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

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NCAR Societal Impacts Program (SIP) Research Integrating Social Science and Meteorology


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ncar societal impacts program sip research integrating social science and meteorology

NCAR Societal Impacts Program (SIP) Research Integrating Social Science and Meteorology

Jeff Lazo

Rebecca Morss

Julie Demuth

July 8, 2009

Picture “borrowed” from http://www.atmos.washington.edu/~houze/

overview
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
meteorology social science putting together the pieces
Meteorology + Social Science: Putting Together the Pieces
  • Ultimate goal of weather forecasting = to create societal value by providing usableinformation for decision making
  • For information to be usable, must be
    • Scientifically sound
    • Communicated effectively
    • Interpretable
    • Actionable
  • SIP supports these goals through multiple mechanisms
societal impacts program
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
information resources
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
weather and society integrated studies was is
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

was is inspired nws integrated warning team iwt workshops
WAS*IS-Inspired NWS Integrated Warning Team (IWT) Workshops
  • WFO Kansas City / Pleasant Hill held 1st 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
slide9

Develop and Support Weather Impacts Community (Examples)

  • NOAA
      • Social Science Working Group
      • Weather & Water Social Science Strategic Plan
      • Hurricane Forecast SocioEconomic Working Group  NOAA-NSF Call for Proposals

American Meteorological Society

- Boards, Editorial Boards, Committees, Council

  • National Research Council
      • Estimating & Communicating Uncertainty
      • Multifunction Phased Array Radar
      • Weather Research Progress and Priorities
  • 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
overall u s sector sensitivity assessment
Overall U.S. Sector Sensitivity Assessment

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)

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

Storm Data Assessment
  • 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
300 billion served
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
value of weather forecasts
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
communication of forecast uncertainty
Communication of Forecast Uncertainty
  • Same survey: Suppose you are watching the news
  • Channel A: high temperature will be 76°F tomorrow
  • Channel B: high temperature will be between 74°F and 78°F tomorrow.

Prefer Channel A

22%

(deterministic)

Prefer Channel B

45%

(uncertainty)

27%

Like both channels

2%

Like neither channel

4%

I don't know

0%

10%

20%

30%

40%

50%

Percent of Respondents

nws service assessments
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
broadcasters views on forecast uncertainty
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.”

improving nws public forecasts
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
benefits of improved hurricane forecasting
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
benefits of improved hurricane forecasting22
Benefits of Improved Hurricane Forecasting

Survey Outline

  • personal impact / vulnerability
  • perceived risk
  • preparation for hurricane
  • evacuation decisionmaking
  • likely impact on household
  • hurricane forecasts
    • attributes
    • perceived accuracy
  • improved hurricane forecasts
    • attributes
    • choice sets
  • current hurricane forecasts
  • socio-demographics
benefits of improved hurricane forecasting26
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
hurricane forecast improvement project
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
hurricane forecast improvement project30
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
warning decisions in extreme weather events wdewe
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
wdewe collaborators participants
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
wdewe methods
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
communicating hurricane info chi
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?
chi collaborators participants
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
chi methods
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
meteorology social science putting together the pieces40
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!
slide41

Thank You!

Jeff Lazo – lazo@ucar.edu

Rebecca Morss – morss@ucar.edu

Julie Demuth – jdemuth@ucar.edu

www.sip.ucar.edu