• Barker, D. M., , W. Huang, , Y-R. Guo, , A. J. Bourgeois, , and Q. N. Xiao, 2004a: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132 , 897914.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., , M. S. Lee, , Y-R. Guo, , W. Huang, , Q-N. Xiao, , and R. Rizvi, 2004b: WRF variational data assimilation development at NCAR. Fifth WRF/14th MM5 Users’ Workshop, Boulder, CO, NCAR, 5 pp. [Available online at http://www.mmm.ucar.edu/mm5/workshop/ws04/Session5/dale.pdf.].

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., , M. S. Lee, , Y-R. Guo, , W. Huang, , S. Rizvi, , and Q. Xiao, 2005: WRF-Var—A unified 3/4D-Var variational data assimilation system for WRF. Sixth WRF/15th MM5 Users’ Workshop, Boulder, CO, NCAR, 17 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/workshop/WS2005/presentations/session10/1-Barker.pdf.].

    • Search Google Scholar
    • Export Citation
  • Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting. Quart. J. Roy. Meteor. Soc., 131 , 10131043.

    • Search Google Scholar
    • Export Citation
  • Courtier, P., , J-N. Thépaut, , and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120 , 13671387.

    • Search Google Scholar
    • Export Citation
  • Gauthier, P., , and J-N. Thépaut, 2001: Impact of the digital filter as a weak constraint in the preoperational 4DVAR assimilation system of Météo France. Mon. Wea. Rev., 129 , 20892102.

    • Search Google Scholar
    • Export Citation
  • Gauthier, P., , M. Tanguay, , S. Laroche, , and S. Pellerin, 2007: Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada. Mon. Wea. Rev., 135 , 23392364.

    • Search Google Scholar
    • Export Citation
  • Giering, R., , and T. Kaminski, 2003: Applying TAF to generate efficient derivative code of Fortran 77-95 programs. PAMM, 2 , 5457.

  • Grell, G. A., , and D. Devenyi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29 .1693, doi:10.1029/2002GL015311.

    • Search Google Scholar
    • Export Citation
  • Guo, Y. R., , H-C. Lin, , X. X. Ma, , X-Y. Huang, , C. T. Terng, , and Y-H. Kuo, 2006: Impact of WRF-Var (3DVar) background error statistics on typhoon analysis and forecast. Seventh WRF Users’ Workshop, Boulder, CO, NCAR, 7 pp. [Available online at /http://www.mmm.ucar.edu/wrf/users/workshops/WS2006/abstracts/PSession04/P4_2_Guo.pdf/.].

    • Search Google Scholar
    • Export Citation
  • Gustafsson, N., 1992: Use of a digital filter as weak constraint in variational data assimilation. Proc. Workshop on Variational Assimilation, with Special Emphasis on Three-Dimensional Aspects, Reading, United Kingdom, ECMWF, 327–338.

    • Search Google Scholar
    • Export Citation
  • Honda, Y., , M. Nishijima, , K. Koizumi, , Y. Ohta, , K. Tamiya, , T. Kawabata, , and T. Tsuyuki, 2005: A pre-operational variational data assimilation system for a non-hydrostatic model at the Japan Meteorological Agency: Formulation and preliminary results. Quart. J. Roy. Meteor. Soc., 131 , 34653475.

    • Search Google Scholar
    • Export Citation
  • Hong, S-Y., , J. Dudhia, , and S-H. Chen, 2004: A revised approach to ice microphysical processes or the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132 , 103120.

    • Search Google Scholar
    • Export Citation
  • Huang, X-Y., , X. Yang, , N. Gustafsson, , K. Mogensen, , and M. Lindskog, 2002: Four-dimensional variational data assimilation for a limited area model. HIRLAM Tech Rep 57, 41 pp. [Available from SMHI, S-601 76 Norrkoping, Sweden.].

  • Huang, X-Y., , Q. Xiao, , W. Huang, , D. Barker, , Y-H. Kuo, , J. Michalakes, , and Z. Ma, 2005: The weather research and forecasting model based on the 4-dimensional variational data assimilation system. [WRF-(4D)Var]. Sixth WRF/15th MM5 Users’ Workshop, Boulder, CO, NCAR, 15 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/workshops/WS2005/presentations/session10/5-Huang.pdf.].

    • Search Google Scholar
    • Export Citation
  • Huang, X-Y., and Coauthors, 2006: Preliminary results of WRF 4D-Var. Seventh WRF Users’ Workshop, Boulder, CO, NCAR, 20 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/workshops/WS2006/presentations/Session4/4_5.pdf.].

    • Search Google Scholar
    • Export Citation
  • Jang, K-I., , X. Zou, , M. Pondeca, , M. Shapiro, , C. Davis, , and A. Krueger, 2003: Incorporating TOMS ozone measurements into the prediction of the Washington, D.C., winter storm during 24–25 January 2000. J. Appl. Meteor., 42 , 797812.

    • Search Google Scholar
    • Export Citation
  • Lee, M-S., , and D. Barker, 2005: Preliminary tests of first guess at appropriate time (FGAT) with WRF 3DVAR and WRF model. J. Kor. Meteor. Soc., 41 , 495505.

    • Search Google Scholar
    • Export Citation
  • Le Dimet, F., , and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretic aspects. Tellus, 38A , 97110.

    • Search Google Scholar
    • Export Citation
  • Lewis, J., , and J. Derber, 1985: The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus, 37A , 309327.

    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., 2003: Modelling of error covariances by 4D-Var data assimilation. Quart. J. Roy. Meteor. Soc., 129 , 31673182.

  • Lorenc, A. C., , and F. Rawlins, 2005: Why does 4D-Var beat 3D-Var? Quart. J. Roy. Meteor. Soc., 131 , 32473257.

  • Lynch, P., , and X-Y. Huang, 1992: Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev., 120 , 10191034.

  • Navon, I. M., , X. Zou, , J. Derber, , and J. Sela, 1992: Variational data assimilation with an adiabatic version of the NMC spectral model. Mon. Wea. Rev., 120 , 14331446.

    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., , and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120 , 17471763.

    • Search Google Scholar
    • Export Citation
  • Rabier, F., and Coauthors, 1997: Recent experimentation on 4D-var and first results from a simplified Kalman filter. ECMWF Tech. Memo. 240, Reading, United Kingdom, 42 pp.

  • Rabier, F., , H. Järvinen, , E. Klinker, , J-F. Mahfouf, , and A. Simmons, 2000: The ECMWF operational implementation of four dimensional variational assimilation. Quart. J. Roy. Meteor. Soc., 126 , 11431170.

    • Search Google Scholar
    • Export Citation
  • Rawlins, F., , S. P. Ballard, , K. J. Bovis, , A. M. Clayton, , D. Li, , G. W. Inverarity, , A. C. Lorenc, , and T. J. Payne, 2007: The Met Office global 4-Dimensional data assimilation system. Quart. J. Roy. Meteor. Soc., 133 , 347362.

    • Search Google Scholar
    • Export Citation
  • Ruggiero, F. H., , J. Michalakes, , T. Nehrkorn, , G. D. Modica, , and X. Zou, 2006: Development of a new distributed-memory MM5 adjoint. J. Atmos. Oceanic Technol., 23 , 424436.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., , J. B. Klemp, , J. Dudhia, , D. O. Gill, , D. M. Barker, , W. Wang, , and J. G. Powers, 2005: A description of the advanced research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp.

  • Sun, J., , and N. A. Crook, 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 , 16421661.

    • Search Google Scholar
    • Export Citation
  • Thépaut, J-N., , and P. Courtier, 1991: Four dimensional variational data assimilation using the adjoint of a multilevel primitive-equation model. Quart. J. Roy. Meteor. Soc., 117 , 12251254.

    • Search Google Scholar
    • Export Citation
  • Thépaut, J-N., , P. Courtier, , G. Belaud, , and G. Lemaitre, 1996: Dynamic structure functions in a four-dimensional variational assimilation: A case study. Quart. J. Roy. Meteor. Soc., 122 , 535561.

    • Search Google Scholar
    • Export Citation
  • Veersé, F., , and J-N. Thépaut, 1998: Multi-truncation incremental approach for four-dimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 124 , 18891908.

    • Search Google Scholar
    • Export Citation
  • Wee, T-K., , and Y-H. Kuo, 2004: Impact of a digital filter as a weak constraint in MM5 4DVAR. Mon. Wea. Rev., 132 , 543559.

  • Xiao, Q., , Y-H. Kuo, , Z. Ma, , W. Huang, , X-Y. Huang, , X-Y. Zhang, , D. M. Barker, , J. Michalakes, , and J. Dudhia, 2008: Application of an Adiabatic WRF adjoint to the investigation of the May 2004 McMurdo Antarctica severe wind event. Mon. Wea. Rev., 136 , 36963713.

    • Search Google Scholar
    • Export Citation
  • Xu, L., , T. Rosmond, , and R. Daley, 2005: Development of NAVDAS-AR: Formulation and initial tests of the linear problem. Tellus, 57A , 546559.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., 2005: Dynamics and structure of mesoscale error covariance of a winter cyclone estimated through short-range ensemble forecasts. Mon. Wea. Rev., 133 , 28762893.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., , C. Snyder, , and R. Rotunno, 2002: Mesoscale predictability of the “Surprise” snowstorm of 24–25 January 2000. Mon. Wea. Rev., 130 , 16171632.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., , C. Snyder, , and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60 , 11731185.

  • Zhang, F., , N. Bai, , R. Rotunno, , C. Snyder, , and C. C. Epifanio, 2007: Mesoscale predictability of moist baroclinic waves: Convection-permitting experiments and multistage error growth dynamics. J. Atmos. Sci., 64 , 35793594.

    • Search Google Scholar
    • Export Citation
  • Zou, X., , Y-H. Kuo, , and Y-R. Guo, 1995: Assimilation of atmospheric radio refractivity using a nonhydrostatic mesoscale model. Mon. Wea. Rev., 123 , 22292249.

    • Search Google Scholar
    • Export Citation
  • Zou, X., , F. Vandenberghe, , M. Pondeca, , and Y-H. Kuo, 1997: Introduction to adjoint techniques and he MM5 adjoint modeling system. NCAR Tech. Note NCAR/TN-435-STR, 110 pp.

  • Zupanski, D., , D. F. Parrish, , E. Rogers, , and G. DiMego, 2002: Four-dimensional variational data assimilation for the blizzard of 2000. Mon. Wea. Rev., 130 , 19671988.

    • Search Google Scholar
    • Export Citation
  • Zupanski, M., 1993: Regional four-dimensional variational data assimilation in a quasi-operational forecasting environment. Mon. Wea. Rev., 121 , 23962408.

    • Search Google Scholar
    • Export Citation
  • Zupanski, M., , D. Zupanski, , T. Vukicevic, , K. Eis, , and T. V. Haar, 2005: CIRA/CSU four-dimensional variational data assimilation system. Mon. Wea. Rev., 133 , 829843.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 241 241 60
PDF Downloads 222 222 61

Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results

© Get Permissions
Restricted access

Abstract

The Weather Research and Forecasting (WRF) model–based variational data assimilation system (WRF-Var) has been extended from three- to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encourage testing under different weather conditions and model configurations.

* The National Center for Atmospheric Research is sponsored by the National Science Foundation

Corresponding author address: Dr. Xiang-Yu Huang, NCAR/MMM, P.O. Box 3000, Boulder, CO 80307. Email: huangx@ucar.edu

Abstract

The Weather Research and Forecasting (WRF) model–based variational data assimilation system (WRF-Var) has been extended from three- to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encourage testing under different weather conditions and model configurations.

* The National Center for Atmospheric Research is sponsored by the National Science Foundation

Corresponding author address: Dr. Xiang-Yu Huang, NCAR/MMM, P.O. Box 3000, Boulder, CO 80307. Email: huangx@ucar.edu

Save