• 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, 18051824, https://doi.org/10.1175/2008MWR2691.1.

    • Crossref
    • 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, 18431862, https://doi.org/10.1175/MWR-D-11-00212.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Crossref
    • 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, 27412758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., and N. Collins, 2007: Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Technol., 24, 14521463, https://doi.org/10.1175/JTECH2049.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brewster, K. A., M. Hu, M. Xue, and J. Gao, 2005: Efficient assimilation of radar data at high resolution for short-range numerical weather prediction. Int. Symp. on Nowcasting Very Short Range Forecasting, Toulouse, France, WMO/World Weather Research Program, 3.06.

    • Search Google Scholar
    • Export Citation
  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 13, https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar. Cambridge University Press, 636 pp.

  • Brooks, H. E., 2004: Tornado-warning performance in the past and future. Bull. Amer. Meteor. Soc., 85, 837843, https://doi.org/10.1175/BAMS-85-6-837.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CAPS, 2017: 2017 CAPS Spring Forecast Experiment Program Plan. Center for The Analysis and Prediction of Storms, 28 pp., http://forecast.caps.ou.edu/SpringProgram2017_Plan-CAPS.pdf.

  • Caya, A., J. Sun, and C. Snyder, 2005: A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation. Mon. Wea. Rev., 133, 30813094, https://doi.org/10.1175/MWR3021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, W., K.-S. Chung, L. Fillion, and S.-J. Baek, 2014: Radar data assimilation in the Canadian high-resolution ensemble Kalman filter system: Performance and verification with real summer cases. Mon. Wea. Rev., 142, 21182138, https://doi.org/10.1175/MWR-D-13-00291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chipilski, H. G., X. Wang, and D. B. Parsons, 2020: Impact of assimilating PECAN profilers on the prediction of bore-driven nocturnal convection: A multiscale forecast evaluation for the 6 July 2015 case study. Mon. Wea. Rev., 148, 11471175, https://doi.org/10.1175/MWR-D-19-0171.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and et al. , 2018: The Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. Bull. Amer. Meteor. Soc., 99, 14331448, https://doi.org/10.1175/BAMS-D-16-0309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and et al. , 2019: Spring Forecasting Experiment 2019: Program overview and operations plan. NOAA, 39 pp., https://hwt.nssl.noaa.gov/sfe/2019/docs/HWT_SFE2019_operations_plan.pdf.

  • Curtis, C. D., and S. M. Torres, 2011: Adaptive range oversampling to achieve faster scanning on the National Weather Radar Testbed phased-array radar. J. Atmos. Oceanic Technol., 28, 15811597, https://doi.org/10.1175/JTECH-D-10-05042.1.

    • Crossref
    • 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, 696716, https://doi.org/10.1175/MWR-D-11-00008.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doviak, R., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 pp.

  • Dowell, D. C., and L. J. Wicker, 2009: Additive noise for storm-scale ensemble data assimilation. J. Atmos. Oceanic Technol., 26, 911927, https://doi.org/10.1175/2008JTECHA1156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dowell, D. C., L. J. Wicker, and C. Snyder, 2011: Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma city supercell: Influences of reflectivity observations on storm-scale analyses. Mon. Wea. Rev., 139, 272294, https://doi.org/10.1175/2010MWR3438.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 19822005, https://doi.org/10.1175/1520-0493(2004)132<1982:WATRIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Droegemeier, K. K., and R. B. Wilhelmson, 1985: Three-dimensional numerical modeling of convection produced by interacting thunderstorm outflows. Part I: Control simulation and low-level moisture variations. J. Atmos. Sci., 42, 23812403, https://doi.org/10.1175/1520-0469(1985)042<2381:TDNMOC>2.0.CO;2.

    • Crossref
    • 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 14310 162, https://doi.org/10.1029/94JC00572.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343367, https://doi.org/10.1007/s10236-003-0036-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, J., and M. Xue, 2008: An efficient dual-resolution approach for ensemble data assimilation and tests with simulated Doppler radar data. Mon. Wea. Rev., 136, 945963, https://doi.org/10.1175/2007MWR2120.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757, https://doi.org/10.1002/qj.49712555417.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 27762790, https://doi.org/10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heinselman, P. L., and S. M. Torres, 2011: High-temporal-resolution capabilities of the National Weather Radar Testbed Phased-Array Radar. J. Appl. Meteor. Climatol., 50, 579593, https://doi.org/10.1175/2010JAMC2588.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S. Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • 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, 796811, https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, M., and M. Xue, 2007: Impact of configurations of rapid intermittent assimilation of WSR-88D radar data for the 8 May 2003 Oklahoma City tornadic thunderstorm case. Mon. Wea. Rev., 135, 507525, https://doi.org/10.1175/MWR3313.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1990: The step-mountain coordinate: Physical package. Mon. Wea. Rev., 118, 14291443, https://doi.org/10.1175/1520-0493(1990)118<1429:TSMCPP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1996: The surface layer in the NCEP Eta Model. 11th Conf. on Numerical Weather Prediction, Norfolk, VA, Amer. Meteor. Soc., 354355.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 2001: Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP Meso Model. NCEP Office Note 437, 61 pp.

  • Jazwinski, A. H., 1970: Stochastic Processes and Filtering Theory. Academic Press, 376 pp.

  • Johnson, A., and X. Wang, 2017: Design and implementation of a GSI-based convection-allowing ensemble data assimilation and forecast system for the PECAN field experiment. Part I: Optimal configurations for nocturnal convection prediction using retrospective cases. Wea. Forecasting, 32, 289315, https://doi.org/10.1175/WAF-D-16-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, J. R. Carley, L. J. Wicker, and C. Karstens, 2015: A comparison of multiscale GSI-based EnKF and 3DVar data assimilation using radar and conventional observations for midlatitude convective-scale precipitation forecasts. Mon. Wea. Rev., 143, 30873108, https://doi.org/10.1175/MWR-D-14-00345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, and S. Degelia, 2017: Design and implementation of a GSI-based convection-allowing ensemble-based data assimilation and forecast system for the PECAN field experiment. Part II: Overview and evaluation of a real-time system. Wea. Forecasting, 32, 12271251, https://doi.org/10.1175/WAF-D-16-0201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, T. A., K. H. Knopfmeier, D. M. Wheatley, G. J. Creager, P. Minnis, and R. Palikonda, 2016: Storm-scale data assimilation and ensemble forecasting with the NSSL experimental Warn-on-Forecast system. Part II: Combined radar and satellite data experiments. Wea. Forecasting, 31, 297327, https://doi.org/10.1175/WAF-D-15-0107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, T. A., P. S. Skinner, K. H. Knopfmeier, E. R. Mansell, P. Minnis, R. Palikonda, and W. J. Smith, 2018a: Comparison of cloud microphysics schemes in a Warn-on-Forecast system using synthetic satellite objects. Wea. Forecasting, 33, 16811708, https://doi.org/10.1175/WAF-D-18-0112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, T. A., X. Wang, P. Skinner, A. Johnson, and Y. Wang, 2018b: Assimilation of GOES-13 imager clear-sky water vapor (6.5 μm) radiances into a Warn-on-Forecast system. Mon. Wea. Rev., 146, 10771107, https://doi.org/10.1175/MWR-D-17-0280.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, Y., M. Xue, G. Zhang, and J. M. Straka, 2008: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part I: Observation operators for reflectivity and polarimetric variables. Mon. Wea. Rev., 136, 22282245, https://doi.org/10.1175/2007MWR2083.1.

    • Crossref
    • 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, 14571475, https://doi.org/10.1175/MWR-D-11-00032.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, Y., and et al. , 2018a: Development of GSI-based EnKF and hybrid EnVar data assimilation capabilities for continental-scale 3-km convection-permitting ensemble forecasting and testing via NOAA Hazardous Weather Testbed Spring Forecasting Experiments. 29th Conf. on Weather Analysis and Forecasting, Denver, CO, 7A.3, https://ams.confex.com/ams/29WAF25NWP/webprogram/Paper345776.html.

    • Search Google Scholar
    • Export Citation
  • Jung, Y., M. Xue, G. Zhao, F. Kong, K. Thomas, T. Supinie, K. Brewster, and N. Snook, 2018b: CAPS real-time storm-scale EnKF data assimilation and forecasts for the NOAA Hazardous Weather Testbed Spring Forecasting Experiments: Towards the goal of operational ensemble-variational data assimilation for convection-permitting models. Eighth EnKF Data Assimilation Workshop, Sainte-Adèle, Québec, Canada, McGill University, https://web.meteo.mcgill.ca/enkf/abstracts_html/5966e38b03.php.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and et al. , 2010: Assessing advances in the assimilation of radar data and other mesoscale observations within a collaborative forecasting–research environment. Wea. Forecasting, 25, 15101521, https://doi.org/10.1175/2010WAF2222405.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. J. Fluids Eng. Trans. ASME, 82, 3545, https://doi.org/10.1115/1.3662552.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Labriola, J., N. Snook, Y. Jung, B. J. Putnam, and M. Xue, 2017: Ensemble hail prediction for the storms of 10 May 2010 in south-central Oklahoma using single- and double-moment microphysical schemes. Mon. Wea. Rev., 145, 49114936, https://doi.org/10.1175/MWR-D-17-0039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Labriola, J., N. Snook, Y. Jung, and M. Xue, 2019: Explicit ensemble prediction of hail in 19 May 2013 Oklahoma city thunderstorms and analysis of hail growth pocesses with several multimoment microphysics schemes. Mon. Wea. Rev., 147, 11931213, https://doi.org/10.1175/MWR-D-18-0266.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawson, J. R., J. S. Kain, N. Yussouf, D. C. Dowell, D. M. Wheatley, K. H. Knopfmeier, and T. A. Jones, 2018: Advancing from convection-allowing NWP to Warn-on-Forecast: Evidence of progress. Wea. Forecasting, 33, 599607, https://doi.org/10.1175/WAF-D-17-0145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lynch, P., and X.-Y. Huang, 1992: Initialization of the HIRLAM model using a digital filter. Mon. Wea. Rev., 120, 10191034, https://doi.org/10.1175/1520-0493(1992)120<1019:IOTHMU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mansell, E. R., C. Ziegler, and E. Bruning, 2010: Simulated electrification of a small thunderstorm with two-moment bulk microphysics. J. Atmos. Sci., 67, 171194, https://doi.org/10.1175/2009JAS2965.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Melhauser, C., and F. Zhang, 2012: Practical and intrinsic predictability of severe and convective weather at the mesoscales. J. Atmos. Sci., 69, 33503371, https://doi.org/10.1175/JAS-D-11-0315.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part II: A proposed three-moment closure and scheme description. J. Atmos. Sci., 62, 30653081, https://doi.org/10.1175/JAS3535.1.

    • Crossref
    • 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 atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., and W. W. Grabowski, 2008: A novel approach for representing ice microphysics in models: Description and tests using a kinematic framework. J. Atmos. Sci., 65, 15281548, https://doi.org/10.1175/2007JAS2491.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., and J. A. Milbrandt, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. J. Atmos. Sci., 72, 287311, https://doi.org/10.1175/JAS-D-14-0065.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595600, https://doi.org/10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, https://doi.org/10.2151/jmsj.87.895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NOAA, 2019: Billion-dollar weather and climate disasters: Overview. National Centers for Environmental Information, accessed 11 July 2019, https://www.ncdc.noaa.gov/billions/.

  • Pan, Y., and M. Wang, 2019: Impact of the assimilation frequency of radar data with the ARPS 3DVar and cloud analysis system on forecasts of a squall line in Southern China. Adv. Atmos. Sci., 36, 160172, https://doi.org/10.1007/s00376-018-8087-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parrish, D., and J. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 17471763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purser, R. J., W. S. Wu, D. F. Parrish, and N. M. Roberts, 2003: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131, 15241535, https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Putnam, B. J., M. Xue, Y. Jung, N. Snook, and G. Zhang, 2019: Ensemble Kalman filter assimilation of polarimetric radar observations for the 20 May 2013 Oklahoma tornadic supercell case. Mon. Wea. Rev., 147, 25112533, https://doi.org/10.1175/MWR-D-18-0251.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romine, G. S., C. S. Schwartz, C. Snyder, J. L. Anderson, and M. L. Weisman, 2013: Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts. Mon. Wea. Rev., 141, 12631284, https://doi.org/10.1175/MWR-D-12-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romine, G. S., C. S. Schwartz, J. Berner, K. R. Fossell, C. Snyder, J. L. Anderson, and M. L. Weisman, 2014: Representing forecast error in a convection-permitting ensemble system. Mon. Wea. Rev., 142, 45194541, https://doi.org/10.1175/MWR-D-14-00100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45, 463485, https://doi.org/10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., S. E. Giangrande, V. M. Melnikov, and T. J. Schuur, 2005: Calibration issues of dual-polarization radar measurements. J. Atmos. Oceanic Technol., 22, 11381155, https://doi.org/10.1175/JTECH1772.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and R. A. Sobash, 2017: Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations. Mon. Wea. Rev., 145, 33973418, https://doi.org/10.1175/MWR-D-16-0400.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and et al. , 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263280, https://doi.org/10.1175/2009WAF2222267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and et al. , 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Smith, T. M., and et al. , 2016: Multi-Radar Multi-Sensor (MRMS) severe weather and aviation products: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 16171630, https://doi.org/10.1175/BAMS-D-14-00173.1.

    • Crossref
    • 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, 34463468, https://doi.org/10.1175/MWR-D-10-05053.1.

    • Crossref
    • 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, 21262146, https://doi.org/10.1175/MWR-D-11-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snook, N., M. Xue, and Y. Jung, 2013: Impacts of assumed observation errors in EnKF analyses and ensemble forecasts of a tornadic mesoscale convective system. 17th Conf. on Integrated Observing and Assimilation Systems for the Athmosphere, Oceans, and Land Surface (IOAS-AOLS), Austin, TX, Amer. Meteor. Soc., 13.2, https://ams.confex.com/ams/93Annual/webprogram/Paper219933.html.

    • Search Google Scholar
    • Export Citation
  • Snook, N., M. Xue, and Y. Jung, 2015: Multiscale EnKF assimilation of radar and conventional observations and ensemble forecasting for a tornadic mesoscale convective system. Mon. Wea. Rev., 143, 10351057, https://doi.org/10.1175/MWR-D-13-00262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snook, N., Y. Jung, J. Brotzge, B. J. Putnam, and M. Xue, 2016: Prediction and ensemble forecast verification of hail in the supercell storms of 20 May 2013. Wea. Forecasting, 31, 811825, https://doi.org/10.1175/WAF-D-15-0152.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snook, N., F. Kong, K. A. Brewster, M. Xue, K. W. Thomas, T. A. Supinie, S. Perfater, and B. Albright, 2019: Evaluation of convection-permitting precipitation forecast products using WRF, NMMB, and FV3 for the 2016–17 NOAA hydrometeorology testbed flash flood and intense rainfall experiments. Wea. Forecasting, 34, 781804, https://doi.org/10.1175/WAF-D-18-0155.1.

    • Crossref
    • 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, 16631677, https://doi.org/10.1175//2555.1.

    • Crossref
    • 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, 36913709, https://doi.org/10.1175/MWR-D-12-00203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., and D. J. Stensrud, 2015: Assimilating surface mesonet observations with the EnKF to improve ensemble forecasts of convection initiation on 29 May 2012. Mon. Wea. Rev., 143, 37003725, https://doi.org/10.1175/MWR-D-14-00126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., and L. J. Wicker, 2015: On the impact of additive noise in storm-scale EnKF experiments. Mon. Wea. Rev., 143, 30673086, https://doi.org/10.1175/MWR-D-14-00323.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and et al. , 2009: Convective-scale Warn-on-Forecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 14871500, https://doi.org/10.1175/2009BAMS2795.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and et al. , 2013: Progress and challenges with Warn-on-Forecast. Atmos. Res., 123, 216, https://doi.org/10.1016/j.atmosres.2012.04.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stratman, D., N. Yussouf, Y. Jung, T. Supinie, M. Xue, P. S. Skinner, and B. J. Putnam, 2020: Optimal temporal frequency of NSSL phased-array radar observations for an experimental Warn-on-Forecast system. Wea. Forecasting, 35, 193214, https://doi.org/10.1175/WAF-D-19-0165.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Supinie, T. A., Y. Jung, M. Xue, D. J. Stensrud, M. M. French, and H. B. Bluestein, 2016: Impact of VORTEX2 observations on analyses and forecasts of the 5 June 2009 Goshen County, Wyoming, supercell. Mon. Wea. Rev., 144, 429449, https://doi.org/10.1175/MWR-D-15-0171.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Supinie, T. A., N. Yussouf, Y. Jung, M. Xue, J. Cheng, and S. Wang, 2017: Comparison of the analyses and forecasts of a tornadic supercell storm from assimilating phased-array radar and WSR-88D observations. Wea. Forecasting, 32, 13791401, https://doi.org/10.1175/WAF-D-16-0159.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71, 36363658, https://doi.org/10.1175/JAS-D-13-0305.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tong, C. C., Y. Jung, M. Xue, and C. Liu, 2020: Direct assimilation of radar data with ensemble Kalman filter and hybrid ensemble-variational method in the National Weather Service operational data assimilation system GSI for the stand-alone regional FV3 model at a convection-allowing resolution. Geophys. Res. Lett., 47, e2020GL090179, https://doi.org/10.1029/2020GL090179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a comprssible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 17891807, https://doi.org/10.1175/MWR2898.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vivekanandan, J., W. M. Adams, and V. N. Bringi, 1991: Rigorous approach to polarimetric radar modeling of hydrometeor orientation distributions. J. Appl. Meteor., 30, 10531063, https://doi.org/10.1175/1520-0450(1991)030<1053:RATPRM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S., M. Xue, and J. Min, 2013: A four-dimensional asynchronous ensemble square-root filter (4DEnSRF) algorithm and tests with simulated radar data. Quart. J. Roy. Meteor. Soc., 139, 805819, https://doi.org/10.1002/qj.1987.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., and X. Wang, 2017: Direct assimilation of radar reflectivity without tangent linear and adjoint of the nonlinear observation operator in the GSI-based EnVar system: Methodology and experiment with the 8 May 2003 Oklahoma City tornadic supercell. Mon. Wea. Rev., 145, 14471471, https://doi.org/10.1175/MWR-D-16-0231.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weber, M. E., J. Y. N. Cho, J. S. Herd, J. M. Flavin, W. E. Benner, and G. S. Torok, 2007: The next-generation multimission U.S. surveillance radar network. Bull. Amer. Meteor. Soc., 88, 17391752, https://doi.org/10.1175/BAMS-88-11-1739.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheatley, D. M., K. H. Knopfmeier, T. A. Jones, and G. J. Creager, 2015: Storm-scale data assimilation and ensemble forecasting with the NSSL experimental Warn-on-Forecast system. Part I : Radar data experiments. Wea. Forecasting, 30, 17951817, https://doi.org/10.1175/WAF-D-15-0043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 19131924, https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2012: Evaluating methods to account for system errors in ensemble data assimilation. Mon. Wea. Rev., 140, 30783089, https://doi.org/10.1175/MWR-D-11-00276.1.

    • Crossref
    • 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 the impact of data from radar networks on thunderstorm analysis and forecasting. J. Atmos. Oceanic Technol., 23, 4666, https://doi.org/10.1175/JTECH1835.1.

    • Crossref
    • 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, 685700, https://doi.org/10.1002/qj.593.

    • Search Google Scholar
    • Export Citation
  • Ying, Y., F. Zhang, and J. L. Anderson, 2018: On the selection of localization radius in ensemble filtering for multiscale quasigeostrophic dynamics. Mon. Wea. Rev., 146, 543560, https://doi.org/10.1175/MWR-D-17-0336.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yussouf, N., and D. J. Stensrud, 2010: Impact of phased-array radar observations over a short assimilation period: Observing system simulation experiments using an ensemble Kalman filter. Mon. Wea. Rev., 138, 517538, https://doi.org/10.1175/2009MWR2925.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yussouf, N., E. R. Mansell, L. J. Wicker, D. Wheatley, and D. 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, 33883412, https://doi.org/10.1175/MWR-D-12-00237.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yussouf, N., J. S. Kain, and A. J. Clark, 2016: Short-term probabilistic forecasts of the 31 May 2013 Oklahoma tornado and flash flood event using a continuous-update-cycle storm-scale ensemble system. Wea. Forecasting, 31, 957983, https://doi.org/10.1175/WAF-D-15-0160.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 12381253, https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., D. J. Stensrud, and F. Zhang, 2019: Simultaneous assimilation of radar and all-sky satellite infrared radiance observations for convection-allowing ensemble analysis and prediction of severe thunderstorms. Mon. Wea. Rev., 147, 43894409, https://doi.org/10.1175/MWR-D-19-0163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., and et al. , 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 17531766, https://doi.org/10.1175/BAMS-88-11-1753.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluating Forecast Performance and Sensitivity to the GSI EnKF Data Assimilation Configuration for the 28–29 May 2017 Mesoscale Convective System Case

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  • 1 School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 2 Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma
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Abstract

In an effort to improve radar data assimilation configurations for potential operational implementation, GSI EnKF data assimilation experiments based on the operational system employed by the Center for Analysis and Prediction of Storms (CAPS) real-time Spring Forecast Experiments are performed. These experiments are followed by 6-h forecasts for an MCS on 28–29 May 2017. Configurations examined include data thinning, covariance localization radii and inflation, observation error settings, and data assimilation frequency for radar observations. The results show experiments that assimilate radar observations more frequently (i.e., 5–10 min) are initially better at suppressing spurious convection. However, assimilating observations every 5 min causes spurious convection to become more widespread with time, and modestly degrades forecast skill through the remainder of the forecast window. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data predict fewer spurious storms and better predict the location of observed storms. Optimized data thinning and horizontal covariance localization radii have positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance toward optimizing the configuration of the GSI EnKF system. Among the DA configurations tested, the one employed by the CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining computationally efficient for real-time use.

Significance Statement

High-resolution ensemble forecasts that can skillfully predict thunderstorms provide an opportunity for warning severe weather further in advance. However, forecast accuracy is dependent upon many factors including the data assimilation system used to create the forecasts. This study optimizes the design of a real-time ensemble forecast and data assimilation system for a severe weather event where a line of thunderstorms produced hail, wind, and tornadoes in the southern United States. The 0–6-h forecasts predict the thunderstorms with moderate skill. Forecast accuracy is most sensitive to how frequently the data assimilation system assimilates radar observations, the degree to which radar observations are thinned, and the covariance localization radius. Results of this study can be used to design future real-time forecast systems for severe weather events.

Current affiliation: National Severe Storms Laboratory, Norman, Oklahoma.

Currrent affiliation: Office of Science and Technology Integration, Silver Spring, Maryland.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jonathan Labriola, jonathan.labriola@noaa.gov

Abstract

In an effort to improve radar data assimilation configurations for potential operational implementation, GSI EnKF data assimilation experiments based on the operational system employed by the Center for Analysis and Prediction of Storms (CAPS) real-time Spring Forecast Experiments are performed. These experiments are followed by 6-h forecasts for an MCS on 28–29 May 2017. Configurations examined include data thinning, covariance localization radii and inflation, observation error settings, and data assimilation frequency for radar observations. The results show experiments that assimilate radar observations more frequently (i.e., 5–10 min) are initially better at suppressing spurious convection. However, assimilating observations every 5 min causes spurious convection to become more widespread with time, and modestly degrades forecast skill through the remainder of the forecast window. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data predict fewer spurious storms and better predict the location of observed storms. Optimized data thinning and horizontal covariance localization radii have positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance toward optimizing the configuration of the GSI EnKF system. Among the DA configurations tested, the one employed by the CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining computationally efficient for real-time use.

Significance Statement

High-resolution ensemble forecasts that can skillfully predict thunderstorms provide an opportunity for warning severe weather further in advance. However, forecast accuracy is dependent upon many factors including the data assimilation system used to create the forecasts. This study optimizes the design of a real-time ensemble forecast and data assimilation system for a severe weather event where a line of thunderstorms produced hail, wind, and tornadoes in the southern United States. The 0–6-h forecasts predict the thunderstorms with moderate skill. Forecast accuracy is most sensitive to how frequently the data assimilation system assimilates radar observations, the degree to which radar observations are thinned, and the covariance localization radius. Results of this study can be used to design future real-time forecast systems for severe weather events.

Current affiliation: National Severe Storms Laboratory, Norman, Oklahoma.

Currrent affiliation: Office of Science and Technology Integration, Silver Spring, Maryland.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jonathan Labriola, jonathan.labriola@noaa.gov
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