• Andra, D. L., Jr., Quoetone E. M. , and Bunting W. F. , 2002: Warning decision making: The relative roles of conceptual models, technology, strategy, and forecaster expertise on 3 May 1999. Wea. Forecasting, 17, 559566, doi:10.1175/1520-0434(2002)017<0559:WDMTRR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Boyatzis, R. E., 1998: Transforming Qualitative Information: Thematic Analysis and Code Development. Sage Publications, 184 pp.

  • Creswell, J. W., 2002: Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications, 227 pp.

  • Gao, J., and Stensrud D. J. , 2012: Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeor classification. J. Atmos. Sci., 69, 10541065, doi:10.1175/JAS-D-11-0162.1.

    • Search Google Scholar
    • Export Citation
  • Gao, J., Xue M. , Brewster K. , and Droegemeier K. K. , 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457469, doi:10.1175/1520-0426(2004)021<0457:ATVDAM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gao, J., and Coauthors, 2013: A real-time weather-adaptive 3DVAR analysis system for severe weather detections and warnings with automatic storm positioning capability. Wea. Forecasting, 28, 727745, doi:10.1175/WAF-D-12-00093.1.

    • Search Google Scholar
    • Export Citation
  • Ge, G., Gao J. , and Xue M. , 2012: Diagnostic pressure equation as a weak constraint in a storm-scale three-dimensional variational radar data assimilation system. J. Atmos. Oceanic Technol., 29, 10751092, doi:10.1175/JTECH-D-11-00201.1.

    • Search Google Scholar
    • Export Citation
  • Heinselman, P. L., Cheong B. L. , Palmer R. D. , Bodine D. , and Hondl K. , 2009: Radar refractivity retrievals in Oklahoma: Insights into operational benefits and limitations. Wea. Forecasting, 24, 13451361, doi:10.1175/2009WAF2222256.1.

    • Search Google Scholar
    • Export Citation
  • Heinselman, P. L., LaDue D. S. , and Lazrus H. , 2012: Exploring impacts of rapid-scan radar data on NWS warning decisions. Wea. Forecasting, 27, 10311044, doi:10.1175/WAF-D-11-00145.1.

    • Search Google Scholar
    • Export Citation
  • Lusk, C., Kucera P. , Roberts W. , and Johnson L. , 1999: The process and methods used to evaluate prototype operational hydrometeorological workstations. Bull. Amer. Meteor. Soc., 80, 5764, doi:10.1175/1520-0477(1999)080<0057:TPAMUT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Morss, R. E., and Ralph F. M. , 2007: Use of information by National Weather Service forecasters and emergency managers during CALJET and PACJET-2001. Wea. Forecasting, 22, 539555, doi:10.1175/WAF1001.1.

    • Search Google Scholar
    • Export Citation
  • Smith, T. M., and Coauthors, 2013: Examination of a real-time 3DVAR analysis system in the Hazardous Weather Testbed. Wea. Forecasting, 29, 63–77, doi:10.1175/WAF-D-13-00044.1.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system. Bull. Amer. Meteor. Soc., 90, 14871499, doi:10.1175/2009BAMS2795.1.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Coauthors, 2013: Progress and challenges with warn-on-forecast. Atmos. Res., 123, 216, doi:10.1016/j.atmosres.2012.04.004.

    • Search Google Scholar
    • Export Citation
  • Stewart, T. R., Moninger W. R. , Grassia J. , Brady R. H. , and Merrem F. H. , 1989: Analysis of expert judgment in a hail forecasting experiment. Wea. Forecasting, 4, 2434, doi:10.1175/1520-0434(1989)004<0024:AOEJIA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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Forecaster Use and Evaluation of Real-Time 3DVAR Analyses during Severe Thunderstorm and Tornado Warning Operations in the Hazardous Weather Testbed

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  • 1 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
  • | 2 NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
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Abstract

A weather-adaptive three-dimensional variational data assimilation (3DVAR) system was included in the NOAA Hazardous Weather Testbed as a first step toward introducing warn-on-forecast initiatives into operations. NWS forecasters were asked to incorporate the data in conjunction with single-radar and multisensor products in the Advanced Weather Interactive Processing System (AWIPS) as part of their warning-decision process for real-time events across the United States. During the 2011 and 2012 experiments, forecasters examined more than 36 events, including tornadic supercells, severe squall lines, and multicell storms. Products from the 3DVAR analyses were available to forecasters at 1-km horizontal resolution every 5 min, with a 4–6-min latency, incorporating data from the national Weather Surveillance Radar-1988 Doppler (WSR-88D) network and the North American Mesoscale model. Forecasters found the updraft, vertical vorticity, and storm-top divergence products the most useful for storm interrogation and quickly visualizing storm trends, often using these tools to increase the confidence in a warning decision and/or issue the warning slightly earlier. The 3DVAR analyses were most consistent and reliable when the storm of interest was in close proximity to one of the assimilated WSR-88D, or data from multiple radars were incorporated into the analysis. The latter was extremely useful to forecasters in blending data rather than having to analyze multiple radars separately, especially when range folding obscured the data from one or more radars. The largest hurdle for the real-time use of 3DVAR or similar data assimilation products by forecasters is the data latency, as even 4–6 min reduces the utility of the products when new radar scans are available.

Corresponding author address: Kristin Calhoun, National Severe Storms Laboratory, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: kristin.kuhlman@noaa.gov

Abstract

A weather-adaptive three-dimensional variational data assimilation (3DVAR) system was included in the NOAA Hazardous Weather Testbed as a first step toward introducing warn-on-forecast initiatives into operations. NWS forecasters were asked to incorporate the data in conjunction with single-radar and multisensor products in the Advanced Weather Interactive Processing System (AWIPS) as part of their warning-decision process for real-time events across the United States. During the 2011 and 2012 experiments, forecasters examined more than 36 events, including tornadic supercells, severe squall lines, and multicell storms. Products from the 3DVAR analyses were available to forecasters at 1-km horizontal resolution every 5 min, with a 4–6-min latency, incorporating data from the national Weather Surveillance Radar-1988 Doppler (WSR-88D) network and the North American Mesoscale model. Forecasters found the updraft, vertical vorticity, and storm-top divergence products the most useful for storm interrogation and quickly visualizing storm trends, often using these tools to increase the confidence in a warning decision and/or issue the warning slightly earlier. The 3DVAR analyses were most consistent and reliable when the storm of interest was in close proximity to one of the assimilated WSR-88D, or data from multiple radars were incorporated into the analysis. The latter was extremely useful to forecasters in blending data rather than having to analyze multiple radars separately, especially when range folding obscured the data from one or more radars. The largest hurdle for the real-time use of 3DVAR or similar data assimilation products by forecasters is the data latency, as even 4–6 min reduces the utility of the products when new radar scans are available.

Corresponding author address: Kristin Calhoun, National Severe Storms Laboratory, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: kristin.kuhlman@noaa.gov
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