• Adger, W. N., 2006: Vulnerability. Global Environ. Change, 16, 268281, https://doi.org/10.1016/j.gloenvcha.2006.02.006.

  • Anthony, K. E., K. R. Cowden-Hodgson, H. Dan O’Hair, R. L. Heath, and G. M. Eosco, 2014: Complexities in communication and collaboration in the hurricane warning system. Commun. Stud., 65, 468483, https://doi.org/10.1080/10510974.2014.957785.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ash, K. D., 2017: A qualitative study of mobile home resident perspectives on tornadoes and tornado protective actions in South Carolina, USA. GeoJournal, 82, 533552, https://doi.org/10.1007/s10708-016-9700-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ash, K. D., R. L. Schumann III, and G. C. Bowser, 2014: Tornado warning trade-offs: Evaluating choices for visually communicating risk. Wea. Climate Soc., 6, 104118, https://doi.org/10.1175/WCAS-D-13-00021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, L. R., E. C. Gruntfest, M. H. Hayden, D. M. Schultz, and C. Benight, 2007: False alarms and close calls: A conceptual model of warning accuracy. Wea. Forecasting, 22, 11401147, https://doi.org/10.1175/WAF1031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baumgart, L. A., E. J. Bass, B. Philips, and K. Kloesel, 2008: Emergency management decision making during severe weather. Wea. Forecasting, 23, 12681279, https://doi.org/10.1175/2008WAF2007092.1.

    • 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
  • Chaney, P. L., and G. S. Weaver, 2010: The vulnerability of mobile home residents in tornado disasters: The 2008 Super Tuesday tornado in Macon County, Tennessee. Wea. Climate Soc., 2, 190199, https://doi.org/10.1175/2010WCAS1042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, R. A., J. J. Gourley, Z. L. Flamig, Y. Hong, and E. Clark, 2014: CONUS-wide evaluation of National Weather Service flash flood guidance products. Wea. Forecasting, 29, 377392, https://doi.org/10.1175/WAF-D-12-00124.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coffey, J. W., and R. R. Hoffman, 2003: Knowledge modeling for the preservation of institutional memory. J. Knowl. Manage., 7, 3852, https://doi.org/10.1108/13673270310485613.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Creswell, J. W., and J. D. Creswell, 2017: Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications, 304 pp.

    • Search Google Scholar
    • Export Citation
  • Daipha, P., 2015: Masters of Uncertainty: Weather Forecasters and the Quest for Ground Truth. University of Chicago Press, 271 pp.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, R. S., 2001: Flash flood forecast and detection methods. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 481–526, https://doi.org/10.1175/0065-9401-28.50.481.

    • Crossref
    • Export Citation
  • Demuth, J. L., 2015: Developing a valid scale of past tornado experiences. Ph.D. dissertation, Colorado State University, 195 pp.

  • Demuth, J. L., 2018: Explicating experience: Development of a valid scale of past hazard experience for tornadoes. Risk Anal., 38, 19211943, https://doi.org/10.1111/risa.12983.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Demuth, J. L., and Coauthors, 2020: Recommendations for developing useful and usable convection-allowing model ensemble information for NWS forecasters. Wea. Forecasting, 35, 13811406, https://doi.org/10.1175/WAF-D-19-0108.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donner, W. R., 2007: An integrated model of risk perception and protective action: Public response to tornado warnings. Ph.D. dissertation, University of Delaware, 212 pp.

  • Evans, C., and Coauthors, 2017: The extratropical transition of tropical cyclones. Part I: Cyclone evolution and direct impacts. Mon. Wea. Rev., 145, 43174344, https://doi.org/10.1175/MWR-D-17-0027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fine, G. A., 2007: Authors of the Storm: Meteorologists and the Culture of Prediction. University of Chicago Press, 280 pp.

  • Gourley, J. J., and Coauthors, 2013: A unified flash flood database across the United States. Bull. Amer. Meteor. Soc., 94, 799805, https://doi.org/10.1175/BAMS-D-12-00198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hardy, C., and N. Phillips, 2004: Discourse and power. The Sage Handbook of Organizational Discourse, D. Grant et al., Eds., SAGE, 299–316.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heinselman, P., D. LaDue, D. M. Kingfield, and R. Hoffman, 2015: Tornado warning decisions using phased-array radar data. Wea. Forecasting, 30, 5778, https://doi.org/10.1175/WAF-D-14-00042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henderson, J. J., 2017: “To err on the side of caution:” Ethical dimensions of the National Weather Service warning process. Ph.D. dissertation, Virginia Polytechnic Institute, 200 pp.

  • Hoekstra, S., K. Klockow, R. Riley, J. Brotzge, H. Brooks, and S. Erickson, 2011: A preliminary look at the social perspective of warn-on-forecast: Preferred tornado warning lead time and the general public’s perceptions of weather risks. Wea. Climate Soc., 3, 128140, https://doi.org/10.1175/2011WCAS1076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffman, R. R., 1991: Human factors psychology in the support of forecasting: The design of advanced meteorological workstations. Wea. Forecasting, 6, 98110, https://doi.org/10.1175/1520-0434(1991)006<0098:HFPITS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffman, R. R., J. W. Coffey, and K. M. Ford, 2000: A case study in the research paradigm of human-centered computing: Local expertise in weather forecasting. Report on contract, “Human-centered system prototype,” National Technology Alliance.

  • Hoffman, R. R., J. W. Coffey, K. M. Ford, and M. J. Carnot, 2001: STORM-LK: A human-centered knowledge model for weather forecasting. Proc. Hum. Factors Ergon. Soc., 45, 752, https://doi.org/10.1177/154193120104500807.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffman, R. R., J. W. Coffey, K. M. Ford, and J. D. Novak, 2006: A method for eliciting, preserving, and sharing the knowledge of forecasters. Wea. Forecasting, 21, 416428, https://doi.org/10.1175/WAF927.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joslyn, S., K. Pak, D. Jones, J. Pyles, and E. Hunt, 2007: The effect of probabilistic information on threshold forecasts. Wea. Forecasting, 22, 804812, https://doi.org/10.1175/WAF1020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kasperson, J. X., R. E. Kasperson, N. Pidgeon, and P. Slovic, 2003: The social amplification of risk: Assessing fifteen years of research and theory. The Feeling of Risk: New Perspectives on Risk Perception, P. Slovic, Ed., Routledge, 317–344.

    • Crossref
    • Export Citation
  • Kasperson, R. E., O. Renn, P. Slovic, H. S. Brown, J. Emel, R. Goble, J. X. Kasperson, and S. Ratick, 1988: The social amplification of risk: A conceptual framework. Risk Anal., 8, 177187, https://doi.org/10.1111/j.1539-6924.1988.tb01168.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, J. R., M. D. Parker, K. D. Sherburn, and G. M. Lackmann, 2017: Rapid evolution of cool season, low-cape severe thunderstorm environments. Wea. Forecasting, 32, 763779, https://doi.org/10.1175/WAF-D-16-0141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LaDue, D. S., 2011: How meteorologists learn to forecast the weather: Social dimensions of complex learning. Ph.D. dissertation, University of Oklahoma, 253 pp.

  • Lazo, J., R. Morss, J. Demuth, and A. Bostrom, 2010: Forecasters’ mental models of flash flood forecasts and warnings. Fifth Symp. on Policy and Socio-economic Research/Second Conf. on International Cooperation in the Earth System Sciences and Services, Atlanta, GA, Amer. Meteor. Soc., J5.1, https://ams.confex.com/ams/90annual/techprogram/paper_164543.htm.

  • Lazrus, H., R. E. Morss, J. L. Demuth, J. K. Lazo, and A. Bostrom, 2016: “Know what to do if you encounter a flash flood”: Mental models analysis for improving flash flood risk communication and public decision making. Risk Anal., 36, 411427, https://doi.org/10.1111/risa.12480.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • League, C. E., W. Díaz, B. Philips, E. J. Bass, K. Kloesel, E. Gruntfest, and A. Gessner, 2010: Emergency manager decision-making and tornado warning communication. Meteor. Appl., 17, 163172, https://doi.org/10.1002/MET.201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, B. F., M. Egnoto, and J. R. Lim, 2019: How mobile home residents understand and respond to tornado warnings. Wea. Climate Soc., 11, 521534, https://doi.org/10.1175/WCAS-D-17-0080.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lusk, C. M., T. R. Stewart, K. R. Hammond, and R. J. Potts, 1990: Judgment and decision making in dynamic tasks: The case of forecasting the microburst. Wea. Forecasting, 5, 627639, https://doi.org/10.1175/1520-0434(1990)005<0627:JADMID>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morss, R. E., and E. Wahl, 2007: An ethical analysis of hydrometeorological prediction and decision making: The case of the 1997 red river flood. Environ. Hazards, 7, 342352, https://doi.org/10.1016/J.ENVHAZ.2007.09.004.

    • Search Google Scholar
    • Export Citation
  • Morss, R. E., O. V. Wilhelmi, M. W. Downton, and E. Gruntfest, 2005: Flood risk, uncertainty, and scientific information for decision making: Lessons from an interdisciplinary project. Bull. Amer. Meteor. Soc., 86, 15931602, https://doi.org/10.1175/BAMS-86-11-1593.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morss, R. E., J. L. Demuth, A. Bostrom, J. K. Lazo, and H. Lazrus, 2015: Flash flood risks and warning decisions: A mental models study of forecasters, public officials, and media broadcasters in Boulder, Colorado. Risk Anal., 35, 20092028, https://doi.org/10.1111/risa.12403.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morss, R. E., and Coauthors, 2017: Hazardous weather prediction and communication in the modern information environment. Bull. Amer. Meteor. Soc., 98, 26532674, https://doi.org/10.1175/BAMS-D-16-0058.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nielsen, E. R., 2019: Insights into extreme short-term precipitation associated with supercells and mesovortices. Ph.D. thesis, Colorado State University, 182 pp.

    • Crossref
    • Export Citation
  • Nielsen, E. R., and R. S. Schumacher, 2018: Dynamical insights into extreme short-term precipitation associated with supercells and mesovortices. J. Atmos. Sci., 75, 29833009, https://doi.org/10.1175/JAS-D-17-0385.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nielsen, E. R., and R. S. Schumacher, 2020a: Dynamical mechanisms supporting extreme rainfall accumulations in the Houston “Tax Day” 2016 flood. Mon. Wea. Rev., 148, 83109, https://doi.org/10.1175/MWR-D-19-0206.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nielsen, E. R., and R. S. Schumacher, 2020b: Observations of extreme short-term precipitation associated with supercells and mesovortices. Mon. Wea. Rev., 148, 159182, https://doi.org/10.1175/MWR-D-19-0146.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nielsen, E. R., G. R. Herman, R. C. Tournay, J. M. Peters, and R. S. Schumacher, 2015: Double impact: When both tornadoes and flash floods threaten the same place at the same time. Wea. Forecasting, 30, 16731693, https://doi.org/10.1175/WAF-D-15-0084.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NWS, 2017: National Weather Service manual 10-950. Hydrologic Services Program, NWSPD 10-9, National Weather Service, 3 pp., http://www.nws.noaa.gov/directives/sym/pd01009050curr.pdf.

  • NWS, 2019: Building a weather ready nation: National Weather Service 2019-2022 strategic plan. NOAA’s National Weather Service, 23 pp., https://www.weather.gov/news/192203-strategic-plan.

  • Pliske, R. M., B. Crandall, and G. Klein, 2004: Competence in weather forecasting. Psychological Investigations of Competence in Decision Making, K. Smith et al., Eds., Cambridge University Press, 40–68.

  • Ripberger, J. T., C. L. Silva, H. C. Jenkins-Smith, and M. James, 2015: The influence of consequence-based messages on public responses to tornado warnings. Bull. Amer. Meteor. Soc., 96, 577590, https://doi.org/10.1175/BAMS-D-13-00213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmidlin, T. W., B. O. Hammer, Y. Ono, and P. S. King, 2009: Tornado shelter-seeking behavior and tornado shelter options among mobile home residents in the United States. Nat. Hazards, 48, 191201, https://doi.org/10.1007/s11069-008-9257-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schroeder, A., J. Basara, J. M. Shepherd, and S. Nelson, 2016a: Insights into atmospheric contributors to urban flash flooding across the United States using an analysis of rawinsonde data and associated calculated parameters. J. Appl. Meteor. Climatol., 55, 313323, https://doi.org/10.1175/JAMC-D-14-0232.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schroeder, A., and Coauthors, 2016b: The development of a flash flood severity index. J. Hydrol., 541, 523532, https://doi.org/10.1016/j.jhydrol.2016.04.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., 2009: Mechanisms for quasi-stationary behavior in simulated heavy-rain-producing convective systems. J. Atmos. Sci., 66, 15431568, https://doi.org/10.1175/2008JAS2856.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., D. T. Lindsey, A. B. Schumacher, J. Braun, S. D. Miller, and J. L. Demuth, 2010: Multidisciplinary analysis of an unusual tornado: Meteorology, climatology, and the communication and interpretation of warnings. Wea. Forecasting, 25, 14121429, https://doi.org/10.1175/2010WAF2222396.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sherburn, K. D., and M. D. Parker, 2014: Climatology and ingredients of significant severe convection in high-shear, low-cape environments. Wea. Forecasting, 29, 854877, https://doi.org/10.1175/WAF-D-13-00041.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sherburn, K. D., M. D. Parker, J. R. King, and G. M. Lackmann, 2016: Composite environments of severe and nonsevere high-shear, low-CAPE convective events. Wea. Forecasting, 31, 18991927, https://doi.org/10.1175/WAF-D-16-0086.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, K. M., and D. Sutter, 2009: False alarms, tornado warnings, and tornado casualties. Wea. Climate Soc., 1, 3853, https://doi.org/10.1175/2009WCAS1005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spinney, J., 2019: Weathering storms and flooded waters: Anthropological perspectives of policy and risk in Toronto, Ontario. Ph.D. thesis, Western University of Ontario, 250 pp.

  • Spinney, J., J. Henderson, M. Bica, L. Palen, E. R. Nielsen, and J. Demuth, 2020: Keeping calm in the chaos: An examination of forecaster sense-making and partner response to TORFFs during Hurricane Florence. 15th Symp. on Societal Applications: Policy, Research and Practice, Boston, MA, Amer. Meteor. Soc., 3.3, https://ams.confex.com/ams/2020Annual/webprogram/Paper370444.html.

  • Stewart, T. R., R. W. Katz, and A. H. Murphy, 1984: Value of weather information: A descriptive study of the fruit-frost problem. Bull. Amer. Meteor. Soc., 65, 126137, https://doi.org/10.1175/1520-0477(1984)065<0126:VOWIAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strader, S. M., and W. S. Ashley, 2018: Finescale assessment of mobile home tornado vulnerability in the central and southeast United States. Wea. Climate Soc., 10, 797812, https://doi.org/10.1175/WCAS-D-18-0060.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sutter, D., and K. M. Simmons, 2010: Tornado fatalities and mobile homes in the United States. Nat. Hazards, 53, 125137, https://doi.org/10.1007/s11069-009-9416-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Terti, G., I. Ruin, S. Anquetin, and J. J. Gourley, 2015: Dynamic vulnerability factors for impact-based flash flood prediction. Nat. Hazards, 79, 14811497, https://doi.org/10.1007/s11069-015-1910-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, S. P., 2013: The Politics of Expertise. Routledge, 338 pp.

  • Walters, J. E., L. R. Mason, K. Ellis, and B. Winchester, 2020: Staying safe in a tornado: A qualitative inquiry into public knowledge, access, and response to tornado warnings. Wea. Forecasting, 35, 6781, https://doi.org/10.1175/WAF-D-19-0090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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A Hazard Multiple: Overlapping Tornado and Flash Flood Warnings in a National Weather Service Forecast Office in the Southeastern United States

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  • 1 Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, and NOAA/OAR/ESRL/Global Systems Laboratory, Boulder, Colorado
  • 2 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
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Abstract

The U.S. weather warning system is designed to help operational forecasters identify hazards and issue alerts to assist people in taking life-saving actions. Assessing risks for separate hazards, such as flash flooding, can be challenging for individuals, depending on their contexts, resources, and abilities. When two or more hazards co-occur in time and space, such as tornadoes and flash floods, which we call TORFFs, risk assessment and available actions people can take to stay safe become increasingly complex and potentially dangerous. TORFF advice can suggest contradictory action—that people get low for a tornado and seek higher ground for a flash flood. The origin of risk information about such threats is the National Weather Service (NWS) Weather Forecast Office. This article contributes to an understanding of the warning and forecast system though a naturalistic study of the NWS during a TORFF event in the southeastern United States. Drawing on literature for the Social Amplification of Risk Framework, this article argues that during TORFFs, elements of the NWS warning operations can unintentionally amplify or attenuate one threat over the other. Our results reveal three ways this amplification or attenuation might occur: 1) underlying assumptions that forecasters understandably make about the danger of different threats; 2) threat terminology and coordination with national offices that shape the communication of risks during a multihazard event; and 3) organizational arrangements of space and forecaster expertise during operations. We conclude with suggestions for rethinking sites of amplification and attenuation and additional areas of future study.

Current affiliation: Amazon.com, Seattle, Washington.

Corresponding author: Jen Henderson, jennifer.henderson-1@colorado.edu

Abstract

The U.S. weather warning system is designed to help operational forecasters identify hazards and issue alerts to assist people in taking life-saving actions. Assessing risks for separate hazards, such as flash flooding, can be challenging for individuals, depending on their contexts, resources, and abilities. When two or more hazards co-occur in time and space, such as tornadoes and flash floods, which we call TORFFs, risk assessment and available actions people can take to stay safe become increasingly complex and potentially dangerous. TORFF advice can suggest contradictory action—that people get low for a tornado and seek higher ground for a flash flood. The origin of risk information about such threats is the National Weather Service (NWS) Weather Forecast Office. This article contributes to an understanding of the warning and forecast system though a naturalistic study of the NWS during a TORFF event in the southeastern United States. Drawing on literature for the Social Amplification of Risk Framework, this article argues that during TORFFs, elements of the NWS warning operations can unintentionally amplify or attenuate one threat over the other. Our results reveal three ways this amplification or attenuation might occur: 1) underlying assumptions that forecasters understandably make about the danger of different threats; 2) threat terminology and coordination with national offices that shape the communication of risks during a multihazard event; and 3) organizational arrangements of space and forecaster expertise during operations. We conclude with suggestions for rethinking sites of amplification and attenuation and additional areas of future study.

Current affiliation: Amazon.com, Seattle, Washington.

Corresponding author: Jen Henderson, jennifer.henderson-1@colorado.edu
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