Emergency Manager Severe Weather
Information Needs and Use of
Experimental Warning Information
Daphne LaDue, Ph.D.
OU CAPS
Christopher Karstens, Ph.D.
OU CIMMS/NSSL
Sean Ernst
OU SoM
James Correia, Jr., Ph.D.
OU CIMMS/SPC
James Hocker
Oklahoma Climatological
Survey
Jonathan Wolfe
NOAA NWS Charleston WV Forecast
Office
This work was funded by NOAA/NSSL and OU CIMMS
MOTIVATION
Main Goal of FACETs:
• provide useful information to key decision makers
• to enable effective response
We can provide more, such as via Prototype Probabilistic
Hazard Information
• Research Question: does the Prototype PHI tool
enable forecasters to provide what emergency
managers need?
Two parts to this work:
• Critical Incident Interviews
• 2015 Hazardous Weather Testbed
PART I:
CRITICAL INCIDENT STUDY
Purpose: identify strengths and limitations of current severe
weather information flow to EMs
Methodology: Critical Incident Technique (CIT) interviews
CIT interviews:
• go beyond participants’ opinions
• seek critical incidents that illustrate the competency (or
lack thereof)
• developed in 1950s by a psychologist to help AF identify
good pilots
Both Studies: data-driven, thematic analyses
PART I:
PARTICIPANTS
Interviews:
• 5 county-level
• 2 city-level
• 1 state-level regional
coordinator (of 15
counties)
• 1 state level, ESF 8
• 1 military
• 1 EM for school district
PART I FINDINGS:
STORM HISTORY
CIT stories revealed that storm history gives EMs a better
idea of what to expect in their community:
Need to know
what storm has
done, expected
strength changes
— EM5
“In real time I was able to
redirect [medical response
resources], ‘cause I have
the latest, greatest that the
National Weather Service
is providing…” —EM8
Storm history info
and track help give
1-1.5 hours heads
up on inbound
storms and
potential impact —
EM4
“Getting to know that storm a little better” —EM9
PART I FINDINGS:
RELATIONSHIP
1. Specific events build relationships —EM7
•
Forecaster on duty didn’t realize impact of sub-severe
storm and EM needs related to impacts
• EM used existing relationships to solve problem
• Built longer-term understanding of EM information needs
2. Building open lines of communication
• EMs know they can call NWS when they need to
• NWS might even reach out prior to event
3. Knowing NWS forecasters creates trust in forecast
information
Knowing NWS
forecasters builds
trust in information
—EM5
“If I see things that concern me, I’ll either
start chatting or get on the phone with my
local NWS and ask them if they’re going to
put a warning out” —EM2
PART I FINDINGS:
CONFIDENCE
1. EM’s understand forecasts carry uncertainty, and they’d like
to hear about forecaster opinions on it
Want confidence on threat
timing and likelihood to
shelter large events well in
advance —EM4
“[NWSChat] gives me a
certain level of confidence,
and I find out what they really
think too” —EM10
1. Example of good information
“It was high, I think it was high confidence, of, supercell
development, into individual supercell development.
Moderate to high confidence, that it will affect the metro, and
then they gave the eta, like between 7 and 9 pm.” —EM11
PART I:
PRELIMINARY OUTCOMES
1. EMs looking for the NWS story of a weather event
•
•
Want the narrative of the storm as it unfolds
Want to know how the forecasters perceive the event
2. Want to build and maintain strong relationship with
forecasters to build trust in forecast
3. Want forecaster’s insights into inherent uncertainty
•
EMs aware that no forecast is exact, want to know
forecaster’s honest assessment of forecast
PART II:
HAZARDOUS WEA. TESTBED
Purpose: bring key stakeholder group into PHI development
early in the R&D to assure resulting work is useful, usable
Methodology:
• Pre-week survey to establish current views of uncertainty
• EMs viewed PHI generated by NWS forecasters and noted
decision/action points
• Researcher observations and questions
• Joint debriefing discussions after each case or live event
• End-of-week EM-only and joint w/NWS discussions
Traditional SVR
warning polygon
Polygon extends beyond echo behind
and to the sides of the storm.
Polygon forward spreads out in width.
One polygon for tornado, wind, hail.
vs.
PHI Object and
SVR Plume
Object tightly surrounds intense part
of echo.
Plume forward spreads out in width.
Separate objects for tornado vs.
wind/hail.
HWT PARTICIPANTS
See also talk by Karstens et al. @ 9:45am Tuesday
6 NWS from 6 states:
Arizona (1)
Alabama (1)
Maine (1)
Missouri (1)
Oklahoma (1)
Virginia (1)
10 EMs from 5 states:
Alabama (1)
Michigan (1)
Minnesota (1)
Oklahoma (6)
Wyoming (1)
PART II:
HWT RESEARCH DESIGN
See also talk by Karstens et al. @ 9:45am Tuesday
Forecasters in
HWT working
w/PHI
Communication
via NWSChat &
PHI
Log of
actions
EMs in another
room, using EDD
to see PHI output
Each object has an associated
set of information (yellow box):
Contents of the discussion box
evolved each week as forecasters and
EMs interacted.
Time of
arrival
Time of
departure
FINDINGS:
HAZARD PLUMES VS.
TRADITIONAL WARNINGS
The main difference: “Uncertainty” —Co4
Advantages:
• PHI  Focus Gives you what areas to focus on, and what areas
likely won’t be affected —Co2
Helps identify which cells in a line might do
something —St1
• Still need trigger points; EMs cannot devote 100% attention to
weather
• Polygon  Confidence of NWS  of EM’s  of others, too.
“Confidence is contagious.”
“...if you’re confident enough to...warn [x number
of] people...maybe I should be certain, too.” —Co4
FINDINGS:
PROBABILITY IS USEFUL.
IS CHANGE MEANINGFUL?
Liked seeing the increases, decreases in probabilities
Changes after warning issuance could be meaningful
“I’m at an 85%, where maybe the warning came
out at a 60%, and that’s like, boom. It gives me a
lot more information.” —Co5
EMs: A few percentage change probably not meaningful, and
may fluctuate too much.
• 10% was suggested,
• or have the forecaster tell decide what was is a
meaningful increase or decrease for that day
FINDINGS:
THIS IS MANAGEABLE
Initially some concern about the increase in information
Iterated w/ forecasters on discussion box contents
EMs started injecting information to the NWS.
“I think we need to send some realistic injects back to them so that
the scenario works just a little bit better. They can...see some of the
challenges that we have” —Co2
Need spot forecast:
Need spot forecast:
Hail hitting hazardous
chemical tanker truck,
can’t take much more.
Will the hail get any
bigger? When will it
stop?
College football team
on a bus with bow
echo heading toward
the highway. How
strong will winds be?
Need spot
forecast:
Water loading on
factory roof, might
collapse. How much
longer will rain last?
CO-CREATION OF WHAT PHI
SHOULD BE
Amazing dynamic — participants became researchers, asking
each other insightful questions to understand the others’ point of
view.
Each week iterated toward the same things:
• Discussion box to contain:
• history, such as reports
• forecaster thinking, but not bland warning-type statements
NWS: “I’m not, I’m not completely, I’m not sold
EM: ”Why
enough to drop the probabilities in light...of the
didn’t you write
reports we’ve gotten [and] how strong it was there for that in the
awhile.”
box?”
• Forecaster touch critical; did not trust automated forecast
• Did forecaster agree? Or that they changed numbers with
his/her expertise & knowledge beyond radar
CONCLUSIONS
Contact:
dzaras@ou.edu
Critical Incident Study
EMs want the narrative of storm as it unfolds
Relationships need to be built and maintained
EMs want forecasters’ ongoing assessment, including
uncertainty
Hazardous Weather Testbed
PHI is more specific, focused  useful for EM decisions
Still need trigger points for action; confidence of forecaster
On our Research in the HWT:
Presence of EMs gave forecasters focus & rapid feedback
 Rapid co-creation of potential PHI vision