Fire Weather User Meeting 2003

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Fire Weather User Meeting 2003

• Sponsored by NWS-ABQ

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Outline

• Fire Weather Zone Boundaries
• Data Areas within Fire Weather Zones
• Statistics
• Ventilation Forecasts
• Open Forum

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Fire Weather Zone Boundaries

• Advantages
• More Weather and Climate designed
• New Mexico experiences unique weather patterns
• Weather and Climate designed means better Fuel
  Model designed
• Fire Weather Forecaster Area Spot Forecast Area
• Lessens confusion for NWS and Land Agencies
• Well known Boundaries Used when possible
• Forest and District
• County
• Roads
• NFDRS anyone??

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Fire Weather Zone Boundaries

• Disadvantages
• Have to Re-learn boundaries
• Many changes have to be made
• Getting the Word Out and Training

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Proposed Change Map Political
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Proposed Change Map Elevation
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Old Zone Map
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New Zone over Old Zone Map
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NFDRS and Proposed New Zones
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Data Areas within Fire Zones

• New Forecast System is Adaptable.Somewhat
• elevation boundaries
• political boundaries
• geographical boundaries
• Text can Fall Out based on user defined data or
  edit areas within a fire weather zone
• Local NWS office will coordinate with the
  customer on these data areas

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Data Areas within Fire Zones

• Advantages
• Create critical areas within a fire zone which
  have more weight.
• Various ways to create critical areas.
• Create a more Useful Forecast for the Customer.

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Data Areas within Fire Zones

• Disadvantages
• Technologically Challenged
• Cant create aspect defined data areas (Ex North
  Slopes) ..2.5 km Grid resolution around 1.5 mile
• Cant create Fuel Type defined data areas based
  on GIS overlays
• Local Effects Above 9000 ft, 8000 to 9000 ft,
  6500 to 8000 ft and below 6500 ft can be placed
  in Zone Text

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Example of Hand Drawn Data Areas
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Example of Elevation Based Data Areas 9000 feet
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Example of Elevation Based Data Areas 10000 feet
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Statistics

• Red Flag Verification 2003
• Zones Warned 150
• Total Zones Occurred 79
• Warned Zones Verified 77
• Warned Zone UnVerified 71
• Unwarned Zones Missed 2
• POD (Prob of Detection) 0.97
• FAR (False Alarm Rate) 0.47
• CSI (Critical Success Index) 0.51
• Average Lead Time 5.2 hours

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Statistics

• Spot Forecasts

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Statistics

• Turn Around Time for Spot Forecasts
• Calculated by subtracting Requested time from
  Forecast Complete time

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Statistics

• Spot Forecast Feedback
• Feedback either by a faxed observation sheet, use
  of portable RAWS or verbal via NWS Spot internet
  program or Phone Call
• Looked at August, September, October, November
  2003

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Statistics

• Feedback
• Hourly afternoon or burn period T,RH and wind
  (Max T, Min RH, General Wind)
• If needs are critical for overnight hours (Min T,
  Max RH and General Wind)
• Critical weather such as precipitation duration
  and accumulation (estimate), Start of upslope or
  downslope winds and afternoon mixing
• Misc such as snow on the ground and sky cover
• Pertinent information for the burn site
• Slope (ridgetop-mid-valley bottom)
• Unusual topography (saddle, chute etc)
• Rule of Thumb Wx
• Burn area defined Topo Maps

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Statistics

• Why?
• Efficiency
• Get Forecast Out Quicker as Forecasters use
  Trends
• Customers wait for forecasts are less
• Forecasters dont have to guess on what happened
  thus less busy time spent
• Correct trends applied to good ground truthing
  provides more accurate forecasts.

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Ventilation Forecasts

• NWS understands the demand for ventilation
  forecasts.
• Due to Customer and NWS needs a compromise was
  struck.
• Special Smoke Management forecast for the 8
  designated points during the off season.
• Displayed on the web as a text product
• Issuance of the product at 330 pm
• Can parse with a program

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Example of the Afternoon Smoke Management Product
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Web Page Example
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Ventilation Forecasts

• Future. will be graphical maps of mixing height
  and transport winds
• Query Capability by the Customer for site
  specific forecasts

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Example of Mixing Height Graphic on the Web
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Example of Transport Wind Graphic on the Web
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Ventilation Forecasts

• Future.Future.GACC Mets????

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Ventilation Forecasts

• Limitations
• Lack of Verification
• Lack of Direction
• Science
• Each state has different policiesdifferent
  entities
• Model data used for initial 1st Guess wide
  ranging
• Thus you can get day to day changes
• Nature of the Beast..Subjective Forecasts
• Moral of the StoryUse with a Grain of Salt!!!