EFFECTIVE WARNING PROCESS: RECENT SOCIAL SCIENCE CONTRIBUTIONS

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EFFECTIVE WARNING PROCESS: RECENT SOCIAL SCIENCE CONTRIBUTIONS International Workshop on Tropical Cyclones 04 December 2014 Republic of Korea Dr. Betty Morrow, SocResearch Miami Dr. Jeffrey Lazo, National Center for Atmospheric Research 1

Overview I. Increased Attention To Reducing Impacts Of TCs II. Recognizing the Need for Social Science Expertise III. Findings from Recent Social Science Research IV. Shortcomings and Unmet Needs V. Way Forward 2

2004-2005 Atlantic Seasons Danielle Alex Emily Cindy Wilma Jeanne 3

2004-2006 Retired Typhoon Names Rananim Morakot Longwan 4

Typhoons Haiyan (Yolanda) - 2013 Deadliest TC to make landfall in the Philippines. Also impacted Micronesia, Southern China, Vietnam Highest winds (10-min. sustained): 230 km/h (145 mph) Fatalities: 6,340 confirmed 1,061 missing Typhoon Phalin 2013 2 nd strongest to make landfall in India 5

Some Social Science Initiatives National Science Board Task Force (U. S.) NCAR Societal Impacts Program WAS*IS Workshops N.A. THORPEX Program Addition of SS to WMO Hurricane Workshops HFIP (% for social science research) National Science Foundation/NOAA grants on Communication Hurricane Information NOS Storm Surge Roadmap (Some SS funding) 6

Examples of Questions Social Science Can Address How do the various components of the forecast message communication chain relate to each other? What value do various stakeholders place on forecasts? How do different groups receive their TC information? How much confidence do they have in it? How are messages interpreted? What factors are associated with risk perception? How do people react to various colors and text? Can most people locate their home on a map? 7

Major Research Foci: Forecast communication process Forecast/warning messages Societal impacts of tropical cyclones (King & Anderson-Berry 2010) 8

FORECAST COMMUNICATION PROCESS Simplified Model of Chain from Forecast to Value Realization (Morss et al. 2008) 9

Components of Tropical Cyclone Forecast Communication Process (Demuth et al. 2012) 10

Weather Information Value Chain National Hydro-Meteorological Services (e.g., NWS) Media / Broadcasters Vendors / Private Sector Meteorology Emergency Managers / Public Officials Economic Sectors / Stakeholders Weather Monitoring Observation Modeling Forecasting Dissemination Communication Perception Interpretation Information Use / Decision Making Economic Values (Lazo, J. K.)

Some general issues identified: 1) Worse case scenario vs. what s most likely to happen 2) How to communicate forecast uncertainty and confidence level 3) Clarity vs. precision 4) Timeliness vs. accuracy constraints (Anthony et al. 2014) 5) Fewer products vs. completeness 6) Access vs. concerns of information attribution 7) Demand for high quality graphics 12

Forecast/Warning Messages Do people get the message and understand what it means to them? That s the only question that matters. Bryan Norcross Official Blog 04 September 2012 13

Steps To Effective Warning Response Understand Hazard Receive Message Perceive Risk Believe It Applies Know What To Do RESOURCES Appropriate Protective Action

Understand Hazard Slow Rising Water? UltimateChase.com

(Rappaport 2014) 16

FLOODING FROM RAIN WIND WATER FROM OCEAN WIND DON'T KNOW TORNADOES 0% 5% 10% 15% 20% 25% 30% 35% 40% N = 459 STORM SURGE Coastal Public On-Line Survey on Tropical and Extratropical Cyclone Forecast Communication Products Report to NOAA. 2012. Eastern Research Group, Inc. RAIN FLOODING 20% 0% 40% 60% 80% 100% N = 1189 5/7/14 Morrow HFIP-SEIA Storm Surge Panel Survey. Likelihood of Deaths from Major Hurricane NCAR 2010. Lazo, Jeffrey.

TC Level of Threat from Each Hazard Experimental product released by U.S. Local Weather Forecast Offices 18

Datum: Above Ground Level Data Visualization 19

Understand Hazard PDFs for Distribution Educational aids to show surge and its potential effects Animations http://www.nhc.noaa.gov/surge/ New Surge Video http://youtu.be/brmwe0fvhv4 http://www.nhc.noaa.gov/surge/resources.php 20

Receive Message Multiple channels; old and new technologie s Information Sources Used Great Deal During Sandy: 62% local TV 53% national TV 48% The Weather Channel 29% local radio 21% Internet 8% social media (such as Facebook, Twitter) 8% NOAA radio Gladwin and Morrow. 2013. Communication and Understanding of Hurricane Sandy Storm Surge Forecast and Warning Information. National Science Foundation. Morrow and Gladwin. 2014. Puerto Rico Hurricane Evacuation Study Behavioral Analysis. Through Dewberry for FEMA and USACE. Different groups, different modes. Nova Scotia after Hurricane Juan. (Silver, A. and C. Conrad, 2010. Public perception of and response in severe weather warnings in Nova Scotia. Meteorol. Appl., 17, 173-179) 21

Some Research Findings: Perceive Risk Clear, known terminology, vivid impacts information Risk perception is socially constructed out of past experiences (mental models) Strong social component Cultural differences in attitudes toward risk Those who ve gone through severe storms likely to take protective action next time, but with diminishing effects Lots of false experience

Expressing Uncertainty Public infers uncertainty from deterministic forecasts (Joslyn & Savelli 2010; Morss et al. 2010) More likely to reduce exposure when uncertainty information provided (Roulston et al. 2006) Weather Roulette tool used to gauge effectiveness of probabilistic predictions (Hagedorn et al. 2009) Broadcast mets in unique position to explain level of uncertainty (Demuth et al. 2009) 23

NHC Cone of Uncertainty Color Probability Cone (Radford et al. 2013) Track Uncertainty and Wind Probability - Exploratory Testing Arrival of TS Force Winds Currently being tested with EMs and media 24

Possible vs. Expected China: Warning, Urgent Warning U.S., Australia: Watch, Warning India: Alert, Warning, Post-landfall Outlook Important questions remain 25

Believe It Applies Local forecasts, maps, photos, references People often are not aware of the danger associated with their location Relationship between forecast and location needs to be clear 26

Each dot = one interview Likelihood Would Be Flooded in Major Hurricane: Green Not Very Likely they d be flooded in a major hurricane! Not Very Likely Somewhat Likely Very Likely Coastal Georgia Evacuation Study. 2010. Morrow & Gladwin through Dewberry. 2009 for FEMA and USACE.

Coastal Alabama Behavioral Survey. 2011. Morrow & Gladwin through Dewberry for Morrow 28 FEMA and USACE

Know What To Do Action statements Policy Question: Who should be responsible for advising the public about protective actions? 29

Steps To Effective Warning Response Understand Hazard Receive Message Perceive Risk Believe It Applies Know What To Do RESOURCES Appropriate Protective Action

Shortcomings and Unmet Needs Short-term, one-shot studies Involvement of social scientists late in the process Non-random samples Use of social studies methods without sufficient training/education Lack of sustained funding 31

Which Social Science? Anthropology Communication Cultural studies Decision sciences Economics Education Environment Human Geography Law Linguistics Media Politics Psychology Social psychology Sociology and More. 32

ONE RESEARCH MODEL DISCUSSIONS, TASK FORCES, PROTOTYPES STAKEHOLDER INTERVIEWS REVISED PROTOTYPES FOCUS GROUPS PRODUCTS: STORM SURGE FLOODING MAP STORM SURGE WARNING ( FOLLOW-UP INTERVIEWS REVISED PRODUCTS EXPERIMENTAL Work completed through Eastern Research Group & National Center for Atmospheric Research and funded by HFIP and NOS Surge Roadmap.

EXPERIMENTAL 2014 34

Recommendations: Way Forward Expand focus of TC research from improving the forecast to improving societal outcomes Integrate social scientists into meteorology Involve social scientists early in the development process Establish session on social science research as continuing part of IWTC program 35

REFERENCES Anthony, K. E., K. R. Cowden-Hodgson, H. D. O Hair, R. L. Heath & G. M. Eosco, 2014. Complexities in communication and collaboration in the hurricane warning system. Comm Studies, 65, 5, 468-483. Demuth, J. L., B. H. Morrow and J. K. Lazo, 2009. Weather forecast uncertainty information: An exploratory study with broadcast meteorologists. Bull. Am. Meteorol., November, 1614-1611. Demuth, J. L., R. E. Morss, B. H. Morrow and J. L. Lazo, 2012. Creation and communication of hurricane risk information. Bull. Am. Meteorol. Soc., August, 1133 1145. Hagedorn, R. and L. A. Smith, 2009. Communicating the value of probabilistic forecasts with weather roulette. Meteorol. Appl., 16, 2, 143-155. Joslyn, S. and S. Savelli, 2010. Communicating forecast uncertainty: public perception of weather forecast uncertainty. Metereol. Appl., 17, 180-195. King, D. and L. Anderson-Berry 2010. Societal impacts of cyclones. Presentation at International Workshop on Tropical Cyclones. Lazo, J. K., A. Bostrom, R. E. Morss, J. L. Demuth and H. Lazrus, 2014. Communicating hurricane warnings: Factors affecting protective behavior. Submitted to Risk Analysis, June 2014. Morrow, B. H., 2007. Storm surge social science project final report. Submitted to NOAA Coastal Services Center. Morrow, B. H., J. K. Lazo, J. Rhome and J. Feyen, 2014. Improving storm surge risk communication: Stakeholder perspectives. Bull. Am. Meteorol. Soc. Preliminary approval. Morss, R. E., J. L. Demuth and J. K. Lazo, 2008. Communicating uncertainty in weather forecasts: A survey of the U.S. Public. Weather Forecast., 23, 5, 974-991. Morss, R. E., J. K. Lazo and J. Demuth, 2010. Examining the use of weather forecasts in decision scenarios; Results from a US survey with implications for uncertainty information. Meteorol. Appl., 17, May, 149-162. Radford, L., J. C. Senkbeil and M. Rockman, 2013. Suggestions for alternative tropical cyclone warning graphics in the USA. Disaster Prev. Manage., 22, 2, 192-209. Rappaport, E.N., 2014. Fatalities in the United States from Atlantic Tropical Cyclones New Data and Interpretation. Bulletin of the American Meteorological Society 95:341-346. Roulston, M. S., G. E. Bolton, A. N. Kleit and A. L. Sears-Collins, 2006. A laboratory study of the benefits of including uncertainty information in weather forecasts. Weather Forecast. 21,1, 116-122)

Contact Information Betty Morrow betty@bmorrow.com Jeff Lazo lazo@ucar.edu 37