NCAR’s Real-Time Convection-Allowing Ensemble Project

Craig S. Schwartz National Center for Atmospheric Research, Boulder, Colorado

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

Search for other papers by Glen S. Romine in
Current site
Google Scholar
PubMed
Close
,
Ryan A. Sobash National Center for Atmospheric Research, Boulder, Colorado

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

Search for other papers by Kathryn R. Fossell in
Current site
Google Scholar
PubMed
Close
, and
Morris L. Weisman National Center for Atmospheric Research, Boulder, Colorado

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

Abstract

Beginning 7 April 2015, scientists at the U.S. National Center for Atmospheric Research (NCAR) began producing daily, real-time, experimental, 10-member ensemble forecasts with 3-km horizontal grid spacing across the entire conterminous United States. Graphical forecast products were posted in real time to the Internet, where they attracted a large following from both forecasters and researchers across government, academia, and the private sector. Although these forecasts were initially planned to terminate after one year, the project was extended through 30 December 2017 because of the enthusiastic community response. This article details the motivation for the NCAR ensemble project and describes the project’s impacts throughout the meteorological community. Classroom and operational use of the NCAR ensemble are discussed in addition to the diverse application of NCAR ensemble output for research purposes. Furthermore, some performance statistics are provided, and the NCAR ensemble website and data visualization approach are described. We hope the NCAR ensemble’s success will motivate additional experimental forecast demonstrations that transcend current operational capabilities, as forward-looking forecast systems are needed to accelerate operational development and provide students, young scientists, and forecasters with glimpses of what future modeling systems may look like. Additionally, the NCAR ensemble dataset is publicly available and can be used for meaningful research endeavors concerning many meteorological topics.

© 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: Craig Schwartz, schwartz@ucar.edu

Abstract

Beginning 7 April 2015, scientists at the U.S. National Center for Atmospheric Research (NCAR) began producing daily, real-time, experimental, 10-member ensemble forecasts with 3-km horizontal grid spacing across the entire conterminous United States. Graphical forecast products were posted in real time to the Internet, where they attracted a large following from both forecasters and researchers across government, academia, and the private sector. Although these forecasts were initially planned to terminate after one year, the project was extended through 30 December 2017 because of the enthusiastic community response. This article details the motivation for the NCAR ensemble project and describes the project’s impacts throughout the meteorological community. Classroom and operational use of the NCAR ensemble are discussed in addition to the diverse application of NCAR ensemble output for research purposes. Furthermore, some performance statistics are provided, and the NCAR ensemble website and data visualization approach are described. We hope the NCAR ensemble’s success will motivate additional experimental forecast demonstrations that transcend current operational capabilities, as forward-looking forecast systems are needed to accelerate operational development and provide students, young scientists, and forecasters with glimpses of what future modeling systems may look like. Additionally, the NCAR ensemble dataset is publicly available and can be used for meaningful research endeavors concerning many meteorological topics.

© 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: Craig Schwartz, schwartz@ucar.edu
Save
  • Accadia, C., S. Mariani, M. Casaioli, A. Lavagnini, and A. Speranza, 2003: Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids. Wea. Forecasting, 18, 918932, https://doi.org/10.1175/1520-0434(2003)018<0918:SOPFSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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
  • Barker, D. M., 2005: Southern high-latitude ensemble data assimilation in the Antarctic Mesoscale Prediction System. Mon. Wea. Rev., 133, 34313449, https://doi.org/10.1175/MWR3042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barth, M. C., and Coauthors, 2015: The Deep Convective Clouds and Chemistry (DC3) field campaign. Bull. Amer. Meteor. Soc., 96, 12811309, https://doi.org/10.1175/BAMS-D-13-00290.1.

    • 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
  • Berner, J., and Coauthors, 2017: Stochastic parameterization: Toward a new view of weather and climate models. Bull. Amer. Meteor. Soc., 98, 565588, https://doi.org/10.1175/BAMS-D-15-00268.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., G. S. Romine, R. Rotunno, D. W. Reif, and C. C. Weiss, 2018: On the anomalous counterclockwise turning of the surface wind with time in the plains of the United States. Mon. Wea. Rev., 146, 467484, https://doi.org/10.1175/MWR-D-17-0297.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bowler, N. E., A. Arribas, K. R. Mylne, K. B. Robertson, and S. E. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703722, https://doi.org/10.1002/qj.234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., M. Leutbecher, and L. Isaksen, 2008: Potential use of an ensemble of analyses in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 134, 20512066, https://doi.org/10.1002/qj.346.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carlberg, B. R., W. A. Gallus Jr., and K. J. Franz, 2018: A preliminary examination of WRF ensemble prediction of convective mode evolution. Wea. Forecasting, 33, 783798, https://doi.org/10.1175/WAF-D-17-0149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cavallo, S. M., R. D. Torn, C. Snyder, C. Davis, W. Wang, and J. Done, 2013: Evaluation of the Advanced Hurricane WRF data assimilation system for the 2009 Atlantic hurricane season. Mon. Wea. Rev., 141, 523541, https://doi.org/10.1175/MWR-D-12-00139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cerrai, D., and Coauthors, 2016: Enhanced outage prediction modeling for strong extratropical storms and hurricanes in the northeastern United States. Fall Meeting of the American Geophysical Union, San Francisco, CA, Amer. Geophys. Union, NH51B-1930, https://agu.confex.com/agu/fm16/meetingapp.cgi/Paper/166625.

  • 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
  • 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
  • Duc, L., K. Saito, and H. Seko, 2013: Spatial–temporal fractions verification for high-resolution ensemble forecasts. Tellus, 65A, 18171, https://doi.org/10.3402/tellusa.v65i0.18171.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., 2008: Fuzzy verification of high resolution gridded forecasts: A review and proposed framework. Meteor. Appl., 15, 5164, https://doi.org/10.1002/met.25.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, C., D. F. Van Dyke, and T. Lericos, 2014: How do forecasters utilize output from a convection-permitting ensemble forecast system? Case study of a high-impact precipitation event. Wea. Forecasting, 29, 466486, https://doi.org/10.1175/WAF-D-13-00064.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 14310 162, https://doi.org/10.1029/94JC00572.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gagne, D. J., A. McGovern, S. E. Haupt, R. A. Sobash, J. K. 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
  • Gebhardt, C., S. E. Theis, M. Paulat, and Z. Ben Bouallègue, 2011: Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmos. Res., 100, 168177, https://doi.org/10.1016/j.atmosres.2010.12.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Golding, B., N. Roberts, G. Leoncini, K. Mylne, and R. Swinbank, 2016: MOGREPS-UK convection-permitting ensemble products for surface water flood forecasting: Rationale and first results. J. Hydrometeor., 17, 13831406, https://doi.org/10.1175/JHM-D-15-0083.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gowan, T. M., W. J. Steenburgh, and C. S. Schwartz, 2018: Validation of mountain precipitation forecasts from the convection-permitting NCAR ensemble and operational forecast systems over the western United States. Wea. Forecasting, 33, 739765, https://doi.org/10.1175/WAF-D-17-0144.1.

    • 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 2016 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
  • Gustafsson, N., and Coauthors, 2018: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres. Quart. J. Roy. Meteor. Soc., 144, 12181256, https://doi.org/10.1002/qj.3179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hacker, J., 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
  • Hagelin, S., J. Son, R. Swinbank, A. McCabe, N. Roberts, and W. Tennant, 2017: The Met Office convective-scale ensemble, MOGREPS-UK. Quart. J. Roy. Meteor. Soc., 143, 28462861, https://doi.org/10.1002/qj.3135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., and F. Zhang, 2016: Review of the ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 144, 44894532, https://doi.org/10.1175/MWR-D-15-0440.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsiao, L.-F., D.-S. Chen, Y.-H. Kuo, Y.-R. Guo, T.-C. Yeh, J.-S. Hong, C.-T. Fong, and C.-S. Lee, 2012: Application of WRF 3DVAR to operational typhoon prediction in Taiwan: Impact of outer loop and partial cycling approaches. Wea. Forecasting, 27, 12491263, https://doi.org/10.1175/WAF-D-11-00131.1.

    • Crossref
    • 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. 26th Conf. on Severe Local Storms, Nashville, TN, Amer. Meteor. Soc., 137, https://ams.confex.com/ams/26SLS/webprogram/Paper211729.html.

  • Jirak, I. L., C. J. Melick, and S. J. Weiss, 2016: Comparison of the SPC storm-scale ensemble of opportunity to other convection-allowing ensembles for severe weather forecasting. 28th Conf. on Severe Local Storms, Portland, OR, Amer. Meteor. Soc., 102, https://ams.confex.com/ams/28SLS/webprogram/Session41668.html.

  • Johnson, A., and X. Wang, 2017: Design and implementation of a GSI-based convection-allowing ensemble data assimilation and forecast system for the PECAN field experiment. Part I: Optimal configurations for nocturnal convection prediction. Wea. Forecasting, 32, 289315, https://doi.org/10.1175/WAF-D-16-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, F. Kong, and M. Xue, 2011: 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, 36733693, https://doi.org/10.1175/MWR-D-11-00015.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klasa, C., M. Arpagaus, A. Walser, and H. Wernli, 2018: An evaluation of the convection-permitting ensemble COSMO-E for three contrasting precipitation events in Switzerland. Quart. J. Roy. Meteor. Soc., 144, 744764, https://doi.org/10.1002/qj.3245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kong, F., and Coauthors, 2007: Preliminary analysis on the real-time storm-scale ensemble forecasts produced as a part of the NOAA Hazardous Weather Testbed 2007 spring experiment. Preprints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Salt Lake City, UT, Amer. Meteor. Soc., 3B.2, http://ams.confex.com/ams/pdfpapers/124667.pdf.

  • Kuchera, E., S. Rentschler, G. Creighton, and J. Hamilton, 2014: The Air Force weather ensemble prediction suite. 15th Annual WRF Users’ Workshop, Boulder, CO, UCAR–NCAR, www2.mmm.ucar.edu/wrf/users/workshops/WS2014/ppts/2.3.pdf.

  • Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. Preprints, 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2, http://ams.confex.com/ams/pdfpapers/83847.pdf.

  • Murphy, A. H., 1993: What is a good forecast? An essay on the nature of goodness in weather forecasting. Wea. Forecasting, 8, 281293, https://doi.org/10.1175/1520-0434(1993)008<0281:WIAGFA>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peralta, C., Z. B. Bouallègue, S. E. Theis, C. Gebhardt, and M. Buchhold, 2012: Accounting for initial condition uncertainties in COSMO-DE-EPS. J. Geophys. Res., 117, D07108, https://doi.org/10.1029/2011JD016581.

    • Search Google Scholar
    • Export Citation
  • Powers, J. G., and Coauthors, 2017: The Weather Research and Forecasting Model: Overview, system efforts, and future directions. Bull. Amer. Meteor. Soc., 98, 17171737, https://doi.org/10.1175/BAMS-D-15-00308.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raynaud, L., and F. Bouttier, 2016: Comparison of initial perturbation methods for ensemble prediction at convective scale. Quart. J. Roy. Meteor. Soc., 142, 854866, https://doi.org/10.1002/qj.2686.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raynaud, L., and F. Bouttier, 2017: The impact of horizontal resolution and ensemble size for convective-scale probabilistic forecasts. Quart. J. Roy. Meteor. Soc., 143, 30373047, https://doi.org/10.1002/qj.3159.

    • 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
  • Romine, G., C. S. Schwartz, C. Snyder, J. Anderson, and M. Weisman, 2013: Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts. Mon. Wea. Rev., 141, 12631284, https://doi.org/10.1175/MWR-D-12-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romine, G., C. S. Schwartz, J. Berner, K. R. Fossell, C. Snyder, J. L. Anderson, and M. L. Weisman, 2014: Representing forecast error in a convection-permitting ensemble system. Mon. Wea. Rev., 142, 45194541, https://doi.org/10.1175/MWR-D-14-00100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schraff, C., H. Reich, A. Rhodin, A. Schomburg, K. Stephan, A. Periáñez, and R. Potthast, 2016: Kilometre-scale ensemble data assimilation for the COSMO model (KENDA). Quart. J. Roy. Meteor. Soc., 142, 14531472, https://doi.org/10.1002/qj.2748.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., and A. J. Clark, 2014: Evaluation of ensemble configurations for the analysis and prediction of heavy-rain-producing mesoscale convective systems. Mon. Wea. Rev., 142, 41084138, https://doi.org/10.1175/MWR-D-13-00357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., 2016: Improving large-domain convection-allowing forecasts with high-resolution analyses and ensemble data assimilation. Mon. Wea. Rev., 144, 17771803, https://doi.org/10.1175/MWR-D-15-0286.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., 2017: A comparison of methods used to populate neighborhood-based contingency tables for high-resolution forecast verification. Wea. Forecasting, 32, 733741, https://doi.org/10.1175/WAF-D-16-0187.1.

    • 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
  • 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, 263280, https://doi.org/10.1175/2009WAF2222267.1.

    • 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, M. L. Weisman, R. A. Sobash, K. R. Fossell, K. W. Manning, and S. B. Trier, 2015a: 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
  • Schwartz, C. S., G. S. Romine, R. A. Sobash, K. R. Fossell, and M. L. Weisman, 2015b: 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
  • 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
  • Smirnov, D., D. McGlone, A. J. Clark, C. Schwartz, and K. Stewart, 2018: On the use of high-resolution ensembles for operational heavy rainfall forecasting in the Denver Metro Area. 32nd Conf. on Hydrology, Austin, TX, Amer. Meteor. Soc., J53.6, https://ams.confex.com/ams/98Annual/webprogram/Paper333117.html.

  • Smith, T. M., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) severe weather and aviation products. 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., C. S. Schwartz, G. S. Romine, K. Fossell, and M. Weisman, 2016a: 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
  • Sobash, R. A., G. S. Romine, C. S. Schwartz, D. J. Gagne, and M. L. Weisman, 2016b: 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
  • Theis, S. E., A. Hense, and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257268, https://doi.org/10.1017/S1350482705001763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and C. A. Davis, 2012: The influence of shallow convection on tropical cyclone track forecasts. Mon. Wea. Rev., 140, 21882197, https://doi.org/10.1175/MWR-D-11-00246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 24902502, https://doi.org/10.1175/MWR3187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • UCAR, 2015: Report of the UCACN Model Advisory Committee. UCAR Rep., 72 pp., www.ncep.noaa.gov/director/ucar_reports/ucacn_20151207/UMAC_Final_Report_20151207-v14.pdf.

  • Weisman, M. L., and Coauthors, 2015: The Mesoscale Predictability Experiment (MPEX). Bull. Amer. Meteor. Soc., 96, 21272149, https://doi.org/10.1175/BAMS-D-13-00281.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Academic Press, 676 pp.

    • Crossref
    • Export Citation
  • Xue, M., and Coauthors, 2007: CAPS real-time storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2007 Spring Experiment. Preprints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Salt Lake City, UT, Amer. Meteor. Soc., 3B, http://ams.confex.com/ams/pdfpapers/124587.pdf.

  • Yang, J., M. Astitha, and C. S. Schwartz, 2017: Improvement of storm forecasts using gridded Bayesian linear regression for northeast United States. Fall Meeting of the American Geophysical Union, New Orleans, LA, Amer. Geophys. Union, NG24A-04, https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/272811.

  • Zhang, X., E. N. Anagnostou, and C. S. Schwartz, 2018: NWP-based adjustment of IMERG precipitation for flood-inducing complex terrain storms: Evaluation over CONUS. Remote Sens ., 10, 642, https://doi.org/10.3390/rs10040642.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, X., Y. Zhu, D. Hou, Y. Luo, J. Peng, and D. Wobus, 2017: Performance of the new NCEP Global Ensemble Forecast System in a parallel experiment. Wea. Forecasting, 32, 19892004, https://doi.org/10.1175/WAF-D-17-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1271 380 80
PDF Downloads 1055 182 30