Info

0 90 180 270 360 0 90 180 270 360

Figure 20. Comparison of Vd and V^D/Rt displays for two different radii (delineated as red and blue lines) for two vortices with different RMWs: (a) Vd display for a pure rotating vortex with Rmax = 30 km; (b) Vd profiles at R = 30 and 60 km; (c) same as (a) except for the VdD/Rt display; and (d) same as (b) except for the VdD/Rx profiles; (e)—(h) are the same as (a)-(d) except for Rmax = 80 km > Rt and the two Vd profiles are at R = 80 and 110 km. The center, T, is located at (x, y) = (200 km, 200 km) and the hypothetical Doppler radar, O, is located at (x, y) = (150 km, 150 km) with RT = 50^2km. (From Jou et al. 2008.)

0 90 180 270 360 0 90 180 270 360

Figure 20. Comparison of Vd and V^D/Rt displays for two different radii (delineated as red and blue lines) for two vortices with different RMWs: (a) Vd display for a pure rotating vortex with Rmax = 30 km; (b) Vd profiles at R = 30 and 60 km; (c) same as (a) except for the VdD/Rt display; and (d) same as (b) except for the VdD/Rx profiles; (e)—(h) are the same as (a)-(d) except for Rmax = 80 km > Rt and the two Vd profiles are at R = 80 and 110 km. The center, T, is located at (x, y) = (200 km, 200 km) and the hypothetical Doppler radar, O, is located at (x, y) = (150 km, 150 km) with RT = 50^2km. (From Jou et al. 2008.)

disturbance possesses an elliptic streamline (e.g. Lee et al., 2006a). Lee et al. (2006a) also showed that a deformation field is essentially a wave number 2 disturbance in cylindrical coordinates. They demonstrated that the GBVTD-retrieved signatures are different between these two types of elliptical vortices. Lee et al. (2002) applied the simple vortex edge wave model in the GBVTD analysis of Typhoon Herb (1996) and resolved two pairs of counter rotating vortices propagating downstream, consistent with the counterclockwise rotation of the elliptical reflectivity pattern. However, the sharp vorticity gradient of the Rankine vortex created an unrealistic discontinuity near the RMW. One of the challenges is how to formulate the VRW disturbance in realistic axisymmetric, atmospheric vortices where the simple form of the equations illustrated by Lamb (1932) is no longer valid.

8.3. Quantifying uncertainties of the GBVTD-simplex center-finding algorithms

Efforts have been made to quantify the uncertainties of the GBVTD-simplex algorithm. Murillo et al. (2001) statistically evaluated the center information (including location, RMW, and MATW) over time from independently derived centers from two separate WSR-88Ds in Hurricane Danny (1997). They found that the individual tracks computed from two radars are consistent but that variations of individual centers can be large. A more consistent set of centers can be obtained by simultaneously considering time continuity in center locations RMW and MATW (Murillo et al., 2002). Bell and Lee (2002, 2003) incorporated statistical information on locations, RMWs, and MATWs of past centers to objectively select the most probable center at each time, and the procedure can be automated after 67 time periods, when reliable statistics can be built. Future research needs to address the uncertainties in the GBVTD-simplex algorithm resulting from multiple wind maxima (e.g.

concentric or nonconcentric eyewalls) and the biases in the axisymmetric tangential winds from wave number 2 radial winds.

8.4. Operational aspects

Algorithms using the vortex reflectivity and dipole Doppler velocity structure to identify the vortex center and maximum wind speed have been implemented for many years (e.g. Lemon et al., 1978; Wood and Brown, 1992; Wood, 1994). The EVTD-derived TC inner core structure in Hurricane Olivia (1994) was transmitted to the Tropical Prediction Center (TPC) via satellite link (Dodge et al., 1995). More complicated suites of algorithms that combine these simple algorithms with several advanced techniques described in this review have drawn attention in recent years from operational centers such as the Central Weather Bureau in Taiwan (P.-L. Chang, personal communication) and the TPC (e.g. McAdie et al., 2001; Harasti et al., 2004). Digital radar data are available at operational centers, opening up the possibility of applying algorithms discussed in this article in operational settings in real time.

The ability to estimate the absolute central pressure and capture the rapid intensification in Hurricane Charley was attempted in Harasti et al. (2005) where the GBVTD-HVVP-derived pressure fields were within 2-3 hPa of the central pressure measured by the GPS dropwindsondes. A software package, Vortex Objective Radar Tracking and Circulation (VORTRAC), based on the GBVTD-HVVP framework, has been developed and transferred to the TPC and field-tested in the 2007 hurricane season (Lee et al., 2006b; Harasti et al., 2007). VORTRAC has been officially accepted by the TPC/NHC for operation use starting from the 2008 hurricane season.

Adequate scanning strategy selected by radar operators is essential for the success of retrieving TC wind fields using the VTD family of SDWR techniques. Radar operators have to choose the tradeoff between range folding (covering more storms) and velocity folding (detecting maximum wind). Examples shown by Lee and Bell (2004) suggested that it is more important to choose a pulse repetition frequency that gives the maximum range when sampling TCs because velocity folding is generally recoverable.

Further research aimed at integrating and capitalizing on the strengths of the individual algorithms described in this article, along with improved physical understanding of the underlying vortex dynamics, will help to better constrain the vortex solutions obtained from single Doppler radar data. These dynamically consistent wind fields can then be used for operational guidance, as initial conditions or data assimilations in numerical models, and as research tools for gaining further insight into atmospheric vortex structure.

Acknowledgements

The authors thank Mr M. Bell, Mr S. Ellis, and the two anonymous reviewers for their thoughtful comments that drastically improved this article.

[Received 26 July 2007; Revised 26 August 2007; Accepted 30 September 2007.]

References

Armijo, L., 1969: A theory for the determination of wind and precipitation velocities with Doppler radars. J. Atmos. Sci., 26, 570-573. Baynton, H. W., 1979: The case for Doppler radars along our hurricane affected coasts. Bull. Amer. Meteor. Soc., 60, 1014-1023. Bell, M., and W.-C. Lee, 2002: An objective method to select a consistent set of tropical cyclone circulation centers derived from the GBVTD-simplex algorithm. Preprints, 25th Conf. on Hurricane and Tropical Meteorology, (29 Apr. 1-3 May 2002, San Diego, CA, USA, Amer. Meteor. Soc.), pp. 642-643.

Bell, M., and W.-C. Lee, 2003: Improved tropical cyclone circulation centers derived from the GBVTD-simplex algorithm. Preprints, 31st Conf. on Radar Meteorology, (Seattle, Washington, USA, AMS), pp. 1012-1014.

Biggerstaff, M. I., L. J. Wicker, J. Guynes, C. Ziegler, J. M. Straka, E. N. Rasmussen, A. Doggett, IV, L. D. Carey, J. L. Schroeder, and C. Weiss, 2005: The shared mobile atmospheric research and teaching radar: a collaboration to enhance research and teaching. Bull. Amer. Meteor. Soc., 86, 1263-1274.

Bluestein, H. B., and R. M. Wakimoto, 2003: Mobile radar observations of severe convective storms. In Radar and Atmospheric Science: A Collection of Essays in Honor of David Atlas, eds. R. Wakimoto and R. Srivastava (Amer. Meteor. Soc.), pp. 105-136.

Bluestein, H. B., A. L. Pazmany, J. C. Galloway, and R. E. Mcintosh, 1995: Studies of the substructure of severe convective storms using a mobile 3-mm-wavelength Doppler radar. Bull. Amer. Meteor. Soc., 76, 2155-2169.

Bluestein, H. B., W.-C. Lee, M. Bell, C. Weiss, and A. Pazmany, 2003: Mobile-Doppler-radar observations of a tornado in a supercell near Bassett, Nebraska on 5 June 1999. Part II: Tornado-vortex structure. Mon. Wea. Rev., 131, 2968-2984.

Boccippio, D. J., 1995: A diagnostic analysis of the VVP single-Doppler retrieval technique. J. Atmos. Oceanic Technol., 12, 230-248.

Brown, R. A., L. R. Lemon, and D. W. Burgess, 1978: Tornado detection by pulsed Doppler radar. Mon. Wea. Rev., 106, 29-38.

Brown, R. A., and V. T. Wood, 1991: On the interpretation of single-Doppler velocity patterns within severe thunderstorms. Wea. Forecasting,

Browning, K. A., and R. Wexler, 1968: The determination of kinematic properties of a wind field using Doppler radar. J. Appl. Meteor.,

Burgess, D. W., 1976: Single-Doppler radar vortex recognition. Part I. Mesocyclone signatures. Preprints, 17th Conf. on Radar Meteorology, (Seattle, USA, Amer. Meteor. Soc.), pp. 97-103.

Carbone, R. E., and F. D. Marks, Jr., 1988: Velocity track display (VTD): a real-time application for airborne Doppler radar data in hurricanes. Preprints, 18th Conf. on Hurricanes and Tropical Meteorology (San Diego, USA, Amer. Meteor. Soc.), pp. 11-12.

Caya, D., and I. Zawadzki, 1992: VAD analysis of nonlinear wind fields. J. Atmos. Oceanic Technol. 9, 575-587.

Chan, J. C. L., and W. M. Gray, 1982: Tropical cyclone movement and surrounding flow relationships. Mon. Wea. Rev., 110, 1354-1374.

Chen, Y., and M. K. Yau, 2001: Spiral bands in a simulated hurricane. Part I: Vortex Rossby wave verification. J. Atmos. Sci., 58, 2128-2145.

Davies-Jones, R. P., R. J. Trapp, and H. B. Blue-stein, 2001: Tornadoes and tornadic storms. In Severe Convective Storms. ed. C. A. Doswell (Amer. Meteor. Soc.), pp. 167-221.

Desrochers, P. R., and F. I. Harris, 1996: Interpretation of mesocyclone vorticity and divergence structure from single-Doppler radar. J. Appl. Meteor., 35, 2191-2209.

Dodge, P., F. D. Marks, Jr., J. Gamache, J. Griffin, and N. Griffin, 1995: EVTD Doppler radar analyses of the inner core of Hurricane Olivia (1994). Preprints, 27th Conf. on Radar Meteorology, (913 Oct. 1995, Vail, CO, USA, Amer. Meteor. Soc.), pp. 299-301.

Dodge, P., R. W. Burpee, and F. D. Marks Jr., 1999: The kinematic structure of a hurricane with sea level pressure less than 900 mb. Mon. Wea. Rev., 127, 987-1004.

Donaldson, R. J., Jr., 1970: Vortex signature recognition by a Doppler radar. J. Appl. Meteor., 9, 661-670.

Donaldson, R. J., Jr., 1991: A proposed technique for diagnosis by radar of hurricane structure. J. Appl. Meteor., 30, 1636-1645.

Dong, K., and C. J. Neumann, 1986: The relationship between tropical cyclone motion and environmental geostrophic flows. Mon. Wea. Rev., 114, 115-122.

Dowell, D. C., C. R. Alexander, J. Wurman, and L. J. Wicker, 2005: Centrifuging of hydrometeors and debris in tornadoes: radar-reflectivity patterns and wind-measurement errors. Mon. Wea. Rev., 133, 1501-1524.

Franklin, J. L., S. J. Lord, S. E. Feuer, and F. D. Marks, 1993: The kinematic structure of Hurricane Gloria (1985) determined from nested analyses of dropwindsonde and Doppler radar data. Mon. Wea. Rev., 121, 2433-2451.

Gall, R., and J. Tuttle, 1999: A single radar technique for estimating the wind in tropical cyclones. Bull. Amer. Meteor. Soc., 80, 653-668.

Gamache, J. F., J. S. Griffin, P. Dodge, and N. F. Griffin, 2004: Automatic Doppler analysis of three-dimensional wind fields in hurricane eyewalls. Preprints, 26th Conf. on Hurricane and Tropical Meteorology, 5D4, (3-7 May 2004, Miami, FL, USA, AMS).

George, J. E., and W. M. Gray, 1976: Tropical cyclone motion and surrounding parameter relationships. J. Appl. Meteor., 15, 1252-1264.

Gray, W. M., 2003: On the transverse circulation of the hurricane. In Cloud Systems, Hurricanes, and the Tropical Rainfall Measuring Mission (TRMM): A Tribute to Dr Joanne Simpson, eds. W.-K. Tao and R. Adler, pp. 149-164.

Griffin, J. S., R. W. Burpee, F. D. Marks, Jr., and J. L. Franklin, 1992: Real-time airbone analysis of aircraft data supporting operational hurricane forecasting. Wea. Forecasting, 7, 480-490.

Guinn, T. A., and W. H. Schubert, 1993: Hurricane spiral bands. J. Atmos. Sci., 50, 3380-3403.

Hanley, D., J. Molinari, and D. Keyser, 2001: A composite study of the interactions between tropical cyclones and upper-tropospheric troughs. Mon. Wea,. Rev., 129, 2570-2584.

Harasti, P. R., 2003: The hurricane volume velocity processing method. Preprints, 31st Conf. on Radar Meteorology, (Seattle, WA, USA, Amer. Meteor. Soc.), pp. 1008-1011.

Harasti, P. R., 2004: Separation of hurricane and environmental winds via single-Doppler radar volumetric data analysis: results from Hurricane Bret (1999). Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, (Miami, FL, USA, Amer. Meteor. Soc.), pp. 124-125.

Harasti, P. R., and R. List, 1995: A single-Doppler radar analysis method for intense axisymmetric cyclones. Preprints, 27th Conf. on Radar Meteorology, (Vail, CO, USA, Amer. Meteor. Soc.), pp. 221-223.

Harasti, P. R., and R. List. 2005: Principal component analysis of doppler radar data. Part I: Geometric connections between eigenvectors and the core region of atmospheric vortices. J. Atmos. Sci., 62, 4027-4042.

Harasti, P. R., W.-C. Lee, and M. Bell, 2005: Rapid intensification of Hurricane Charley (2004) derived from WSR-88D data prior to landfall. Part II: HVVP adjustment for the environmental wind and central pressure retrieval. Preprints, 30th Int. Conf. on Radar Meteorology, (Albuquerque, New Mexico, USA, Amer. Meteor. Soc.), CD volume, paper J2J.3.

Harasti, P. R., W. C. Lee, and M. Bell, 2007: Real-time implementation of VORTRAC at the National Hurricane Center. 33rd Conf. on Radar Meteorology, (6-11 August 2007, Cairns, Australia).

Harasti, P. R., C. J. McAdie, P. P. Dodge, W.-C. Lee, J. Tuttle, S. T. Murillo, and F. D. Marks,

Jr., 2004: Real-time implementation of single-Doppler analysis methods for tropical cyclones: algorithm improvements and use with WSR-88D display data. Wea. Forecasting, 19, 219-239.

Holland, G. J., 1984: Tropical cyclone motion: a comparison of theory and observation. J. Atmos. Sci, 41, 68-75.

Houze, R. A. Jr., S. A. Rutledge, M. I. Biggerstaff, and B. F. Smull, 1989: Interpretation of Doppler weather radar displays of midlatitude mesoscale convective systems. Bull. Amer. Meteor. Soc., 70, 608-619.

Houze, R. A., Jr., S. S. Chen, W.-C. Lee, R. F. Rogers, J. A. Moore, G. J. Stossmeister, J. L. Cetrone, W. Zhao, and M. M. Bell, 2006: The Hurricane Rainband and Intensity Change Experiment (RAINEX): Observations and modeling of Hurricanes Katrina, Ophelia, and Rita (2005). Submitted to Bull. Amer. Meteor. Soc.

Jorgensen, D. P., 1984a: Mesoscale and convective-scale characteristics of mature hurricanes. Part I: General observations by research aircraft. J. Atmos. Sci., 41, 1268-1286.

Jorgensen, D. P., 1984b: Mesoscale and convective-scale characteristics of mature hurricanes. Part II. Inner core structure of Hurricane Allen (1980). J. Atmos. Sci., 41, 1287-1311.

Jou, B. J.-D., and S.-M. Deng, 1997: Determination of typhoon center and radius of maximum wind by using Doppler radar. 22nd Conf. on Hurricanes and Tropical Meteorology, (May 1997, Fort Collins, CO, USA), pp. 104-105.

Jou, B. J.-D., B.-L. Chang, and W.-C. Lee, 1994: Analysis of typhoon circulation using ground-based Doppler radar (in Chinese, with English abstract). Atm,os. Sci., 22, 163-187.

Jou, B. J.-D., S.-M. Deng, and B.-L. Chang, 1996: Determination of typhoon center and radius of maximum wind by using Doppler radar (in Chinese, with English abstract). Atmos. Sci., 24, 1-23.

Jou, B. J.-D., J.-M. Chou, P.-L. Chang, and W.-C. Lee, 2001: Landfall typhoons observed by Taiwan Doppler radar network: a case study of typhoon Billis (2000). Int. Conf. on Mesoscale Meteorology and Typhoons in East Asia, (Sept. 2001, Taipei, Taiwan), pp. 171-178.

Jou, B. J.-D., W.-C. Lee, S.-P. Liu, and Y. C. Kao, 2008: Generalized VTD (GVTD) retrieval of atmospheric vortex kinematic structure. Part I: Formulation and error analysis. Mon. Wea. Rev. (in press).

Koscielny, A. J., R. J. Doviak, and R. Rabin, 1982: Statistical considerations in the estimation of divergence from single-Doppler radar and applications to prestorm boundary layer observations. J. Appl. Meteor., 21, 197-210.

Kossin, J. P., W. H. Schubert, and M. T. Montgomery. 2000: Unstable interactions between a hurricane's primary eyewall and a secondary ring of enhanced vorticity. J. Atmos. Sci., 57, 38933917.

Lamb, H., 1932: Hydrodynamics, 6th edn. (Dover), 732 pp.

Lee, J. J., T. Samaras, and C. Young, 2004: Pressure measurements at the ground in an F-4 tornado. Preprints, 22nd Conf. on Severe Local Storms. (Amer. Meter. Soc.), CD volume, paper 15.3.

Lee, J. L., Y.-H. Kuo, and A. E. MacDonald, 2003: The vorticity method: extension to mesoscale vertical velocity and validation for tropical storms. Quart. J. Roy. Meteor. Soc., 129, 10291050.

Lee, J. L., W.-C. Lee, and A. E. MacDonald, 2006: Estimating vertical velocity and flow from Doppler radar observations of tropical cyclones. Quart. J. Roy. Meteor. Soc., 132, 125-145.

Lee, W.-C., and M. M. Bell, 2004: Kinematic structure of Hurricane Isabel (2003) near landfall from the Morehead City WSR-88D. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, (Miami, FL, USA, Amer. Meteor. Soc.), pp. 566-567.

Lee, W.-C., and M. M. Bell, 2007: Rapid intensification, eyewall contraction and breakdown of Hurricane Charley (2004) Near Landfall. Geophys. Res. Lett., 34, L02802, doi:10.1029/ 2006GL027889.

Lee, W.-C., and F. D. Marks, Jr., 2000: Tropical cyclone kinematic structure retrieved from single Doppler radar observations. Part II: The GBVTD-simplex center finding algorithm. Mon. Wea. Rev., 128, 1925-1936.

Lee, W.-C., and J. Wurman, 2005: Diagnosed three-dirmensional axisymmetric structure of the Mulhall tornado on 3 May 1999. J. Atmos. Sci., 62, 2373-2393.

Lee, W.-C., P. R. Harasti, and M. Bell, 2002: An improved algorithm to resolve circulations of wavenumber two tropical cyclones. Preprints, 25th Conf. on Hurricane and Tropical Meteorology, (29 Apr.-3 May 2002, San Diego, CA, USA, Amer. Meteor. Soc.), pp. 611-612.

Lee, W.-C., P. R. Harasti, and M. Bell, 2006b: VORTRAC: a utility to deduce central pressure and radius of maximum wind of landfalling tropical cyclones using WSR-88D data. Preprints, 60th Interdepartmental Hurricane Conference (20-24 Mar. 2006, Mobile, Alabama, Office of the Federal Coordinator for Meteorological Services and Supporting Research).

Lee, W.-C., F. D. Marks, Jr., and R. E. Carbone, 1994: Velocity track display (VTD): a technique to extract primary vortex circulation of a tropical cyclone in real time using single airborne Doppler radar data. J. Atmos. Oceanic Technol, 11, 337356.

Lee, W.-C., J.-D. Jou, P.-L. Chang, and S.-M. Deng, 1999: Tropical cyclone kinematic structure retrieved from single Doppler radar observations. Part I: Interpretation of Doppler velocity patterns and the GBVTD technique. Mon. Wea. Rev., 127, 2419-2439.

Lee, W.-C., J.-D. Jou, P.-L. Chang, and F. D. Marks, 2000: Tropical cyclone kinematic structure retrieved from single-Doppler radar observations. Part III: Evolution and structure of Typhoon Alex (1987). Mon. Wea. Rev., 128, 3892-4001.

Lee, W.-C., P. R. Harasti, M. Bell, B. J.-D. Jou, and M.-H. Chang, 2006a: Doppler velocity signatures of idealized elliptical vortices. Terr. Atmos. Oceanic Sci., 17, 429-446.

Lemon, L. R., D. W. Burgess, and R. A. Brown, 1978: Tornadic storm sirflow and morphology derived from single-Doppler radar measurement. Mon. Wea. Rev., 106, 48-61.

Liou, Y.-C., 1999a: A preliminary study of retrieving low level vortex circulation using a Lagrangian coordinate and single Doppler radar data, Terr. Atmos. Oceanic Sci., 10, 321-340.

Liou, Y.-C., 1999b: Single radar recovery of crossbeam wind components using a modified moving frame of reference technique. J. Atmos. Oceanic Technol., 16, 1003-1016.

Liou, Y.-C., T.-C. C. Wang, W.-C. Lee, and Y.-J. Chang, 2006: The retrieval of asymmetric tropical cyclone structures using Doppler radar observations and the method of extended GBVTD. Mon. Wea. Rev., 134, 1140-1160.

Mallen, K. J., M. T. Montgomery, and B. Wang, 2005: Re-examining tropical cyclone near-core radial structure using aircraft observations: implications for vortex resiliency. J. Atmos. Sci., 62, 408-425.

Marks, F. D., 2003: State of the science: radar view of tropical cyclones. In Radar and Atmospheric Science: A Collection of Essays in Honor of David Atlas, eds. R. Wakimoto and R. Sriva-stava (Amer. Meteor. Soc.), pp. 33-74.

Marks, F. D., and R. A. Houze, 1987: Inner core structure of Hurricane Alicia from Doppler radar observations. J. Atmos. Sci., 44, 1296-1317.

Marks, F. D., R. A. Houze, and J. F. Gamache, 1992: Dual aircraft investigation of the inner core of Hurricane Norbert. Part I: Kinematic structure. J. Atmos. Sci., 49, 919-942.

Matejka, T., and R. C. Srivastava. 1991: An improved version of the extended velocity-azimuth display analysis of single-doppler radar data. J. Atmos. Oceanic Technol, 8, 453-466.

McAdie, C. J., P. R. Harasti, P. Dodge, W.-C. Lee, S. Murillo, and F. D. Marks, Jr., 2001: Real-time implementation of tropical cyclone-specific radar data processing algorithms. Preprints, 30th Int. Conf. on Radar Meteorology, (Munich, Germany, Amer. Meteor. Soc.), pp. 468-470.

Montgomery, M. T., and R. J. Kallenbach, 1997: A theory for vortex Rossby-waves and its application to spiral bands and intensity changes in hurricanes. Quart. J. Roy. Meteor. Soc., 123, 435-465.

Murillo, S. T., W.-C. Lee, and F. D. Marks, 2000: Evaluating the GBVTD-tropical center finding simplex algorithm. Preprints, 24th Conf. on Hurricane and Tropical Meteorology (29 May-2 June 2000, Fort Lauderdale, FL, USA, Amer. Meteor. Soc.), pp. 312-313.

Murillo, S. T., W.-C. Lee, F. D. Marks, and P. Dodge, 2002: Examining structural changes and circulation center of Hurricane Danny (1997) using a single-Doppler radar wind retrieval technique. Preprints, 25th Conf. on Hurricane and Tropical Meteorology (29 April-3 May 2002, San Diego, CA, USA, Amer. Meteor. Soc.), pp. 485486.

Neldar, J. A., and R. Mead, 1965: A simplex method for function minimization. Comp. J., 7, 308-313.

Qiu, C.-J., and Q. Xu, 1992: A simple adjoint method of the wind analysis for single-Doppler data. J. Atmos. Oceanic Technol., 9, 588-598.

Rankine, W. J. M., 1901: A Manual of Applied Mechanics. 16th edn. (Charles Griff), pp. 574578.

Ray, P., 1990: Convective dynamics. IN Radar in Meteorology, ed. D. Atlas (Amer. Metror. Soc.), pp. 348-390.

Rinehart, R., 1979. Internal storm motions from a single non-Doppler weather radar. (NCAR/TN-146+STR), 262 pp.

Rinehart, R., and E. T. Garvey, 1978: Three-dimensional storm motion detection by conventional weather radar. Nature, 273, 287-289.

Rotunno, R., 1979: A study in tornado-like vortex dynamics. J. Atmos. Sci., 36, 140-155.

Rotunno, R., and K. A. Emanuel, 1987: An air-sea interaction theory for tropical cyclones. Part II: Evolutionary study using a nonhydrostatic axi-symmetric numerical model. J. Atmos. Sci., 44, 542-561.

Roux, F., F. Chane-Ming, A. Lasserre-Bigorry, and O. Nuissier, 2004: Structure and evolution of intense tropical cyclone Dina near La Reunion on 22 January 2002: GB-EVTD analysis of single Doppler radar observations. J. Atmos. Oceanic Technol., 21, 1501-1518.

Roux, F., and F. D. Marks, Jr., 1996: Extended velocity track display (EVTD): an improved processing method for Doppler radar observations of tropical cyclones. J. Atmos. Oceanic Technol., 13, 875-899.

Schubert, W. H., M. T. Montgomery, R. K., Taft, T. A. Guinn, S. R. Fulton, J. P. Kossin, and J. P. Edwards, 1999: Polygonal eyewalls, asymmetric eye contraction, and potential vorticity mixing in hurricanes. J. Atmos. Sci., 56, 1197-1223.

Shapiro, A., S. Ellis, and J. Shaw, 1995: Single-Doppler retrievals with Phoenix 11 data: clear air and microburst wind retrievals in the planetary boundary layer. J. Atmos. Sci., 52, 1265-1536.

Stirling, J., and R. M. Wakimoto, 1989: Mesoscale vortices in the stratiform region of a decaying midlatitude squall line. Mon. Wea. Rev., 117, 452-458.

Symthe, G. R., and D. S. Zrnic, 1983: Correlation analysis of Doppler radar data and retrieval of the horizontal wind. J. Climate Appi. Meteor., 22, 297-311.

Tanamachi, R. L., H. B. Bluestein, W.-C. Lee, M. Bell, and A. Pazmany, 2006: Ground-based velocity track display (GBVTD) analysis of a W-band Doppler radar data in a tornado near Stockton, Kansas, on 15 May 1999. Mon. Wea. Rev. (accepted with revision).

Terwey, W. D., and M. T. Montgomery, 2002: Wavenumber-2 and wavenumber-m vortex rossby wave instabilities in a generalized three-region model. J. Atmos. Sci., 59, 2421-2427.

Tuttle, J. D., and G. B. Foote, 1990: Determination of the boundary layer airflow from a single Doppler radar. J. Atmos. Oceanic Technol., 7, 218-232.

Waldteufel, P., and H. Corbin, 1979: On the analysis of single-Doppler radar data. J. Appl. Meteor., 18, 532-542.

Wang, Y., 2002a: Vortex Rossby waves in a numerically simulated tropical cyclone. Part I: Overall structure, potential vorticity and kinetic energy budgets. J. Atmos. Sci., 59, 1213-1238.

Wang, Y., 2002b: Vortex Rossby waves in a numerically simulated tropical cyclone. Part II: The role in tropical cyclone structure and intensity changes. J. Atmos. Sci., 59, 1239-1262.

Wang, Y., and C.-C. Wu, 2004: Current understanding of tropical cyclone structure and intensity changes: a review. Meteorol. Atmos. Phys., 87, 257-278.

Ward, N. B., 1972: The exploration of certain features of tornado dynamics using a laboratory model. J. Atmos. Sci., 29, 1194-1204.

Willoughby, H. E., 1992: Linear motion of a shallow-water barotropic vortex as an initial-value problem. J. Atmos. Sci., 49, 2015-2031.

Willoughby, H. E., and M. B. Chelmow, 1982: Objective determination of hurricane tracks from aircraft observations. Mon. Wea. Rev., 110, 1298-1305.

Winn, W. P., S. J., Hunyady, and G. D. Aulich, 1999: Pressure at the ground in a large tornado. J. Geophy. Res., 104: D18, 22067-22082.

Wood, V. T., 1994: A technique for detecting a tropical cyclone center using a Doppler radar. J. Atmos. Oceanic Technol., 11, 1207-1216.

Wood, V. T., and F. D. Marks, Jr., 1989: Hurricane Gloria: simulated land-based Doppler velocities reconstructed from airborne Doppler radar measurements. Preprints, 18th Conf. on Hurricane and Tropical Meteorology (San Diego, USA, Amer. Meteor. Soc.), pp. 115-116.

Wood, V. T., and R. A. Brown, 1992: Effects of radar proximity on single-Doppler velocity of axi-symmetric rotation and divergence. Mon. Wea. Rev., 120, 2798-2807.

Wurman, J., 2002: The multiple vortex structure of a tornado. Wea. Forecasting, 17, 473-505.

Wurman, J., and S. Gill, 2000: Finescale radar observations of the Dimmitt, Texas (2 June 1995), tornado. Mon. Wea. Rev., 128, 2135-2164.

Wurman, J., and T. Samaras, 2004: Comparison of in-situ and DOW Doppler winds in a tornado and RHI vertical slices through 4 tornadoes during 1996-2004. Preprints, 22nd Conf. on Severe Local Storms (Amer. Meteor. Soc.), CD volume, paper 15.4.

Wurman, J., J. M. Straka, E. N. Rasmussen, M. Randall, and A. Zahrai, 1997: Design and deployment of a portable, pencil-beam, pulsed, 3-cm Doppler radar. J. Atmos. Oceanic Technol., 14, 1502-1512.

Xu, Q., C. Qiu, and J. Yu, 1994: Adjoint method retrieving of horizontal wind from single-Doppler reflectivity measured during Phoenix II. J. Atmos. Oceanic Technol, 11, 275-288.

Xu, Q., and C. J. Qiu, 1994: Simple adjoint methods for single-Doppler wind analysis with a strong constraint of mass conservation. J. Atmos. Oceanic Tech., 11, 289-298.

Yamauchi, H., O. Suzuki, and Akaeda, 2002: Asymmetry in wind field of Typhoon 0115 analyzed by triple Doppler radar observations. Preprints, Int. Conf. on Mesoscale Convective Systems and Heavy Rainfall/Snowfall in East Asia (Tokyo, Japan, Japan Science and Technology Corporation and Chinese Academy of Meteorological Sciences), pp. 245-250.

Zawadzki, I. I., 1973: Statistical properties of precipitation patterns. J. Appi. Meteor., 12, 459-472.

Zhao, K., B. J.-D. Jou, Y.-J. Pan, and W.-Z. Ge, 2007: Structural characteristics of a meso-vortex in Taiwan Strait from single Doppler radar analysis. Acta Meteor Sinica (in Chinese), in press.

Typhoon—Ocean Interactions Inferred by Multisensor Observations

I-I Lin and Chun-Chieh Wu Department of Atmospheric Sciences, National Taiwan University, Taipei,, Taiwan

[email protected]

The western North Pacific Ocean and the surrounding seas are among the world's oceans where tropical cyclones, highest both in number and in intensity, are found. There has long been interest in studying the typhoon-ocean interaction processes in this vast oceanic region. However, observations are rare and it has been difficult to study these complex, dynamic, and interdisciplinary processes. With the advancement of satellite remote sensing, especially microwave remote sensing with cloud-penetrating capabilities, it has finally become possible to catch a glimpse of some of these processes in the western North Pacific. In this article, we review a number of recent papers using these new satellite observations to study (1) the interaction between typhoons and warm ocean eddies, (2) enhancement of ocean primary production induced by typhoons, and (3) posttyphoon air-sea interaction.

1. Introduction

The interaction between tropical cyclones and the ocean is complex, dynamic, and interdisciplinary. Tropical cyclones form on the ocean, which is the energy source that fuels a cyclone's intensification (Emanuel, 1986, 1988, 1991, 1995; Holland, 1997; Black et al., 2007). As cyclones intensify, they impact back on the ocean and cause cold water from the deeper ocean to be entrained and upwelled to the upper ocean layer (Chang and Anthes, 1979; Price, 1981; Stramma et al., 1986; Shay et al., 1992; Dickey et al., 1998; Jacob et al., 2000). This self-induced ocean cooling in turn plays a critical negative feedback role in the cyclone's intensification (Gallacher et al., 1989; Emanuel, 1999; Schade and Emanuel, 1999, Bender and Ginis, 2000; Cione and Uhlhorn, 2003; Emanuel et al., 2004; Lin et al., 2005; Zhu and Zhang, 2006; Wu et al., 2007). After the cyclone's departure, cold wakes, typically more evident to the right of the cyclone tracks (Change and Anthes, 1978;

Price, 1981; Cornillon et al., 1987; Bender and Ginis, 2000; Monaldo et al., 1997; Wentz et al., 2000; Lin et al, 2003b), are left behind. These cold wakes can exist in the ocean for days to weeks and generate continual feedback with the atmosphere (Emanuel, 2001; Lin et al., 2003a).

Besides the above physical interactions, there can be biogeochemical interactions between cyclones and the ocean because the intense cyclone wind mixes and transports not only the cold water from the deeper ocean but also nutrients in the deeper layer (Lin et al., 2003b; Babin et al., 2004; Davis and Yan, 2004; Siswanto et al., 2007). As most of the tropical and subtropical oceans are oligotrophic, i.e. nutrient-poor (Eppley, 1989; Behrenfeld and Falkowski, 1997; McGillicuddy et al., 1998; McGillicuddy et al., 2001; Uz et al., 2001), the nutrients brought to the upper ocean are critical for phytoplankton, inducing phytoplankton blooms (Lin et al, 2003b; Babin et al, 2004; Siswanto et al., 2007). Also, since phytoplankton is the base of the marine food chain, these blooms may produce a chain reaction to affect the fishery yield. This phenomenon has not been lost on experienced fishermen who harvest in the wake long after a tropical cyclone's passage. Besides being the base of the ocean food chain, these phytoplankton blooms can have even more profound impact on the earthy system. Like all other plants, phytoplankton uses carbon dioxide, sunlight, and nutrients to photosyn-thesize; additionally, half of the world's oxygen is produced by phytoplankton. Therefore, phy-toplankton production also affects the uptake of carbon dioxide, an important greenhouse gas and a major cause of natural and man-made climate changes (Eppley and Peterson, 1979; Eppley, 1989; Bates et al., 1998; McGillicuddy et al., 1998; Lin et al., 2003b; Uz et al., 2001).

Pre-existing ocean features are known to cause even more complex air-sea interactions in the tropical cyclone and ocean system (Qiu, 1999; Shay et al., 2000; Goni and Trinanes 2003; Emanuel et al., 2004; Lin et al., 2005; Scharroo et al., 2005; Oey et al., 2007; Wu et al., 2007). It has been reported that some major category 5a storms, e.g. Hurricane Opal of 1995 (Shay et al., 2000), Katrina of 2005 (Scharroo et al., 2005), supertyphoon Maemi of 2003 (Lin et al., 2005), and Dianmu of 2004 (Pun et al., 2007), rapidly intensified when encountering warm ocean features.

The western North Pacific basin and the surrounding seas are where most tropical cyclone (i.e. typhoon) activities are located. But due to the lack of in situ and airborne observations, it has been difficult to credibly analyze the complex typhoon-ocean interaction processes in this basin. Available opportune in situ observations in this region are too sparse to lend meaningful spatial correlations. Visible and IR satellite remote sensing images (e.g. Advanced Very High Resolution Radiometer, AVHRR) are frequently contaminated by clouds (Wentz et al.,

2000), especially near typhoons, limiting the usefulness of these images for our purposes. Therefore, observations of typhoon-ocean interactions in the northwestern Pacific have been extremely difficult. With recent advances in microwave remote sensing (Fu et al., 1994; Fu and Cazenave, 2001; Liu et al., 1998; Wentz et al., 2000), however, some inroads have been made on exploring the processes that occur at the air-sea interface as typhoons translate over the ocean.

The major advantage of using microwave remote sensing is its ability to penetrate clouds and its independence from sunlight. Therefore, observations can be made both during the day and the night without bias toward fairweather at the air-sea interface. For the 50th anniversary of the Department of Atmospheric Sciences of the National Taiwan University, we demonstrate in this review the use of three types of microwave remote sensors and one optical remote sensor to study a number of little-studied or infrequently observed typhoon-ocean interaction processes in the western North Pacific based on a series of recent papers (Lin et al., 2003a; Lin et al., 2003b; Lin et al., 2005). The three types of microwave data are (1) the ocean surface wind vector from the QuikSCAT active microwave scatterometer (Liu et al., 1998), (2) the sea surface temperature (SST) data from the TRMM (Tropical Rainfall Measuring Mission) passive microwave imager and the Advanced Microwave Scanning Radiometer (AMSR-E) (Wentz et al., 2000), and (3) the sea surface height anomaly (SSHA) data from the TOPEX-Poseidon and JASON-1 active microwave altimeters (Fu et al., 1994; Fu and Cazenave, 2001). The optical remote sensor used is the ocean color data from the NASA Sea-viewing Wide Field-of-view (SeaWiFS) sensor (O'Reilly et al., 1998). In this work, the QuikSCAT, TOPEX-Poseidon, and JASON-1

aSaffir—Simpson Tropical Cyclone Scale, based on the 1 min maximum sustained winds: Category 1: 34—43 ms 1; Category 2: 44—50 ms-1; Category 3: 51—59 ms-1; Category 4: 59—71 ms-1; and Category 5: > 71ms-1.

data are from the daily level 2 product of the NASA/Jet Propulsion Laboratory. The SeaWiFS data are the daily level 2 chlorophyll-a data of the NASA Goddard Space Flight Center while the TRMM/SST data are the daily product of the Remote Sensing Systems (Wentz et al., 2000). All products have been validated with in situ observations and readers are referred to the original references (Fu et al., 1994; Liu et al., 1998; O'Reilly et al., 1998; Wentz et al., 2000) for their respective accuracies.

In Sec. 2, we will discuss the interaction between supertyphoon Maemi (2003) and a warm ocean eddy (Lin et al., 2005), and show that warm ocean eddies play a critical role in Maemi's intensification to category 5. In Sec. 3, we will show the drastic biological response induced by typhoon Kai-Tak (2000) in the South China Sea (Lin et al., 2003b). In Sec. 4, we present observations in previously unobserved posttyphoon air-sea interaction processes that the cold wakes, left behind by typhoons, can have evident feedback to the atmosphere by reducing the ocean surface wind speed (Lin et al., 2003a). In Sec. 5, a summary is presented.

2. The Interaction between Supertyphoon Maemi (2003) and a Warm Ocean Eddy (Based on Lin et al., 2005)

Since the observation of a noticeable number of intense category 4 or 5 Atlantic/Gulf of Mexico hurricanes [e.g. Opal (1995), Mitch (1998), Bret (1999)] rapidly intensified to category 5 when encountering warm mesoscale ocean eddies (Shay et al., 2000; Goni and Trinanes, 2003; Emanuel et al., 2004), there has been much interest in studying the interaction between tropical cyclones and ocean features (e.g. eddies and currents) (Shay et al., 2000; Hong et al., 2000; Goni and Trinanes, 2003; Emanuel et al., 2004; Lin et al., 2005; Scharroo et al., 2005; Oey et al., 2006; Pun et al., 2007; Wu et al., 2007). In particular, it is suggested that the warm eddy in the Gulf of Mexico may have induced the intensification of the devastating hurricane Katrina of 2005 to category 5 before its landfall (Scharroo et al., 2005). The western North Pacific Ocean is among the world's oceans in which the greatest number of intense category 4 and 5 cyclones are found.b It is interesting to find out whether these intense typhoons are associated with ocean features. In this section, we review the first event observed in the western North Pacific of such an encounter.

In September 2003, typhoon Maemi passed directly over a prominent warm ocean eddy in the western North Pacific, as observed by the satellite SSHA data from the TOPEX/Poseidon and JASON-1 altimeters (Fu et al., 1994). This warm ocean eddy is around 700 km x 500 km in size and is characterized by its large positive SSHAc of 10-45 cm (Fig. 1). Joint analysis with the best track data from the Joint Typhoon Warning Center (JTWC) shows that during the 36 h of the Maemi eddy encounter, Maemi's intensity (in 1 min sustained wind) shot up from the modest category 1 (41ms-1) to its peak in category 5 (77 ms-1). As can be observed in Fig. 1, Maemi entered the eddy region at 1800 UTC, 8 September 2003, when its intensity was at category 1 (green bullet). It then rapidly intensified to category 2, (blue bullet) in 6 h and jumped to category 4 (yellow bullet) in the next 6h. At 0000 UTC, 10 September 2003 (i.e. 36 h from category 1), Maemi reached its peak at category 5 (77ms-1; black bullet) and became the most intense tropical cyclone globally in 2003. As Maemi left the eddy region, its intensity declined.

bFrom the best-track data of the Joint Typhoon Warning Center during 1960—2005.

cFrom the existing literature, warm ocean features with SSHA > 8 cm are considered prominent. (Qiu, 1999; Shay et al., 2000).

Was this article helpful?

0 0

Post a comment