Evolution of WRF-HAILCAST during the 2014–16 NOAA/Hazardous Weather Testbed Spring Forecasting Experiments

Rebecca D. Adams-Selin Atmospheric and Environmental Research, Inc., Lexington, Massachusetts

Search for other papers by Rebecca D. Adams-Selin in
Current site
Google Scholar
PubMed
Close
,
Adam J. Clark NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Adam J. Clark in
Current site
Google Scholar
PubMed
Close
,
Christopher J. Melick 557th Weather Wing/16th Weather Squadron, Offutt AFB, Nebraska

Search for other papers by Christopher J. Melick in
Current site
Google Scholar
PubMed
Close
,
Scott R. Dembek NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma

Search for other papers by Scott R. Dembek in
Current site
Google Scholar
PubMed
Close
,
Israel L. Jirak NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma

Search for other papers by Israel L. Jirak in
Current site
Google Scholar
PubMed
Close
, and
Conrad L. Ziegler NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Conrad L. Ziegler in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Four different versions of the HAILCAST hail model have been tested as part of the 2014–16 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments. HAILCAST was run as part of the National Severe Storms Laboratory (NSSL) WRF Ensemble during 2014–16 and the Community Leveraged Unified Ensemble (CLUE) in 2016. Objective verification using the Multi-Radar Multi-Sensor maximum expected size of hail (MRMS MESH) product was conducted using both object-based and neighborhood grid-based verification. Subjective verification and feedback was provided by HWT participants. Hourly maximum storm surrogate fields at a variety of thresholds and Storm Prediction Center (SPC) convective outlooks were also evaluated for comparison. HAILCAST was found to improve with each version due to feedback from the 2014–16 HWTs. The 2016 version of HAILCAST was equivalent to or exceeded the skill of the tested storm surrogates across a variety of thresholds. The post-2016 version of HAILCAST was found to improve 50-mm hail forecasts through object-based verification, but 25-mm hail forecasting ability declined as measured through neighborhood grid-based verification. The skill of the storm surrogate fields varied widely as the threshold values used to determine hail size were varied. HAILCAST was found not to require such tuning, as it produced consistent results even when used across different model configurations and horizontal grid spacings. Additionally, different storm surrogate fields performed at varying levels of skill when forecasting 25- versus 50-mm hail, hinting at the different convective modes typically associated with small versus large sizes of hail. HAILCAST was able to match results relatively consistently with the best-performing storm surrogate field across multiple hail size thresholds.

© 2019 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: Rebecca D. Adams-Selin, rselin@aer.com

Abstract

Four different versions of the HAILCAST hail model have been tested as part of the 2014–16 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments. HAILCAST was run as part of the National Severe Storms Laboratory (NSSL) WRF Ensemble during 2014–16 and the Community Leveraged Unified Ensemble (CLUE) in 2016. Objective verification using the Multi-Radar Multi-Sensor maximum expected size of hail (MRMS MESH) product was conducted using both object-based and neighborhood grid-based verification. Subjective verification and feedback was provided by HWT participants. Hourly maximum storm surrogate fields at a variety of thresholds and Storm Prediction Center (SPC) convective outlooks were also evaluated for comparison. HAILCAST was found to improve with each version due to feedback from the 2014–16 HWTs. The 2016 version of HAILCAST was equivalent to or exceeded the skill of the tested storm surrogates across a variety of thresholds. The post-2016 version of HAILCAST was found to improve 50-mm hail forecasts through object-based verification, but 25-mm hail forecasting ability declined as measured through neighborhood grid-based verification. The skill of the storm surrogate fields varied widely as the threshold values used to determine hail size were varied. HAILCAST was found not to require such tuning, as it produced consistent results even when used across different model configurations and horizontal grid spacings. Additionally, different storm surrogate fields performed at varying levels of skill when forecasting 25- versus 50-mm hail, hinting at the different convective modes typically associated with small versus large sizes of hail. HAILCAST was able to match results relatively consistently with the best-performing storm surrogate field across multiple hail size thresholds.

© 2019 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: Rebecca D. Adams-Selin, rselin@aer.com
Save
  • Adams-Selin, R., and C. Ziegler, 2016: Forecasting hail using a one-dimensional hail growth model within WRF. Mon. Wea. Rev., 144, 49194939, https://doi.org/10.1175/MWR-D-16-0027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ben Bouallègue, Z., and S. E. Theis, 2014: Spatial techniques applied to precipitation ensemble forecasts: From verification results to probabilistic products. Meteor. Appl., 21, 922929, https://doi.org/10.1002/met.1435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brimelow, J. C., G. W. Reuter, and E. R. Poolman, 2002: Modeling maximum hail size in Alberta thunderstorms. Wea. Forecasting, 17, 10481062, https://doi.org/10.1175/1520-0434(2002)017<1048:MMHSIA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cintineo, J. L., T. M. Smith, and V. Lakshmanan, 2012: An objective high-resolution hail climatology of the contiguous United States. Wea. Forecasting, 27, 12351248, https://doi.org/10.1175/WAF-D-11-00151.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C., B. Brown, and R. Bullock, 2006a: Object-based verification of precipitation forecasts. Part I: Methods and application to mesoscale rain areas. Mon. Wea. Rev., 134, 17721784, https://doi.org/10.1175/MWR3145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C., B. Brown, and R. Bullock, 2006b: Object-based verification of precipitation forecasts. Part II: Application to convective rain systems. Mon. Wea. Rev., 134, 17851795, https://doi.org/10.1175/MWR3146.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dennis, E. J., and M. R. Kumjian, 2017: The impact of vertical wind shear on hail growth in simulated supercells. J. Atmos. Sci., 74, 641663, https://doi.org/10.1175/JAS-D-16-0066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, J., and Coauthors, 2014: NCEP regional ensemble update: Current systems and planned storm-scale ensembles. 26th Conf. on Weather Analysis and Forecasting/22nd Conf. on Numerical Weather Prediction, Atlanta, GA, Amer. Meteor. Soc., J1.4, https://ams.confex.com/ams/94Annual/webprogram/Paper239030.html.

  • Foote, G. B., 1984: A study of hail growth utilizing observed storm conditions. J. Climate Appl. Meteor., 23, 84101, https://doi.org/10.1175/1520-0450(1984)023<0084:ASOHGU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Foote, G. B., and H. W. Frank, 1983: Case study of a hailstorm in Colorado. Part III: Airflow from triple-Doppler measurements. J. Atmos. Sci., 40, 686707, https://doi.org/10.1175/1520-0469(1983)040<0686:CSOAHI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gagne, D. J., A. McGovern, S. Haupt, R. Sobash, J. Williams, and M. Xue, 2017: Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles. Wea. Forecasting, 32, 18191840, https://doi.org/10.1175/WAF-D-17-0010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gallo, B. T., and Coauthors, 2017: Breaking new ground in severe weather prediction: The 2015 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. Wea. Forecasting, 32, 15411568, https://doi.org/10.1175/WAF-D-16-0178.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herman, G. R., E. R. Nielsen, and R. S. Schumacher, 2018: Probabilistic verification of Storm Prediction Center convective outlooks. Wea. Forecasting, 33, 161184, https://doi.org/10.1175/WAF-D-17-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., 1982: A comparative study of the rates of development of potential graupel and hail embryos in high plains storms. J. Atmos. Sci., 39, 28672897, https://doi.org/10.1175/1520-0469(1982)039<2867:ACSOTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., 1983a: A technique for investigating graupel and hail development. J. Climate Appl. Meteor., 22, 11431160, https://doi.org/10.1175/1520-0450(1983)022<1143:ATFIGA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., 1983b: Case study of a hailstorm in Colorado. Part IV: Graupel and hail growth mechanisms deduced through particle trajectory calculations. J. Atmos. Sci., 40, 14821509, https://doi.org/10.1175/1520-0469(1983)040<1482:CSOAHI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., and D. J. Musil, 1982: Case study of a hailstorm in Colorado. Part II: Particle growth processes at mid-levels deduced form in-situ measurements. J. Atmos. Sci., 39, 28472866, https://doi.org/10.1175/1520-0469(1982)039<2847:CSOAHI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., A. R. Jameson, and H. W. Frank, 1980: Hail growth mechanisms in a Colorado storm. Part II: Hail formation processes. J. Atmos. Sci., 37, 17791813, https://doi.org/10.1175/1520-0469(1980)037<1779:HGMIAC>2.0.CO;2.

    • Crossref
    • 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.

    • Crossref
    • 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, 129151.

  • Jewell, R., and J. Brimelow, 2009: Evaluation of Alberta hail growth model using severe hail proximity soundings from the United States. Wea. Forecasting, 24, 15921609, https://doi.org/10.1175/2009WAF2222230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jirak, I., and Coauthors, 2014: An overview of the 2014 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. 27th Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 46, https://ams.confex.com/ams/27SLS/webprogram/Paper254650.html.

  • Kain, J. S., S. R. Dembek, S. J. Weiss, J. L. Case, J. J. Levit, and R. A. Sobash, 2010: Extracting unique information from high-resolution forecast models: Monitoring selected fields and phenomena every time step. Wea. Forecasting, 25, 15361542, https://doi.org/10.1175/2010WAF2222430.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., T. Smith, K. Hondl, G. J. Stumpf, and A. Witt, 2006: A real-time, three-dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity, and derived products. Wea. Forecasting, 21, 802823, https://doi.org/10.1175/WAF942.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Magono, C., and T. Nakamura, 1965: Aerodynamic studies of falling snowflakes. J. Meteor. Soc. Japan, 43, 139147, https://doi.org/10.2151/jmsj1965.43.3_139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manzato, A., 2007: A note on the maximum Peirce skill score. Wea. Forecasting, 22, 11481154, https://doi.org/10.1175/WAF1041.1.

  • Melick, C. J., I. L. Jirak, and J. Correia Jr., A. R. Dean, and S. J. Weiss, 2014: Exploration of the NSSL maximum expected size of hail (MESH) product for verifying experimental hail forecasts in the 2014 Spring Forecast Experiment. 27th Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 76, https://ams.confex.com/ams/27SLS/webprogram/Paper254292.html.

  • Miller, L. J., J. D. Tuttle, and C. A. Knight, 1988: Airflow and hail growth in a severe northern high plains supercell. J. Atmos. Sci., 45, 736762, https://doi.org/10.1175/1520-0469(1988)045<0736:AAHGIA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, L. J., J. D. Tuttle, and G. B. Foote, 1990: Precipitation production in a large Montana hailstorm: Airflow and particle growth trajectories. J. Atmos. Sci., 47, 16191646, https://doi.org/10.1175/1520-0469(1990)047<1619:PPIALM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, A., 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
  • NCEI, 2017: U.S. billion-dollar weather and climate disasters: Overview. National Centers for Environmental Information, https://www.ncdc.noaa.gov/billions/.

  • Nelson, S. P., 1983: The influence of storm flow structure on hail growth. J. Atmos. Sci., 40, 19651983, https://doi.org/10.1175/1520-0469(1983)040<1965:TIOSFS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nelson, S. P., and N. C. Knight, 1987: The hybrid multicellular–supercellular storm—An efficient hail producer. Part I: An archetypal example. J. Atmos. Sci., 44, 20422059, doi.org/10.1175/1520-0469(1987)044%3c2042:THMSEH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortega, K., 2018: Evaluating multi-radar, multi-sensor products for surface hailfall diagnosis. Electron. J. Severe Storms Meteor., 13 (1), http://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/163.

  • Peirce, C. S., 1884: The numerical measure of the success of predictions. Science, 4, 453454, https://doi.org/10.1126/science.ns-4.93.453-a.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poolman, E. R., 1992: Die voorspelling van haelkorrelgroei in Suid-Afrika (The forecasting of hail growth in South Africa). M.S. thesis, Faculty of Engineering, University of Pretoria, 113 pp.

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

    • 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
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., http://dx.doi.org/10.5065/D68S4MVH.

    • Crossref
    • 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.

    • 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
  • Wendt, N., I. Jirak, and C. Melick, 2016: Verification of severe weather proxies from the NSSL-WRF for hail forecast. 28th Conf. on Severe Local Storms, Portland, OR, Amer. Meteor. Soc., 110, https://ams.confex.com/ams/28SLS/webprogram/Paper300913.html.

  • Wilks, D., 2006: Forecast verification. Statistical Methods in the Atmospheric Sciences, D. S. Wilks, Ed., 2nd ed., Academic Press, 260–268.

  • Wilson, C. J., K. L. Ortega, and V. Lakshmanan, 2009: Evaluating multi-radar, multi-sensor hail diagnosis with high resolution hail reports. 25th Conf. on Interactive Information Processing Systems, Phoenix, AZ, Amer. Meteor. Soc., P2.9, https://ams.confex.com/ams/89annual/techprogram/paper_146206.htm.

  • Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. Mitchell, and K. W. Thomas, 1998: An enhanced hail detection algorithm for the WSR-88D. Wea. Forecasting, 13, 286303, https://doi.org/10.1175/1520-0434(1998)013<0286:AEHDAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woodcock, F., 1976: The evaluation of yes/no forecasts for scientific and administrative purposes. Mon. Wea. Rev., 104, 12091214, https://doi.org/10.1175/1520-0493(1976)104<1209:TEOYFF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ziegler, C. L., P. S. Ray, and N. C. Knight, 1983: Hail growth in an Oklahoma multicell storm. J. Atmos. Sci., 40, 17681791, https://doi.org/10.1175/1520-0469(1983)040<1768:HGIAOM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2722 1526 133
PDF Downloads 1338 219 10