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stuart by ajizai


									Societal Impacts of Severe Weather – The Future of
Prediction, Communication and Post-Event Analysis

                 Neil A. Stuart
             NOAA/NWS Albany, NY
                   NROW X
               5 November 2008
Increasing importance to evaluation
        of societal impacts
• Urbanization of America/World
• Increasing population density
• Increasing exposure to hazardous weather
• Huge diversity in user community
• Huge spectrum in levels of vulnerability in
  user community
• Increased liability for economic impact and
  loss of life
  Current system of pre-event and post-
    event evaluation of forecast value
• Sources of guidance contribute to forecast confidence of
  forecast scenario
• Individual forecaster perception of probability of event
  initiates forecast/watch/warning
• Graphics and text products convey level of urgency
  depending on perceived potential impact
• Observed weather – calculate statistics for POD, FAR,
• Future trends – Ensemble guidance provides more
  quantitative probabilities for various hazards
• Impacts from various hazard types evaluated
• Create text and graphical products (Including PQPF?)
  conveying potential impact of various hazards
                   Valentine’s Day 2007
•   Despite advances in data analysis,
    assimilation and visualization,
    some important user groups are not
     –   Widespread 20-42” of snow Capital
         Region of NY and north and west
     –   NESIS Category 3 – ranked near
         Blizzard of ’78 in SE New England
     –   I-80 shut down in PA due to
         accidents in mixed precipitation
     –   Many planes stranded on runways
         for hours at JFK airport
     –   35 deaths

•   Winter Weather Impact checklist
    developed at NWS Buffalo for Lake
    Effect events
•   Distributed across eastern region of
•   Evaluation of potential utility with
    synoptic-scale snowstorms
•   Ranking of multiple types of
•   Accumulated rank defines High,
    Moderate or Low Impact
Interior New York/New England
•Timing – 3: Covered multiple rush hours
•Seasonality – 2: Mid Season Infrequent
•Phenomena – 3: Visibility <1/4 mile in
heavy snow
•Post Storm – 3 Windy and Temperatures
•Total = 11 - High

PA, Southern NY, NJ, MD
•Timing – 3: Covered multiple rush hours
•Seasonality – 2: Mid Season Infrequent
•Phenomena – 2: Moderate/heavy sleet,
wet snow or mix
•Phenomena – 3: Freezing precipitation,
black ice
•Post Storm – 2: Windy or Temperatures
•Total = 8 or 9 - High
                      October 2008 storm
    Despite advances in data analysis,
    assimilation and visualization,
    some important user groups are not
     –   Very elevation dependent snowfall
     –   >12” snow Catskills, Adirondacks
     –   >6” snow Helderbergs, Schoharie
         Valley, parts of Green Mountains
     –   Trace of snow Capital District, Lake
         George, Saratoga Regions,
     –   I-84 shut down in NY/PA due to
         mixed precipitation
     –   >100,000 without power
     –   Many trees/wires down

•   How can meteorologists help users
    to reduce the societal impacts of
    major winter storms like the
    Valentine’s Day 2007 and October
    2008 Storms?
     –   Need to communicate forecast
         information in a manner understood
         by the most user groups
     –   Need to educate users on how to
         best use current forecast products
         and services
     –   Need to coordinate with users to
         best tailor current and future
         products for their needs
Catskills, Adirondacks and Schoharie
•Timing – 3: Covered multiple rush hours
(if rush hour exists in the mountains!)
•Seasonality – 3: First storm of season
•Phenomena – 2: Moderate/Heavy wet
snow or mix
•Post Storm – 2: Windy or Temperatures
•Total = 10 - High

Albany, valley locations
•Timing – 1: overnight
•Seasonality – No factor since no
accumulation, but could be 3 if considered
1st storm of season
•Phenomena – 1: Light intensity snow or
•Post Storm – 1: Temperatures slowly
moderating above freezing

•Total = 3 – Low, but could be moderate if
considered 1st storm of season
                                      •Higher elevations received accumulating
10/08-Valleys                     1   snow – low impact for valley areas,
                2/0710/08 -Mtns   2   including densest population centers
   1                 2
                                           •Divide into different elevations or divide
POD=0 FAR=1      POD=1 FAR=0
                                           rural vs. urban– sub county?
                                           •Limited ability to delineate sub county
                                           areas in text products due to software
                                           •Increasing use of graphical forecasts
                                           •All synoptic-scale storms are high impact
                                           somewhere, so some division necessary
                                           •Maybe consider pavement/ground
                                           temperatures for accumulation and road
                                           treatment factors?
                                      Call to action statements – text or
                                           •Overstated threat in valley locations – any
                                           impact due to unnecessary preparation?
                                           •Road crews in impacted areas – best use
                                           of resources- winter still >1 month away?
                                           •Very wet snow – trees and power lines
                                           down - power companies most efficient use
                                           of resources?
                                           •Addressing variety of vulnerability factors
                                           such as age, health, wealth, gender?
                                           •Graphics of hazards on map with
                                           census/demographic data: Assist
                                           EMs/Specialized Users – see SVR slide 18
                                           •Largely up to broadcast media to convey
                                           message to most of the user community
Ensemble products as guidance but
       must be calibrated
Experimental Probabilistic QPF: Forecaster produced but based on POP and QPF grids –
   Once ensemble and forecaster probabilities calibrated, potential use in PQPF grid
    Severe Thunderstorm and Tornado Warnings

•   Current – Polygon warnings for part of a county/counties
•   Successive polygon warnings for locations downstream
•   Verified by observation of severe weather – POD, FAR, CSI
•   Future trends – Probabilistic severe weather information
•   Polygons composed of a range of probabilities for various severe
    weather types
•   Polygons for estimated time of arrival for various severe weather types
•   Forecaster generated probabilities based on perceived threat –
    mesoscale/storm scale analyses, radar based or observed
•   Short-range microscale numerical models being developed for
    thunderstorm evolution predictions on the scale of minutes
•   Some private sector capability to produce future radar graphics for
    severe thunderstorms (not presented here)
•   What probability would activate EAS?
•   Will EAS exist in the future? – Cell phones, cable TV, NYAlert
•   Inspiration from probabilistic hurricane wind and surge graphics
•   Gridded verification – including quantifying choosing not to warn
                          Current Methodology
                                 New polygon issued with short lead time
Long lead time for location A
                                 for location B

      Possible future Methodology – with assistance from
      meso/micro scale models (currently in development)
 Polygon with range of                In this case – rapid updates move polygon
 probabilities updated frequently,    east little by little so locations A and B
 perhaps every 5-15 minutes           receive similar progression of information
         Example of recent event – 24 July EF2
                  tornado in NH/ME
1533 UTC GYX radar image –
Tornado touch down, no
reports yet, SVR issued

Probabilistic polygon/accum.
threat could have provided
tornado probabilities based on
radar data

Any non-zero probability can
provide critical information to
different user groups
       Example of recent event – 24 July EF2
                tornado in NH/ME

1538 UTC GYX radar image
– Tornado on ground, 1st
reports beginning to come
in, TOR issued 1546 UTC

Estimated time of arrival
compliments probabilities
on previous page
 Some current cutting edge severe weather
display products – Google Earth applications
    Some current cutting edge severe weather
       warning and verification products

                                    Graphical and text warnings with radar and severe reports

Demographic data for each warning               Experimental verification statistics
Gridded/Probabilistic severe warning
                  • White – forecast area
                    gridded by Lat/Lon
                  • Gray – Watch area
                  • Black – Polygon Warning
                  • Red – Severe Weather
                  • Many new statistics can
                    be calculated
                  • Red could also be radar
                    detected mesocyclone or
                  • Quantifying choice of no
                    warning – if warning not
                    issued and no severe
                    weather reported
                  • Probabilistic warnings –
                    even more new statistics
                         Other considerations
•   Social science collaborations
     – Emphasis on surveys and behavioral science to evaluate perceptions and
       psychology – input into probabilistic forecast guidance
         • What do users know/understand?
         • What are their preferred information sources?
         • How can the message be most clearly conveyed, minimizing misinterpretation of
           the risk?
         • What influences their decisions under stress and uncertainty?
         • What motivates them to prepare and respond to potentially life threatening
         • What capabilities do they have to prepare and respond? (Exposure and
           vulnerability issues)
     – Emphasis on economic and human impacts
         • NWS Service Assessments – Super Tuesday and Picher OK
         • Influencing government/insurance policy - Katrina
         • Input into community hazard mitigation
              – Ft. Collins floods and Katrina/Ike
              – Potential local collaborative study of Schoharie County NY
•   Partnering between all sectors
     – AMS Ad Hoc Committee on Communicating Uncertainty in Forecasting
     – National Weather Center – Government, Private Sector and Academia all in
       one complex
     – Hazardous Weather Test Bed
•   Iowa Environmental Mesonet division of the Iowa State University Department
    of Agronomy
•   NWS Fort Worth and the North Central Texas Council of Governments
•   Greg Stumpf and Travis Smith for probabilistic warning graphics
•   Harold Brooks for gridded severe weather graphic
•   Howard Altshule of Forensic Weather Consultants for pictures from 28 October
    2008 storm
•   Eve Gruntfest and WAS*IS/SSWIM founders for promoting physical and social
    science collaborations that are directing future product development

    Thank you for your time and attention –
    are there any questions or comments?
     Organizations paving the way for increased Social and
                Physical Science collaborations

                                       Web Sites
•Http:// - WAS*IS web site
•Http:// - Probabilistic warning workshop
•Http:// – Source of many ensemble forecast guidance products

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