• Aksoy, A., , Dowell D. C. , , and Snyder C. , 2009: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses. Mon. Wea. Rev., 137, 18051824.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., , and Collins N. , 2007: Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Technol., 24, 14521463.

    • Search Google Scholar
    • Export Citation
  • Daley, R., , and Barker E. , 2001: NAVDAS source book 2001. Naval Research Laboratory Rep. NRL/PU/7530-01-441, 161 pp.

  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 14310 162.

    • Search Google Scholar
    • Export Citation
  • Gaspari, G., , and Cohn S. , 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., , Dudhia J. , , and Stauffer D. R. , 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-3981STR, 138 pp.

  • Hodur, R. M., 1997: The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev., 125, 14141430.

    • Search Google Scholar
    • Export Citation
  • Hogan, T. F., , and Rosmond T. E. , 1991: The description of the Navy Operational Global Atmospheric Prediction System’s spectral forecast model. Mon. Wea. Rev., 119, 17861815.

    • Search Google Scholar
    • Export Citation
  • McLay, J. G., , Bishop C. H. , , and Reynolds C. A. , 2008: Evaluation of the ensemble transform analysis perturbation scheme at NRL. Mon. Wea. Rev., 136, 10931108.

    • Search Google Scholar
    • Export Citation
  • McLay, J. G., , Bishop C. H. , , and Reynolds C. A. , 2010: A local formulation of the ensemble transform (ET) analysis perturbation scheme. Wea. Forecasting, 25, 985993.

    • Search Google Scholar
    • Export Citation
  • Meng, Z., , and Zhang F. , 2007: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments. Mon. Wea. Rev., 135, 14031423.

    • Search Google Scholar
    • Export Citation
  • Meng, Z., , and Zhang F. , 2008a: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon. Wea. Rev., 136, 552540.

    • Search Google Scholar
    • Export Citation
  • Meng, Z., , and Zhang F. , 2008b: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Comparison with 3DVAR in a month-long experiment. Mon. Wea. Rev., 136, 36713682.

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

  • Snyder, C., , and Zhang F. , 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 16631677.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., , Anderson J. L. , , Bishop C. H. , , Hamill T. M. , , and Whitaker J. S. , 2003: Ensemble square root filters. Mon. Wea. Rev., 131, 14851490.

    • Search Google Scholar
    • Export Citation
  • Tong, M., , and Xue M. , 2005: Ensemble Kalman filter assimilation of Doppler radar data with compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 17891807.

    • Search Google Scholar
    • Export Citation
  • Tong, M., , and Xue M. , 2008: Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part II: Parameter estimation experiments. Mon. Wea. Rev., 136, 16491668.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., , and Hamill T. M. , 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 19131924.

  • Zhang, F., , Snyder C. , , and Sun J. , 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 12381253.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., , Meng Z. , , and Aksoy A. , 2006: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: Perfect model experiments. Mon. Wea. Rev., 134, 722736.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., , Weng Y. , , Sippel J. , , Meng Z. , , and Bishop C. , 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 21052125.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 11 11 3
PDF Downloads 4 4 1

Development of a Mesoscale Ensemble Data Assimilation System at the Naval Research Laboratory

View More View Less
  • 1 Marine Meteorology Division, Naval Research Laboratory, Monterey, California
  • 2 The Pennsylvania State University, University Park, Pennsylvania
  • 3 Marine Meteorology Division, Naval Research Laboratory, Monterey, California
  • 4 National Severe Storms Laboratory, Norman, Oklahoma
© Get Permissions
Restricted access

Abstract

An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system’s computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications.

Corresponding author address: Dr. Qingyun Zhao, Naval Research Laboratory, 7 Grace Hopper Ave., Mail Stop II, Monterey, CA 93943. E-mail: allen.zhao@nrlmry.navy.mil

Abstract

An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system’s computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications.

Corresponding author address: Dr. Qingyun Zhao, Naval Research Laboratory, 7 Grace Hopper Ave., Mail Stop II, Monterey, CA 93943. E-mail: allen.zhao@nrlmry.navy.mil
Save