Advances in Atmospheric Sciences

, Volume 21, Issue 2, pp 220–226 | Cite as

Parallel computing of a variational data assimilation model for GPS/MET observation using the ray-tracing method

  • Zhang Xin 
  • Liu Yuewei 
  • Wang Bin 
  • Ji Zhongzhen 


The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate meteorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with the variational technique. Verified by radiosonde, including GPS/MET observations into the analysis makes an overall improvement to the analysis variables of temperature, winds, and water vapor. However, the variational model with the ray-tracing method is quite expensive for numerical weather prediction and climate research. For example, about 4 000 GPS/MET refraction angles need to be assimilated to produce an ideal global analysis. Just one iteration of minimization will take more than 24 hours CPU time on the NCEP’s Cray C90 computer. Although efforts have been taken to reduce the computational cost, it is still prohibitive for operational data assimilation. In this paper, a parallel version of the three-dimensional variational data assimilation model of GPS/MET occultation measurement suitable for massive parallel processors architectures is developed. The divide-and-conquer strategy is used to achieve parallelism and is implemented by message passing. The authors present the principles for the code’s design and examine the performance on the state-of-the-art parallel computers in China. The results show that this parallel model scales favorably as the number of processors is increased. With the Memory-IO technique implemented by the author, the wall clock time per iteration used for assimilating 1420 refraction angles is reduced from 45 s to 12 s using 1420 processors. This suggests that the new parallelized code has the potential to be useful in numerical weather prediction (NWP) and climate studies.

Key words

parallel computing variational data assimilation GPS/MET 


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  1. Fjeldbo, G. F., A. J. Kliore, and V. R. Eshleman, 1971: The neutral atmosphere of Venus as studied with the Mariner V radio occultation experiment.Astron. J.,76, 123–140.CrossRefGoogle Scholar
  2. Gorbunov, M. E., A. S. Gurvich, and L. Bengtsson, 1996: Advanced algorithms of inversion of GPS/MET satellite data and their application to reconstruction of temperature and humidity.Tech. Rep.,211, Max Plank Inst. For Meteorol., Hamburg, 40pp.Google Scholar
  3. Kravtso, Y. A., and Y. I. Orlov, 1990:Geometrical Optics of Inhomogeneous Media. Springer-Verlag, New York, 312pp.Google Scholar
  4. Kuo, Y. -H., and coauthors, 1998: A GPS/MET sounding through an intense upper-level front.Bull. Amer. Meteor. Soc.,79, 617–626.CrossRefGoogle Scholar
  5. Kursinski, E. R., G. A. Hajj, K. R. Hardy, L. J. Romans, and J. T. Schofield, 1995: Observing tropospheric water vapor by radio occultation using the Global Positioning System.Geophys. Res. Lett.,22, 2365–2368.CrossRefGoogle Scholar
  6. Kursinski, E. R., and coauthors, 1996: Initial results of radio occultation observations of earth’s atmosphere using the Global Positioning System.Science,271, 1107–1110.CrossRefGoogle Scholar
  7. Li Shuyong, Wang Bin, and Zhang Xin, 2001: The parallel computing of GPS Ray shooting model.Adv. Atmos. Sci.,18(6), 1185–1191.CrossRefGoogle Scholar
  8. Liebe, H. J., 1989: MPM-An atmospheric millmeter-wave propagation model.Int. J. Infrared Millimeter Waves,10, 631–650.CrossRefGoogle Scholar
  9. Liu, D. C., and J. Nocedal, 1989: On the limited memory BFGS method for large scale optimization.Mathematical Programming,45, 503–528.CrossRefGoogle Scholar
  10. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical interpolation analysis system.Mon. Wea. Rev.,120, 1747–1763.CrossRefGoogle Scholar
  11. Peaceman, D. W., and H. H. Rachford, 1955: The numerical solution of parabolic and elliptic differential equations.J. Soc. Ind. Appl. Math.,3, 28–41.CrossRefGoogle Scholar
  12. Phinney, R. A., and D. L. Anderson, 1968: On the radio occultation method for studying planetary atmosphere.J. Geophys. Res.,73, 1819–1827.CrossRefGoogle Scholar
  13. Rocken, C., and coauthors, 1997: Analysis and validation of GPS/MET data in the neutral atmosphere.J. Geophys. Res.,102, 29849–29866.CrossRefGoogle Scholar
  14. Smith, E. K., and S. Weintraub, 1953: The constants in the equations for atmospheric refractive index at radio frequencies.Proc. IRE.,41, 1035–1037.CrossRefGoogle Scholar
  15. Thayer, G. D., 1974: An improved equation for radio refractive index of air.Radio Sci.,9, 803–807.CrossRefGoogle Scholar
  16. Ware, R., and coauthors, 1996: GPS sounding of atmosphere from low earth orbit: Preliminary results.Bull. Amer. Meteor. Soc.,77, 19–40.CrossRefGoogle Scholar
  17. Yanenko, N. N., 1971:The Method of Fractional Steps., Springer-Verlag, New York.Google Scholar
  18. Zou, X., B. Wang, H. Liu, R. A. Anthes, T. Matsumura, and Y. -J. Zhu, 2000: Use of GPS/MET refraction angles in three-dimensional variational analysis,Quart. J. Roy. Meteor. Soc.,126, 3013–3040.CrossRefGoogle Scholar
  19. Zou, X., and coauthors, 1999: A raytracing operator and its adjoint for the use of GPS/MET refraction angle measurements.J. Geophys. Res.,104, 22301–22318.CrossRefGoogle Scholar

Copyright information

© Advances in Atmospheric Sciences 2004

Authors and Affiliations

  • Zhang Xin 
    • 1
    • 3
  • Liu Yuewei 
    • 2
  • Wang Bin 
    • 3
  • Ji Zhongzhen 
    • 3
  1. 1.Key Laboratory of Pure and Applied Mathematics, Center for Computational Science and Engineering, School of Mathematical SciencesPeking UniversityBeijing
  2. 2.National Meteorological CenterBeijing
  3. 3.State Key Laboratory of Numerical Modeling for Atmospherics Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing

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