Skip to main content
Have a personal or library account? Click to login
Application of conditional non-linear optimal perturbations to tropical cyclone adaptive observation using theWeather Research Forecasting (WRF) model Cover

Application of conditional non-linear optimal perturbations to tropical cyclone adaptive observation using theWeather Research Forecasting (WRF) model

Tellus
Volume 63 (2011): Issue 5
By: ,   and    
Open Access
|Jan 2011
References

References

  1. Aberson , S. D . 2003 . Targeted observations to improve operational trop-ical cyclone track. Mon . Weather Re v . 131 , 1613 – 1628 .
    Search in Google ScholarBack to article
  2. Aberson , S. D. and Etherton , B. J . 2006 . Targeting and data assimila-tion studies during Hurricane Humberto (2001). J. Atmos. Sc i . 63 , 175 – 186 .
    Search in Google ScholarBack to article
  3. Barker , D. M. , Huang , W. , Guo , Y-R. , Bourgeois , A. J. and Xiao , Q. N. 2004. A three-dimensional variational data assimilation sys-tem for MM5: implementation and initial results. Mon. Weather Rev. 132 , 897 - 914 .
    Search in Google ScholarBack to article
  4. Barker , D. M. , Lee , M. S. , Guo , Y.-R. , Huang , W. , Rizvi , S. and co-authors . 2005 . WRF-Var—a unified 3/4D-Var variational data assim-ilation system for WRF. In: Proceedings of the Sixth WRFI15th MM5 Users’ Workshop , NCAR, Boulder, CO, 17 pp .
    Search in Google ScholarBack to article
  5. Birgin , E. G. , Martinez , J. E. and Marcos , R. 2001. Algorithm 813: SPG—software for convex-constrained optimization. ACM Trans. Math. Softw 27 , 340 - 349 .
    Search in Google ScholarBack to article
  6. Bishop , C. H. and Toth , Z . 1999 . Ensemble transformation and adaptive observations. J. Atmos. Sc i . 56 , 1748 – 1765 .
    Search in Google ScholarBack to article
  7. Bishop , C. H. , Etherton , B. J. and Majumdar , S. J. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Weather Rev. 129 , 430 - 436 .
    Search in Google ScholarBack to article
  8. Cardinali , C. , Buizza , R. , Kelly , G. , Shapiro , M. and Thepaut , J.-N . 2007 . The value of observations. Part DI: influence of weather regimes on targeting . Q. J. R. MeteoroL Soc . 133 , 1833 – 1842 .
    Search in Google ScholarBack to article
  9. Duan , W. S. , Mu , M. and Wang , B . 2004 . Conditional nonlinear op-timal perturbations as the optimal precursors for El Nino-Southern Oscillation events . J. Geophys. Res . 109 , 1029 – 1041 .
    Search in Google ScholarBack to article
  10. Ehrendorfer , M. and Errico , R. M . 1995 . Mesoscale predictability and the spectrum of optimal perturbations. J. Atmos. Sc i . 52 , 3475 – 3500 .
    Search in Google ScholarBack to article
  11. Ehrendorfer , M. , Errico , R. M. and Raeder , K. D . 1999 . Singular-vector perturbation growth in a primitive equation model with moist physics. J. Atmos. Sc i . 56 , 1627 – 1648 .
    Search in Google ScholarBack to article
  12. Errico , R. M . 1985 . Spectra computed from a limited area grid. Mon . Weather Re v . 113 , 1554 – 1562 .
    Search in Google ScholarBack to article
  13. Grell , G. A. , Dudhia , J. and Stauffer , D. R . 1995 . A description of the fifth generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCARTTN-398±STR, 122 pp .
    Search in Google ScholarBack to article
  14. Gustafsson , N. and Huang , X.-Y . 1996 . Sensitivity experiments with the spectral HIRLAM and its adjoint . Tellus 48A , 501 – 517 .
    Search in Google ScholarBack to article
  15. Hamill , T. M. and Snyder , C . 2002 . Using improved background error covariances from an ensemble Kalman filter for adaptive observations. Mon . Weather Re v . 130 , 1552 – 1572 .
    Search in Google ScholarBack to article
  16. Hoover , B. T. and Morgan , M. C . 2010 . Validation of a tropical cyclone steering response function with a barotropic adjoint model. J. Atmos. Sc i . 67 , 1806 – 1816 .
    Search in Google ScholarBack to article
  17. Hsiao , L.-E , Liou , C.-S. , Yeh , T.-C. , Guo , Y.-R. , Chen , D.-S. and co-authors . 2010. A vortex relocation scheme for tropical cyclone initialization in Advanced Research WRF. Mon. Weather Rev . 138 , 3298-331 5 .
    Search in Google ScholarBack to article
  18. Huang , X.-Y. , Xiao , Q. N. , Barker , D. M. , Zhang , X. , Michalakes , J. and co-authors . 2009 . Four-dimensional variational data assimilation for WRF: formulation and preliminary results. Mon. Weather Rev . 137 , 299 - 314 .
    Search in Google ScholarBack to article
  19. Jiang , Z. N. and Mu , M . 2009 . A comparison study of the methods of conditional nonlinear optimal perturbations and singular vectors in ensemble prediction. Adv . Atmos. Sc i . 26 , 465 – 470 .
    Search in Google ScholarBack to article
  20. Langland R. H . 2005 . Issues in targeted observing . Q. J. R. MeteoroL Soc . 131 , 3409 – 3425 .
    Search in Google ScholarBack to article
  21. Langland , R. H. and Rohaly , G. D . 1996 . Analysis error and adjoint sensi-tivity in prediction of a North Atlantic frontal cyclone. In: Proceedings of the 11th Conf on Numerical Weather Prediction , Preprints, Amer. Meteor. Soc. 150 - 152 .
    Search in Google ScholarBack to article
  22. Lorenz , E. N. and Emanuel , K. A . 1998 . Optimal sites for supplementary weather observations: simulation with a small model. J. Atmos. Sc i . 55 , 399 – 414 .
    Search in Google ScholarBack to article
  23. Majumdar , S. J. , Aberson , S. D. , Bishop , C. H. , Buizza , R. , Peng , M. S. and co-authors. 2006. A comparison of adaptive observing guidance for Atlantic tropical cyclones. Mon. Weather Rev. 134 , 2354 - 2372.
    Search in Google ScholarBack to article
  24. Montani , A. , Thorpe , A. J. , Buizza , R. and Unden , P. 2006. Forecast skill of the ECMWF model using targeted observations during FASTEX. Q. J. R. MeteoroL Soc. 125 , 3219 - 3240 .
    Search in Google ScholarBack to article
  25. Mu , M. and Duan , W. S . 2003 . A new approach to studying ENSO predictability: conditional nonlinear optimal perturbation . Chin. Sci. Bull . 48 , 1045 – 1047 .
    Search in Google ScholarBack to article
  26. Mu , M. and Zhang , Z. Y . 2006 . Conditional nonlinear optimal pertur-bations of a two-dimensional quasigeostrophic model. J. Atmos. Sc i . 63 , 1587 – 1604 .
    Search in Google ScholarBack to article
  27. Mu , M. , Wang , H. L. and Zhou , F. F . 2007 . Application of conditional op-timal perturbation to adaptive observation: preliminary results. Chin. J. Atmos. Sc i . 31 , 1102 – 1112 .
    Search in Google ScholarBack to article
  28. Mu , M. , Zhou , F . E and Wang, H. L. 2009. A method for identifying the sensitive areas in targeted observations for tropical cyclone prediction: conditional nonlinear optimal perturbation . Mon. Weather Rev . 137 , 1623– 1639 .
    Search in Google ScholarBack to article
  29. Palmer , T. N. , Gelaro , R. , Barameijer , J. and Buizza , R . 1998 . Sin-gular vectors, metrics, and adaptive observations. J. Atmos. Sc i . 55 , 633 – 653 .
    Search in Google ScholarBack to article
  30. Parrish , D. F. and Derber , J. C. 1992. The National Meteorological Cen-ter’s spectral statistical-interpolation analysis system . Mon. Weather Rev . 120 , 1747– 1763 .
    Search in Google ScholarBack to article
  31. Pu , Z. X. and Kalnay , E . 1999 . Targeting observations with the quasi-linear inverse and adjoint NCEP global models: performance during FASTEX . Q. J. R. MeteoroL Soc . 125 , 3329 – 3337 .
    Search in Google ScholarBack to article
  32. Qin , Q. H. and Mu , M . 2011 . A study on the reduction of forecast error variance by three adaptive observation approaches for tropical cyclone prediction. Mon . Weather Re v . 139 , 2218 – 2232 .
    Search in Google ScholarBack to article
  33. Rabier , F. , Klinker , E. , Courtier , P. and Hollingsworth , A . 1996 . Sensi-tivity of forecast errors to initial conditions . Q. J. R. MeteoroL Soc . 122 , 121 – 150 .
    Search in Google ScholarBack to article
  34. Reynolds , C. A. , Peng , M. S. , Majumdar , S. J. , Aberson , S. D. , Bishop , C. H. and co-authors. 2007. Interpretation of adaptive observing guidance for Atlantic tropical cyclones. Mon. Weather Rev. 135 , 4006 - 4029 .
    Search in Google ScholarBack to article
  35. Skamarock , W. C. , Klemp , J. B. , Dudhia , J. , Gill , D. O. , Barker , D. M. , and co-authors. 2008. A description of the advanced research WRF version 3. NCAR Tech. Note TN-475±STR , 113 pp .
    Search in Google ScholarBack to article
  36. Wang , H. L ., Sun J. Z ., Guo Y.-R. and Huang X.-Y. 2011. Radar re-flectivity assimilation with the four-dimensional variational system of the Weather Research and Forecast Model. In: Proceedings of the 91st American Meteorological Society Annual Meeting. Seattle, WA. Available at: http://ams.confex.com/ams/91Annua1/webprogram/Manuscript/Paper185272/4DVAR_RF_AMS2011_Wang.pdf. Accessed on 23-27 January 2011.
    Search in Google ScholarBack to article
  37. Wu , C. C. , Chen , J. H. and Lin , P. H . 2007a . The impact of drop-windsonde data on typhoon track forecasts in DOTSTAR . Weather Forecast . 22 , 1157 – 1176 .
    Search in Google ScholarBack to article
  38. Wu , C. C. , Chen , J. H. , Lin , P. H. and Chou , K. H. 2007b. Targeted ob-servations of tropical cyclone movement based on the adjoint-derived sensitivity steering vector. J. Atmos. Sci. 64 , 2611 - 2626 .
    Search in Google ScholarBack to article
  39. Wu , C. C. , Chen , J. H. , Majumdar , S. J. , Peng , M. S. , Reynolds , C. A. and co-authors. 2009a. Intercomparison of targeted observation guidance for tropical cyclones in the northwestern Pacific. Mon. Weather Rev. 137 , 2471 - 2492 .
    Search in Google ScholarBack to article
  40. Wu , C. C. , Chen , S.-G. , Chen , J. H. , Chou , K.-H. and Lin , P.-H. 2009b. Interaction of typhoon Shanshan (2006) with the midlatitude trough from both adjoint-derived sensitivity steering vector and potential voracity perspectives. Mon. Weather Rev. 137 , 852 - 862 .
    Search in Google ScholarBack to article
  41. Xiao , Q. N. , Kuo , Y.-H. , Ma , Z. Z. , Huang , W. , Huang , X.-Y. , and co-authors . 2008. Development of the WRF adjoint modeling system and its application to the investigation of the May 2004 McMurdo Antarctica severe wind event. Mon. Weather Rev . 136 , 3696-371 3 .
    Search in Google ScholarBack to article
  42. Zhang , M. , Zhang F. Q. , Huang X.-Y. and Zhang X . 2010 . Inter-comparison of an ensemble Kalman filter with three-and four-dimensional variational data assimilation methods in a limited-area model over the month ofJune 2003. Mon . Weather Re v . 139 , 566 – 572 .
    Search in Google ScholarBack to article
  43. Zhou F. F. and Mu M . 2011 . The impact of verification area design on tropical cyclone targeted observations based on the CNOP method. Adv. Atmos. Sci . In press.
    Search in Google ScholarBack to article
  44. Zou , X. L. , Vandenberghe , E , Pondeca , M. and Kuo Y.-H . 1997 . Introduction to adjoint techniques and the MM5 adjoint model-ing system. NCAR Technical Note, NCAR/TN2435 STR, 110 pp, [Available from NCAR, P.O. Box 3000, Boulder, CO 80307-3000].
    Search in Google ScholarBack to article
DOI: https://doi.org/10.1111/j.1600-0870.2011.00536.x | Journal eISSN: 3035-9554
Journal RSS Feed
Language: English
Page range: 939 - 957
Submitted on: Apr 19, 2011
Accepted on: Jun 28, 2011
Published on: Jan 1, 2011
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2011 Hongli Wang, Mu Mu, Xiang-Yu Huang, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 63 (2011): Issue 5
Previous article
Next article