced drag coefficients for high wind speeds in tropical cyclones. Nature, 422, 279-283 • .