Advances in Atmospheric Sciences

, Volume 21, Issue 2, pp 220–226

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 
Article

DOI: 10.1007/BF02915708

Cite this article as:
Zhang, X., Liu, Y., Wang, B. et al. Adv. Atmos. Sci. (2004) 21: 220. doi:10.1007/BF02915708

Abstract

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 computingvariational data assimilationGPS/MET

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