• Anderson, J. L., , and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 27412758.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., , W. Huang, , Y.-R. Guo, , A. J. Bourgeois, , and Q. Xiao, 2004: A three-dimensional (3DVAR) variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914.

    • Search Google Scholar
    • Export Citation
  • Buehner, M., , P. L. Houtekamer, , C. Charette, , H. L. Mitchell, , and B. He, 2010a: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: Description and single-observation experiments. Mon. Wea. Rev., 138, 15501566.

    • Search Google Scholar
    • Export Citation
  • Buehner, M., , P. L. Houtekamer, , C. Charette, , H. L. Mitchell, , and B. He, 2010b: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-month experiments with real observations. Mon. Wea. Rev., 138, 15671586.

    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., , and M. J. Suarez, 1999: A shortwave radiation parameterization for atmospheric studies. NASA Tech. Memo. 15 (104606), 40 pp.

  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 472 pp.

  • Evensen, G., , and P. J. van Leeuwen, 2000: An ensemble Kalman smoother for nonlinear dynamics. Mon. Wea. Rev., 128, 18521867.

  • Fertig, E. J., , J. Harlim, , and B. R. Hunt, 2007: A comparative study of 4D-VAR and a 4D ensemble Kalman filter: Perfect model simulations with Lorenz-96. Tellus, 59A, 96100.

    • Search Google Scholar
    • Export Citation
  • Gustafsson, N., 2007: Discussion on ‘4D-Var or EnKF?' Tellus, 59A, 774777.

  • Hamill, T., , and C. Snyder, 2000: A hybrid ensemble Kalman filter–3D variational analysis scheme. Mon. Wea. Rev., 128, 29052915.

  • Houtekamer, P. L., , and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 32693289.

  • Huang, X.-Y., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299314.

    • Search Google Scholar
    • Export Citation
  • Hunt, B. R., and Coauthors, 2004: Four-dimensional ensemble Kalman filtering. Tellus, 56A, 273277.

  • Janjic, Z. I., 1996: The Mellor–Yamada Level 2.5 scheme in the NCEP Eta model. Preprints, 11th Conf. on Numerical Weather Prediction, Norfolk, VA, Amer. Meteor. Soc., 354–355.

  • Janjic, Z. I., 2002: Nonsingular implementation of the Mellor–Yamada Level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, 61 pp.

  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181.

  • Kalnay, E., , H. Li, , T. Miyoshi, , S.-C. Yang, , and J. Ballabrera-Poy, 2007: 4D-Var or ensemble Kalman filter? Tellus, 59A, 758773.

  • Lawless, A. S., 2010: A note on the analysis error associated with 3D-FGAT. Quart. J. Roy. Meteor. Soc., 136, 10941098.

  • Le Dimet, F.-X., , and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97110.

    • Search Google Scholar
    • Export Citation
  • Lewis, J. M., , and J. C. Derber, 1985: The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus, 37A, 309322.

    • Search Google Scholar
    • Export Citation
  • Liu, C., , Q. Xiao, , and B. Wang, 2008: An ensemble-based four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test. Mon. Wea. Rev., 136, 33633373.

    • Search Google Scholar
    • Export Citation
  • Liu, C., , Q. Xiao, , and B. Wang, 2009: An ensemble-based four-dimensional variational data assimilation scheme. Part II: Observing System Simulation Experiments with the Advanced Research WRF (ARW). Mon. Wea. Rev., 137, 16871704.

    • Search Google Scholar
    • Export Citation
  • Lorenc, A., 2003: The potential of the ensemble Kalman filter for NWP: A comparison with 4DVar. Quart. J. Roy. Meteor. Soc., 129, 31833203.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., , S. J. Taubman, , P. D. Brown, , M. J. Iacono, , and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 (D14), 16 66316 682.

    • Search Google Scholar
    • Export Citation
  • Powers, J. G., , A. J. Monaghan, , A. M. Cayette, , D. H. Bromwich, , Y.-H. Kuo, , and K. W. Manning, 2003: Real-time mesoscale modeling over Antarctica: The Antarctic mesoscale prediction system. Bull. Amer. Meteor. Soc., 84, 15331545.

    • Search Google Scholar
    • Export Citation
  • Rabier, F., , J.-N. Thépaut, , and P. Courtier, 1998: Extended assimilation and forecast experiments with a four-dimensional variational assimilation system. Quart. J. Roy. Meteor. Soc., 124, 18611887.

    • Search Google Scholar
    • Export Citation
  • Rabier, F., , H. Jarvinen, , E. Klinker, , J.-F. Mahfouf, , and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation. Part I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 11431170.

    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., , and A. Hollingsworth, 2002: Some aspects of the improvement in skill of numerical weather prediction. Quart. J. Roy. Meteor. Soc., 128, 647677.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 125 pp.

  • Wang, X., 2010: Incorporating ensemble covariance in the Gridpoint Statistical Interpolation variational minimization: A mathematical framework. Mon. Wea. Rev., 138, 29902995.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , T. M. Hamill, , J. S. Whitaker, , and C. H. Bishop, 2007a: A comparison of hybrid ensemble transform Kalman filter-OI and ensemble square-root filter analysis schemes. Mon. Wea. Rev., 135, 10551076.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , C. Snyder, , and T. M. Hamill, 2007b: On the theoretical equivalence of differently proposed ensemble/3D-VAR hybrid analysis schemes. Mon. Wea. Rev., 135, 222227.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , D. Barker, , C. Snyder, , and T. M. Hamill, 2008a: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 51165131.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , D. Barker, , C. Snyder, , and T. M. Hamill, 2008b: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 51325147.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., , X. Zou, , M. Pondeca, , M. A. Shapiro, , and C. S. Velden, 2002: Impact of GMS-5 and GOES-9 satellite-derived winds on the prediction of a NORPEX extratropical cyclone. Mon. Wea. Rev., 130, 507528.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., and Coauthors, 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
  • Zhang, F., , M. Zhang, , and J. A. Hansen, 2009: Coupling ensemble Kalman filter with four- dimensional variational data assimilation. Adv. Atmos. Sci., 26, 19.

    • Search Google Scholar
    • Export Citation
  • Zhang, M., , and F. Zhang, 2012: E4DVar: Coupling an ensemble Kalman filter with four-dimensional variational data assimilation in a limited-area weather prediction model. Mon. Wea. Rev., 140, 587600.

    • Search Google Scholar
    • Export Citation
  • Zhang, M., , F. Zhang, , X.-Y. Huang, , and X. Zhang, 2011: Intercomparison of an ensemble Kalman filter with three- and four-dimensional variational data assimilation methods in a limited-area model over the month of June 2003. Mon. Wea. Rev., 139, 566572.

    • Search Google Scholar
    • Export Citation
  • Zupanski, M., 2005: Maximum likelihood ensemble filter: Theoretical aspects. Mon. Wea. Rev., 133, 17101726.

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An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part III: Antarctic Applications with Advanced Research WRF Using Real Data

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  • 1 College of Marine Science, University of South Florida, St. Petersburg, Florida
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Abstract

A four-dimensional ensemble-based variational data assimilation (4DEnVar) algorithm proposed in Part I of the 4DEnVar series (denoted En4DVar in Part I, but here we refer to it as 4DEnVar according to WMO conference recommendation to differentiate it from En4DVar algorithm using adjoint model) uses a flow-dependent background error covariance calculated from ensemble forecasts and performs 4DVar optimization based on an incremental approach and a preconditioning algorithm. In Part II, the authors evaluated 4DEnVar with observing system simulation experiments (OSSEs) using the Advanced Research Weather Research and Forecasting Model (ARW-WRF, hereafter WRF). The current study extends the 4DEnVar to assimilate real observations for a cyclone in the Antarctic and the Southern Ocean in October 2007. The authors performed an intercomparison of four different WRF variational approaches for the case, including three-dimensional variational data assimilation (3DVar), first guess at the appropriate time (FGAT), and ensemble-based three-dimensional (En3DVar) and four-dimensional (4DEnVar) variational data assimilations. It is found that all data assimilation approaches produce positive impacts in this case. Applying the flow-dependent background error covariance in En3DVar and 4DEnVar yields forecast skills superior to those with the homogeneous and isotropic background error covariance in 3DVar and FGAT. In addition, the authors carried out FGAT and 4DEnVar 3-day cycling and 72-h forecasts. The results show that 4DEnVar produces a better performance in the cyclone prediction. The inflation factor on 4DEnVar can effectively improve the 4DEnVar analysis. The authors also conducted a short period (10-day lifetime of the cyclone in the domain) of analysis/forecast intercomparison experiments using 4DEnVar, FGAT, and 3DVar. The 4DEnVar scheme demonstrates overall superior and robust performance.

Corresponding author address: Dr. Chengsi Liu, Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072. E-mail: cliu@ou.edu

Abstract

A four-dimensional ensemble-based variational data assimilation (4DEnVar) algorithm proposed in Part I of the 4DEnVar series (denoted En4DVar in Part I, but here we refer to it as 4DEnVar according to WMO conference recommendation to differentiate it from En4DVar algorithm using adjoint model) uses a flow-dependent background error covariance calculated from ensemble forecasts and performs 4DVar optimization based on an incremental approach and a preconditioning algorithm. In Part II, the authors evaluated 4DEnVar with observing system simulation experiments (OSSEs) using the Advanced Research Weather Research and Forecasting Model (ARW-WRF, hereafter WRF). The current study extends the 4DEnVar to assimilate real observations for a cyclone in the Antarctic and the Southern Ocean in October 2007. The authors performed an intercomparison of four different WRF variational approaches for the case, including three-dimensional variational data assimilation (3DVar), first guess at the appropriate time (FGAT), and ensemble-based three-dimensional (En3DVar) and four-dimensional (4DEnVar) variational data assimilations. It is found that all data assimilation approaches produce positive impacts in this case. Applying the flow-dependent background error covariance in En3DVar and 4DEnVar yields forecast skills superior to those with the homogeneous and isotropic background error covariance in 3DVar and FGAT. In addition, the authors carried out FGAT and 4DEnVar 3-day cycling and 72-h forecasts. The results show that 4DEnVar produces a better performance in the cyclone prediction. The inflation factor on 4DEnVar can effectively improve the 4DEnVar analysis. The authors also conducted a short period (10-day lifetime of the cyclone in the domain) of analysis/forecast intercomparison experiments using 4DEnVar, FGAT, and 3DVar. The 4DEnVar scheme demonstrates overall superior and robust performance.

Corresponding author address: Dr. Chengsi Liu, Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072. E-mail: cliu@ou.edu
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