1. NCAR Societal Impacts Program (SIP) Research Integrating Social Science and Meteorology Picture “borrowed” from http://www.atmos.washington.edu/~houze/ Jeff Lazo Rebecca Morss Julie Demuth July 8, 2009 Jeff Lazo Rebecca Morss Julie Demuth July 8, 2009

2. Overview Why integrate meteorology and social sciences? What is the Societal Impacts Program (SIP)? Brief overview of SIP activities –Capacity building –Research In-depth research discussion –Current hurricane research projects –Current NSF (and partially NOAA) funded research on hurricanes and flash floods Summary

3. Meteorology + Social Science: Putting Together the Pieces Ultimate goal of weather forecasting = to create societal value by providing usable information for decision making For information to be usable, must be –Scientifically sound –Communicated effectively –Interpretable –Actionable SIP supports these goals through multiple mechanisms

4. Societal Impacts Program Overview: –NCAR – RAL, MMM, ISP, COMET –Funded by NCAR, NOAA’s USWRP, external grants –Initiated April 1, 2004 Objective: –Infuse social science and economic research, methods, and capabilities into the planning, execution, and analysis of weather information, applications, and research directions through:  Information Resources  Weather and Society * Integrated Studies (WAS*IS)  Develop and Support Weather Impacts Community  Primary Research

5. SIP’s Capacity-Building Activities

6. Information Resources –Extreme Weather Sourcebook – updated to 2008 $11.6B / year weather damages (1955-2006) Led to research project on quality of damage data –Weather and Society Watch 250+ subscribers Quarterly newsletter – including special issue for AMS mtg. Always looking for contributions! Any ideas??? –Societal Impacts Program Discussion Board 250+ participants

7. Weather and Society * Integrated Studies (WAS*IS) Capacity-building program – build an interdisciplinary community & learn about integrating social science & meteorology –6 workshops, 171 people to date –Upcoming 2009 summer workshop, August 6-14 (NWS sponsored) –Additional WAS*IS- inspired workshops  NWS WAS*IS workshop (October 2007) 2008

8. WAS*IS-Inspired NWS Integrated Warning Team (IWT) Workshops WFO Kansas City / Pleasant Hill held 1 st IWT (January 2009) –Envisioned, led by Andy Bailey (WCM) with WAS*IS –Build stronger partnerships among NWS, broadcast meteorologists, emergency managers –Introduce and discuss social science NWS Central Region to continue –IWT in Omaha/Valley (September 2009) –Possibly another in 2009

9. Develop and Support Weather Impacts Community (Examples) American Meteorological Society -Boards, Editorial Boards, Committees, Council NOAA -Social Science Working Group -Weather & Water Social Science Strategic Plan -Hurricane Forecast SocioEconomic Working Group  NOAA-NSF Call for Proposals World Meteorological Organization -WWRP Social and Economic Research and Applications Working Group -Forum on Social and Economic Applications and Benefits -Economics Primer for Meteorological and Hydrological Services National Research Council -Estimating & Communicating Uncertainty -Multifunction Phased Array Radar -Weather Research Progress and Priorities

10. SIP Research: Brief Overview

11. Econometric model of U.S. state-level and sector-level sensitivity to weather variability 24 Years Economics Data 70 Years Weather Data Sensitivity: - Relative: 3.36% - Absolute: $470 billion (2007) Overall U.S. Sector Sensitivity Assessment

12. Storm Data Assessment NWS Performance Branch – Brent MacAloney Survey of NWS personnel on Storm Data processes Two part survey –Part A - July 2008 – WFO based  Data creation  Perception of accuracy  Additional training/ resources –Part B - December 2008 – Event based  Specific estimation for randomly selected recent events  Individual responsible for each event’s loss estimate Desired Outcomes –Identify training and resource needs –Understand potential bias in loss estimation

13. 300 Billion Served! Conducted nationwide, web survey on people’s sources, perceptions, uses, and values of weather forecasts –Average respondent gets weather forecasts 115 times per month! –226 million U.S. adults  300 billion forecasts obtained per year!  N=1465, 3.6% never use weather forecasts

14. Value of Weather Forecasts Same nationwide survey asked about people’s willingness-to-pay for NWS services –Median fitted value of $286 per household –114.4 million households  estimated value of forecasts to U.S. public is $31.5 billion

15. Communication of Forecast Uncertainty –Channel A: high temperature will be 76°F tomorrow –Channel B: high temperature will be between 74°F and 78°F tomorrow. 0%10%20%30%40%50% Prefer Channel A (deterministic) Prefer Channel B (uncertainty) Like both channels Like neither channel I don't know Percent of Respondents Same survey: Suppose you are watching the news 22% 45% 27% 2% 4%

16. NWS Service Assessments Super Tuesday Tornadoes –Led social science efforts on  Who died, where, what warning information they had  Survivors’ knowledge, interpretations, and decisions –Partnerships with NWS team members were essential!  Mike Vescio (PDT), Kevin Barjenbruch (SLC), Daniel Nietfeld (OAX) SIP working with NWS on including social scientists in other Service Assessments –Jen Sprague, Brent MacAloney, Doug Young

17. Broadcasters’ Views on Forecast Uncertainty Broadcasters are users and providers of forecast uncertainty … verbally, graphically, numerically 3 focus groups with 14 broadcast meteorologists at AMS Broadcast Meteorology Conference –Collaboration with Paul Hirschberg, Elliott Abrams, John Gaynor, Betty Morrow in support of AMS ACUF Results: BAMS InBox article (Demuth et al., in press) “If it’s a complicated forecast, I’ll say that I’m not too sure what’s going to happen. But not with the numbers… We show 3 tombstones. This is what you can expect the next 3 days. And then at the end I show the other 3, but I just kind of brush those off… So there’s a way of expressing uncertainty without having to get into the numbers and PoPs and stuff like that.”

18. Improving NWS Public Forecasts NWS web pages accessed millions of times daily Public forecast information can be inconsistent or misleading, which can lead to suboptimal decisions Partnering with NWS to integrate social science into evaluation –Joint funding with OST, OCWWS –Collaborating with Doug Hilderbrand, many others

19. SIP’s Current Hurricane Research Projects

20. Hurricane Forecast Socio-Economic Working Group

21. Benefits of Improved Hurricane Forecasting What is the value to households of potentially improved hurricane forecasts? Stated-preference method –Stated Choice (conjoint analysis) Current Survey Development and Empirical Results 80 subjects not representative sample –non-random nature of recruiting –small sample size

22. Benefits of Improved Hurricane Forecasting Survey Outline 1.personal impact / vulnerability 2.perceived risk 3.preparation for hurricane 4.evacuation decisionmaking 5.likely impact on household 6.hurricane forecasts  attributes  perceived accuracy 7.improved hurricane forecasts  attributes  choice sets 8.current hurricane forecasts 9.socio-demographics

24. Choice Set Attributes and Levels Level Time of expected landfall Maximum wind speed Projected location of landfall Expected storm surge Increase in Annual Cost to Your Household Currently accurate to within (baseline) 8 hours 48 hours in advance 20 miles per hour 48 hours in advance 100 miles 48 hours in advance plus or minus 8 feet of height above sea level 48 hours in advance $12 Intermediate Improvement 6 hours 15 miles per hour 80 miles 48 hours in advance 6 feet of height above sea level $24 Maximum Improvement 4 hours 10 miles per hour 65 miles 48 hours in advance 4 feet of height above sea level $48

26. Benefits of Improved Hurricane Forecasting Econometric modeling and analysis – Don Waldman –random utility behavioral model –parameter estimates represent marginal utilities  landfall time, windspeed, location, storm surge  cost (marginal utility of income) –estimation is by bivariate probit  first choice between A and B  second choice between A/B and “do nothing” –analyzed only choice occasions 2 – 8 –80 subjects – 7 choices each = 560 “observations”  quadrature to account for intra-subject correlation

27. Modeling Results Choice Sets 2-8 Only (n = 560) Bivariate Probit w/quadrature Est.t-ratio Marginal WTP Landfall Time-0.067-3.57$2.18 Maximum wind speed-0.008-1.08$0.26 Landfall location-0.007-3.22$0.23 Storm surge-0.062-3.50$2.04 Annual Cost-0.030-11.27

28. WTP Calculation: Improve Baseline to Intermediate on All Attributes Attribute Baseline (all 48 hours in advance) Intermediate Improvement Diff. Marg. WTP WTP Time of expected landfall ± 8 hours± 6 hours2$2.18$4.36 Maximum wind speed ± 20 mph± 15 mph5$0.26$1.30 Projected location of landfall ± 100 miles± 80 miles20$0.23$4.60 Expected storm surge ±8’ of height above sea level ± 6’ of height above sea level 2$2.04$4.08 Total WTP$14.34

29. Hurricane Forecast Improvement Project HFIP Metrics –Reduce average track error by 50% for Days 1 through 5. –Reduce average intensity error by 50% for Days 1 through 5. –Increase the probability of detection (POD) for rapid intensity change to 90% at Day 1 decreasing linearly to 60% at Day 5, and decrease the false alarm ratio (FAR) for rapid intensity change to 10% for Day 1 increasing linearly to 30% at Day 5. –Extend the lead time for hurricane forecasts out to Day 7

30. Hurricane Forecast Improvement Project Socio-Economic Impacts Assessment –Assessment of Emergency Managers - Betty Morrow  in-depth focused interviews  emergency managers  stakeholder communities (hospitals / transportation / etc) –Household valuation – Jeff Lazo  non-market stated choice assessment  adapted Benefits of Improved Hurricane Forecasting  attribute set from HFIP  400 sample across the vulnerable region

31. SIP’s NSF- and NOAA- Funded Research on Hurricanes and Flash Floods

32. Warning Decisions in Extreme Weather Events (WDEWE) 3-year NSF-funded study How are warnings communicated, obtained, interpreted, and used in decision making by participants in the warning process? –Role of uncertainty in information dissemination and decision making –Factors influencing organizational and public decision making and actions –Public preferences for attributes of forecast and warning information

33. WDEWE Collaborators & Participants Multi-disciplinary research team –Meteorology, economics, sociology, risk communication and decision analysis –NCAR, CU Hazards Center, Univ. of Washington Key support and collaboration with Larry Mooney, MIC at WFO Denver/Boulder 4 groups: forecasters, public officials, media, public

34. WDEWE Methods Parallel studies: hurricanes in Miami area, flash floods in Boulder area Multiple methods –In progress (in Boulder)  Interviews (organizational & mental modeling) & survey with forecasters, local officials, media –Planned  Mental modeling interviews with public on perceptions and decision making  Focus groups with officials, media, public on interpretation and use of warning messages  Survey on public on preferences for warning messages  Synthesis and feedback

35. WDEWE: Methods examples

36. Communicating Hurricane Info (CHI) 2-year joint NSF- & NOAA-funded study How is the content of hurricane forecast and warning messages generated? What are the channels through which hurricane forecast and warning information is communicated? How do at-risk people (including vulnerable populations) comprehend and react to specific components of the forecast and warning messages?

37. CHI Collaborators & Participants Multi-disciplinary research team –Meteorology, economics, communication, sociology –NCAR, Univ of Oklahoma, Univ of Houston Key support and collaboration –Jamie Rhome, Storm Surge Team Lead at NHC –Advisory committee of researchers, forecasters, broadcasters 4 groups: NHC and WFO forecasters, broadcasters, emergency managers, public

38. CHI Methods Parallel studies in Miami and Houston areas Multiple methods –In progress (in Miami):  Interviews and observations with forecasters, broadcasters, emergency managers –Planned  Survey on public access, comprehension, and reaction to messages  Focus groups with vulnerable populations  Laboratory tests of sample messages with public  Synthesis and feedback

39. CHI: Product examples

40. Meteorology + Social Science: Putting Together the Pieces Ultimate goal of weather forecasting = to create societal value by providing usable information for decision-making Integrated meteorology-social science research is in its infancy!! (Think Ted Fujita studying tornadoes 30-40 years ago!) We have a long way to go … but it’s an exciting road to travel!

41. Thank You! Jeff Lazo – lazo@ucar.edu Rebecca Morss – morss@ucar.edu Julie Demuth – jdemuth@ucar.edu Thank You! Jeff Lazo – lazo@ucar.edu Rebecca Morss – morss@ucar.edu Julie Demuth – jdemuth@ucar.edu www.sip.ucar.edu