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Advances in Atmospheric Sciences

, Volume 22, Issue 3, pp 415–427 | Cite as

Assimilation and simulation of typhoon Rusa (2002) using the WRF system

  • Gu JianfengEmail author
  • Qingnong Xiao
  • Ying-Hwa Kuo
  • Dale M. Barker
  • Xue Jishan
  • Ma Xiaoxing
Article

Abstract

Using the recently developed Weather Research and Forecasting (WRF) 3DVAR and the WRF model, numerical experiments are conducted for the initialization and simulation of typhoon Rusa (2002). The observational data used in the WRF 3DVAR are conventional Global Telecommunications System (GTS) data and Korean Automatic Weather Station (AWS) surface observations. The Background Error Statistics (BES) via the National Meteorological Center (NMC) method has two different resolutions, that is, a 210-km horizontal grid space from the NCEP global model and a 10-km horizontal resolution from Korean operational forecasts. To improve the performance of the WRF simulation initialized from the WRF 3DVAR analyses, the scale-lengths used in the horizontal background error covariances via recursive filter are tuned in terms of the WRF 3DVAR control variables, streamfunction, velocity potential, unbalanced pressure and specific humidity. The experiments with respect to different background error statistics and different observational data indicate that the subsequent 24-h the WRF model forecasts of typhoon Rusa’s track and precipitation are significantly impacted upon the initial fields. Assimilation of the AWS data with the tuned background error statistics obtains improved predictions of the typhoon track and its precipitation.

Key words

3DVAR data assimilation background error statistics numerical simulation typhoon 

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References

  1. Barker, D. M., W. Huang, Y.-R. Guo, and Q. Xiao, 2004: A three-dimensional variational (3DVAR) data assimilation system for use with MM5: Implementation and initial results.Mon. Wea. Rev.,132, 897–914.CrossRefGoogle Scholar
  2. Courtier, P., 1985: Experiments in data assimilation using the adjoint model technique. Preprints.Workshop on High-Resolution Analysis, Reading, United Kingdom, European Centre for Medium-Range Weather Forecasts, 1–20.Google Scholar
  3. Courtier, P., E. Anderson, W. Heckley, J. Pailleux, D. Vasiljevic, M. Hamrud, A. Hollingsworth, F. Rabier, and M. Fischer, 1998: The ECMWF implementation of three dimensional variational (3DVAR) data assimilation. Part I: Formulation.Quart. J. Roy. Meteor. Soc.,123, 1–26.Google Scholar
  4. Courtier, P., J. -N. Thepaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach.Quart. J. Roy. Meteor. Soc.,120, 1367–1387.CrossRefGoogle Scholar
  5. Derber, J. C., 1985: The variational 4-D assimilation of analyses using filtered models as constraints. Ph.D. dissertation, University of Wisconsin-Madison, 142pp.Google Scholar
  6. Hayden, C. M., and R. J. Purser, 1995: Recursive filter objective analysis of meteorological fields: Applications to NESDIS operational processing.J. Appl. Meteor.,34, 3–15.Google Scholar
  7. Le Dimet, F. X., 1982: A general formalism of variational analysis. CIMMS Rep. 22, 1–34. [Available from Sarkeys Energy Center, Rm 1110, University of Oklahoma, Norman, OK 73019.]Google Scholar
  8. Le Dimet, F. X., and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects.Tellus,38A, 97–110.Google Scholar
  9. Lewis, J. M., and J. C. Derber, 1985: The use of the adjoint equation to solve a variational adjustment problem with advective constraints.Tellus,37A, 309–322.CrossRefGoogle Scholar
  10. Li, Z., I. M. Navon, and Y. Zhu, 2000: Performance of 4D-var with different strategies for the use of adjoint physics with the FSU global spectral model.Mon. Wea. Rev.,128, 668–688.CrossRefGoogle Scholar
  11. Li, Z., and I. M. Navon, 2001: Optimality of variational data assimilation and its relationship with the Kalman filter and smoother.Quart, J. Roy. Meteor. Soc.,127, 661–883.CrossRefGoogle Scholar
  12. Lin, Y. L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model.J. Climate Appl. Meteor.,22, 1065–1092.CrossRefGoogle Scholar
  13. Lorenc, A. C., 1986: Analysis methods for numerical weather prediction.Quart. J. Roy. Meteor. Soc.,112, 1177–1194.CrossRefGoogle Scholar
  14. Lorenc, A. C., and Coauthors, 2000: The Met. Office global three-dimensional variational data assimilation scheme.Quart. J. Roy. Meteor. Soc.,126, 2991–3012.CrossRefGoogle Scholar
  15. Navon, I. M., X. Zou, J. Derber, and J. Sela, 1992: Variational data assimilation with an adiabatic version of the NMC spectral model.Mon. Wea. Rev.,120, 1433–1446.CrossRefGoogle Scholar
  16. Parish, D. F., and J. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system.Mon. Wea. Rev.,120, 1747–1763.CrossRefGoogle Scholar
  17. Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts, 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances.Mon. Wea. Rev.,131, 1524–1535.CrossRefGoogle Scholar
  18. Rabier, F., A. McNally, E. Anderson, P. Courtier, P. Unden, J. Eyre, A. Hollingsworth, and F. Bouttier, 1997: The ECMWF implementation of three dimensional variational (3DVar) data assimilation. Part II: Structure function.Quart. J. Roy. Meteor. Soc.,123, 27–52.Google Scholar
  19. Sasaki, Y. 1958: An objective analysis based on variational methods.J. Meteor. Soc. Japan,36, 77–88.Google Scholar
  20. Talagrand, O., and P. Courtier, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation-Part I. Theory.Quart. J. Roy. Meteor. Soc.,113, 1311–1328.CrossRefGoogle Scholar
  21. Wu, W.-S., J. Purser, and D. E. Parrish, 2002: Threedimensional variational analysis with spatial inhomogeneous covariances.Mon. Wea. Rev.,130, 2905–2916.CrossRefGoogle Scholar
  22. Xiao, Q., X. Zou, and B. Wang, 2000: Initialization and simulation of a landfalling hurricane using a variational bogus data assimilation scheme.Mon. Wea. Rev.,128, 2252–2269.CrossRefGoogle Scholar
  23. Xiao, Q., X. Zou, M. Pondeca, M. A. Shapiro, and C. Velden, 2002: Impact of GMS-5 and GOES-9 satellitederived winds on the prediction of a NORPEX extratropical cyclone.Mon. Wea. Rev.,130, 507–528.CrossRefGoogle Scholar
  24. Zhang, X., B. Wang, Z. Ji, Q. Xiao, and X. Zhang, 2003: Initialization and simulation of a typhoon using 4dimensional variational data assimilation—Research on typhoon Herb (1996).Adv. Atmos. Sci.,20(4), 612–622.CrossRefGoogle Scholar
  25. Zou, X., I. M. Navon, and J. G. Sela, 1993a: Control of gravitational oscillations in variational data assimilation.Mon. Wea. Rev.,121, 272–289.CrossRefGoogle Scholar
  26. Zou, X., I. M. Navon, and J. G. Sela, 1993b: Variational data assimilation with moist threshold processes using the NMC spectral model.Tellus,45A, 370–387.Google Scholar
  27. Zou, X., and Q. Xiao, 2000: Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme.J. Atmos. Sci.,57, 836–860.CrossRefGoogle Scholar
  28. Zupanski, M., 1993: Regional four-dimensional variational data assimilation in a quasi-operational forecasting environment.Mon. Wea. Rev.,121, 2396–2408.CrossRefGoogle Scholar

Copyright information

© Advances in Atmospheric Sciences 2003

Authors and Affiliations

  • Gu Jianfeng
    • 1
    • 2
    • 3
    Email author
  • Qingnong Xiao
    • 2
  • Ying-Hwa Kuo
    • 2
  • Dale M. Barker
    • 2
  • Xue Jishan
    • 1
  • Ma Xiaoxing
    • 3
  1. 1.Chinese Academy of Meteorological SciencesBeijing
  2. 2.National Center for Atmospheric ResearchBoulderUSA
  3. 3.Shanghai Weather Forecast CenterShanghai

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