Editorial Type:
Article
Article Type:
Research Article

Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective System

Nathan Snook Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

Search for other papers by Nathan Snook in
Current site
Google Scholar
PubMed Close
,
Ming Xue Center for Analysis and Prediction of Storms, and School of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Ming Xue in
Current site
Google Scholar
PubMed Close
, and
Youngsun Jung Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

Search for other papers by Youngsun Jung in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Apr 2015
DOI:
https://doi.org/10.1175/MWR-D-13-00262.1
Page(s):
1035–1057
Restricted access

Abstract

In recent studies, the authors have successfully demonstrated the ability of an ensemble Kalman filter (EnKF), assimilating real radar observations, to produce skillful analyses and subsequent ensemble-based probabilistic forecasts for a tornadic mesoscale convective system (MCS) that occurred over Oklahoma and Texas on 9 May 2007. The current study expands upon this prior work, performing experiments for this case on a larger domain using a nested-grid EnKF, which accounts for mesoscale uncertainties through the initial ensemble and lateral boundary condition perturbations. In these new experiments, conventional observations (including surface, wind profiler, and upper-air observations) are assimilated in addition to the WSR-88D and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar data used in the previous studies, better representing meso- and convective-scale features. The relative impacts of conventional and radar data on analyses and forecasts are examined, and biases within the ensemble are investigated.

The new experiments produce a substantially improved forecast, including better representation of the convective lines of the MCS. Assimilation of radar data substantially improves the ensemble precipitation forecast. Assimilation of conventional data together with radar observations substantially improves the forecast of near-surface mesovortices within the MCS, improves forecasts of surface temperature and dewpoint, and imparts a slight but noticeable improvement to short-term precipitation forecasts. Furthermore, ensemble analyses and forecasts are found to be sensitive to the localization radius applied to conventional data within the EnKF.

Corresponding author address: Nathan Snook, Center for Analysis and Prediction of Storms, University of Oklahoma, 120 David Boren Blvd., Rm. 5425, Norman, OK 73072. E-mail: nsnook@ou.edu

Abstract

In recent studies, the authors have successfully demonstrated the ability of an ensemble Kalman filter (EnKF), assimilating real radar observations, to produce skillful analyses and subsequent ensemble-based probabilistic forecasts for a tornadic mesoscale convective system (MCS) that occurred over Oklahoma and Texas on 9 May 2007. The current study expands upon this prior work, performing experiments for this case on a larger domain using a nested-grid EnKF, which accounts for mesoscale uncertainties through the initial ensemble and lateral boundary condition perturbations. In these new experiments, conventional observations (including surface, wind profiler, and upper-air observations) are assimilated in addition to the WSR-88D and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar data used in the previous studies, better representing meso- and convective-scale features. The relative impacts of conventional and radar data on analyses and forecasts are examined, and biases within the ensemble are investigated.

The new experiments produce a substantially improved forecast, including better representation of the convective lines of the MCS. Assimilation of radar data substantially improves the ensemble precipitation forecast. Assimilation of conventional data together with radar observations substantially improves the forecast of near-surface mesovortices within the MCS, improves forecasts of surface temperature and dewpoint, and imparts a slight but noticeable improvement to short-term precipitation forecasts. Furthermore, ensemble analyses and forecasts are found to be sensitive to the localization radius applied to conventional data within the EnKF.

Corresponding author address: Nathan Snook, Center for Analysis and Prediction of Storms, University of Oklahoma, 120 David Boren Blvd., Rm. 5425, Norman, OK 73072. E-mail: nsnook@ou.edu
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Aksoy, A., D. C. Dowell, and C. Snyder, 2009: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses. Mon. Wea. Rev., 137, 1805–1824, doi:10.1175/2008MWR2691.1.

    • Search Google Scholar
    • Export Citation

  • Aksoy, A., D. C. Dowell, and C. Snyder, 2010: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part II: Short-range ensemble forecasts. Mon. Wea. Rev., 138, 1273–1292, doi:10.1175/2009MWR3086.1.

    • Search Google Scholar
    • Export Citation

  • Aksoy, A., S. Lorsolo, T. Vukicevic, K. J. Sellwood, S. D. Aberson, and F. Zhang, 2012: The HWRF Hurricane Ensemble Data Assimilation System (HEDAS) for high-resolution data: The impact of airborne Doppler radar observations in an OSSE. Mon. Wea. Rev., 140, 1843–1862, doi:10.1175/MWR-D-11-00212.1.

    • Search Google Scholar
    • Export Citation

  • Aksoy, A., S. D. Aberson, T. Vukicevic, K. J. Sellwood, S. Lorsolo, and X. Zhang, 2013: Assimilation of high-resolution tropical cyclone observations with an ensemble Kalman filter using NOAA/AOML/HRD’s HEDAS: Evaluation of the 2008–11 vortex-scale analyses. Mon. Wea. Rev., 141, 1842–1865, doi:10.1175/MWR-D-12-00194.1.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • 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, 2741–2758, doi:10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., W. A. Gallus Jr., M. Xue, and F. Kong, 2009: A comparison of precipitation forecast skill between small convection-permitting and large convection-parameterizing ensembles. Wea. Forecasting, 24, 1121–1140, doi:10.1175/2009WAF2222222.1.

    • Search Google Scholar
    • Export Citation

  • Crum, T. D., R. L. Alberty, and D. W. Burgess, 1993: Recording, archiving, and using WSR-88D data. Bull. Amer. Meteor. Soc., 74, 645–653, doi:10.1175/1520-0477(1993)074<0645:RAAUWD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dawson, D. T., II, L. J. Wicker, E. R. Mansell, and R. L. Tanamachi, 2012: Impact of the environmental low-level wind profile on ensemble forecasts of the 4 May 2007 Greensburg, Kansas, tornadic storm and associated mesocyclones. Mon. Wea. Rev., 140, 696–716, doi:10.1175/MWR-D-11-00008.1.

    • Search Google Scholar
    • Export Citation

  • Dirren, S., R. D. Torn, and G. J. Hakim, 2007: A data assimilation case study using a limited-area ensemble Kalman filter. Mon. Wea. Rev., 135, 1455–1473, doi:10.1175/MWR3358.1.

    • Search Google Scholar
    • Export Citation

  • Dong, J., M. Xue, and K. K. Droegemeier, 2011: The analysis and impact of simulated high-resolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter. Meteor. Atmos. Phys., 112, 41–61, doi:10.1007/s00703-011-0130-3.

    • Search Google Scholar
    • Export Citation

  • Dowell, D. C., and L. J. Wicker, 2009: Additive noise for storm-scale ensemble forecasting and data assimilation. J. Atmos. Oceanic Technol., 26, 911–927, doi:10.1175/2008JTECHA1156.1.

    • Search Google Scholar
    • Export Citation

  • Dowell, D. C., L. J. Wicker, and D. J. Stensrud, 2004: High-resolution analyses of the 8 May 2003 Oklahoma City storm. Part II: EnKF data assimilation and forecast experiments. 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 12.5. [Available online at https://ams.confex.com/ams/pdfpapers/81393.pdf.]

  • Dowell, D. C., L. J. Wicker, and C. Snyder, 2011: Ensemble Kalman filter data assimilation of radar observations of the 8 May 2003 Oklahoma City supercell: Influences of reflectivity observations on storm-scale analyses. Mon. Wea. Rev., 139, 272–294, doi:10.1175/2010MWR3438.1.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 2461–2480, doi:10.1175/1520-0493(2001)129<2461:AOAPMS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143–10 162, doi:10.1029/94JC00572.

    • Search Google Scholar
    • Export Citation

  • Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343–367, doi:10.1007/s10236-003-0036-9.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter–3D variational analysis scheme. Mon. Wea. Rev., 128, 2905–2919, doi:10.1175/1520-0493(2000)128<2905:AHEKFV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., and J. S. Whitaker, 2011: What constrains spread growth in forecasts initialized from ensemble Kalman filters? Mon. Wea. Rev., 139, 117–131, doi:10.1175/2010MWR3246.1.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796–811, doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604–620, doi:10.1175/MWR-2864.1.

    • Search Google Scholar
    • Export Citation

  • Jung, Y., G. Zhang, and M. Xue, 2008: Assimilation of simulated polarimetric radar data for a convective storm using ensemble Kalman filter. Part I: Observation operators for reflectivity and polarimetric variables. Mon. Wea. Rev., 136, 2228–2245, doi:10.1175/2007MWR2083.1.

    • Search Google Scholar
    • Export Citation

  • Jung, Y., M. Xue, and M. Tong, 2012: Ensemble Kalman filter analyses of the 29–30 May 2004 Oklahoma tornadic thunderstorm using one- and two-moment bulk microphysics schemes, with verification against polarimetric radar data. Mon. Wea. Rev., 140, 1457–1475, doi:10.1175/MWR-D-11-00032.1.

    • Search Google Scholar
    • Export Citation

  • Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • McLaughlin, D., and Coauthors, 2009: Short-wavelength technology and the potential for distributed networks of small radar systems. Bull. Amer. Meteor. Soc., 90, 1797–1817, doi:10.1175/2009BAMS2507.1.

    • Search Google Scholar
    • Export Citation

  • Putnam, B. J., M. Xue, Y. Jung, N. Snook, and G. Zhang, 2014: The analysis and prediction of microphysical states and polarimetric variables in a mesoscale convective system using double-moment microphysics, multinetwork radar data, and the ensemble Kalman filter. Mon. Wea. Rev., 142, 141–162, doi:10.1175/MWR-D-13-00042.1.

    • Search Google Scholar
    • Export Citation

  • Schenkman, A. D., M. Xue, A. Shapiro, K. Brewster, and J. Gao, 2011a: The analysis and prediction of the 8–9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR. Mon. Wea. Rev., 139, 224–246, doi:10.1175/2010MWR3336.1.

    • Search Google Scholar
    • Export Citation

  • Schenkman, A. D., M. Xue, A. Shapiro, K. Brewster, and J. Gao, 2011b: Impact of CASA radar and Oklahoma mesonet data assimilation on the analysis and prediction of tornadic mesovortices in a MCS. Mon. Wea. Rev., 139, 3422–3445, doi:10.1175/MWR-D-10-05051.1.

    • Search Google Scholar
    • Export Citation

  • Schwartz, C. S., and Coauthors, 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263–280, doi:10.1175/2009WAF2222267.1.

    • Search Google Scholar
    • Export Citation

  • Snook, N., and M. Xue, 2008: Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms. Geophys. Res. Lett., 35, L24803, doi:10.1029/2008GL035866.

    • Search Google Scholar
    • Export Citation

  • Snook, N., M. Xue, and Y. Jung, 2011: Analysis of a tornadic mesoscale convective vortex based on ensemble Kalman filter assimilation of CASA X-band and WSR-88D radar data. Mon. Wea. Rev., 139, 3446–3468, doi:10.1175/MWR-D-10-05053.1.

    • Search Google Scholar
    • Export Citation

  • Snook, N., M. Xue, and Y. Jung, 2012: Ensemble probabilistic forecasts of a tornadic mesoscale convective system from ensemble Kalman filter analyses using WSR-88D and CASA radar data. Mon. Wea. Rev., 140, 2126–2146, doi:10.1175/MWR-D-11-00117.1.

    • Search Google Scholar
    • Export Citation

  • Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663–1677, doi:10.1175/2555.1.

    • Search Google Scholar
    • Export Citation

  • Sobash, R. A., and D. J. Stensrud, 2013: The impact of covariance localization for radar data on EnKF analyses of a developing MCS: Observing system simulation experiments. Mon. Wea. Rev., 141, 3691–3709, doi:10.1175/MWR-D-12-00203.1.

    • Search Google Scholar
    • Export Citation

  • Stensrud, D. J., and Coauthors, 2009: Convective-scale Warn-on-Forecast System: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 1487–1499, doi:10.1175/2009BAMS2795.1.

    • Search Google Scholar
    • Export Citation

  • Tong, M., 2006: Ensemble Kalman filer assimilation of Doppler radar data for the initialization and prediction of convective storms. School of Meteorology, University of Oklahoma, 243 pp.

  • Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789–1807, doi:10.1175/MWR2898.1.

    • Search Google Scholar
    • Export Citation

  • Tong, M., and M. Xue, 2008a: Simultaneous estimation of microphysical parameters and atmospheric state with radar data and ensemble square-root Kalman filter. Part I: Sensitivity analysis and parameter identifiability. Mon. Wea. Rev., 136, 1630–1648, doi:10.1175/2007MWR2070.1.

    • Search Google Scholar
    • Export Citation

  • Tong, M., and M. Xue, 2008b: Simultaneous estimation of microphysical parameters and atmospheric state with radar data and ensemble square-root Kalman filter. Part II: Parameter estimation experiments. Mon. Wea. Rev., 136, 1649–1668, doi:10.1175/2007MWR2071.1.

    • Search Google Scholar
    • Export Citation

  • Wang, Y., Y. Jung, T. A. Supinie, and M. Xue, 2013: A hybrid MPI/OpenMP parallel algorithm and performance analysis for an ensemble square root filter suitable for dense observations. J. Atmos. Oceanic Technol., 30, 1382–1397, doi:10.1175/JTECH-D-12-00165.1.

    • Search Google Scholar
    • Export Citation

  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913–1924, doi:10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wood, V. T., and R. A. Brown, 1997: Effects of radar sampling on single-Doppler velocity signatures of mesocyclones and tornadoes. Wea. Forecasting, 12, 928–938, doi:10.1175/1520-0434(1997)012<0928:EORSOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wu, C.-C., G.-Y. Lien, J.-H. Chen, and F. Zhang, 2010: Assimilation of tropical cyclone track and structure based on the ensemble Kalman filter (EnKF). J. Atmos. Sci., 67, 3806–3822, doi:10.1175/2010JAS3444.1.

    • Search Google Scholar
    • Export Citation

  • Xue, M., and Coauthors, 2001: The Advanced Regional Prediction System (ARPS)—A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications. Meteor. Atmos. Phys., 76, 143–165, doi:10.1007/s007030170027.

    • Search Google Scholar
    • Export Citation

  • Xue, M., and Coauthors, 2008: CAPS realtime storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2008 Spring Experiment. 24th Conf. on Several Local Storms, Savannah, GA, Amer. Meteor. Soc., 12.2. [Available online at https://ams.confex.com/ams/24SLS/techprogram/paper_142036.htm.]

  • Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)—A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161–193, doi:10.1007/s007030070003.

    • Search Google Scholar
    • Export Citation

  • Xue, M., M. Tong, and K. K. Droegemeier, 2006: An OSSE framework based on the ensemble square-root Kalman filter for evaluating impact of data from radar networks on thunderstorm analysis and forecast. J. Atmos. Oceanic Technol., 23, 46–66, doi:10.1175/JTECH1835.1.

    • Search Google Scholar
    • Export Citation

  • Xue, M., Y. Jung, and G. Zhang, 2010: State estimation of convective storms with a two-moment microphysics scheme and an ensemble Kalman filter: Experiments with simulated radar data. Quart. J. Roy. Meteor. Soc., 136, 685–700, doi:10.1002/qj.593.

    • Search Google Scholar
    • Export Citation

  • Yussouf, N., and D. J. Stensrud, 2012: Comparison of single-parameter and multiparameter ensembles for assimilation of radar observations using the ensemble Kalman filter. Mon. Wea. Rev., 140, 562–586, doi:10.1175/MWR-D-10-05074.1.

    • Search Google Scholar
    • Export Citation

  • Yussouf, N., E. R. Mansell, L. J. Wicker, D. M. Wheatley, and D. J. Stensrud, 2013: The ensemble Kalman filter analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell storm using single- and double-moment microphysics schemes. Mon. Wea. Rev., 141, 3388–3411, doi:10.1175/MWR-D-12-00237.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observations on the convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 1238–1253, doi:10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zhu, K., Y. Pan, M. Xue, X. Wang, J. S. Whitaker, S. G. Benjamin, S. S. Weygandt, and M. Hu, 2013: A regional GSI-based ensemble Kalman filter data assimilation system for the Rapid Refresh configuration: Testing at reduced resolution. Mon. Wea. Rev., 141, 4118–4139, doi:10.1175/MWR-D-13-00039.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 707 350 12
PDF Downloads 364 121 9