Cover Journal of Atmospheric and Oceanic Technology
Journal of Atmospheric and Oceanic Technology

  • Benjamin, S. G., and Coauthors, 2004: An hourly assimilation–forecast cycle: The RUC. Mon. Wea. Rev., 132 , 495–518.

  • Bluestein, H. B., and Hazen D. S. , 1989: Doppler-radar analysis of a tropical cyclone over land: Hurricane Alicia (1983) in Oklahoma. Mon. Wea. Rev., 117 , 2594–2611.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Briggs, W. L., Henson V. E. , and McCormick S. F. , 2000: A Multigrid Tutorial. 2nd ed. SIAM, 193 pp.

  • Browning, K. A., and Wexler R. , 1968: The determination of kinematic properties of a wind field using Doppler radar. J. Appl. Meteor., 7 , 105–113.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Byrd, R. H., Lu P. , Nocedal J. , and Zhu C. , 1995: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput., 16 , 1190–1208.

  • Derber, J., and Rosati A. , 1989: A global oceanic data assimilation system. J. Phys. Oceanogr., 19 , 1333–1347.

  • Fletcher, R., 1987: Practical Methods of Optimization. 2nd ed. John Wiley & Sons, 450 pp.

  • Gao, J., Xue M. , Shapiro A. , and Droegemeier K. , 1999: A variational method for the analysis of three-dimensional wind fields from two Doppler radars. Mon. Wea. Rev., 127 , 2128–2142.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Horn, B. K. P., and Schunck B. G. , 1980: Determining optical flow. Massachusetts Institute of Technology Artificial Intelligence Laboratory Memo. 572, 27 pp.

    • Search Google Scholar
    • Export Citation

  • Lee, W-C., Jou B. J. , Chang P-L. , and Deng S-M. , 1999: Tropical cyclone kinematic structure retrieved from single-Doppler radar observation. Part I: Interpretation of Doppler patterns and GBVTD technique. Mon. Wea. Rev., 128 , 3982–4001.

    • Search Google Scholar
    • Export Citation

  • Lhermitte, R. M., and Atlas D. , 1961: Precipitation motion by pulse Doppler. Preprints, Ninth Conf. on Radar Meteorology, Kansas City, MO, Amer. Meteor. Soc., 218–223.

    • Search Google Scholar
    • Export Citation

  • Li, W., Xie Y. , He Z. , Liu K. , Han G. , Ma J. , and Li D. , 2008: Application of the multigrid data assimilation method to the China Seas’ temperature forecast. J. Atmos. Oceanic Technol., 25 , 2106–2116.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lindskog, M., Järvinen H. , and Michelson D. B. , 2002: Development of radar wind data assimilation for the HIRLAM 3DVAR. HIRLAM Tech. Rep. 52, 22 pp.

    • Search Google Scholar
    • Export Citation

  • Liou, Y. C., Gal-Chen T. , and Lilly D. K. , 1991: Retrieval of wind temperature and pressure from single Doppler radar and a numerical model. Proc. 25th Int. Conf. on Radar Meteorology, Paris, France, Amer. Meteor. Soc., 151–154.

    • Search Google Scholar
    • Export Citation

  • Peace R. L. Jr., , Brown R. A. , and Gamnitz H. G. , 1969: Horizontal motion field observations with a single pulse Doppler radar. J. Atmos. Sci., 26 , 1096–1103.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Qiu, C. J., and Xu Q. , 1992: A simple adjoint method of wind analysis for single-Doppler data. J. Atmos. Oceanic Technol., 9 , 588–598.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rinehart, R. E., 1979: Internal storm motions from a single non-Doppler weather radar. NCAR Tech. Note NCAR/TN-146+STR, 262 pp.

  • Sun, J., and Crook N. A. , 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54 , 1642–1661.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sun, J., and Crook N. A. , 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55 , 835–852.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sun, J., and Crook N. A. , 2001: Real-time low-level wind and temperature analysis using single WSR-88D data. Wea. Forecasting, 16 , 117–132.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sun, J., Flicker D. W. , and Lilly D. K. , 1991: Recovery of three-dimensional wind and temperature fields from simulated single-Doppler radar data. J. Atmos. Sci., 48 , 876–890.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tuttle, J. D., and Foote G. B. , 1990: Determination of the boundary layer airflow from a single Doppler radar. J. Atmos. Oceanic Technol., 7 , 218–232.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Waldteufel, P., and Corbin H. , 1979: On the analysis of single-Doppler radar data. J. Appl. Meteor., 18 , 532–542.

  • Weygandt, S. S., Shapiro A. , and Droegemeier K. K. , 2002a: Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part I: Single-Doppler velocity retrieval. Mon. Wea. Rev., 130 , 433–453.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Weygandt, S. S., Shapiro A. , and Droegemeier K. K. , 2002b: Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part II: Thermodynamic retrieval and numerical prediction. Mon. Wea. Rev., 130 , 454–476.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xiao, Q., Ying-hwa K. , Sun J. , Wen-chau L. , Lim E. , Yong-run G. , and Barker D. M. , 2005: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Meteor., 44 , 768–788.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xie, Y., Koch S. E. , McGinley J. A. , Albers S. , and Wang N. , 2005: A sequential variational analysis approach for mesoscale data assimilation. Preprints, 21st Conf. on Weather Analysis and Forecasting/17th Conf. on Numerical Weather Prediction, Washington, D.C., Amer. Meteor. Soc., 15B.7. [Available online at http://ams.confex.com/ams/pdfpapers/93468.pdf].

    • Search Google Scholar
    • Export Citation

  • Xu, Q., Liu S. , and Xue M. , 2006: Background error covariance functions for vector wind analyses using Doppler radar radial-velocity observations. Quart. J. Roy. Meteor. Soc., 132 , 2887–2904.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xue, M., Liu S. , and Yu T-Y. , 2007: Variational analysis of oversampled dual-Doppler radial velocity data and application to the analysis of tornado circulations. J. Atmos. Oceanic Technol., 24 , 403–414.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 356 124 10
PDF Downloads 458 80 10
Article Type:
Research Article

Application of the Multigrid Method to the Two-Dimensional Doppler Radar Radial Velocity Data Assimilation

Wei LiCollege of Physical and Environmental Oceanography, Ocean University of China, Qingdao, and National Marine Data and Information Service, State Oceanic Administration, Tianjin, China

Search for other papers by Wei Li in
Current site
Google Scholar
PubMedClose
,
Yuanfu XieNOAA/Earth System Research Laboratory, Boulder, Colorado

Search for other papers by Yuanfu Xie in
Current site
Google Scholar
PubMedClose
,
Shiow-Ming DengCentral Weather Bureau, Taipei, Taiwan

Search for other papers by Shiow-Ming Deng in
Current site
Google Scholar
PubMedClose
, and
Qi WangNational Marine Data and Information Service, State Oceanic Administration, Tianjin, China

Search for other papers by Qi Wang in
Current site
Google Scholar
PubMedClose
Print Publication:
01 Feb 2010
DOI:
https://doi.org/10.1175/2009JTECHA1271.1
Page(s):
319–332
Restricted access

Abstract

In recent years, the Earth System Research Laboratory (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) has developed a space and time mesoscale analysis system (STMAS), which is currently a sequential three-dimensional variational data assimilation (3DVAR) system and is developing into a sequential 4DVAR in the near future. It is implemented by using a multigrid method based on a variational approach to generate grid analyses. This study is to test how STMAS deals with 2D Doppler radar radial velocity and to what degree the 2D Doppler radar radial velocity can improve the conventional (in situ) observation analysis. Two idealized experiments and one experiment with real Doppler radar radial velocity data, handled by STMAS, demonstrated significant improvement of the conventional observation analysis. Because the radar radial wind data can provide additional wind information (even it is incomplete: e.g., missing tangential wind vector), the analyses by assimilating both radial wind data and conventional data showed better results than those by assimilating only conventional data. Especially in the case of sparse conventional data, radar radial wind data can provide significant information and improve the analyses considerably.

Corresponding author address: Wei Li, 93 Liuwei Road, Hedong District, Tianjin 300171, China. Email: liwei@mail.nmdis.gov.cn

Abstract

In recent years, the Earth System Research Laboratory (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) has developed a space and time mesoscale analysis system (STMAS), which is currently a sequential three-dimensional variational data assimilation (3DVAR) system and is developing into a sequential 4DVAR in the near future. It is implemented by using a multigrid method based on a variational approach to generate grid analyses. This study is to test how STMAS deals with 2D Doppler radar radial velocity and to what degree the 2D Doppler radar radial velocity can improve the conventional (in situ) observation analysis. Two idealized experiments and one experiment with real Doppler radar radial velocity data, handled by STMAS, demonstrated significant improvement of the conventional observation analysis. Because the radar radial wind data can provide additional wind information (even it is incomplete: e.g., missing tangential wind vector), the analyses by assimilating both radial wind data and conventional data showed better results than those by assimilating only conventional data. Especially in the case of sparse conventional data, radar radial wind data can provide significant information and improve the analyses considerably.

Corresponding author address: Wei Li, 93 Liuwei Road, Hedong District, Tianjin 300171, China. Email: liwei@mail.nmdis.gov.cn

Save