Four-dimensional variational data assimilation for a limited area model

References

• Andersson E. Järvinen H. Variational quality control. Q. J. R. Meteorol. Soc. 1999; 125: 697–722.
   Web of Science ®Google Scholar
• Berre L. Estimation of synoptic and mesoscale forecast error covariances in a limited-area model. Mon. Wea. Rev. 2000; 128: 644–667.
   Web of Science ®Google Scholar
• 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.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Dahlgren, P and Gustafsson, N. 2012. Assimilating host model information into a limited area model. Tellus A. 64, doi: 10.3402/tellusa.v64i0.15836.
   Google Scholar
• Evensen G. Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. 1994; 99: 10143–10162.
   Google Scholar
• 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.
   Google Scholar
• Ghil M. Malanotte-Rizzoli P. Data assimilation in meteorology and oceanography. Mon. Wea. Rev. 1991; 33: 141–266.
   Google Scholar
• Gilbert J. C. Lemaréchal C. Some numerical experiments with variable-storage quasi-Newton algorithms. Adv. Geophys. 1989; B45: 407–435.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Gustafsson N. Huang X.-Y. Sensitivity experiments with the spectral HIRLAM and its adjoint. Tellus. 1996; 48A: 501–517.
   Google Scholar
• Gustafsson N. Källén E. Thorsteinsson S. Sensitivity of forecast errors to initial and lateral boundary conditions. Tellus. 1998; 50A: 167–185.
   Google Scholar
• Gustafsson N. McDonald A. A comparison of the HIRLAM gridpoint and spectral semi-Lagrangian models. Tellus. 1996; 124: 2008–2022.
   Google Scholar
• 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.
   Google Scholar
• Harris B. A. Kelly G. A satellite radiance-bias correction scheme for data assimilation. Q. J. R. Meteorol. Soc. 2001; 127: 1453–1468.
   Web of Science ®Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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/.
   Google Scholar
• Janisková M. Thépaut J.-N. Geleyn J.-F. Simplified and regular physical parameterizations for incremental four-dimensional variational assimilation. 1999; 127: 26–45.
   Google Scholar
• Kain J. The Kain-Fritsch convective parameterization: an update. Mon. Wea. Rev. 2004; 43: 170–181.
   Google Scholar
• Kalnay E. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press: Cambridge, 2003
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Le Dimet F. X. Talagrand O. Variational algorithms for analysis and assimilation of meteorological observations. Theoretical aspects. Mon. Wea. Rev. 1986; 38A: 97–110.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Lorenc A. C. The potential of the ensemble Kalman filter for NWP – a comparison with 4D-Var. Tellus. 2003; 129: 3183–3203.
   Google Scholar
• Lynch P. The Dolph-Chebyshev window: a simple optimal filter. Q. J. R. Meteorol. Soc. 1997; 125: 655–660.
   Google Scholar
• Lynch P. Huang X.-Y. Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev. 1992; 120: 1019–1034.
   Web of Science ®Google Scholar
• 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.
   Google Scholar
• Noilhan J. Mahfouf J.-F. The ISBA land surface parameterization scheme. Beitr. Phys. Atmos. 1996; 13: 145–159.
   Google Scholar
• Parrish D. F. Derber J. C. The National Meteorological Center's spectral statistical interpolation analysis system. Global Planet. Change. 1992; 120: 1747–1763.
   Google Scholar
• 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.
   Google Scholar
• Rantakokko J. Strategies for parallel variational data assimilation. Tellus. 1997; 23: 2017–2039.
   Google Scholar
• 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.
   Google Scholar
• Savijärvi H. Fast radiation parameterization schemes for mesoscale and short-range forecast models. J. Clim. 1990; 29: 437–447.
   Google Scholar
• 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/.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Trémolet Y. Accounting for an imperfect model in 4D-Var. Q. J. R. Meteorol. Soc. 2006; 132: 2483–2504.
   Web of Science ®Google Scholar
• Undén, P, Rontu, L, Järvinen, H, Lynch, P, Calvo, J and co-authors. 2002. HIRLAM-5 Scientific Documentation. Online at: http://hirlam.org/.
   Google Scholar
• Veersé F. Thépaut J.-N. Multiple-truncation incremental approach for four-dimensional variational data assimilation. 1998; 124: 1889–1908.
   Google Scholar
• 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.
   Google Scholar
• 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/.
   Google Scholar