About this Journal Submit a Manuscript Table of Contents
Advances in Meteorology
Volume 2010 (2010), Article ID 797265, 14 pages
http://dx.doi.org/10.1155/2010/797265
Research Article

A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations

1National Severe Storms Laboratory, Norman, OK 73072, USA
2Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK 73072, USA
3Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Science Application International Corporation, Greenbelt, MD 20771, USA
4National Meteorological Center, China Meteorological Administration, Beijing 100081, China
5Marine Meteorology Division, Naval Research Laboratory, Monterey, CA 93943-5502, USA

Received 1 January 2010; Accepted 3 April 2010

Academic Editor: Zhaoxia Pu

Copyright © 2010 Qin Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Sun, D. W. Flicker, and D. K. Lilly, “Recovery of three-dimensional wind and temperature fields from simulated single-Doppler radar data,” Journal of the Atmospheric Sciences, vol. 48, no. 6, pp. 876–890, 1991. View at Scopus
  2. H. Kapitza, “Numerical experiments with the adjoint of a nonhydrostatic mesoscale model,” Monthly Weather Review, vol. 119, no. 12, pp. 2993–3011, 1991. View at Scopus
  3. C. Qiu and Q. Xu, “A simple adjoint method of wind analysis for single-Doppler data,” Journal of Atmospheric & Oceanic Technology, vol. 9, no. 5, pp. 588–598, 1992. View at Scopus
  4. J. Sun and A. Crook, “Wind and thermodynamic retrieval from single-Doppler measurements of a gust front observed during Phoenix II,” Monthly Weather Review, vol. 122, no. 6, pp. 1075–1091, 1994. View at Scopus
  5. Q. Xu, C. Qiu, and J. Yu, “Adjoint-method retrievals of low-altitude wind fields from single-Doppler reflectivity measured during Phoenix II,” Journal of Atmospheric & Oceanic Technology, vol. 11, no. 2, pp. 275–288, 1994. View at Scopus
  6. Q. Xu, C. Qiu, and J. Yu, “Adjoint-method retrievals of low-altitude wind fields from single-Doppler wind data,” Journal of Atmospheric & Oceanic Technology, vol. 11, no. 2, pp. 579–585, 1994. View at Scopus
  7. S. Laroche and I. Zawadzki, “A variational analysis method for the retrieval of three-dimensional wind field from single-Doppler radar data,” Journal of the Atmospheric Sciences, vol. 51, pp. 2664–2682, 1994. View at Scopus
  8. A. Shapiro, S. Ellis, and J. Shaw, “Single-Doppler velocity retrievals with Phoenix II data: clear air and microburst wind retrievals in the planetary boundary layer,” Journal of the Atmospheric Sciences, vol. 52, no. 9, pp. 1265–1287, 1995. View at Scopus
  9. J. Zhang and T. Gal-Chen, “Single-Doppler wind retrieval in the moving frame of reference,” Journal of the Atmospheric Sciences, vol. 53, no. 18, pp. 2609–2623, 1996. View at Scopus
  10. Y.-C. Liou, “Single radar recovery of cross-beam wind components using a modified moving frame of reference technique,” Journal of Atmospheric and Oceanic Technology, vol. 16, no. 8, pp. 1003–1016, 1999. View at Scopus
  11. J. Gao, M. Xue, A. Shapiro, Q. Xu, and K. K. Droegemeier, “Three-dimensional simple adjoint velocity retrievals from single-Doppler radar,” Journal of Atmospheric and Oceanic Technology, vol. 18, no. 1, pp. 26–38, 2001. View at Scopus
  12. S. S. Weygandt, A. Shapiro, and K. K. Droegemeier, “Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part I: single-Doppler velocity retrieval,” Monthly Weather Review, vol. 130, no. 3, pp. 433–453, 2002. View at Scopus
  13. J. Sun and N. A. Crook, “Real-time low-level wind and temperature analysis using single WSR-88D data,” Weather and Forecasting, vol. 16, no. 1, pp. 117–132, 2001. View at Scopus
  14. Q. Xu, H. Gu, and W. Gu, “A variational method for Doppler radar data assimilation,” in Proceedings of the 5th Symposium on Integrated Observing Systems, pp. 118–121, The American Meteor Society, Albuquerque, New Mexico, 2001.
  15. W. Gu, H. Gu, and Q. Xu, “Impact of single-Doppler radar observations on numerical prediction of 7 May 1995 Oklahoma squall line,” in Proceedings of the 5th Symposium on Integrated Observing Systems, pp. 139–142, The American Meteor Society, Albuquerque, New Mexico, 2001.
  16. J. Gao, M. Xue, K. Brewster, and K. K. Droegemeier, “A three-dimensional variational data analysis method with recursive filter for Doppler radars,” Journal of Atmospheric and Oceanic Technology, vol. 21, no. 3, pp. 457–469, 2004. View at Scopus
  17. M. Hu, M. Xue, J. Gao, and K. Brewster, “3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part II: impact of radial velocity analysis via 3DVAR,” Monthly Weather Review, vol. 134, no. 2, pp. 699–721, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. C.-J. Qiu and Q. Xu, “Least squares retrieval of microburst winds from single-Doppler radar data,” Monthly Weather Review, vol. 124, no. 6, pp. 1132–1144, 1996. View at Scopus
  19. Q. Xu, H. Gu, and S. Yang, “Simple adjoint method for three-dimensional wind retrievals from single-Doppler radar,” Quarterly Journal of the Royal Meteorological Society, vol. 127, no. 573, pp. 1053–1067, 2001. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Gal-Chen, “A method for the initialization of the anelastic equations: implications for matching models with observations,” Monthly Weather Review, vol. 106, pp. 587–606, 1978.
  21. E. C. Hane and B. C. Scott, “Temperature and pressure perturbations within convective clouds derived from detailed air motion information: preliminary testing,” Monthly Weather Review, vol. 106, pp. 654–661, 1978.
  22. R. M. Hodur, “The naval research laboratory's coupled ocean/atmosphere mesoscale prediction system (COAMPS),” Monthly Weather Review, vol. 125, no. 7, pp. 1414–1430, 1997. View at Scopus
  23. A. H. Jazwinski, Stochastic Processes and Filtering Theory, Academic Press, New York, NY, USA, 1970.
  24. A. F. Bennett, Inverse Method in Physical Oceanography, Cambridge University Press, Cambridge, UK, 1992.
  25. J.-M. Lewis and J. C. Derber, “The use of adjoint Equations to solve a variational adjustment problem with advective constraints,” Tellus, vol. 37A, pp. 309–322, 1985. View at Scopus
  26. F.-X. Le Dimet and O. Talagrand, “Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects,” Tellus, vol. 38, no. 2, pp. 97–110, 1986.
  27. D. F. Parrish and J. C. Derber, “The National Meteorological Center's spectral statistical- interpolation analysis system,” Monthly Weather Review, vol. 120, no. 8, pp. 1747–1763, 1992. View at Scopus
  28. J. Gong, L. Wang, and Q. Xu, “A three-step dealiasing method for Doppler velocity data quality control,” Journal of Atmospheric and Oceanic Technology, vol. 20, no. 12, pp. 1738–1748, 2003. View at Publisher · View at Google Scholar · View at Scopus
  29. R. J. Doviak and D. S. Zrnić, Doppler Radar and Weather Observations, Dover Publications, New York, NY, USA, 2nd edition, 2006.
  30. E. Kessler, “On the distribution and continuity of water substance in atmospheric circulation. Meteor. Monogr,” The American Meteor Society, vol. 10, no. 32, p. 84, 1969. View at Scopus
  31. R. J. Purser, W.-S. Wu, D. F. Parrish, and N. M. Roberts, “Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: spatially homogeneous and isotropic Gaussian covariances,” Monthly Weather Review, vol. 131, no. 8, pp. 1524–1535, 2003. View at Publisher · View at Google Scholar · View at Scopus
  32. Q. Xu, “Representations of inverse covariances by differential operators,” Advances in Atmospheric Sciences, vol. 22, no. 2, pp. 181–198, 2005. View at Scopus
  33. Q. Xu, K. Nai, and L. Wei, “An innovation method for estimating radar radial-velocity observation error and background wind error covariances,” Quarterly Journal of the Royal Meteorological Society, vol. 133, no. 623, pp. 407–415, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. Q. Xu, K. Nai, L. Wei, et al., “Progress in Doppler radar data assimilation,” in Proceedings of the 32nd Conference on Radar Meteorology, pp. 24–29, The American Meteor Society, Albuquerque, New Mexico, 2005, CD-ROM, JP1J7. View at Scopus
  35. Q. Xu and L. Wei, “Estimation of three-dimensional error covariances. Part II: analysis of wind innovation vectors,” Monthly Weather Review, vol. 129, no. 12, pp. 2939–2954, 2001. View at Scopus
  36. L. Wei, Q. Xu, and Q. Zhao, “Using GOES data to improve COAMPS cloud analysis and forecast,” in Proceedings of the 5th Symposium on Integrated Observing Systems, pp. 126–129, The American Meteor Society, Albuquerque, New Mexico, 2001.
  37. Q. Zhao, J. Cook, and L. Phegley, “Assimilation of radar observations into a high-resolution numerical weather analysis and prediction system at NRL,” in Proceedings of the 31th Conference on Radar Meteorology, pp. 169–172, The American Meteor Society, Seattle, Wash, USA, 2001.
  38. Q. Zhao, J. Cook, Q. Xu, and P. R. Harasti, “Improving short-term storm predictions by assimilating both radar radial-wind and reflectivity observations,” Weather and Forecasting, vol. 23, pp. 373–391, 2008.
  39. Q. Xu, L. Wang, P. Zhang, et al., “Progress in radar data quality control and assimilation,” in Proceedings of the 6th International Symposium of Hydrological Applications of Weather Radar, Bureau of Meteorology & Australian Meteor, Melbourne, Australia, 2001, The Oceanography Society, Conference CD.
  40. Q. Zhao, J. Cook, Q. Xu, and P. R. Harasti, “Using radar wind observations to improve mesoscale numerical weather prediction,” Weather and Forecasting, vol. 21, no. 4, pp. 502–522, 2006. View at Publisher · View at Google Scholar · View at Scopus
  41. A. Protat and I. Zawadzki, “Optimization of dynamic retrievals from a multiple-Doppler radar network,” Journal of Atmospheric and Oceanic Technology, vol. 17, no. 6, pp. 753–760, 2000. View at Scopus
  42. R. Daley, Atmospheric Data Analysis, Cambridge University Press, Cambridge, UK, 1991.
  43. Q. Xu and J. Gong, “Background error covariance functions for Doppler radial-wind analysis,” Quarterly Journal of the Royal Meteorological Society, vol. 129, no. 590, pp. 1703–1720, 2003. View at Publisher · View at Google Scholar · View at Scopus