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Warning decision making – Austria 2003
A successful integrated convective warning system:
A successful integrated convective warning system:
Workshop in Österreich – The integrated warning system Workshop in Österreich – The integrated warning system 20-23 May 2003 20-23 May 2003
Presented by
Jim LaDue
Warning Decision Training Branch
Norman, Oklahoma
Warning decision making – Austria 2003
ObjectiveObjective
To share our experiences with what makes an effective warning system
Warning decision making – Austria 2003
An integrated warning systemAn integrated warning system1. A research program for science, technology, human factors
2. Rapidly updating stream of information about storms and their environment from radar, satellite, point observations, model information, and spotters
3. An office with an effective warning operations plan to help forecasters maintain situational awareness
4. Knowledgeable forecasters in the science, technology and human factors recognize the threats and issue timely watches, warnings and updates
5. Multiple and redundant methods of communicating warnings to the media, emergency preparedness community and the general public
6. A public knowledgeable in using the watches and warnings to protect life and property
7. Post-mortem on events to review mistakes
Warning decision making – Austria 2003
OverviewOverview1. Intro to the warning program
I. Pre-event products
II. Warnings and statements
2. The information flow
I. Radar, spotters, environment
3. Situational awareness
4. Warning Operations – maximizing SA
I. Office strategies
II. Individual storm assessment strategies
5. Maintaining proficiency
I. training
II. Learning from past mistakes
Warning decision making – Austria 2003
Pre-event awarenessPre-event awareness
The Storm Prediction Center (SPC) issues
outlooks from 1 to 3 days before the event
The Norman Weather Forecast Office translates the SPC products to enhance public awareness of the risks
Warning decision making – Austria 2003
An example SPC outlookAn example SPC outlook
50 kt
50 kt
70 kt
LShort wavetrough
MDT risk
These outlooks are intended for
forecasters
-9 hr -6 hr -3 hr -0 hr +3hr
Warning decision making – Austria 2003
An example WFO hazardous weather outlook
An example WFO hazardous weather outlook
THUNDERSTORM OUTLOOK
NATIONAL WEATHER SERVICE NORMAN OK
1230 PM CDT MON MAY 3 1999
THERE IS A MODERATE RISK OF SEVERE THUNDERSTORMS OVER THE WESTERN HALF OF OKLAHOMA AND WESTERN NORTH TEXAS LATER THIS AFTERNOON THROUGH TONIGHT. THE RISK AREA IS EAST OF A HOLLIS TO BUFFALO LINE AND WEST OF U.S.HIGHWAY 177. AREAS OF CENTRAL AND SOUTHEAST OKLAHOMA EAST OF HIGHWAY 177 ARE IN A SLIGHT RISK.
DISCUSSION... (stuff deleted)
WIND SHEAR IN THE ATMOSPHERE IS EXPECTED TO BE FAVORABLE FOR STORM ORGANIZATION AND SOME SUPERCELL THUNDERSTORMS ARE LIKELY. ALTHOUGH HAIL AND DAMAGING WINDS ARE THE MAIN SEVERE WEATHER THREATS...THE COMBINATION OF MODERATE INSTABILITY AND MODERATELY STRONG WIND FIELDS SUGGEST THAT ISOLATED TORNADOES ARE ALSO POSSIBLE INTO THE MID-EVENING. AS THE THUNDERSTORMS ORGANIZE INTO A SQUALL LINE LATER THIS EVENING...THE MAIN SEVERE THREATS WILL BE HAIL AND STRONG WINDS.
EMERGENCY MANAGERS AND SPOTTER GROUPS ACROSS CENTRAL AND WESTERN OKLAHOMA AND WESTERN NORTH TEXAS SHOULD BE PREPARED FOR POSSIBLE ACTIVATION LATER THIS AFTERNOON AND THROUGH THE EVENING.
Location of the moderate risk in
Norman’s area
Weather discussion
Call to action
Warning decision making – Austria 2003
Local offices disseminate which counties are included in the watch
Spotters are activated
SPC watch – Threat imminentSPC watch – Threat imminent
Issued before storms mature
Valid for 6 hrs
-9 hr -6 hr -3 hr -0 hr +3hr
Warning decision making – Austria 2003
Warning OperationsWarning Operations
-9 hr -6 hr -3 hr -0 hr +3hr
•Severe Tstm•>2cm hail•>25 m/s•Valid 1 hr
•Tornado•Radar/spotter indications•Valid <1hr
•Flash flood•Life threatening flood•Spotter reports•Valid >2 hr
Warning decision making – Austria 2003
Warning OperationsWarning Operations
-9 hr -6 hr -3 hr -0 hr +3hr
Warning decision making – Austria 2003
Warning geometryWarning geometry
• The warning is drafted with latitude/longitude vertices
Warning decision making – Austria 2003
Warning geometryWarning geometryBULLETIN - IMMEDIATE BROADCAST REQUESTED
SEVERE THUNDERSTORM WARNING
NATIONAL WEATHER SERVICE NORMAN OK
415 PM CDT MON MAY 3 1999
THE NATIONAL WEATHER SERVICE IN NORMAN HAS ISSUED A
* SEVERE THUNDERSTORM WARNING FOR...
COMANCHE COUNTY IN SOUTHWEST OKLAHOMA
* UNTIL 500 PM CDT
* AT 415 PM CDT...DOPPLER RADAR INDICATED A SEVERE THUNDERSTORM 3 MILES SOUTHWEST OF LAWTON...MOVING NORTHEAST AT 30 MPH.
* LOCATIONS IN THE WARNING INCLUDE CACHE...ELGIN...FLETCHER…FORT ILL...GERONIMO...LAWTON...MEDICINE PARK...MEERS AND STERLING
HAIL UP TO THE SIZE OF QUARTERS AND WIND GUSTS TO AT LEAST 60 MPH ARE LIKELY.
LAT...LON 3454 9868 3447 9842 3454 9817 3485 9810 3483 9862
Warning decision making – Austria 2003
Warning geometryWarning geometry
• Most users refer to the political boundaries for which the warning has been issued
• The body of the warning specify which towns are in the path
• And expected wind and hail size
• All warnings are tone alerted on weather radio
Warning decision making – Austria 2003
Warning geometryWarning geometry• The warning is followed
by severe weather statements describing the progress of the warning
SEVERE WEATHER STATEMENT
NATIONAL WEATHER SERVICE NORMAN OK
421 PM CDT MON MAY 3 1999
AT 420 PM QUARTER SIZE HAIL WAS REPORTED IN LAWTON. A SEVERE THUNDERSTORM WARNING REMAINS IN EFFECT FOR
COMANCHE COUNTY UNTIL 5 PM.LAT...LON 3454 9868 3447 9842 3454 9817 3485 9810 3483 9862
Warning decision making – Austria 2003
Experimental warning productsExperimental warning products
Significant weather advisory or pre-warning
Warning decision making – Austria 2003
Experimental Warning ProductsExperimental Warning ProductsWARNING DECISION UPDATE
NATIONAL WEATHER SERVICE NORMAN OK
345 PM CDT THU MAY 8 2003
THIS WARNING DECISION UPDATE CONCERNS SOUTHWEST AND CENTRAL OKLAHOMA.
NORTHEAST COMANCHE COUNTY STORM IS STRENGTHENING AND POLARIMENTRIC RADAR DATA (ZDR) FROM NSSL SUGGESTS LIQUID WATER ABOVE FREEZING
LEVEL INDICATIVE OF STRENGTHENING UPDRAFT. NOW LOOKING CAREFULLY FOR COLUMN OF HIGH Z (>50 DBZ) BETWEEN 15-30 KFT. THIS MAY BE AN INCIPIENT SUPERCELL.
NOTE: THIS IS AN EXPERIMENTAL PRODUCT MEANT TO INCREASE INFORMATION EXCHANGE ON THE STORM SCALE.
Warning decision making – Austria 2003
Local Storm ReportsLocal Storm Reports
• Required to relay all incoming storm reports immediately
LOCAL STORM REPORT
NATIONAL WEATHER SERVICE NORMAN OK
1025 AM CDT WED MAY 07 2003
TIME (CDT) .....CITY LOCATION.....STATE ...EVENT/REMARKS...
....COUNTY LOCATION....
1040 PM 5 E STRINGTOWN OK .88 INCH HAIL
05/06/03 ATOKA PUBLIC REPORTED HAIL
COVERED THE GROUND.
Warning decision making – Austria 2003
Other Warning operations tasksOther Warning operations tasks• Relay all warnings on the National Warning System (NAWAS)
• All products are related out to spotters via amateur radio networks
• Some offices also relay warnings out via pager services
• Emergency managers in populated areas receive personal phone calls from NWS personel when warnings are issued
• Some offices use instant messaging to describe their thought processes to selected customers
Warning decision making – Austria 2003
Data inputData input
RadarData
(others)
SpotterReports
RadarData
(yours)
ProbingCalls
RadarData
(others)
Model
Guidance
Yea Nay
Radar
(others)
Satellite
UpdatedMesoscaleAnalysis
???
Data
Point soundingsSurface data
Lightning
Warning decision making – Austria 2003
Radar dataRadar data
The most important input tool for short term warnings.
Warning decision making – Austria 2003
Influence of spotter reports on warnings
Influence of spotter reports on warnings
• Warning frequency is strongly correlated to the number of reports
• Therefore, spotters are the second most important input in warning decision making
• Consider this example from St. Louis
• Carroll et al. 2002 - Research Experiences for Undergraduates program at OU
Warning decision making – Austria 2003
St. Louis CWA Population DensitySt. Louis CWA Population Density
People per km2
Warning decision making – Austria 2003
Events per 1,000 km2Events per 1,000 km2
Events per 1,000 km2
Warning decision making – Austria 2003
Warnings per 1,000 km2Warnings per 1,000 km2
Warnings per 1,000 km2
Carroll et al., 2002
Warning decision making – Austria 2003
The Norman WFO amateur radio liaison network
A ham radio operator at the NWS OUN office relays the latest warnings and storm updates out to one of three networks
Dennis McCarthy – KC5EVH
WX5OUN
Warning decision making – Austria 2003
The Norman WFO amateur radio liaison network
Managers of repeater networks coordinate radio traffic between the NWS and local spotter networks, the media and emergency managers.
Example: The Southwest Independent Repeater Association (SWIRA) ismanaged by Terry Mahorney KB5LLI
SWIRA WX5OUN
Warning decision making – Austria 2003
The Norman WFO amateur radio liaison network
Managers of repeater networks coordinate radio traffic between the NWS and local spotter networks, the media and emergency managers.
Example: The Southwest Independent Repeater Association (SWIRA) ismanaged by Terry Mahorney KB5LLI
SWIRA WX5OUN
Warning decision making – Austria 2003
The Norman WFO amateur radio liaison network
146.79 Altus
Chasers receive the NWS update, and may respond back with reports directly to the repeater or to a local spotter group
The local spotter net controller relays spotter reports through the liaison network
SWIRA WX5OUN
Warning decision making – Austria 2003
The Norman WFO amateur radio liaison network
The Norman WFO amateur radio liaison network
146.79 Altus
Media stormchasers and helicopter pilots relay their observations back to their stations.
These reports are fed back to the NWS via TV broadcasts, and by amateur radio.
Other chasers/spotters listen in on these reports too.
WX5OUN
Warning decision making – Austria 2003
The Norman WFO amateur radio liaison network
The Norman WFO amateur radio liaison network
Rick Smith, WCM – NWS OUN
Terry Mahorney KB5LLI SWIRA
Andy Wallace, Lawton KC5GHH Ch 7 Lawton
Charlie Byers SPS EM
Robert Moose 'Moose' Ch4 OKC NBC
Jay Kruckenberg, Woodward
Mike Honigsburg, Garfield CO EM
Putnam Ryder KC5GVD OK state EM office OKC
Gayland Kitch, KC5MMU Moore EM
Brent Myers, WA5NWS, Chillocothe, TX Police
Herb Gunther, Seminole CO EM
Dave Ewoldt
Acknowledged contributors
EM
EM
EM
EM
EM
EM
EM
Warning decision making – Austria 2003
Where media assists the NWSWhere media assists the NWS
Get to the video!
Realtime chaser data from
multiple stations
Warning decision making – Austria 2003
Environmental data inputEnvironmental data input
• Radar cannot adequately observe hail size, downbursts or tornadoes
• Environmental data becomes important in the process
Warning decision making – Austria 2003
04 May 2001 04 June 2001
Pick the storm most likely to be tornadic
Warning decision making – Austria 2003
Storm Types/Hazards TableStorm Types/Hazards TableDmg winds Hail Tornado FF
Ordinary cell
(0-6km shear <15 m/s)
Steep LL lapse rates
High LCL, dry midlevels, high DCAPE
Intense elevated core
Descending core bottom
Elevated radial convergence
Cold temps aloft
Large buoyancy ~
-20 C
Intense elevated core ~ -20 C and colder
High VIL density, TBSS
No CIN, steep LL lapse rates
Sharp boundary with LL vertical vorticity
Rapidly growing and new CBs
high RH in deep layer; deep warm cloud; small mean wind
Slow storm motion
Large storm core
Super-cell
(0-6km shear > 15 – 20 m/s)
Similar environ as above except for shear and high CAPE & DCAPE, strong 0-1 km shear can assist
In addition to above, LL mesocyclogenesis; developing hook, deep convergence zone
Large buoyancy @-20 C level, strong 0-6 km shear, stg mid- upper SR flow;
WER BWER, intense elevated core,
mesocyclone,
TBSS, high VIL density
Strong 0-1km shear in addition to 0-6 km shear; low LCL; low CIN
LL TVS, meso, inflow notch; sign of a hook, strong LL convergence below mesocyclone;BWER
High RH in deep layer; deep warm cloud; small SR anvil flow
Low supercell motion
Not an LP storm
Multicell
(organized group of
ordinary/supercells)
>40kt 0-6km shear
Strong >30kt 700-500 wind;
Stg leading Gradient;
Bookend vortex pair;
MARC, deep convergence zone, rear inflow notch
Separated cores; cells exposed to favorable environment
Similar to supercells?
Mostly left of rear inflow notches along leading edge of core, front inflow notch with WER and vert vorticity
Slow MBE motion; triple pt anchoring; upwind instability, LL jet, high PW, high mean RH
Intrastorm seeding
Echo training, slow motion
Source: IC 5.7 Student Guide http://wdtb.noaa.gov/DLCourses/dlocFY03/ic57/ic57-0210-2-screen.pdf
Warning decision making – Austria 2003
Lightning data Lightning data
• Cloud to ground lightning sometimes is useful in severe thunderstorm detection
• However, the most severe storms often elevate charging layers resulting in less LTGCG http://www.cira.colostate.edu/ramm/visit/ltgmet2.html
Warning decision making – Austria 2003
Satellite dataSatellite data• Supercells often exhibit a
warm wake downstream of the updraft.
• However, these wakes only occur with isothermal or inversion layers above the equilibrium level
http://www.cira.colostate.edu/ramm/visit/ev.htmlhttp://www.nssl.noaa.gov/istpds/icu624/
Warning decision making – Austria 2003
Overwhelming data input rateOverwhelming data input rate
RadarData
(others)
SpotterReports
RadarData
(yours)
ProbingCalls
RadarData
(others)
Model
Guidance
Yea Nay
Radar
(others)
Satellite
UpdatedMesoscaleAnalysis
???
Data
Point soundingsSurface data
Lightning
Warning decision making – Austria 2003
And excessive workloadAnd excessive workload
• Can lead to lower performance
Stress/Performance
Curve
StressPer
form
ance
Team Building Associates (1997)
Warning decision making – Austria 2003
A more robust look at events could yield valuable associations
0
10
20
30
40
50
60
70
80
91 92 93 94 95 96 97 98 99
Skill – based Errors are:
•Poor technique
•Improper use of equipment
•Omitting required procedures
•Failure to observe critical data
Percentage of Human Error Mishaps Associated with skill-based Errors (FY 91-99)
From analysis of Naval Safety Center accident database
Shappell and Weigman, 2001
Warning decision making – Austria 2003
Aviation industry findingsMechanical errors decreased, human error did not
Cla
ss A
, B,&
C M
isha
ps/1
00,0
00 F
ligh
t Hou
rsC
lass
A, B
,& C
Mis
haps
/100
,000
Fli
ght H
ours
00
22
44
66
88
1010
1212
1414
1616
1977
1977
1979
1979
1981
1981
1983
1983
1985
1985
1987
1987
1989
1989
1991
1991
YearYear
Mechanical
Human
Shappell, S. and Wiegmann, D. (1996). U.S. Naval aviation mishaps 1977-1992 All NAVY/MARINE Class A, B, & C Mishaps
Reason: Much emphasis on relatively easy to see mechanical problems… very little on human factors contribution.
Warning decision making – Austria 2003
OrganizationalFactors
Technology
Science
Latent Conditions Training
Infrastructure, policyCharacteristics•Radar( RF, Dealiasing ,sampling)
Models
Stability of equipment
What we don’t know NSE
Conceptual models
Failed orAbsent Defenses
HumanFactors
Unwarned eventDeath and injury
Active Conditions Teamwork
Coordination
SA
Experience
It’s never just one thing
*Human Factors Analysis and Classification System (Shappell/Wiegman)
Warning decision making – Austria 2003
Situation Awareness -review The ability to maintain the big picture
Only one of these guys has good SA.
Warning decision making – Austria 2003
Situation AwarenessOfficial definition
Situation AwarenessOfficial definition
• Perception of the elements in the environment within a volume of space and time (level I)
• Comprehension of their meaning (level II)
• Projection of their status in the near future (level III)
Endsley 1988
Warning decision making – Austria 2003
Situation AwarenessSituation Awareness
• Perception of the elements in the environment within a volume of space and time (level I)
Or did you see this as well?
Is this what your decision is based on?
Same time…different radar
Warning decision making – Austria 2003
Situation AwarenessSituation Awareness• Comprehension of their meaning (level II)
Now that you’ve seen this, do you understand what this is?
Did you see this?
Perceive
Hook echo with 65dBZ in the hook: debris
Warning decision making – Austria 2003
Situation AwarenessSituation Awareness
• Projection of their status in the near future (level III)
Now do you realize what is likely to happen? And what you should do?
Do you understand what this is?
(Hook echo with 65dBZ in the hook: debris)
Did you see this?
Perceive
Comprehend Project
…Tornado Emergency for the OKC Metro……...
Warning decision making – Austria 2003
Factors affecting your ability to get or maintain SA
Factors affecting your ability to get or maintain SA
• Attention
Limited; affected by task priority
• Working memory
Information stored but easily accessed
• Use of conceptual models
Perception of meaningful patterns
Relationships between different pieces of information
Workload
As workload increases, SA decreases
Warning decision making – Austria 2003
SA and workloadSA and workload• Low SA, low workload
Don’t know anything, don’t want to know
• Low SA, high workload
Don’t know anything, but am trying way too hard to find out
• High SA, high workload
Do know plenty, but at great effort (can’t keep this up for long!)
• High SA, low workload
Do know, and it comes easily
• If you are not operating here….find out why and fix it!
Warning decision making – Austria 2003
SA and WorkloadSA and Workload
• Warnings take all three levels of SA
Perceive, comprehend, project
• Decision to warn based on
Knowledge of Conceptual Model
Recognition of Conceptual Model in radar and other supporting data (spotter input, knowledge of environment)
• Requires proactive interrogation of base data
– Which is a workload problem if ratio of forecaster to number of storms is insufficient
» Key: Sectorize (re-distribute workload)
» Assure staffing is appropriate
Warning decision making – Austria 2003
I. What do effective warning events have in common?
Factors for success in NWS warning events
I. What do effective warning events have in common?
Factors for success in NWS warning events
• Science
• Technology
• Human Factors
Warning decision making – Austria 2003
The ScienceThe more we learn, the more we understand about some
things…the less we understand about others
• Atmosphere/phenomena understood
• Representative conceptual models are in place
“Already, some new explanations of aspects of tornadic behavior have been proposed. They await testing with theoretical understanding and more VORTEX cases."
Harold BrooksVORTEX-95
Warning decision making – Austria 2003
The TechnologyTechnology is best when:The Technology
Technology is best when:
• It has the ability to convey science
• Strengths/limitations are understood
• It is reliable
• Software/hardware designs are effective
• It has a positive impact on situation awareness of user
I know about the strengths and limitations of the 88D
I will need to learn a new set of strengths and limitations with any new technology
Warning decision making – Austria 2003
Human FactorsWarnings aren’t issued in a vacuum
Human FactorsWarnings aren’t issued in a vacuum
Does everyone understand their role today?
Does everyone understand what
they’re looking at?
What are each of these people doing?
Did the right person hear that report?
Does someone see what’s happening outside??!!
•Correct application / understanding of conceptual model
•Good situation awareness (individual/team)
•Effective strategies, methodologies
•Effective use of technology
•Organizational and individual contributions are positive
•There is effective communication, coordination, teamwork
WFO OUN Ops area on May 3rd, 1999
Warning decision making – Austria 2003
A good office warning operation depends on good team SA
A good office warning operation depends on good team SA
What I know
What you know
What she knows
What he knows
What we all know
What we share with others
Warning decision making – Austria 2003
Example – A typical NWS Office Layout
Example – A typical NWS Office Layout
MKX operations for “outbreak” event
WS1
CRS
WS2
WS3
CRS
WS5
WS4
Public/Flash Flood(1-2 Mets)
River Flood(1-2 HMT/Intern)
Svr Wx Coordinator(1 Met)
Marine/Aviation(1-2 Mets)
Warning (2 Mets)
Statements(2 Mets)
HAM (2-3 Persons)
Storm Reports(1 HMT/Intern)
QC(1 HMT/Intern)
Asst. Svr Wx Coordinator(1 Met)
Warning decision making – Austria 2003
Roles and dutiesRoles and duties• Warning meteorologists
• Mesoanalysts
• Radio operators
• Event loggers
• Technicians
• Severe weather coordinator
Oversees warning operations
Makes sure workload for each warning forecaster
Ensures uninhibited communication amongst all
Warning decision making – Austria 2003
Splitting up the workloadSplitting up the workload
• Geographical sectorizing
• Sectorizing by severe weather type
• Sectorizing by product type
• All of the above with adequate staffing
• Coordinator is needed to help split up workload and ensure no storms are missed
Warning decision making – Austria 2003
Accident Investigations
Root Cause Analysis
Proximal Cause
Post-Mortems
Post-mortems: learning from the past
Post-mortems: learning from the past
WB-Graph (Why-Because)
Warning decision making – Austria 2003
Some past significant events which weren’t as effective – one example
Some past significant events which weren’t as effective – one example
• Science Severe box(moderate risk)
• Technology Map inaccuracies
• Human Factors Applying conceptual model (tornadic supercell)
• Understanding of conceptual model Situation Awareness
• Lack of real-time reports (visibility, lines of comms)• Procedures, strategies (storm interrogation techniques)
Communication, coordination (internal, external) Roles, responsibilities Wording Relationship with customer
Warning decision making – Austria 2003
Some past significant events which weren’t as effective 12 Tornadic Events*
Number of times each category has played a role in the 12 events we looked at
Some past significant events which weren’t as effective 12 Tornadic Events*
Number of times each category has played a role in the 12 events we looked at
5
7
12
0
2
4
6
8
10
12
Science Technology Human Factors
*All but 1 event had little or no lead time. Ten events F3 or greater.
Warning decision making – Austria 2003
ScienceThe science of the event, and our
understanding of it, help to shape our expectations.
ScienceThe science of the event, and our
understanding of it, help to shape our expectations.
•Watches•Severe - 4•None - 1
5
7
12
0
2
4
6
8
10
12
Number of Events = 12
Science Technology Human Factors
Warning decision making – Austria 2003
TechnologySometimes technological issues
play a role
TechnologySometimes technological issues
play a role
• Range Folding - 2
• Radar sampling – 3
• No algorithm guidance – 2Only mentioned on one report
• Equipment malfunction - 1
• Warning Dissemination - 3Comms, NWR, Maps
5
7
12
0
2
4
6
8
10
12
Science Technology Human Factors
Warning decision making – Austria 2003
Human FactorsUltimately the human must put it all together
Human FactorsUltimately the human must put it all together
• Apply Conceptual Model – 8 Cyclic tornadic supercell Comma head tornadoes
• Situation Awareness - 12 Strategies - 8
• Sectorizing, inadequate procedures or RPS List, failure to use other radars, failure to make PRF changes, equipment distractions (attention)
Workload - 4 Spotter reports delayed or not received – 6
• Organizational - 9 Roles/responsibilities (3), Partnerships (3), Coord/Comms
(3), climate (2), face threat, staffing, shift change, inexperience
• Other wording, time of day
5
7
12
0
2
4
6
8
10
12
Science Technology Human Factors
Warning decision making – Austria 2003
How we improveincluding a review of relevant WDM concepts
(at least for these cases)
How we improveincluding a review of relevant WDM concepts
(at least for these cases)
• Science – (Where severe threat was not realized before
event occurred)
Additional research plus local studies
• Requires better data sets
• Technology
Additional development plus incorporation of local applications
Evaluation of user needs and impacts
Warning decision making – Austria 2003
How we improveincluding a review of relevant WDM concepts
How we improveincluding a review of relevant WDM concepts
• Human Factors
Correct understanding and application of conceptual models
Warning environment which supports good SA
Effective office strategies
Warning environment which supports good communication and coordination
Warning decision making – Austria 2003
• Simulations are the most effective method of training
• Every forecaster in the NWS is required to complete two/year
Warning decision making – Austria 2003
• What are staffing practices during severe weather?
Do you sectorize? Use a coordinator? How is workload?
• What is your organizational environment like?
How does the flow of the office support good SA?
• Access to all data sets (spotters, etc)
How good is teamwork and communication?
How long have you and others worked there and with each other?
Are roles and responsibilities clear during severe weather operations?
What is working relationship with partners (other WFOs, spotters, EMs, etc)
Meeting the ChallengesHow do you and your office stack up in these
areas?
Meeting the ChallengesHow do you and your office stack up in these
areas?
Warning decision making – Austria 2003
ContactsContacts
Storm interpretation and warning methodologies
James LaDue [email protected]
Mesoscale analysis and warning methodologies
Brad Grant [email protected]
Situational Awareness and cognitive task analysis
Liz Quoetone [email protected]
Warning decision making – Austria 2003
ReferencesReferencesAviation Safety Network, http://aviation-safety.net/index.shtmlEndsley, M.R., 1988. Design and Evaluation for Situation Awareness Enhancement. M.R.
Endsley, Proceedings of the Human Factors Society, 32nd annual meeting, Santa Monica, CA
Lemon, L.R., and C. A. Doswell III, 1979b: Severe thunderstorm evolution and mesocyclone structure as related to tornadogenesis. Mon. Wea. Rev., 107,1184-1197.
Orasanu, J., U. Fischer, L. McDonnel, J. Davison, K. Haars, E. Villeda, C. VanAken 1998: How do Flight Crews Detect and Prevent Errors? Findings from a Flight
Simulation Study. Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting, Chicago 191-195.
Shappell, S., D. Wiegmann. A Human Factors Approach to Accident Analysis and Prevention, Workshop, 45th Conference on Human Factors and Ergonomics Society, Minneapolis, 2001
Xiao, Y., C. Mackenzie, R. Patey, and LOTAS Group 1998: Team Coordination and Breakdowns in a Real-life Stressful Environment. Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting, Chicago 186- 190.
NWS – Various Disaster Survey Reports and communications with survey team members.
Warning decision making – Austria 2003
ReferencesSome WDTB presentations online
ReferencesSome WDTB presentations online
WDMISituation Awareness and Decision Making Warning Methodology Office Strategies Warning Operations in the AWIPS EraVortex Findings Techniques for Improving Warnings
WDM IINWS Warnings and Customer Response Team Decision MakingPublic Reaction to Warnings Effective Warning Environments AWIPS Configurations for Warnings Radar Limitations and TVS Detections Environmental Assessment
DLOC WorkshopUsing Near-Storm Environ. Data in WDM Process Convective Initiation/Tornado Warning GuidanceRadar Detection of Severe Tstm Features
WDM IIIMaximizing AWIPS ProceduresFailure ModesThe Role of Effective Communication in the Warning ProcessStrategies for Optimizing Severe Weather PerformanceMesoscale Input into WDMAlgorithms and War GamesImpacts of Automation on Expertise Social Science of WarningsSevere Weather Probability Outlooks WDM IVWhen Bad Things Happen to Good ForecastersSevere Weather Threat AssessmentThe Value of Post-MortemsRadar Precursors to Damaging Winds
www.wdtb.noaa.gov
Warning decision making – Austria 2003
ReferencesReferencesSevere Convection Forecasting and Warning Professional Development Series, http://www.wdtb.noaa.gov/resources/PDS/newconvectpds.htm
Severe storms interpretation guide, see IC57 of the WSR-88D DLOC course, http://www.wdtb.noaa.gov/DLCourses/dloc/dlocmain.html#studentguides
Capabilities of severe weather and thermodynamic parameters in severe storms forecasting, http://www.wdtb.noaa.gov/resources/IC/svrparams/intro/index.htm