The Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment

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
,
Israel L. Jirak NOAA/NWS/Storm Prediction Center, Norman, Oklahoma

Search for other papers by Israel L. Jirak in
Current site
Google Scholar
PubMed
Close
,
Scott R. Dembek NOAA/OAR/National Severe Storms Laboratory, and Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma

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

Search for other papers by Gerry J. Creager in
Current site
Google Scholar
PubMed
Close
,
Fanyou Kong Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Fanyou Kong in
Current site
Google Scholar
PubMed
Close
,
Kevin W. Thomas Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Kevin W. Thomas in
Current site
Google Scholar
PubMed
Close
,
Kent H. Knopfmeier NOAA/OAR/National Severe Storms Laboratory, and Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma

Search for other papers by Kent H. Knopfmeier in
Current site
Google Scholar
PubMed
Close
,
Burkely T. Gallo NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Burkely T. Gallo in
Current site
Google Scholar
PubMed
Close
,
Christopher J. Melick NOAA/NWS/Storm Prediction Center, and Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma

Search for other papers by Christopher J. Melick in
Current site
Google Scholar
PubMed
Close
,
Ming Xue School of Meteorology, University of Oklahoma, and Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Ming Xue in
Current site
Google Scholar
PubMed
Close
,
Keith A. Brewster Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Keith A. Brewster in
Current site
Google Scholar
PubMed
Close
,
Youngsun Jung Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Youngsun Jung in
Current site
Google Scholar
PubMed
Close
,
Aaron Kennedy Department of Atmospheric Science, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Aaron Kennedy in
Current site
Google Scholar
PubMed
Close
,
Xiquan Dong Department of Atmospheric Science, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Xiquan Dong in
Current site
Google Scholar
PubMed
Close
,
Joshua Markel Department of Atmospheric Science, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Joshua Markel in
Current site
Google Scholar
PubMed
Close
,
Matthew Gilmore Department of Atmospheric Science, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Matthew Gilmore in
Current site
Google Scholar
PubMed
Close
,
Glen S. Romine National Center of Atmospheric Research, Boulder, Colorado

Search for other papers by Glen S. Romine in
Current site
Google Scholar
PubMed
Close
,
Kathryn R. Fossell National Center of Atmospheric Research, Boulder, Colorado

Search for other papers by Kathryn R. Fossell in
Current site
Google Scholar
PubMed
Close
,
Ryan A. Sobash National Center of Atmospheric Research, Boulder, Colorado

Search for other papers by Ryan A. Sobash in
Current site
Google Scholar
PubMed
Close
,
Jacob R. Carley NOAA/Environmental Modeling Center, Camp Springs, Maryland

Search for other papers by Jacob R. Carley in
Current site
Google Scholar
PubMed
Close
,
Brad S. Ferrier NOAA/Environmental Modeling Center, Camp Springs, Maryland

Search for other papers by Brad S. Ferrier in
Current site
Google Scholar
PubMed
Close
,
Matthew Pyle NOAA/Environmental Modeling Center, Camp Springs, Maryland

Search for other papers by Matthew Pyle in
Current site
Google Scholar
PubMed
Close
,
Curtis R. Alexander NOAA/OAR/Earth System Research Laboratory/Global Systems Division, Boulder, Colorado

Search for other papers by Curtis R. Alexander in
Current site
Google Scholar
PubMed
Close
,
Steven J. Weiss NOAA/NWS/Storm Prediction Center, Norman, Oklahoma

Search for other papers by Steven J. Weiss in
Current site
Google Scholar
PubMed
Close
,
John S. Kain NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by John S. Kain in
Current site
Google Scholar
PubMed
Close
,
Louis J. Wicker NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Louis J. Wicker in
Current site
Google Scholar
PubMed
Close
,
Gregory Thompson National Center of Atmospheric Research, Boulder, Colorado

Search for other papers by Gregory Thompson in
Current site
Google Scholar
PubMed
Close
,
Rebecca D. Adams-Selin 557th Weather Wing, Offutt Air Force Base, Nebraska

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

Search for other papers by David A. Imy in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.

© 2018 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: Adam J. Clark, adam.clark@noaa.gov

A supplement to this article is available online (10.1175/BAMS-D-16-0309.2)

Abstract

One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.

© 2018 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: Adam J. Clark, adam.clark@noaa.gov

A supplement to this article is available online (10.1175/BAMS-D-16-0309.2)

Supplementary Materials

    • Supplemental Materials (PDF 875.32 KB)
Save
  • Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Arellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 12831296, https://doi.org/10.1175/2009BAMS2618.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atger, F., 2003: Spatial and interannual variability of the reliability of ensemble-based probabilistic forecasts: Consequences for calibration. Mon. Wea. Rev., 131, 15091523, https://doi.org/10.1175//1520-0493(2003)131<1509:SAIVOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Crossref
    • 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, 19721995, https://doi.org/10.1175/2010MWR3595.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berner, J., K. R. Fossell, S.-Y. Ha, J. P. Hacker, and C. Snyder, 2015: Increasing the skill of probabilistic forecasts: Understanding performance improvements from model-error representations. Mon. Wea. Rev., 143, 12951320, https://doi.org/10.1175/MWR-D-14-00091.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Candille, G., 2009: The multiensemble approach: The NAEFS example. Mon. Wea. Rev., 137, 16551665, https://doi.org/10.1175/2008MWR2682.1.

  • Carbone, R. E., J. D. Tuttle, D. A. Ahijevych, and S. B. Trier, 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci., 59, 20332056, https://doi.org/10.1175/1520-0469(2002)059<2033:IOPAWW>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-allowing 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, 2010a: 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., W. A. Gallus Jr., M. Xue, and F. Kong, 2010b: Growth of spread in convection-allowing and convection-parameterizing ensembles. Wea. Forecasting, 25, 594612, https://doi.org/10.1175/2009WAF2222318.1.

    • 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 Forecasting Program Spring Experiment. Bull. Meteor. Amer. Soc., 93, 5574, https://doi.org/10.1175/BAMS-D-11-00040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., R. G. Bullock, T. L. Jensen, M. Xue, and F. Kong, 2014: Application of object-based time-domain diagnostics for tracking precipitation systems in convection-allowing models. Wea. Forecasting, 29, 517542, https://doi.org/10.1175/WAF-D-13-00098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2016: Spring Forecasting Experiment 2016: Preliminary findings and results. NOAA/NSSL/SPC, 50 pp., https://hwt.nssl.noaa.gov/Spring_2016/HWT_SFE_2016_preliminary_findings_final.pdf.

    • Search Google Scholar
    • Export Citation
  • Dawson, L. C., G. S. Romine, R. J. Trapp, and M. E. Baldwin, 2017: Verifying supercellular rotation in a convection-permitting ensemble forecasting system with radar-derived rotation track data. Wea. Forecasting, 32, 781795, https://doi.org/10.1175/WAF-D-16-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, J., J. McQueen, G. DiMego, Z. Toth, D. Jovic, B. Zhou, and H.-Y. Chuang, 2006: New dimension of NCEP Short-Range Ensemble Forecasting (SREF) system: Inclusion of WRF members. Preprints, WMO Expert Team Meeting on Ensemble Prediction Systems, Exeter, United Kingdom, WMO, www.wcrp-climate.org/WGNE/BlueBook/2006/individual-articles/05_Du_Jun_WMO06.pdf.

    • Search Google Scholar
    • Export Citation
  • Gallo, B. T., A. J. Clark, and S. R. Dembek, 2016: Forecasting tornadoes using convection-permitting ensembles. Wea. Forecasting, 31, 273295, https://doi.org/10.1175/WAF-D-15-0134.1.

    • 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
  • Gao, J., 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, 457469, https://doi.org/10.1175/1520-0426(2004)021<0457:ATVDAM>2.0.CO;2.

    • Crossref
    • 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, 625641, https://doi.org/10.1111/j.1600-0870.2010.00497.x.

    • Crossref
    • 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.

    • 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
  • Hu, M., M. Xue, and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675698, https://doi.org/10.1175/MWR3092.1.

    • 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
  • Janjić, Z. I., and R. Gall, 2012: Scientific documentation of the NCEP Nonhydrostatic Multiscale Model on the B Grid (NMMB). Part 1: Dynamics. NCAR/TN-4891STR, 75 pp., http://nldr.library.ucar.edu/repository/assets/technotes/TECH-NOTE-000-000-000-857.pdf.

    • Search Google Scholar
    • Export Citation
  • Jankov, I., and Coauthors, 2017: A performance comparison between multiphysics and stochastic approaches within a North American RAP ensemble. Mon. Wea. Rev., 145, 11611179, https://doi.org/10.1175/MWR-D-16-0160.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jirak, I. L., S. J. Weiss, and C. J. Melick, 2012: The SPC storm-scale ensemble of opportunity: Overview and results from the 2012 Hazardous Weather Testbed Spring Forecasting Experiment. Preprints, 26th Conf. on Severe Local Storms, Nashville, TN, Amer. Meteor. Soc., P9.137, https://ams.confex.com/ams/26SLS/webprogram/Paper211729.html.

  • Jirak, I. L., A. J. Clark, J. Correia Jr., K. Knopfmeier, C. Melick, B. T. Gallo, M. Coniglio, and S. J. Weiss, 2015: Spring Forecasting Experiment 2015: Preliminary findings and results. NOAA/NSSL/SPC Rep., 32 pp., https://hwt.nssl.noaa.gov/Spring_2015/HWT_SFE_2015_Prelim_Findings_Final.pdf.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., P. R. Janish, S. J. Weiss, M. E. Baldwin, R. S. Schneider, and H. E. Brooks, 2003: Collaboration between forecasters and research scientists at the NSSL and SPC: The spring program. Bull. Amer. Meteor. Soc., 84, 17971806, https://doi.org/10.1175/BAMS-84-12-1797.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., and Coauthors, 2014: The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585601, https://doi.org/10.1175/BAMS-D-12-00050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., C. M. Kishtawal, T. E. LaRow, D. R. Bachiochi, Z. Zhang, C. E. Williford, S. Gadgil, and S. Surendran, 1999: Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285, 15481550, https://doi.org/10.1126/science.285.5433.1548.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., V. Kumar, A. Simon, A. Bhardwaj, T. Ghosh, and R. Ross, 2016: A review of multimodel superensemble forecasting for weather, season climate, and hurricanes. Rev. Geophys., 54, 336377, https://doi.org/10.1002/2015RG000513.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2, https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.

  • 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
  • Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291303.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nelson, B. R., O. P. Prat, D.-J. Seo, and E. Habib, 2016: Assessment and implications of NCEP Stage IV quantitative precipitation estimates for product intercomparisons. Wea. Forecasting, 31, 371394, https://doi.org/10.1175/WAF-D-14-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., and Coauthors, 2004: Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER). Bull. Amer. Meteor. Soc., 85, 853872, https://doi.org/10.1175/BAMS-85-6-853.

    • Crossref
    • 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.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., D. M. Schultz, B. A. Colle, and D. J. 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
  • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, R. A. Sobash, K. R. Fossell, and M. L. Weisman, 2015a: 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, M. L. Weisman, R. A. Sobash, K. R. Fossell, K. W. Manning, and S. B. Trier, 2015b: A real-time convection-allowing ensemble prediction system initialized by mesoscale ensemble Kalman filter analyses. Wea. Forecasting, 30, 11581181, https://doi.org/10.1175/WAF-D-15-0013.1.

    • 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
  • 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., G. S. Romine, C. S. Schwartz, D. J. Gagne II, and M. L. Weisman, 2016a: Explicit forecasts of low-level rotation from convection-allowing models for next-day tornado prediction. Wea. Forecasting, 31, 15911614, https://doi.org/10.1175/WAF-D-16-0073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., C. S. Schwartz, G. S. Romine, K. R. Fossell, and M. L. Weisman, 2016b: 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
  • 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, 20772107, https://doi.org/10.1175/1520-0493(2000)128<2077:UICAMP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stratman, D. R., M. C. Coniglio, S. E. Koch, and M. Xue, 2013: Use of multiple verification methods to evaluate forecasts of convection from hot- and cold-start convection-allowing models. Wea. Forecasting, 28, 119138, https://doi.org/10.1175/WAF-D-12-00022.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Towns, J., and Coauthors, 2014: XSEDE: Accelerating scientific discovery. Comput. Sci. Eng., 16, 6274, https://doi.org/10.1109/MCSE.2014.80.

  • UCAR, 2015: Report of the UCACN Model Advisory Committee. University Corporation for Atmospheric Research, Boulder, CO, 72 pp., www.ncep.noaa.gov/director/ucar_reports/ucacn_20151207/UMAC_Final_Report_20151207-v14.pdf.

  • Xue, M., D. Wang, J. 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, 139170.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wandishin, M. S., S. L. Mullen, D. J. Stensrud, and H. E. Brooks, 2001: Evaluation of a short-range multimodel ensemble system. Mon. Wea. Rev., 129, 729747, https://doi.org/10.1175/1520-0493(2001)129<0729:EOASRM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0–36-h explicit convective forecasts with the WRF-ARW Model. Wea. Forecasting, 23, 407437,https://doi.org/10.1175/2007WAF2007005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1678 745 47
PDF Downloads 543 144 12