• Ancell, B., and G. J. Hakim, 2007: Comparing adjoint-and ensemble sensitivity analysis with applications to observation targeting. Mon. Wea. Rev., 135, 41174134, https://doi.org/10.1175/2007MWR1904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baxter, M. A., and P. N. Schumacher, 2017: Distribution of single-banded snowfall in central U.S. cyclones. Wea. Forecasting, 32, 533554, https://doi.org/10.1175/WAF-D-16-0154.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brennan, M. J., and G. M. Lackmann, 2005: The influence of incipient latent heat release on the precipitation distribution of the 24–25 January 2000 U.S. East Coast cyclone. Mon. Wea. Rev., 133, 19131937, https://doi.org/10.1175/MWR2959.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 23942416, https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cerruti, B. J., and S. G. Decker, 2011: The local winter storm scale: A measure of the intrinsic ability of winter storms to disrupt society. Bull. Amer. Meteor. Soc., 92, 721737, https://doi.org/10.1175/2010BAMS3191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Changnon, S. A., 1999: Impacts of 1997/98 El Niño–generated weather in the United States. Bull. Amer. Meteor. Soc., 80, 18191827, https://doi.org/10.1175/1520-0477(1999)080<1819:IOENOG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Changnon, S. A., 2007: Catastrophic winter storms: An escalating problem. Climatic Change, 84, 131139, https://doi.org/10.1007/s10584-007-9289-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Changnon, S. A., D. Changnon, T. R. Karl, and T. G. Houston, 2008: Snowstorms across the Nation: An Atlas about Storms and Their Damages. National Climatic Data Center, 96 pp.

    • Search Google Scholar
    • Export Citation
  • Charles, M. E., and B. A. Colle, 2009: Verification of extratropical cyclones within the NCEP operational models. Part I: Analysis errors and short-term NAM and GFS forecasts. Wea. Forecasting, 24, 11731190, https://doi.org/10.1175/WAF2222169.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001a: 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
  • Chen, F., and J. Dudhia, 2001b: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part II: Preliminary model validation. Mon. Wea. Rev., 129, 587604, https://doi.org/10.1175/1520-0493(2001)129<0587:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., W. A. Gallus Jr., M. Xue, and F. Kong, 2009: A comparison of precipitation forecast skill between small convection-permitting and large convection-parameterizing ensembles. Wea. Forecasting, 24, 11211140, https://doi.org/10.1175/2009WAF2222222.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., W. A. Gallus Jr., and M. L. Weisman, 2010: Neighborhood-based verification of precipitation forecasts from convection-allowing NCAR WRF Model simulations and the operational NAM. Wea. Forecasting, 25, 14951509, https://doi.org/10.1175/2010WAF2222404.1.

    • Crossref
    • 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.

    • Crossref
    • 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, 5574, https://doi.org/10.1175/BAMS-D-11-00040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colle, B. A., and M. E. Charles, 2011: Spatial distribution and evolution of extratropical cyclone errors over North America and its adjacent oceans in the NCEP global forecast system model. Wea. Forecasting, 26, 129149, https://doi.org/10.1175/2010WAF2222422.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., K. L. Elmore, J. S. Kain, S. J. Weiss, M. Xue, and M. L. Weisman, 2010: Evaluation of WRF model output for severe weather forecasting from the 2008 NOAA Hazardous Weather Testbed spring experiment. Wea. Forecasting, 25, 408427, https://doi.org/10.1175/2009WAF2222258.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Connelly, R., and B. A. Colle, 2019: Validation of snow multibands in the comma head of an extratropical cyclone using a 40-member ensemble. Wea. Forecasting, 34, 13431363, https://doi.org/10.1175/WAF-D-18-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dyer, J., and C. Zarzar, 2016: Defining the influence of horizontal grid spacing on ensemble uncertainty within a regional modeling framework. Wea. Forecasting, 31, 19972017, https://doi.org/10.1175/WAF-D-16-0030.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Efron, B., and R. Tibshirani, 1994: An Introduction to the Bootstrap. CRC Press, 456 pp.

  • Eisenberg, D., and K. E. Warner, 2005: Effects of snowfalls on motor vehicle collisions, injuries, and fatalities. Amer. J. Public Health, 95, 120124, https://doi.org/10.2105/AJPH.2004.048926.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fierro, A. O., E. R. Mansell, C. L. Ziegler, and D. R. MacGorman, 2012: Application of a lightning data assimilation technique in the WRF-ARW Model at cloud-resolving scales for the tornado outbreak of 24 May 2011. Mon. Wea. Rev., 140, 26092627, https://doi.org/10.1175/MWR-D-11-00299.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gallus, W. A., 2010: Application of object-based verification techniques to ensemble precipitation forecasts. Wea. Forecasting, 25, 144158, https://doi.org/10.1175/2009WAF2222274.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gallus, W. A., J. Wolff, J. A. Gotway, M. Harrold, L. Blank, and J. Bleck, 2019: The impact of using mixed physics in the Community Leveraged Unified Ensemble. Wea. Forecasting, 34, 849867, https://doi.org/10.1175/WAF-D-18-0197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greybush, S. J., S. Saslo, and R. Grumm, 2017: Assessing the ensemble predictability of precipitation forecasts for the January 2015 and 2016 East Coast winter storms. Wea. Forecasting, 32, 10571078, https://doi.org/10.1175/WAF-D-16-0153.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550560, https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iyer, E. R., A. J. Clark, M. Xue, and F. Kong, 2016: A comparison of 36–60-h precipitation forecasts from convection-allowing and convection-parameterizing ensembles. Wea. Forecasting, 31, 647661, https://doi.org/10.1175/WAF-D-15-0143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., S. J. Weiss, J. J. Levit, M. E. Baldwin, and D. R. Bright, 2006: Examination of cConvection-aAllowing cConfigurations of the WRF mModel for the pPrediction of sSevere cConvective wWeather: The SPC/NSSL SSpring PProgram 2004. Wea. Forecasting, 21 (2), 167181, https://doi.org/10.1175/WAF906.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karstens, C. D., and Coauthors, 2015: Evaluation of a probabilistic forecasting methodology for severe convective weather in the 2014 Hazardous Weather Testbed. Wea. Forecasting, 30, 15511570, https://doi.org/10.1175/WAF-D-14-00163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Korfe, N. G., and B. A. Colle, 2018: Evaluation of cool-season extratropical cyclones in a multimodel ensemble for eastern North America and the western Atlantic Ocean. Wea. Forecasting, 33, 109127, https://doi.org/10.1175/WAF-D-17-0036.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., 2011: GCIP/EOP surface: Precipitation NCEP/EMC 4km Gridded Data (GRIB) Stage IV Data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 9 August 2019, https://doi.org/10.5065/D6PG1QDD.

    • Crossref
    • Export Citation
  • Liu, C., K. Ikeda, G. Thompson, R. Rasmussen, and J. Dudhia, 2011: High-resolution simulations of wintertime precipitation in the Colorado headwaters region: Sensitivity to physics parameterizations. Mon. Wea. Rev., 139, 35333553, https://doi.org/10.1175/MWR-D-11-00009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loken, E. D., A. J. Clark, M. Xue, and F. Kong, 2017: Comparison of next-day probabilistic severe weather forecasts from coarse- and fine-resolution CAMs and a convection-allowing ensemble. Wea. Forecasting, 32, 14031421, https://doi.org/10.1175/WAF-D-16-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1993: The Essence of Chaos. University of Washington Press, 227 pp.

  • Mahoney, K. M., and G. M. Lackmann, 2006: The sensitivity of numerical forecasts to convective parameterization: A case study of the 17 February 2004 East Coast cyclone. Wea. Forecasting, 21, 465488, https://doi.org/10.1175/WAF937.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McMillen, J. D., and W. J. Steenburgh, 2015: Impact of microphysics parameterizations on simulations of the 27 October 2010 Great Salt Lake–effect snowstorm. Wea. Forecasting, 30, 136152, https://doi.org/10.1175/WAF-D-14-00060.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mittermaier, M. P., and G. Csima, 2017: Ensemble versus deterministic performance at the kilometer scale. Wea. Forecasting, 32, 16971709, https://doi.org/10.1175/WAF-D-16-0164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moore, J. T., and P. D. Blakley, 1988: The role of frontogenetical forcing and conditional symmetric instability in the Midwest snowstorm of 30–31 January 1982. Mon. Wea. Rev., 116, 21552171, https://doi.org/10.1175/1520-0493(1988)116<2155:TROFFA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morcrette, J.-J., H. W. Barker, J. N. S. Cole, M. J. Iacono, and R. Pincus, 2008: Impact of a new radiation package, McRad, in the ECMWF integrated forecast system. Mon. Wea. Rev., 136, 47734798, https://doi.org/10.1175/2008MWR2363.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and Coauthors, 2011: The community NOAH land surface model with multiparameterization options (NOAH-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Novak, D. R., L. F. Bosart, D. Keyser, and J. S. Waldstreicher, 2004: An observational study of cold season–banded precipitation in northeast U.S. cyclones. Wea. Forecasting, 19, 9931010, https://doi.org/10.1175/815.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Novak, D. R., B. A. Colle, and A. R. Aiyyer, 2010: Evolution of mesoscale precipitation band environments within the comma head of Northeast U.S. cyclones. Mon. Wea. Rev., 138, 23542374, https://doi.org/10.1175/2010MWR3219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ota, Y., J. C. Derber, E. Kalnay, and T. Miyoshi, 2013: Ensemble-based observation impact estimates using the NCEP GFS. Tellus, 65A, 20038, https://doi.org/10.3402/tellusa.v65i0.20038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robinson, P. J., 1989: The influence of weather on flight operations at the Atlanta Hartsfield International Airport. Wea. Forecasting, 4, 461468, https://doi.org/10.1175/1520-0434(1989)004<0461:TIOWOF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roebber, P., D. Schultz, B. Colle, and D. Stensrud, 2004: Toward improved prediction: High-resolution and ensemble modeling systems in operations. Wea. Forecasting, 19, 936949, https://doi.org/10.1175/1520-0434(2004)019<0936:TIPHAE>2.0.CO;2.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rooney, J. F., 1967: Urban snow hazard in the United States—Appraisal of disruption. Geogr. Rev., 57, 538559, https://doi.org/10.2307/212932.

  • Rothfusz, L. P., R. Schneider, D. Novak, K. Klockow-McClain, A. E. Gerard, C. Karstens, G. J. Stumpf, and T. M. Smith, 2018: FACETs: A proposed next-generation paradigm for high-impact weather forecasting. Bull. Amer. Meteor. Soc., 99, 20252043, https://doi.org/10.1175/BAMS-D-16-0100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., D. M. Schultz, and J. A. Knox, 2010: Convective snowbands downstream of the Rocky Mountains in an environment with conditional, dry-symmetric, and inertial instabilities. Mon. Wea. Rev., 138, 44164438, https://doi.org/10.1175/2010MWR3334.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and R. A. Sobash, 2019: Revisiting sensitivity to horizontal grid spacing in convection-allowing models over the central and eastern United States. Mon. Wea. Rev., 147, 44114435, https://doi.org/10.1175/MWR-D-19-0115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and Coauthors, 2009: Next-day convection-allowing WRF Model guidance: A second look at 2-km versus 4-km grid spacing. Mon. Wea. Rev., 137, 33513372, https://doi.org/10.1175/2009MWR2924.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, R. A. Sobash, K. R. Fossell, and M. L. Weisman, 2015: NCAR’s experimental real-time convection-allowing ensemble prediction system. Wea. Forecasting, 30, 16451654, https://doi.org/10.1175/WAF-D-15-0103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, K. R. Fossell, R. A. Sobash, and M. L. Weisman, 2017: Toward 1-km ensemble forecasts over large domains. Mon. Wea. Rev., 145, 29432969, https://doi.org/10.1175/MWR-D-16-0410.1.

    • Crossref
    • 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., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997: Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev., 125, 18701884, https://doi.org/10.1175/1520-0493(1997)125<1870:PODSMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smirnova, T. G., J. M. Brown, and D. Kim, 2000: Parameterization of cold-season processes in the MAPS land-surface scheme. J. Geophys. Res., 105, 40774086, https://doi.org/10.1029/1999JD901047.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snively, D. V., and W. A. Gallus Jr., 2014: Prediction of convective morphology in near-cloud-permitting WRF Model simulations. Wea. Forecasting, 29, 130149, https://doi.org/10.1175/WAF-D-13-00047.1.

    • Crossref
    • 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.

    • Crossref
    • 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 surrogates from an ensemble forecasting system. Wea. Forecasting, 31, 255271, https://doi.org/10.1175/WAF-D-15-0138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Song, F., and G. J. Zhang, 2018: Understanding and improving the scale dependence of trigger functions for convective parameterization using cloud-resolving model data. J. Climate, 31, 73857399, https://doi.org/10.1175/JCLI-D-17-0660.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sukoriansky, S., B. Galperian, and V. Perov, 2005: Application of a new spectral theory of stable stratified turbulence to the atmospheric boundary layer over sea ice. Bound.-Layer Meteor., 117, 231257, https://doi.org/10.1007/s10546-004-6848-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tewari, M., and Coauthors, 2004: Implementation and verification of the unified Noah land surface model in the WRF model. 20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 14.2a, https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm.

  • 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., M. K. Politovich, and R. M. Rasmussen, 2017: A numerical weather model’s ability to predict characteristics of aircraft icing environments. Wea. Forecasting, 32, 207221, https://doi.org/10.1175/WAF-D-16-0125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1989: The effect of penetrative cumulus convection on the large-scale flow in a general circulation model. Beitr. Phys. Atmos., 57, 216239.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and G. J. Hakim, 2008: Ensemble-based sensitivity analysis. Mon. Wea. Rev., 136, 663677, https://doi.org/10.1175/2007MWR2132.1.

  • Tracton, M. S., 2008: Must surprise snowstorms be a surprise? Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting: A Tribute to Fred Sanders, Meteor. Monogr., No. 55, Amer. Meteor. Soc., 251–268, https://doi.org/10.1175/0065-9401-33.55.251.

    • Crossref
    • Export Citation
  • Tsuboki, K., 2008: High-resolution simulations of high-impact weather systems using the cloud-resolving model on the Earth simulator. High Resolution Numerical Modelling of the Atmosphere and Ocean, K. Hamilton and W. Ohfuchi, Eds., Springer, 141–155.

    • Crossref
    • Export Citation
  • Weigel, A. P., M. A. Liniger, and C. Appenzeller, 2007: The discrete Brier and ranked probability skill scores. Mon. Wea. Rev., 135, 118124, https://doi.org/10.1175/MWR3280.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527548, https://doi.org/10.1175/1520-0493(1997)125<0527:TRDOEM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weygandt, S. S., and N. L. Seaman, 1994: Quantification of predictive skill for mesoscale and synoptic-scale meteorological features as a function of horizontal grid resolution. Mon. Wea. Rev., 122, 5771, https://doi.org/10.1175/1520-0493(1994)122<0057:QOPSFM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. International Geophysics Series, Vol. 59, Elsevier, 467 pp.

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. International Geophysics Series, Vol. 100, Academic Press, 648 pp.

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 704 pp.

  • Willison, J., W. A. Robinson, and G. M. Lackmann, 2013: The importance of resolving mesoscale latent heating in the North Atlantic storm track. J. Atmos. Sci., 70, 22342250, https://doi.org/10.1175/JAS-D-12-0226.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., Y. Wang, and K. Hamilton, 2011: Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme. Mon. Wea. Rev., 139, 34893513, https://doi.org/10.1175/MWR-D-10-05091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and R. Rotunno, 2002: Mesoscale predictability of the “surprise” snowstorm of 24–25 January 2000. Mon. Wea. Rev., 130, 16171632, https://doi.org/10.1175/1520-0493(2002)130<1617:MPOTSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., N. Bei, R. Rotunno, C. Snyder, and C. C. Epifanio, 2007: Mesoscale predictability of moist baroclinic waves: Cloud-resolving experiments and multistage error growth dynamics. J. Atmos. Sci., 64, 35793594, https://doi.org/10.1175/JAS4028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, M., E. K. M. Chang, and B. A. Colle, 2013: Ensemble sensitivity tools for assessing extratropical cyclone intensity and track predictability. Wea. Forecasting, 28, 11331156, https://doi.org/10.1175/WAF-D-12-00132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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An Examination of the Impact of Grid Spacing on WRF Simulations of Wintertime Precipitation in the Mid-Atlantic United States

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  • 1 Department of Earth Sciences, The Hebrew University of Jerusalem, Givat Ram, Jerusalem, Israel
  • 2 Weather It Is, Ltd., Efrat, Israel
  • 3 Department of Meteorology, The Pennsylvania State University, State College, Pennsylvania
  • 4 Department of Applied Physics and Mathematics, Columbia University, New York, New York
  • 5 NASA/Goddard Institute for Space Studies, Columbia University, New York, New York
  • 6 NCAR/Climate and Global Dynamics Laboratory, Boulder, Colorado
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Abstract

A large set of deterministic and ensemble forecasts was produced to identify the optimal spacing for forecasting U.S. East Coast snowstorms. WRF forecasts were produced on cloud-allowing (~1-km grid spacing) and convection-allowing (3–4 km) grids, and compared against forecasts with parameterized convection (>~10 km). Performance diagrams were used to evaluate 19 deterministic forecasts from the winter of 2013–14. Ensemble forecasts of five disruptive snowstorms spanning the years 2015–18 were evaluated using various methods to evaluate probabilistic forecasts. While deterministic forecasts using cloud-allowing grids were not better than convection-allowing forecasts, both had lower bias and higher success ratios than forecasts with parameterized convection. All forecasts were underdispersive. Nevertheless, forecasts on the higher-resolution grids were more reliable than those with parameterized convection. Forecasts on the cloud-allowing grid were best able to discriminate areas that received heavy snow and those that did not, while the forecasts with parameterized convection were least able to do so. It is recommended to use convection-resolving and (if computationally possible) to use cloud-allowing forecast grids when predicting East Coast winter storms.

© 2020 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: Barry H. Lynn, barry.h.lynn@gmail.com

Abstract

A large set of deterministic and ensemble forecasts was produced to identify the optimal spacing for forecasting U.S. East Coast snowstorms. WRF forecasts were produced on cloud-allowing (~1-km grid spacing) and convection-allowing (3–4 km) grids, and compared against forecasts with parameterized convection (>~10 km). Performance diagrams were used to evaluate 19 deterministic forecasts from the winter of 2013–14. Ensemble forecasts of five disruptive snowstorms spanning the years 2015–18 were evaluated using various methods to evaluate probabilistic forecasts. While deterministic forecasts using cloud-allowing grids were not better than convection-allowing forecasts, both had lower bias and higher success ratios than forecasts with parameterized convection. All forecasts were underdispersive. Nevertheless, forecasts on the higher-resolution grids were more reliable than those with parameterized convection. Forecasts on the cloud-allowing grid were best able to discriminate areas that received heavy snow and those that did not, while the forecasts with parameterized convection were least able to do so. It is recommended to use convection-resolving and (if computationally possible) to use cloud-allowing forecast grids when predicting East Coast winter storms.

© 2020 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: Barry H. Lynn, barry.h.lynn@gmail.com
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