Advertisement

Advances in Atmospheric Sciences

, Volume 22, Issue 4, pp 534–544 | Cite as

The effect of three-dimensional variational data assimilation of QuikSCAT data on the numerical simulation of typhoon track and intensity

  • Zeng ZhihuaEmail author
  • Duan Yihong
  • Liang Xudong
  • Ma Leiming
  • Johnny Chung-leung Chan
Article

Abstract

In this paper, the three-dimensional variational data assimilation scheme (3DVAR) in the mesoscale model version 5 (MM5) of the US Pennsylvania State University/National Center for Atmospheric Research is used to study the effect of assimilating the sea-wind data from QuikSCAT on the prediction of typhoon track and intensity. The case of Typhoon Dujuan (2003) is first tested and the results show appreciable improvements. Twelve other cases in 2003 are then evaluated. The assimilation of the QuikSCAT data produces significant impacts on the structure of Dujuan in terms of the horizontal and vertical winds, sealevel pressure and temperature at the initial time. With the assimilation, the 24-h (48-h) track prediction of 11 (10) out of the 12 typhoons is improved. The 24-h (48-h) prediction of typhoon intensity is also improved in 10 (9) of the 12 cases. These experiments therefore demonstrate that assimilation of the QuikSCAT sea-wind data can increase the accuracy of typhoon track and intensity predictions through modification of the initial fields associated with the typhoon.

Key words

QuikSCAT MM5 3DVAR numerical simulation Typhoon Dujuan 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barker, D. M., W. Huang, Y.-R. Guo, and A. Bourgeois, 2003: A three-dimensional variational (3DVAR) data assimilation system for use with MM5. NCAR Technical Note, NCAR/TN-453+STR, National Center for Atmospheric Research, Boulder, Colorado, 68pp.Google Scholar
  2. Bouttier, F., and G. Kelly, 2001: Observing-system experiments in the ECMWF 4D-Var data assimilation system.Quart. J. Roy. Meteor. Soc.,127, 1469–1488.CrossRefGoogle Scholar
  3. Courtier, P., and Coauthors, 1998: The ECMWF implementation of three dimensional variational (3DVAR) data assimilation. Part I: Formulation.Quant. J. Roy. Meteor. Soc.,123, 1–26.Google Scholar
  4. Ebuchi, N., H. C. Graber, and M. J. Caruso, 2002: Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data.J. Atmos. and Oceanic Tech.,19, 2049–2062.CrossRefGoogle Scholar
  5. English, S. J., R. J. Renshaw, P. C. Dibben, A. J. Smith, P. C. Rayer, C. Poulsen, F. W. Saunders, and J. R. Eyre, 2000: A comparison of the impact of TOVS and ATOVS satellite sounding data on the accuracy of numerical weather forecasts.Quart. J. Roy. Meteor. Soc.,126, 2911–2931.Google Scholar
  6. Gelb, A., J. F. Kasper, R. A. Nash, C. F. Price, and A. A. Sutherland (Eds.), 1974:Applied Optimal Estimation. M. I. T. Press, Cambridge, Massachusetts, 323–336.Google Scholar
  7. Grell, G. A., Y.-H. Kuo, and R. Pasch, 1991: Semiprognostic tests of cumulus parameterization schemes in the middle latitudes.Mon. Wea. Rev.,119, 5–31.CrossRefGoogle Scholar
  8. Huddleston, J. N., and B. W. Stiles, 2000: Multidimensional histogram (MUDH) rain flag product description (Version 2.1). Jet Propulsion Laboratory, Pasadena, California, 8 pp. [Available online at podaac.jpl.nasa.gov/quikscat/qscatdoc.html].Google Scholar
  9. JPL, 2001: QuikSCAT science data product user’s manual, Version 2.2. Jet Propulsion Laboratory Publ. D-18053, Pasadena, California, 30–33. [Available online at podaac.jpl.nasa.gov/quikscat/qscatdoc.html].Google Scholar
  10. Kazumori, M., K. Okamoto, and H. Owada, 2003: Operational use of the ATOVS radiances in global data assimilation at the JMA.Proc Thirteenth International TOVS Study Conference, Sainte-Adèle, Québec, Canada, 29 October–4 November 2003, 37–42.Google Scholar
  11. Leidner, S. M., L. Isaksen, and R. N. Hoffman, 2003: Impact of NSCAT winds on tropical cyclones in the ECMWF 4DVAR Assimilation System.Mon. Wea. Rev.,131, 3–26.CrossRefGoogle Scholar
  12. Liu, K. S., and J. C. L. Chan, 2002: Synoptic flow patterns associated with small and large tropical cyclones over the western North Pacific.Mon. Wea. Rev.,130, 2134–2142.CrossRefGoogle Scholar
  13. Long, D. E., and J. M. Mendel, 1991: Identifiability in wind estimation from wind scatterometer measurements.IEEE Trans. Geosci. Remote Sens.,GE-29, 268–276.CrossRefGoogle Scholar
  14. Lorenc, A. C., 1992: Iterative analysis using covariance functions and filters.Quart. J. Roy. Meteor. Soc. 118, 569–591.Google Scholar
  15. Mahfouf, J. F., and F. Rabier, 2000: The ECMWF operational implementation of four-dimensional variational assimilation. II: Experimental results with improved physics.Quart. J. Roy. Meteor. Soc.,126, 1171–1190.CrossRefGoogle Scholar
  16. McNally, A. P., J. C. Derber, W. Wu, and B. B. Katz, 2000: The use of TOVS level-1b radiances in the NCEP SSI analysis system.Quart. J. Roy. Meteor. Soc.,126, 1171–1190.CrossRefGoogle Scholar
  17. Parrish, D., and J. C. Derber, 1992: The National Meteorological Center Spectral Statistical Interpolation analysis.Mon. Wea. Rev.,120, 1747–1763.CrossRefGoogle Scholar
  18. Pasch, R. J., S. R. Stewart, and D. P. Brown, 2003: Comments on “Early Detection of Tropical Cyclones Using SeaWinds-Derived Vorticity”.Bull. Amer. Meteor. Soc.,84, 1415–1416.CrossRefGoogle Scholar
  19. Shaffer, S. J., R. S. Dunbar, S. V. Hisao, and D. G. Long, 1991: A median-filter-based ambiguity removal algorithm for NSCAT.IEEE Trans. Geosci. Remote Sens.,GE-29, 167–174.CrossRefGoogle Scholar
  20. Sharp, R. J., M. A. Bourassa, and J. J. O’Brien, 2002: Early detection of tropical cyclones using SeaWindsderived vorticity.Bull. Amer. Meteor. Soc.,83, 879–889.CrossRefGoogle Scholar
  21. Simmons, A. J., and A. Hollingsworth, 2002: Some aspects of the improvements in skill of numerical weather prediction.Quart. J. Roy. Meteor. Soc.,128, 647–677.CrossRefGoogle Scholar
  22. Xue, J., 2002: Satellite data assimilation plan in CMA. Topic chairman and rapporteur reports of the Fifth WMO International Workshop on Tropical Cyclones (IWTC-V), WMO/TD. No.1136.Google Scholar
  23. Zhang, D.-L., and R. A. Anthes, 1982: A high-resolution model of the planetary boundary layer: Sensitivity tests and comparisons with SESAME-79 data.J. Appl. Meteor.,21, 1594–1609.CrossRefGoogle Scholar

Copyright information

© Advances in Atmospheric Sciences 2003

Authors and Affiliations

  • Zeng Zhihua
    • 1
    Email author
  • Duan Yihong
    • 1
  • Liang Xudong
    • 1
  • Ma Leiming
    • 1
  • Johnny Chung-leung Chan
    • 1
    • 2
  1. 1.Shanghai Typhoon InstituteShanghai
  2. 2.Department of Physics and Materials ScienceCity University of Hong KongHong Kong

Personalised recommendations