Valid Time Shifting for an Experimental RRFS Convection-Allowing EnVar Data Assimilation and Forecast System: Description and Systematic Evaluation in Real Time

Nicholas A. Gasperoni aSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Nicholas A. Gasperoni in
Current site
Google Scholar
PubMed
Close
,
Xuguang Wang aSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Xuguang Wang in
Current site
Google Scholar
PubMed
Close
, and
Yongming Wang aSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Yongming Wang in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This study describes a real-time implementation of valid time shifting (VTS) within the Gridpoint Statistical Interpolation–based ensemble-variational (EnVar) data assimilation system, developed at the Multi-Scale Data Assimilation and Predictability Laboratory. This system, featuring data assimilation of mesoscale conventional observations and storm-scale radar reflectivity observations and interfaced with the next-generation Finite Volume Cubed Sphere Dynamical Core limited-area model (FV3-LAM), was run in real-time during the 2021 Hazardous Weather Testbed Spring Forecast Experiment. The VTS method efficiently increases ensemble size by incorporating ensemble forecast output before and after the central analysis. Two configurations were examined to systematically evaluate VTS: a baseline 36-member system with hourly data assimilation (NOVTS), and an experiment testing VTS for the radar analysis step. Verification across 22 cases shows statistically significant benefits of VTS to increase ensemble spread and better fit first guesses to observations. Control member forecasts launched at 0000 UTC have consistently higher skill, lower bias, and higher reliability in VTS than in NOVTS throughout the 18-h forecast evaluation period, especially from severe cases often featuring upscale growth into mesoscale convective systems. Verification of updraft helicity-based ensemble surrogate severe probabilistic forecasts against observed storm reports shows higher skill of VTS when verifying on finer scales, with benefits to constraining higher probabilities over report locations and reducing probabilities over no-report locations. This study is a first step toward the next-generation Rapid Refresh Forecast System (RRFS), demonstrating the feasibility of such a real-time system and the potential benefits of VTS implementation.

© 2023 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: Nicholas A. Gasperoni, ngaspero@ou.edu

Abstract

This study describes a real-time implementation of valid time shifting (VTS) within the Gridpoint Statistical Interpolation–based ensemble-variational (EnVar) data assimilation system, developed at the Multi-Scale Data Assimilation and Predictability Laboratory. This system, featuring data assimilation of mesoscale conventional observations and storm-scale radar reflectivity observations and interfaced with the next-generation Finite Volume Cubed Sphere Dynamical Core limited-area model (FV3-LAM), was run in real-time during the 2021 Hazardous Weather Testbed Spring Forecast Experiment. The VTS method efficiently increases ensemble size by incorporating ensemble forecast output before and after the central analysis. Two configurations were examined to systematically evaluate VTS: a baseline 36-member system with hourly data assimilation (NOVTS), and an experiment testing VTS for the radar analysis step. Verification across 22 cases shows statistically significant benefits of VTS to increase ensemble spread and better fit first guesses to observations. Control member forecasts launched at 0000 UTC have consistently higher skill, lower bias, and higher reliability in VTS than in NOVTS throughout the 18-h forecast evaluation period, especially from severe cases often featuring upscale growth into mesoscale convective systems. Verification of updraft helicity-based ensemble surrogate severe probabilistic forecasts against observed storm reports shows higher skill of VTS when verifying on finer scales, with benefits to constraining higher probabilities over report locations and reducing probabilities over no-report locations. This study is a first step toward the next-generation Rapid Refresh Forecast System (RRFS), demonstrating the feasibility of such a real-time system and the potential benefits of VTS implementation.

© 2023 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: Nicholas A. Gasperoni, ngaspero@ou.edu
Save
  • 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.

    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., G. A. Grell, J. M. Brown, T. G. Smirnova, and R. Bleck, 2004: Mesoscale weather prediction with the RUC hybrid isentropic–terrain-following coordinate model. Mon. Wea. Rev., 132, 473494, https://doi.org/10.1175/1520-0493(2004)132<0473:MWPWTR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Black, T. L., 1994: The new NMC mesoscale eta model: Description and forecast examples. Wea. Forecasting, 9, 265278, https://doi.org/10.1175/1520-0434(1994)009<0265:TNNMEM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Black, T. L., and Coauthors, 2021: A limited area modeling capability for the finite‐volume cubed‐sphere (FV3) dynamical core and comparison with a global two‐way nest. J. Adv. Model. Earth Syst., 13, e2021MS002483, https://doi.org/10.1029/2021MS002483.

    • Search Google Scholar
    • Export Citation
  • Bowler, N. E., and Coauthors, 2017: Inflation and localization tests in the development of an ensemble of 4D-ensemble variational assimilations. Quart. J. Roy. Meteor. Soc., 143, 12801302, https://doi.org/10.1002/qj.3004.

    • Search Google Scholar
    • Export Citation
  • Carley, J. R., and Coauthors, 2021: Status of NOAA’s next generation convection-allowing ensemble: The Rapid Refresh forecast system. WAF Symp. General Session, Online, Amer. Meteor. Soc., 12.8, https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/378383.

  • Caron, J.-F., and M. Buehner, 2018: Scale-dependent background error covariance localization: Evaluation in a global deterministic weather forecasting system. Mon. Wea. Rev., 146, 13671381, https://doi.org/10.1175/MWR-D-17-0369.1.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2011: Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon. Wea. Rev., 139, 14101418, https://doi.org/10.1175/2010MWR3624.1.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 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.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2019: Spring Forecasting Experiment 2019 conducted by the Experimental Forecast Program of the NOAA Hazardous Weather Testbed. NOAA Preliminary Findings and Results Rep., 77 pp., https://hwt.nssl.noaa.gov/sfe/2019/docs/HWT_SFE_2019_Prelim_Findings_FINAL.pdf.

  • Clark, A. J., and Coauthors, 2021: Spring Forecasting Experiment 2021 conducted by the Experimental Forecast Program of the NOAA Hazardous Weather Testbed. NOAA Preliminary Findings and Results Rep., 86 pp., https://hwt.nssl.noaa.gov/sfe/2021/docs/HWT_SFE_2021_Prelim_Findings_FINAL.pdf.

  • 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Duda, J. D., X. Wang, Y. Wang, and J. R. Carley, 2019: Comparing the assimilation of radar reflectivity using the direct GSI-based ensemble–variational (EnVar) and indirect cloud analysis methods in convection-allowing forecasts over the continental United States. Mon. Wea. Rev., 147, 16551678, https://doi.org/10.1175/MWR-D-18-0171.1.

    • Search Google Scholar
    • Export Citation
  • Gallo, B. T., and Coauthors, 2021: Exploring convection-allowing model evaluation strategies for severe local storms using the finite-volume cubed-sphere (FV3) model core. Wea. Forecasting, 36, 319, https://doi.org/10.1175/WAF-D-20-0090.1.

    • Search Google Scholar
    • Export Citation
  • Gasperoni, N. A., X. Wang, and Y. Wang, 2020: A comparison of methods to sample model errors for convection-allowing ensemble forecasts in the setting of multiscale initial conditions produced by the GSI-based EnVar assimilation system. Mon. Wea. Rev., 148, 11771203, https://doi.org/10.1175/MWR-D-19-0124.1.

    • Search Google Scholar
    • Export Citation
  • Gasperoni, N. A., X. Wang, and Y. Wang, 2022: Using a cost-effective approach to increase background ensemble member size within the GSI-based EnVar system for improved radar analyses and forecasts of convective systems. Mon. Wea. Rev., 150, 667689, https://doi.org/10.1175/MWR-D-21-0148.1.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155167, https://doi.org/10.1175/1520-0434(1999)014<0155:HTFENP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Harris, L. M., and S.-J. Lin, 2013: A two-way nested global-regional dynamical core on the cubed-sphere grid. Mon. Wea. Rev., 141, 283306, https://doi.org/10.1175/MWR-D-11-00201.1.

    • Search Google Scholar
    • Export Citation
  • Harris, L. M., S. L. Rees, M. Morin, L. Zhou, and W. F. Stern, 2019: Explicit prediction of continental convection in a skillful variable‐resolution global model. J. Adv. Model. Earth Syst., 11, 18471869, https://doi.org/10.1029/2018MS001542.

    • Search Google Scholar
    • Export Citation
  • Hitchens, N. M., H. E. Brooks, and M. P. Kay, 2013: Objective limits on forecasting skill of rare events. Wea. Forecasting, 28, 525534, https://doi.org/10.1175/WAF-D-12-00113.1.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., X. Deng, H. L. Mitchell, S.-J. Baek, and N. Gagnon, 2014: Higher resolution in an operational ensemble Kalman filter. Mon. Wea. Rev., 142, 11431162, https://doi.org/10.1175/MWR-D-13-00138.1.

    • Search Google Scholar
    • Export Citation
  • Hsu, W.-R., and A. H. Murphy, 1986: The attributes diagram: A geometrical framework for assessing the quality of probability forecasts. Int. J. Forecasting, 2, 285293, https://doi.org/10.1016/0169-2070(86)90048-8.

    • Search Google Scholar
    • Export Citation
  • Huang, B., and X. Wang, 2018: On the use of cost-effective valid-time-shifting (VTS) method to increase ensemble size in the GFS hybrid 4DEnVar system. Mon. Wea. Rev., 146, 29732998, https://doi.org/10.1175/MWR-D-18-0009.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Jankov, I., J. Beck, J. Wolff, M. Harrold, J. B. Olson, T. Smirnova, C. Alexander, and J. Berner, 2019: Stochastically perturbed parameterizations in an HRRR-based ensemble. Mon. Wea. Rev., 147, 153173, https://doi.org/10.1175/MWR-D-18-0092.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., and X. Wang, 2012: Verification and calibration of neighborhood and object-based probabilistic precipitation forecasts from a multimodel convection-allowing ensemble. Mon. Wea. Rev., 140, 30543077, https://doi.org/10.1175/MWR-D-11-00356.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, Y. Wang, A. Reinhart, A. J. Clark, and I. L. Jirak, 2020: Neighborhood- and object-based probabilistic verification of the OU MAP ensemble forecasts during 2017 and 2018 Hazardous Weather Testbeds. Wea. Forecasting, 35, 169191, https://doi.org/10.1175/WAF-D-19-0060.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., F. Han, Y. Wang, and X. Wang, 2023: Scale-dependent verification of the OU MAP convection allowing ensemble initialized with multi-scale and large-scale perturbations during the 2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Atmosphere, 14, 255, https://doi.org/10.3390/atmos14020255.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Coauthors, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931952, https://doi.org/10.1175/WAF2007106.1.

    • Search Google Scholar
    • Export Citation
  • Kalina, E. A., I. Jankov, T. Alcott, J. Olson, J. Beck, J. Berner, D. Dowell, and C. Alexander, 2021: A progress report on the development of the High-Resolution Rapid Refresh ensemble. Wea. Forecasting, 36, 791804, https://doi.org/10.1175/WAF-D-20-0098.1.

    • Search Google Scholar
    • Export Citation
  • Lei, L., and J. S. Whitaker, 2017: Evaluating the trade-offs between ensemble size and ensemble resolution in an ensemble-variational data assimilation system. J. Adv. Model. Earth Syst., 9, 781789, https://doi.org/10.1002/2016MS000864.

    • 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.

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

    • Search Google Scholar
    • Export Citation
  • Olson, J. B., J. S. Kenyon, W. A. Angevine, J. M. Brown, M. Pagowski, and K. Sušelj, 2019: A description of the MYNN-EDMF scheme and the coupling to other components in WRF–ARW. NOAA Tech. Memo. OAR GSD-61, 42 pp., https://doi.org/10.25923/n9wm-be49.

  • Potvin, C. K., and Coauthors, 2019: Systematic comparison of convection-allowing models during the 2017 NOAA HWT Spring Forecasting Experiment. Wea. Forecasting, 34, 13951416, https://doi.org/10.1175/WAF-D-19-0056.1.

    • Search Google Scholar
    • Export Citation
  • Putman, W. M., and S.-J. Lin, 2007: Finite-volume transport on various cubed-sphere grids. J. Comput. Phys., 227, 5578, https://doi.org/10.1016/j.jcp.2007.07.022.

    • Search Google Scholar
    • Export Citation
  • Roberts, B., B. T. Gallo, I. L. Jirak, A. J. Clark, D. C. Dowell, X. Wang, and Y. Wang, 2020: What does a convection-allowing ensemble of opportunity buy us in forecasting thunderstorms? Wea. Forecasting, 35, 22932316, https://doi.org/10.1175/WAF-D-20-0069.1.

    • Search Google Scholar
    • Export Citation
  • Roberts, B., and Coauthors, 2023: Model configuration versus driving model: Influences on next-day regional convection-allowing model forecasts during a real-time experiment. Wea. Forecasting, 38, 99123, https://doi.org/10.1175/WAF-D-21-0211.1.

    • Search Google Scholar
    • Export Citation
  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897, https://doi.org/10.1175/2007MWR2123.1.

    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., 2009: Visualizing multiple measures of forecast quality. Wea. Forecasting, 24, 601608, https://doi.org/10.1175/2008WAF2222159.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, K. R. Smith, and M. L. Weisman, 2014: Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter. Wea. Forecasting, 29, 12951318, https://doi.org/10.1175/WAF-D-13-00145.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., M. G. Duda, S. Ha, and S.-H. Park, 2018: Limited-area atmospheric modeling using an unstructured mesh. Mon. Wea. Rev., 146, 34453460, https://doi.org/10.1175/MWR-D-18-0155.1.

    • Search Google Scholar
    • Export Citation
  • Smith, T. M., and Coauthors, 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., J. S. Kain, D. R. Bright, A. R. Dean, M. C. Coniglio, and S. J. Weiss, 2011: Probabilistic forecast guidance for severe thunderstorms based on the identification of extreme phenomena in convection-allowing model forecasts. Wea. Forecasting, 26, 714728, https://doi.org/10.1175/WAF-D-10-05046.1.

    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., C. S. Schwartz, G. S. Romine, K. R. Fossell, and M. L. Weisman, 2016: Severe weather prediction using storm surrogate from an ensemble forecasting system. Wea. Forecasting, 31, 255271, https://doi.org/10.1175/WAF-D-15-0138.1.

    • Search Google Scholar
    • Export Citation
  • Stratman, D. R., N. Yussouf, Y. Jung, T. A. 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.

    • 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Toy, M., J. Olson, J. Brown, G. Grell, T. Smirnova, and J. Kenyon, 2020: Implementing the RAP/HRRR orographic drag parameterization suite in the FV3GFS. UFS Users’ Workshop, Online, Development Testbed Center, 10 pp., https://dtcenter.org/sites/default/files/events/2020/1-toy-michael.pdf.

  • Wang, X., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVar-based ensemble–variational hybrid data assimilation for NCEP global forecast system: Single-resolution experiments. Mon. Wea. Rev., 141, 40984117, https://doi.org/10.1175/MWR-D-12-00141.1.

    • Search Google Scholar
    • Export Citation
  • Wang, X., H. G. Chipilski, C. H. Bishop, E. Satterfield, N. Baker, and J. S. Whitaker, 2021: A multiscale local gain form ensemble transform Kalman filter (MLGETKF). Mon. Wea. Rev., 149, 605622, https://doi.org/10.1175/MWR-D-20-0290.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., and X. Wang, 2021a: Rapid update with EnVar direct radar reflectivity data assimilation for the NOAA regional convection-allowing NMMB model over the CONUS: System description and initial experiment results. Atmosphere, 12, 1286, https://doi.org/10.3390/atmos12101286.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., and X. Wang, 2021b: Development of convective-scale static background error covariance within GSI-based hybrid EnVar system for direct radar reflectivity data assimilation. Mon. Wea. Rev., 149, 27132736, https://doi.org/10.1175/MWR-D-20-0215.1.

    • Search Google Scholar
    • Export Citation
  • Wheatley, D. M., N. Yussouf, and D. J. Stensrud, 2014: Ensemble Kalman filter analyses and forecasts of a severe mesoscale convective system using different choices of microphysics schemes. Mon. Wea. Rev., 142, 32433263, https://doi.org/10.1175/MWR-D-13-00260.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Xu, Q., H. Lu, S. Gao, M. Xue, and M. Tong, 2008: Time-expanded sampling for ensemble Kalman filter: Assimilation experiments with simulated radar observations. Mon. Wea. Rev., 136, 26512667, https://doi.org/10.1175/2007MWR2185.1.

    • 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, 33883412, https://doi.org/10.1175/MWR-D-12-00237.1.

    • Search Google Scholar
    • Export Citation
  • Yussouf, N., D. C. Dowell, L. J. Wicker, K. H. Knopfmeier, and D. M. Wheatley, 2015: Storm-scale data assimilation and ensemble forecasts for the 27 April 2011 severe weather outbreak in Alabama. Mon. Wea. Rev., 143, 30443066, https://doi.org/10.1175/MWR-D-14-00268.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., and Coauthors, 2019: How well does an FV3‐based model predict precipitation at a convection‐allowing resolution? Results from CAPS forecasts for the 2018 NOAA hazardous weather testbed with different physics combinations. Geophys. Res. Lett., 46, 35233531, https://doi.org/10.1029/2018GL081702.

    • Search Google Scholar
    • Export Citation
  • Zhao, Q., Q. Xu, Y. Jin, J. McLay, and C. Reynolds, 2015: Time-expanded sampling for ensemble-based data assimilation applied to conventional and satellite observations. Wea. Forecasting, 30, 855872, https://doi.org/10.1175/WAF-D-14-00108.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 478 376 22
Full Text Views 219 182 4
PDF Downloads 215 175 5