Skip to main content
Have a personal or library account? Click to login
Four-dimensional variational data assimilation for a limited area model Cover

Four-dimensional variational data assimilation for a limited area model

Tellus
Volume 64 (2012): Issue 1
By: ,  ,  ,  ,  ,  ,   and    
Open Access
|Dec 2012
References

References

  1. Andersson E. Järvinen H. Variational quality control. Q. J. R. Meteorol. Soc. 1999; 125: 697–722.
    Search in Google ScholarBack to article
  2. Berre L. Estimation of synoptic and mesoscale forecast error covariances in a limited-area model. Mon. Wea. Rev. 2000; 128: 644–667.
    Search in Google ScholarBack to article
  3. Buizza, R. 1993. Impact of simple vertical diffusion scheme and of the optimization time interval on optimal unstable structures. ECMWF Tech. Memo. 192. ECMWF, Shinfield Park, Reading, RG2 9AX, UK.
    Search in Google ScholarBack to article
  4. Courtier P. Thépaut J.-N. Hollingsworth A. A strategy for operational implementation of 4D-Var, using an incremental approach. Q. J. R. Meteorol. Soc. 1994; 120: 1367–1387.
    Search in Google ScholarBack to article
  5. Cuxart J. Bougeault P. Redelsperger J.-L. A turbulence scheme allowing for mesoscale and large-eddy simulations. Q. J. R. Meteorol. Soc. 2000; 126: 1–30.
    Search in Google ScholarBack to article
  6. Dahlgren, P and Gustafsson, N. 2012. Assimilating host model information into a limited area model. Tellus A. 64, doi: 10.3402/tellusa.v64i0.15836.
    Search in Google ScholarBack to article
  7. Evensen G. Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. 1994; 99: 10143–10162.
    Search in Google ScholarBack to article
  8. Gauthier P. Thépaut J.-N. Impact of the digital filter as a weak constraint in the preoperational 4DVAR assimilation system of Météo-France. J. Geophys. Res. 2001; 129: 2089–2102.
    Search in Google ScholarBack to article
  9. Ghil M. Malanotte-Rizzoli P. Data assimilation in meteorology and oceanography. Mon. Wea. Rev. 1991; 33: 141–266.
    Search in Google ScholarBack to article
  10. Gilbert J. C. Lemaréchal C. Some numerical experiments with variable-storage quasi-Newton algorithms. Adv. Geophys. 1989; B45: 407–435.
    Search in Google ScholarBack to article
  11. Gustafsson, N. 1991. The HIRLAM model. In: Proceedings of the ECMWF Seminar on Numerical methods in atmospheric models, 9–13 September 1991, Volume II. ECMWF, Shinfield Park, Reading, RG2 9AX, UK.
    Search in Google ScholarBack to article
  12. Gustafsson, N. 1992. Use of a digital filter as weak constraint in variational data assimilation. In: Proceedings of a workshop on variational assimilation, with special emphasis on three-dimensional aspects. ECMWF, Shinfield Park, Reading, RG2 9AX, UK.
    Search in Google ScholarBack to article
  13. Gustafsson, N. 1999. The numerical scheme and lateral boundary conditions for the spectral HIRLAM and its adjoint. In: Proceedings of the seminar on recent developments in numerical methods for atmospheric modelling. ECMWF, Reading, RG2 9AX, UK.
    Search in Google ScholarBack to article
  14. Gustafsson, N, Berre, L, Hörnquist, S, Huang, X.-Y, Lindskog, M and co-authors. 2001. Three-dimensional variational data assimilation for a limited area model. Part I: general formulation and the background error constraint. 53A, 425–446.
    Search in Google ScholarBack to article
  15. Gustafsson N. Huang X.-Y. Sensitivity experiments with the spectral HIRLAM and its adjoint. Tellus. 1996; 48A: 501–517.
    Search in Google ScholarBack to article
  16. Gustafsson N. Källén E. Thorsteinsson S. Sensitivity of forecast errors to initial and lateral boundary conditions. Tellus. 1998; 50A: 167–185.
    Search in Google ScholarBack to article
  17. Gustafsson N. McDonald A. A comparison of the HIRLAM gridpoint and spectral semi-Lagrangian models. Tellus. 1996; 124: 2008–2022.
    Search in Google ScholarBack to article
  18. Gustafsson N. Thorsteinsson S. Stengel M. Hólm E. Use of a non linear pseudo-relative humidity variable in a multivariate formulation of moisture analysis. Mon. Wea. Rev. 2011; 137: 1004–1018.
    Search in Google ScholarBack to article
  19. Harris B. A. Kelly G. A satellite radiance-bias correction scheme for data assimilation. Q. J. R. Meteorol. Soc. 2001; 127: 1453–1468.
    Search in Google ScholarBack to article
  20. Haugen J.-E. Machenhauer B. A spectral limited-area model formulation with time-dependent boundary conditions applied to the shallow-water equations. Q. J. R. Meteorol. Soc. 1993; 121: 2618–2630.
    Search in Google ScholarBack to article
  21. Hólm, E, Andersson, E, Beljaars, A, Lopez, P, Mahfouf, J.-F and co-authors. 2002. Assimilation and modelling of the hydrological cycle: ECMWF's status and plans. ECMWF Tech. Memo. 383. ECMWF, Shinfield Park, Reading, RG2 9AX, UK.
    Search in Google ScholarBack to article
  22. Hortal M. The development and testing of a new two-time-level semi-Lagrangian scheme (SETTLS) in the ECMWF forecast model. 2002; 128: 1671–1687.
    Search in Google ScholarBack to article
  23. Huang X.-Y. Gustafsson N. Källén E. Using an adjoint model to improve an optimum interpolation based data assimilation system. Q. J. R. Meteorol. Soc. 1997; 49A: 161–176.
    Search in Google ScholarBack to article
  24. Huang, X.-Y, Yang, X, Gustafsson, N, Mogensen, K. S and Lindskog, M. 2002. Four-Dimensional Variational Data Assimilation for a Limited Area Model. HIRLAM Technical Report 57. Online at: http://hirlam.org/.
    Search in Google ScholarBack to article
  25. Janisková M. Thépaut J.-N. Geleyn J.-F. Simplified and regular physical parameterizations for incremental four-dimensional variational assimilation. 1999; 127: 26–45.
    Search in Google ScholarBack to article
  26. Kain J. The Kain-Fritsch convective parameterization: an update. Mon. Wea. Rev. 2004; 43: 170–181.
    Search in Google ScholarBack to article
  27. Kalnay E. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press: Cambridge, 2003
    Search in Google ScholarBack to article
  28. Kawabata, T, Seko, H, Saito, K, Kuroda, T, Tamiya, K and co-authors. 2007. An assimilation and forecasting experiment of the Nerima heavy rainfall with a cloud-resolving nonhydrostatic 4-dimensional variational data assimilation system. 85, 255–276.
    Search in Google ScholarBack to article
  29. Lawless A. S. Nichols N. K. Inner-loop stopping criteria for incremental four-dimensional variational data assimilation. J. Meteor. Soc. Japan. 2006; 134: 3425–3435.
    Search in Google ScholarBack to article
  30. Le Dimet F. X. Talagrand O. Variational algorithms for analysis and assimilation of meteorological observations. Theoretical aspects. Mon. Wea. Rev. 1986; 38A: 97–110.
    Search in Google ScholarBack to article
  31. Lewis J. M. Derber J. C. The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus. 1985; 37A: 309–322.
    Search in Google ScholarBack to article
  32. Lindskog, M, Gustafsson, N, Navascués, B, Mogensen, K. S, Huang, X.-Y and co-authors. 2001. Three-dimensional variational data assimilation for a limited area model. Part II: Observation handling and assimilation experiments. Tellus. 53A, 447–468.
    Search in Google ScholarBack to article
  33. Lorenc A. C. The potential of the ensemble Kalman filter for NWP – a comparison with 4D-Var. Tellus. 2003; 129: 3183–3203.
    Search in Google ScholarBack to article
  34. Lynch P. The Dolph-Chebyshev window: a simple optimal filter. Q. J. R. Meteorol. Soc. 1997; 125: 655–660.
    Search in Google ScholarBack to article
  35. Lynch P. Huang X.-Y. Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev. 1992; 120: 1019–1034.
    Search in Google ScholarBack to article
  36. Machenhauer B. On the dynamics of gravity oscillations in a shallow water model with applications to normal model initialization. Mon. Wea. Rev. 1977; 50: 253–271.
    Search in Google ScholarBack to article
  37. Noilhan J. Mahfouf J.-F. The ISBA land surface parameterization scheme. Beitr. Phys. Atmos. 1996; 13: 145–159.
    Search in Google ScholarBack to article
  38. Parrish D. F. Derber J. C. The National Meteorological Center's spectral statistical interpolation analysis system. Global Planet. Change. 1992; 120: 1747–1763.
    Search in Google ScholarBack to article
  39. Polavarapu S. Tanguay M. Ménard R. Staniforth A. The tangent linear model for semi-Lagrangian schemes: linearizing the process of interpolation. Mon. Wea. Rev. 1996; 48A: 74–95.
    Search in Google ScholarBack to article
  40. Rantakokko J. Strategies for parallel variational data assimilation. Tellus. 1997; 23: 2017–2039.
    Search in Google ScholarBack to article
  41. Rasch P.J. Kristjánsson J.E. A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. Parallel Comput. 1998; 11: 1587–1614.
    Search in Google ScholarBack to article
  42. Savijärvi H. Fast radiation parameterization schemes for mesoscale and short-range forecast models. J. Clim. 1990; 29: 437–447.
    Search in Google ScholarBack to article
  43. Schyberg, H, Landelius, T, Thorsteinsson, S, Tveter, F, Vignes, O and co-authors. 2003. Assimilation of ATOVS data in the HIRLAM 3D-VAR System. HIRLAM Technical Report 60. Online at: http://hirlam.org/.
    Search in Google ScholarBack to article
  44. Stengel M. Lindskog M. Undén P. Gustafsson N. Bennartz R. An extended observation operator in HIRLAM 4D-Var for the assimilation of cloud-affected satellite radiances. 2010; 136: 1064–1074.
    Search in Google ScholarBack to article
  45. Stengel, M, Undén, P, Lindskog, M, Dahlgren, P, Gustafsson, N and co-authors. 2009. Assimilation of SEVIRI infrared radiances with HIRLAM 4D-Var. Q. J. R. Meteorol. Soc. 135, 2100–2109.
    Search in Google ScholarBack to article
  46. Thépaut J.-N. Courtier P. Belaud G. Lemaitre G. Dynamical structure functions in a four-dimensional variational assimilation: a case study. Q. J. R. Meteorol. Soc. 1996; 122: 535–561.
    Search in Google ScholarBack to article
  47. Trémolet Y. Accounting for an imperfect model in 4D-Var. Q. J. R. Meteorol. Soc. 2006; 132: 2483–2504.
    Search in Google ScholarBack to article
  48. Undén, P, Rontu, L, Järvinen, H, Lynch, P, Calvo, J and co-authors. 2002. HIRLAM-5 Scientific Documentation. Online at: http://hirlam.org/.
    Search in Google ScholarBack to article
  49. Veersé F. Thépaut J.-N. Multiple-truncation incremental approach for four-dimensional variational data assimilation. 1998; 124: 1889–1908.
    Search in Google ScholarBack to article
  50. Wang X. Snyder C. Hamill T. M. On the theoretical equivalence of differently proposed ensemble-3DVAR hybrid analysis schemes. Q. J. R. Meteorol. Soc. 2007; 135: 222–227.
    Search in Google ScholarBack to article
  51. Yang, X. 2002. Physical adjoint in HIRLAM 4DVAR. HIRLAM Workshop on Variational Data Assimilation and Remote Sensing. FMI, Helsinki, Finland, 21–23 January 2002. Online at: http://hirlam.org/.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.3402/tellusa.v64i0.14985 | Journal eISSN: 3035-9554
Journal RSS Feed
Language: English
Page range: 14985 - 14985
Submitted on: May 31, 2011
Accepted on: Dec 7, 2011
Published on: Dec 1, 2012
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
data assimilation,
analysis,
numerical weather prediction

© 2012 Nils Gustafsson, Xiang-Yu Huang, Xiaohua Yang, Kristian Mogensen, Magnus Lindskog, Ole Vignes, Tomas Wilhelmsson, Sigurdur Thorsteinsson, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 64 (2012): Issue 1
Previous article
Next article