A Fast Inverse Algorithm Based on the Multigrid Technique for Cloud Tomography

Jun Zhou Key Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Search for other papers by Jun Zhou in
Current site
Google Scholar
PubMed
Close
,
Hengchi Lei Key Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Search for other papers by Hengchi Lei in
Current site
Google Scholar
PubMed
Close
,
Lei Ji Key Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, and Institute of Urban Meteorology, China Meteorological Administration, and Beijing Weather Modification Office, Beijing, China

Search for other papers by Lei Ji in
Current site
Google Scholar
PubMed
Close
, and
Tuanjie Hou Key Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Search for other papers by Tuanjie Hou in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

A fast inverse algorithm based on the half-V cycle scheme (HV) of the multigrid technique is developed for cloud tomography. Fourier analysis shows that the slow convergence problem caused by the smoothing property of the iterative algorithm can be effectively alleviated in HV by performing iterations from the coarsest to the finest grid. In this way, the resolvable scales of information contained in observations can be retrieved efficiently on the coarser grid level and the unresolvable scales are left as errors on the finer grid level. Numerical simulations indicate that, compared with the previous algorithm without HV (NHV), HV can significantly reduce the runtime by 89%–96.9% while retaining a similar level of retrieval accuracy. For the currently feasible two-level flight scheme for a 20-km-wide target area, convergence can be accelerated from 407 s in NHV to 13 s in HV. This reduction in time would be multiplied several times if the target area were much wider; but then segmental retrieval would be required to avoid exceeding the time limit of cloud tomography. This improvement represents an important saving in terms of computing resources and ensures the real-time application of cloud tomography in a much wider range of fields.

Corresponding author address: Dr. Jun Zhou, Key Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Deshengmenwai, Qijiahuozi, Chaoyang District, Beijing 100029, China. E-mail: zhoujun@mail.iap.ac.cn

Abstract

A fast inverse algorithm based on the half-V cycle scheme (HV) of the multigrid technique is developed for cloud tomography. Fourier analysis shows that the slow convergence problem caused by the smoothing property of the iterative algorithm can be effectively alleviated in HV by performing iterations from the coarsest to the finest grid. In this way, the resolvable scales of information contained in observations can be retrieved efficiently on the coarser grid level and the unresolvable scales are left as errors on the finer grid level. Numerical simulations indicate that, compared with the previous algorithm without HV (NHV), HV can significantly reduce the runtime by 89%–96.9% while retaining a similar level of retrieval accuracy. For the currently feasible two-level flight scheme for a 20-km-wide target area, convergence can be accelerated from 407 s in NHV to 13 s in HV. This reduction in time would be multiplied several times if the target area were much wider; but then segmental retrieval would be required to avoid exceeding the time limit of cloud tomography. This improvement represents an important saving in terms of computing resources and ensures the real-time application of cloud tomography in a much wider range of fields.

Corresponding author address: Dr. Jun Zhou, Key Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Deshengmenwai, Qijiahuozi, Chaoyang District, Beijing 100029, China. E-mail: zhoujun@mail.iap.ac.cn
Save
  • Briggs, W. L., Henson V. E. , and McCormick S. F. , 2000: A Multigrid Tutorial. 2nd ed. Society for Industrial and Applied Mathematics, 193 pp.

  • Drake, J. F., and Warner J. , 1988: A theoretical study of the accuracy of tomographic retrieval of cloud liquid with an airborne radiometer. J. Atmos. Oceanic Technol., 5, 844857, doi:10.1175/1520-0426(1988)005<0844:ATSOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • He, Z. J., Xie Y. F. , Li W. , Li D. , Han G. J. , Liu K. X. , and Ma J. R. , 2008: Application of the sequential three-dimensional variational method to assimilating SST in a global ocean model. J. Atmos. Oceanic Technol., 25, 10181033, doi:10.1175/2007JTECHO540.1.

    • Search Google Scholar
    • Export Citation
  • Huang, D., Liu Y. , and Wiscombe W. , 2008: Determination of cloud liquid water distribution using 3D cloud tomography. J. Geophys. Res., 113, D13201, doi:10.1029/2007JD009133.

    • Search Google Scholar
    • Export Citation
  • Li, W., Xie Y. F. , He Z. J. , Han G. J. , Liu K. X. , Ma J. R. , and Li D. , 2008: Application of the multigrid data assimilation method to the China Seas’ temperature forecast. J. Atmos. Oceanic Technol., 25, 21062116, doi:10.1175/2008JTECHO510.1.

    • Search Google Scholar
    • Export Citation
  • Li, W., Xie Y. F. , Deng S. M. , and Wang Q. , 2010: Application of the multigrid method to the two-dimensional Doppler radar radial velocity data assimilation. J. Atmos. Oceanic Technol., 27, 319332, doi:10.1175/2009JTECHA1271.1.

    • Search Google Scholar
    • Export Citation
  • MacDonald, A. E., Xie Y. F. , and Ware R. H. , 2002: Diagnosis of three-dimensional water vapor using a GPS network. Mon. Wea. Rev., 130, 386397, doi:10.1175/1520-0493(2002)130<0386:DOTDWV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Steven, W. S., 1997: The Scientist and Engineer’s Guide to Digital Signal Processing. California Technical Publishing, 626 pp.

  • Wang, Y. F., 2007: Computational Methods for Inverse Problems and Their Applications (in Chinese). Higher Education Press, 83–84.

  • Warner, J., and Drake J. F. , 1985: Determination of cloud liquid water distribution by radiometric data. J. Atmos. Oceanic Technol., 2, 293303, doi:10.1175/1520-0426(1985)002<0293:DOCLWD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Warner, J., and Drake J. F. , 1988: Field tests of an airborne remote sensing technique for measuring the distribution of liquid water in convective cloud. J. Atmos. Oceanic Technol., 5, 833843, doi:10.1175/1520-0426(1988)005<0833:FTOAAR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xie, Y. F., Koch S. E. , McGinley J. A. , Albers S. , and Wang N. , 2005: A sequential variational analysis approach for mesoscale data assimilation. 21st Conf. on Weather Analysis and Forecasting/17th Conf. on Numerical Weather Prediction, Washington, D.C., Amer. Meteor. Soc., 15B.7. [Available online at https://ams.confex.com/ams/WAFNWP34BC/techprogram/paper_93468.htm.]

  • Xie, Y. F., Koch S. E. , McGinley J. A. , Albers S. , Bieringer P. E. , Wolfson M. , and Chan M. , 2011: A space–time multiscale analysis system: A sequential variational analysis approach. Mon. Wea. Rev., 139, 12241240, doi:10.1175/2010MWR3338.1.

    • Search Google Scholar
    • Export Citation
  • Young, K. C., 1996: Weather modification—A theoretician’s viewpoint. Bull. Amer. Meteor. Soc., 77, 27012710, doi:10.1175/1520-0477(1996)077<2701:WMATV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhou, J., Lei H. C. , and Ji L. , 2011: Comparison of non-linear optimum and linear iteration algorithms applied in the microwave tomograph (in Chinese). Plateau Meteor., 30, 760771.

    • Search Google Scholar
    • Export Citation
  • Zhou, J., Lei H. C. , and Ji L. , 2013: Improvement of liquid water content retrieval accuracy by multilevel detection in cloud tomography. J. Atmos. Oceanic Technol., 30, 301312, doi:10.1175/JTECH-D-12-00054.1.

    • Search Google Scholar
    • Export Citation
  • Zhu, C. Y., Byrd R. H. , Lu P. , and Nocedal J. , 1997: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans. Math. Software, 23, 550560, doi:10.1145/279232.279236.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 471 294 128
PDF Downloads 103 41 2