Editorial Type:
Article
Article Type:
Research Article

Using Varied Microphysics to Account for Uncertainty in Warm-Season QPF in a Convection-Allowing Ensemble

Jeffrey D. Duda School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Xuguang Wang School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Fanyou Kong Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Ming Xue School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Print Publication:
01 Jun 2014
DOI:
https://doi.org/10.1175/MWR-D-13-00297.1
Page(s):
2198–2219
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Abstract

Two approaches for accounting for errors in quantitative precipitation forecasts (QPFs) due to uncertainty in the microphysics (MP) parameterization in a convection-allowing ensemble are examined. They include mixed MP (MMP) composed mostly of double-moment schemes and perturbing parameters within the Weather Research and Forecasting single-moment 6-class microphysics scheme (WSM6) MP scheme (PPMP). Thirty-five cases of real-time storm-scale ensemble forecasts produced by the Center for Analysis and Prediction of Storms during the NOAA Hazardous Weather Testbed 2011 Spring Experiment were examined.

The MMP ensemble had better fractions Brier scores (FBSs) for most lead times and thresholds, but the PPMP ensemble had better relative operating characteristic (ROC) scores for higher precipitation thresholds. The pooled ensemble formed by randomly drawing five members from the MMP and PPMP ensembles was no more skillful than the more accurate of the MMP and PPMP ensembles. Significant positive impact was found when the two were combined to form a larger ensemble.

The QPF and the systematic behaviors of derived microphysical variables were also examined. The skill of the QPF among different members depended on the thresholds, verification metrics, and forecast lead times. The profiles of microphysics variables from the double-moment schemes contained more variation in the vertical than those from the single-moment members. Among the double-moment schemes, WDM6 produced the smallest raindrops and very large number concentrations. Among the PPMP members, the behaviors were found to be consistent with the prescribed intercept parameters. The perturbed intercept parameters used in the PPMP ensemble fell within the range of values retrieved from the double-moment schemes.

Corresponding author address: Jeff Duda, School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Rm. 2500, 120 David Boren Blvd., Norman, OK 73072. E-mail: jeffduda319@gmail.com

Abstract

Two approaches for accounting for errors in quantitative precipitation forecasts (QPFs) due to uncertainty in the microphysics (MP) parameterization in a convection-allowing ensemble are examined. They include mixed MP (MMP) composed mostly of double-moment schemes and perturbing parameters within the Weather Research and Forecasting single-moment 6-class microphysics scheme (WSM6) MP scheme (PPMP). Thirty-five cases of real-time storm-scale ensemble forecasts produced by the Center for Analysis and Prediction of Storms during the NOAA Hazardous Weather Testbed 2011 Spring Experiment were examined.

The MMP ensemble had better fractions Brier scores (FBSs) for most lead times and thresholds, but the PPMP ensemble had better relative operating characteristic (ROC) scores for higher precipitation thresholds. The pooled ensemble formed by randomly drawing five members from the MMP and PPMP ensembles was no more skillful than the more accurate of the MMP and PPMP ensembles. Significant positive impact was found when the two were combined to form a larger ensemble.

The QPF and the systematic behaviors of derived microphysical variables were also examined. The skill of the QPF among different members depended on the thresholds, verification metrics, and forecast lead times. The profiles of microphysics variables from the double-moment schemes contained more variation in the vertical than those from the single-moment members. Among the double-moment schemes, WDM6 produced the smallest raindrops and very large number concentrations. Among the PPMP members, the behaviors were found to be consistent with the prescribed intercept parameters. The perturbed intercept parameters used in the PPMP ensemble fell within the range of values retrieved from the double-moment schemes.

Corresponding author address: Jeff Duda, School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Rm. 2500, 120 David Boren Blvd., Norman, OK 73072. E-mail: jeffduda319@gmail.com
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  • Baldwin, M. E., S. Lakshmivarahan, and J. S. Kain, 2001: Verification of mesoscale features in NWP models. Preprints, Ninth Conf. on Mesoscale Processes, Ft. Lauderdale, FL, Amer. Meteor. Soc., 8.3. [Available online at https://ams.confex.com/ams/pdfpapers/23364.pdf.]

  • Berner, J., G. Shutts, M. Leutbecher, and T. Palmer, 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603–626, doi:10.1175/2008JAS2677.1.

    • Search Google Scholar
    • Export Citation

  • Berner, J., S.-Y. Ha, J. P. Hacker, A. Fournier, and C. Snyder, 2011: Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations. Mon. Wea. Rev., 139, 1972–1995, doi:10.1175/2010MWR3595.1.

    • Search Google Scholar
    • Export Citation

  • Buizza, R., and T. N. Palmer, 1995: The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci., 52, 1434–1456, doi:10.1175/1520-0469(1995)052<1434:TSVSOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125B, 2887–2908, doi:10.1002/qj.49712556006.

    • Search Google Scholar
    • Export Citation

  • Candille, G., 2009: The multiensemble approach: The NAEFS example. Mon. Wea. Rev., 137, 1655–1665, doi:10.1175/2008MWR2682.1.

  • Caron, J.-F., 2013: Mismatching perturbations at the lateral boundaries in limited-area ensemble forecasting: A case study. Mon. Wea. Rev., 141, 356–374, doi:10.1175/MWR-D-12-00051.1.

    • Search Google Scholar
    • Export Citation

  • Charron, M., G. Pellerin, L. Spacek, P. L. Houtekamer, N. Gagnon, H. L. Mitchell, and L. Michelin, 2010: Toward random sampling of model error in the Canadian ensemble prediction system. Mon. Wea. Rev., 138, 1877–1901, doi:10.1175/2009MWR3187.1.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., W. A. Gallus Jr., and T. C. Chen, 2008: Contributions of mixed physics versus perturbed initial/lateral boundary conditions to ensemble-based precipitation forecast skill. Mon. Wea. Rev., 136, 2140–2156, doi:10.1175/2007MWR2029.1.

    • 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-allowing and large convection parameterizing ensembles. Wea. Forecasting,24, 1121–1140, doi:10.1175/2009WAF2222222.1.

  • Clark, A. J., W. A. Gallus, M. Xue, and F. Kong, 2010: Growth of spread in convection-allowing and convection-parameterizing ensembles. Wea. Forecasting, 25, 594–612, doi:10.1175/2009WAF2222318.1.

    • 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, 1410–1418, doi:10.1175/2010MWR3624.1.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., and Coauthors, 2012: An overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program Spring Experiment. Bull. Amer. Meteor. Soc.,93, 55–74, doi:10.1175/BAMS-D-11-00040.1.

  • Dawson, D. T., M. Xue, J. A. Milbrandt, and M. K. Yau, 2010: Comparison of evaporation and cold pool development between single-moment and multimoment bulk microphysics schemes in idealized simulations of tornadic thunderstorms. Mon. Wea. Rev., 138, 1152–1171, doi:10.1175/2009MWR2956.1.

    • Search Google Scholar
    • Export Citation

  • Doblas-Reyes, F. J., M. Déqué, and J.-P. Piedelievre, 2000: Multi-model spread and probabilistic seasonal forecasts in PROVOST. Quart. J. Roy. Meteor. Soc., 126, 2069–2807, doi:10.1002/qj.49712656705.

    • Search Google Scholar
    • Export Citation

  • Du, J., S. L. Millen, and F. Sanders, 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125, 2427–2459, doi:10.1175/1520-0493(1997)125<2427:SREFOQ>2.0.CO;2.

    • 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

  • Eckel, F. A., and C. F. Mass, 2005: Aspects of effective mesoscale, short-range ensemble forecasting. Wea. Forecasting, 20, 328–350, doi:10.1175/WAF843.1.

    • Search Google Scholar
    • Export Citation

  • Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarplay, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta Model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation

  • Ferrier, B. S., cited 2013: The cloud and precipitation scheme in the operational NCEP (regional) models: Description and system integration issues. [Available online at http://www.emc.ncep.noaa.gov/mmb/bf/presentations/Stony_Brook_3-1-05.ppt.]

  • Ferrier, B. S., Y. Jin, Y. Lin, T. Black, E. Rogers, and G. DiMego, 2002: Implementation of a new grid-scale cloud and precipitation scheme in an NCEP eta model. Preprints, 19th Conf. on Weather Analysis and Forecasting/15th Conf. on Numerical Weather Prediction, San Antonio, TX, Amer. Meteor. Soc., 10.1. [Available online at https://ams.confex.com/ams/pdfpapers/47241.pdf.]

  • Ferrier, B. S., and Coauthors, cited 2013: Changes to the NCEP meso eta analysis and forecast system: Modified cloud microphysics, assimilation of GOES cloud-top pressure, and assimilation of NEXRAD 88D radial wind velocity data. NCEP Technical Procedures Bull. [Available online at http://www.emc.ncep.noaa.gov/mmb/tpb.spring03/tpb.htm.]

  • Gao, J.-D., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457–469, doi:10.1175/1520-0426(2004)021<0457:ATVDAM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 2610–2627, doi:10.1175/MWR2810.1.

    • Search Google Scholar
    • Export Citation

  • Hacker, J. P., and Coauthors, 2011: The U.S. Air Force Weather Agency’s mesoscale ensemble: Scientific description and performance results. Tellus, 63A, 1–17, doi:10.1111/j.1600-0870.2010.00497.x.

    • Search Google Scholar
    • Export Citation

  • Hagedorn, R., F. J. Doblas-Reyes, and T. N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus, 57A, 219–233, doi:10.1111/j.1600-0870.2005.00103.x.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550–560, doi:10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta-RSM short range ensemble forecasts. Mon. Wea. Rev., 125, 1312–1327, doi:10.1175/1520-0493(1997)125<1312:VOERSR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hohenegger, C., and C. Schär, 2007: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Amer. Meteor. Soc., 88, 1783–1793, doi:10.1175/BAMS-88-11-1783.

    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129–151.

  • Hou, D., E. Kalnay, and K. K. Droegemeier, 2001: Objective verification of the SAMEX ’98 ensemble forecasts. Mon. Wea. Rev., 129, 73–91, doi:10.1175/1520-0493(2001)129<0073:OVOTSE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225–1242, doi:10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hu, M., M. Xue, and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of Fort Worth tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675–698, doi:10.1175/MWR3092.1.

    • Search Google Scholar
    • Export Citation

  • Janjić, Z., 2002: Nonsingular implementation of the Mellor–Yamada_ level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, 61 pp. [Available online at http://www.emc.ncep.noaa.gov/officenotes/newernotes/on437.pdf.]

  • Johnson, A., and X. Wang, 2012: Verification and calibration of neighborhood and object-based probabilistic precipitation forecasts from a multi-model convection-allowing ensemble. Mon. Wea. Rev., 140, 3054–3077, doi:10.1175/MWR-D-11-00356.1.

    • Search Google Scholar
    • Export Citation

  • Johnson, A., and X. Wang, 2013: Object-based evaluation of a storm scale ensemble during the 2009 NOAA Hazardous Weather Testbed Spring Experiment. Mon. Wea. Rev., 141, 1079–1098, doi:10.1175/MWR-D-12-00140.1.

    • Search Google Scholar
    • Export Citation

  • Johnson, A., X. Wang, F. Kong, and M. Xue, 2011a: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of the object-oriented cluster analysis method for precipitation fields. Mon. Wea. Rev., 139, 3673–3693, doi:10.1175/MWR-D-11-00015.1.

    • Search Google Scholar
    • Export Citation

  • Johnson, A., X. Wang, F. Kong, and M. Xue, 2011b: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part II: Ensemble clustering over the whole experiment period. Mon. Wea. Rev., 139, 3694–3710, doi:10.1175/MWR-D-11-00016.1.

    • Search Google Scholar
    • Export Citation

  • Johnson, A., X. Wang, F. Kong, and M. Xue, 2013: Object-based evaluation of the impact of horizontal grid spacing on convection-allowing forecasts. Mon. Wea. Rev., 141, 3413–3425, doi:10.1175/MWR-D-13-00027.1.

    • Search Google Scholar
    • Export Citation

  • Johnson, A., and Coauthors, 2014: Multiscale characteristics and evolution of perturbations for warm season convection-allowing precipitation forecasts: Dependence on background flow and method of perturbation. Mon. Wea. Rev., 142, 1053–1073, doi:10.1175/MWR-D-13-00204.1.

    • Search Google Scholar
    • Export Citation

  • Jung, Y., M. Xue, and G. Zhang, 2010: Simulations of polarimetric radar signatures of a supercell storm using a two-moment bulk microphysics scheme. J. Appl. Meteor. Climatol., 49, 146–163, doi:10.1175/2009JAMC2178.1

    • Search Google Scholar
    • Export Citation

  • Kong, F., K. K. Droegemeier, and N. L. Hickmon, 2006: Multi-resolution ensemble forecasts of an observed tornadic thunderstorm system. Part I: Comparison of coarse and fine-grid experiments. Mon. Wea. Rev., 134, 807–833, doi:10.1175/MWR3097.1.

    • Search Google Scholar
    • Export Citation

  • Kong, F., K. K. Droegemeier, and N. L. Hickmon, 2007a: Multiresolution ensemble forecasts of an observed tornadic thunderstorm system. Part II: Storm-scale experiments. Mon. Wea. Rev., 135, 759–782, doi:10.1175/MWR3323.1.

    • Search Google Scholar
    • Export Citation

  • Kong, F., and Coauthors, 2007b: Preliminary analysis on the real-time storm-scale ensemble forecasts produced as a part of the NOAA Hazardous Weather Testbed 2007 Spring Experiment. 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Salt Lake City, UT, Amer. Meteor. Soc., 3B.2. [Available online at https://ams.confex.com/ams/pdfpapers/124667.pdf.]

  • Kong, F., and Coauthors, 2010: Evaluation of CAPS multi-model storm-scale ensemble forecast for the NOAA HWT 2010 Spring Experiment. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P4.18. [Available online at https://ams.confex.com/ams/pdfpapers/175822.pdf.]

  • Kong, F., and Coauthors, 2011: Storm-scale ensemble forecasting for the NOAA hazardous weather testbed. Extended Abstracts, Sixth European Conf. on Severe Storms, Palma de Mallorca, Spain, European Severe Storms Laboratory. [Available online at http://www.essl.org/ECSS/2011/programme/abstracts/171.pdf.]

  • Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612, doi:10.1175/2009MWR2968.1.

    • Search Google Scholar
    • Export Citation

  • Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291–303.

  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851–875, doi:10.1029/RG020i004p00851.

    • 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, 3065–3081, doi:10.1175/JAS3535.1.

    • Search Google Scholar
    • Export Citation

  • Mittermaier, M. P., 2007: Improving short-range high-resolution model precipitation forecast skill using time-lagged ensembles. Quart. J. Roy. Meteor. Soc., 133, 1487–1500, doi:10.1002/qj.135.

    • Search Google Scholar
    • Export Citation

  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF Ensemble Prediction System: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73–119, doi:10.1002/qj.49712252905.

    • Search Google Scholar
    • Export Citation

  • Morrison, H., and J. Milbrandt, 2011: Comparison of two-moment bulk microphysics schemes in idealized supercell thunderstorm simulations. Mon. Wea. Rev., 139, 1103–1130, doi:10.1175/2010MWR3433.1.

    • Search Google Scholar
    • Export Citation

  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 991–1007, doi:10.1175/2008MWR2556.1.

    • Search Google Scholar
    • Export Citation

  • Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 1665–1677, doi:10.1175/JAS3446.1.

    • Search Google Scholar
    • Export Citation

  • Palmer, T. N., G. J. Shutts, R. Hagedorn, F. J. Doblas-Reyes, T. Jung, and M. Leutbecher, 2005: Representing model uncertainty in weather and climate prediction. Annu. Rev. Earth Planet. Sci., 33, 163–193, doi:10.1146/annurev.earth.33.092203.122552.

    • Search Google Scholar
    • Export Citation

  • Putnam, B. J., M. Xue, Y. Jung, N. A. Snook, and G. Zhang, 2013: 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

  • Ralph, F. M., and Coauthors, 2005: Improving short-term (0–48 h) cool-season quantitative precipitation forecasts: Recommendations from a USWRP workshop. Bull. Amer. Meteor. Soc., 86, 1619–1632, doi:10.1175/BAMS-86-11-1619.

    • Search Google Scholar
    • Export Citation

  • Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124B, 1071–1107, doi:10.1002/qj.49712454804.

    • Search Google Scholar
    • Export Citation

  • Richardson, D. S., 2001: Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size. Quart. J. Roy. Meteor. Soc., 127, 2473–2489, doi:10.1002/qj.49712757715.

    • Search Google Scholar
    • Export Citation

  • Roberts, N. M., and H. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 78–97, doi:10.1175/2007MWR2123.1.

    • Search Google Scholar
    • Export Citation

  • Ropnack, A., A. Hense, C. Gebhardt, and D. Majewski, 2013: Bayesian model verification of NWP ensemble forecasts. Mon. Wea. Rev., 141, 375–387, doi:10.1175/MWR-D-11-00350.1.

    • 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, 463–485, doi:10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.

    • 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

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN–475+STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

  • 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.

  • Stensrud, D. J., J.-W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, 2077–2107, doi:10.1175/1520-0493(2000)128<2077:UICAMP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Testud, J., S. Oury, R. A. Black, P. Amayenc, and X. Dou, 2001: The concept of “normalized” distribution to describe raindrop spectra: A tool for cloud physics and cloud remote sensing. J. Appl. Meteor., 40, 1118–1140, doi:10.1175/1520-0450(2001)040<1118:TCONDT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Theis, S. E., A. Hense, and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257–268, doi:10.1017/S1350482705001763.

    • 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, 5095–5115, doi:10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation

  • Tong, M., and M. Xue, 2008: Simultaneous estimation of microphysical parameters and atmospheric state with simulated 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

  • Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317–2330, doi:10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297–3319, doi:10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Vie, B., O. Nuissier, and V. Ducrocq, 2011: Cloud-resolving ensemble simulations of Mediterranean heavy precipitating events: Uncertainty on initial conditions and lateral boundary conditions. Mon. Wea. Rev., 139, 403–423, doi:10.1175/2010MWR3487.1.

    • Search Google Scholar
    • Export Citation

  • Wang, X., and C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60, 1140–1158, doi:10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wang, X., and C. H. Bishop, 2005: Improvement of ensemble reliability with a new dressing kernel. Quart. J. Roy. Meteor. Soc., 131, 965–986, doi:10.1256/qj.04.120.

    • Search Google Scholar
    • Export Citation

  • Wang, X., C. H. Bishop, and Simon J.Julier, 2004: Which is better, an ensemble of positive–negative pairs or a centered spherical simplex ensemble? Mon. Wea. Rev., 132, 1590–1605, doi:10.1175/1520-0493(2004)132<1590:WIBAEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wei, M., Z. Toth, R. Wobus, and Y. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus, 60A, 62–79.

    • Search Google Scholar
    • Export Citation

  • Xue, M., D.-H. Wang, J.-D. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139–170, doi:10.1007/s00703-001-0595-6.

    • Search Google Scholar
    • Export Citation

  • Xue, M., and Coauthors, 2007: CAPS realtime storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2007 Spring Experiment. 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., 3B.1. [Available online at https://ams.confex.com/ams/pdfpapers/124587.pdf.]

  • 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. Several Local Storms, Savannah, GA, Amer. Meteor. Soc., 12.2. [Available online at https://ams.confex.com/ams/pdfpapers/142036.pdf.]

  • Xue, M., and Coauthors, 2009: CAPS realtime multi-model convection-allowing ensemble and 1-km convection-resolving forecasts for the NOAA Hazardous Weather Testbed 2009 Spring Experiment. 23nd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 16A.2. [Available online at https://ams.confex.com/ams/pdfpapers/154323.pdf.]

  • Xue, M., and Coauthors, 2011: Real-time convection-permitting ensemble and convection-resolving deterministic forecasts of CAPS for the Hazardous Weather Testbed 2010 Spring Experiment. 25th Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 9A.2. [Available online at https://ams.confex.com/ams/91Annual/webprogram/Paper183227.html.]

  • Xue, M., F. Kong, K. A. Brewster, K. W. Thomas, J. Gao, Y. Wang, and K. K. Droegemeier, 2013: Prediction of convective storms at convection-resolving 1 km resolution over continental United States with radar data assimilation: An example case of 26 May 2008 and precipitation forecasts from spring 2009. Adv. Meteor., 2013, 259052, doi:10.1155/2013/259052.

    • 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

  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 1321–1338, doi:10.1175/2011BAMS-D-11-00047.1.

    • Search Google Scholar
    • Export Citation
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