Integrating Meteorology and Social Science

1
Integrating Meteorology and Social Science
Julie Demuth NCAR Societal Impacts Program
NCAR Undergraduate Leadership Workshop June 18,
2008
2
The forecast high temperature for Boulder
tomorrow is 80ºF.
What do you think the actual high temp will be?

• 80ºF
• 79-81ºF
• 78-82ºF
• 75-85ºF
• 70-90ºF

3
Making decisions during a tornado

• Why might someone not take shelter from a
  tornado?
• .
• .
• .

• What is the right decision?
• Is there a right decision?
• How and why do decisions get made?

4
NCAR Societal Impacts Program (SIP)

• SIP Mission
• To improve the societal gains from weather
  forecasting by infusing social science research,
  methods, and capabilities into the planning,
  execution, and analysis of weather information,
  applications, and research directions
• Little is currently known about
• Sources, perceptions, interpretations,
  preferences, uses, values, etc. of weather
  information
• For various user groups
• For a range of weather information contexts
  (e.g., weather phenomena, uncertainty, different
  points in time)

5
Examples of SIP research

• Sources, perceptions, uses, and value of weather
  forecasts
• Communicating uncertainty in weather forecasts
• Broadcast meteorologists preferences for
  conveying uncertainty and perceptions of publics
  needs and wants
• Societal impacts of 2008 Super Tuesday tornado
  event

6
Examples of SIP research

• Overall U.S. economic sector sensitivity
  assessment
• Assessing the transportation sectors use and
  value of weather information
• Examining warning decisions in extreme weather
  events

• Examining the hurricane warning system Content,
  channels, and comprehension (proposed)

7
WASIS Vision To change the weather enterprise
by comprehensively sustainably integrating
social science into meteorological research
practice
8
Weather Forecast Uncertainty Information
9
Motivation

• Weather forecasts are inherently uncertain
• but much current weather forecast information
  provided to the public is deterministic
• Interest in providing uncertainty information
• but we have limited understanding of how to do
  this effectively
• Little (publicly available) empirical knowledge
  about what people think, want, use, etc.
  regarding weather forecast information
• but this is a critical piece to being able to
  effectively communicate weather forecast
  information, including uncertainty

10
Methodology and Research ?s

• Nationwide controlled-access web survey of public
• Uncertainty-related research questions
• How much confidence do people have in different
  types of weather forecasts?
• Do people infer uncertainty into deterministic
  forecasts and, if so, how much?
• How do people interpret a type of uncertainty
  forecast that is already commonly available and
  familiar probability of precipitation forecasts?
• To what extent do people prefer to receive
  forecasts that are deterministic versus those
  that express uncertainty?
• What formats do people prefer for receiving
  forecast uncertainty information?

11
Suppose the forecast high temperature for
tomorrow for your area is 75F.

• What do you think the actual high temperature
  will be?

50
40
30
Percent of Respondents
20
10
0
75F
74-76F
73-77F
70-80F
65-85F
Other
( 1F)
( 2F)
( 5F)
( 10F)
N1465
12
Super Tuesday Tornado OutbreakNWS Service
Assessment
13
Motivation

• 56 direct fatalities highest death toll since
  1985
• Despite extensive efforts to warn the public
  about the impending event, there was still a
  large loss of life. Why?

• NWS conducted national service assessment
• 1st time for societal impacts emphasis in a
  service assessment (and there has already been
  another!)

14
Service assessment scope

• This was a well-warned event Why didnt people
  take shelter? Why dont people do what theyre
  supposed to do to make the right decision?

15
Methodology

• Semi-structured interviews with members of the
  public
• Some of the questions
• When did you first realize there was a threat of
  a tornado in this area?
• How did you learn about the threat? (Sources,
  environmental cues)
• What were you thinking after you received that
  information? (Trust? Confusion? Uncertainty?
  Barriers to action?)
• What did you do next? (Confirmation?)
• Were there any unique circumstances about your
  day that affected your experience during the
  tornado event? What?
• Have you ever been in a similar type of
  extreme-weather situation in the past?
  (Experience, false alarms)
• Did anything from that experience influence what
  you did during this most recent event?
• Have you ever been warned about an extreme
  weather event in the past that did not occur?
• Think back over the entire tornado event, from
  the time you learned there was a tornado threat
  through when the tornado actually occurred.
• Is there any other information you would have
  liked to have had?

16
Findings perception

• Personalization of the threat
• Seeking confirmation of the threat
• Atkins woman
• Atkins couple
• Personal risk perception optimism bias!
• Tennessee family
• Arkansas family

17
Meteorology social science

• To provide products and services to better inform
  and improve peoples decision-making
• Provide people information that they actually
  want and use rather than what we think they do
  (or should) want and use
• Couple results with product development efforts
  and practice
• So much knowledge ? concepts, methodologies,
  theoretical frameworks, tools ? from the social
  sciences that can and should be integrated!
• Communication, anthropology, sociology,
  psychology, econ,

SO many needs, opportunities, and knowledge gaps
Multiple users, creators, channels, messages,
contexts, etc.!
18
The essentials

• Partnerships among social scientist, research
  meteorologists, operational meteorologist, policy
  makers, practitioners, etc.
• Interest and willingness to work together, to
  listen, learn, exchange ideas! To co-produce
  knowledge.
• Passion!

Working toward the overarching goal of better
serving society!
19
Thank you!

• What questions do you have
• about my research?
• about other related research / opportunities?
• about getting into this type of work?
• about anything else?
• Contact info and resources
• jdemuth_at_ucar.edu
• NCAR Societal Impacts Program (www.sip.ucar.edu)
• WASIS (www.sip.ucar.edu/wasis)

20
Extra Slides
21
WASIS mission

• Build an interdisciplinary community of
  practitioners, researchers, stakeholders --
  from the grassroots up -- who are dedicated to
  the integration of meteorology social science

Capacity building -- creating a community for
lifelong collaboration support!
22
WASIS mission

• Providing opportunity to learn and examine ideas,
  methods, and examples related to integrated
  weather-society work

• Tools GIS, surveys, qualitative methods
• Concepts problem definition, speaking the same
  language, end-to-end-to-end process
• Topics risk perception, vulnerability,
  resilience

23
All the choices below are the same as a
probability of precipitation of 20.
Do you like the information given this way?

• Chance of precipitation is 20
• There is a 1 in 5 chance of precipitation
• The odds are 1 to 4 that it will rain
• There is a slight chance of rain tomorrow

? Percent ? Frequency ? Odds ? Text
Asked this question 3 ways -- using PoPs of 20,
50, and 80 with corresponding text descriptions
from NWS
24
Percent of respondents who said yes
25
Findings knowledge

• People get information from multiple sources
• Mostly commonly via television
• Also commonly from other people (family, friends,
  neighbors, co-workers)
• People get information multiple times
• NOAA Weather Radio not common
• Misconceptions about sirens

26
Findings decision-making

• Often think of decision-making as a singular
  event
• Happens numerous times and ways throughout the
  warning process
• Implicit part of peoples gathering and
  interpreting weather information
• Focusing on sheltering
• Vast majority of people who received warning
  information sought shelter in best location
  available to them

27
Findings 56 fatalities

• Collected as much good data as we could
• Nearly 2/3 of victims were in mobile homes
• Distinction between safer and safest shelters
• Most people did not have a safest shelter option
  available
• Of 19 people for which we had information, most
  people heeded the warning and sought shelter in
  best available location