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Project ID: 07-2-1-42

Year: 2007

Date Started: 06/04/2007

Date Completed: 07/23/2010

Title: Predicting Lightning Risk Nationwide

Project Proposal Abstract: Dry thunderstorms are a major source of wildfires, and are disproportionately responsible for igniting major wildfires, yet currently dry thunderstorm predictions at high spatial resolutions are unavailable for most of the country. We propose development of improved dry lightning algorithms to expand their coverage, and improve accuracy and usability of such predictions in fire management. In JFSP project #01-1-6-08, a discriminant algorithm was developed for the Pacific Northwest. Additional work by NOAA?s Storm Prediction Center (SPC) has produced a statistical scheme to predict the location and intensity of lightning outbreaks. We propose here to build on this work in 3 ways: -Incorporate additional data to expand these predictions to other geographic areas as appropriate, including the interior of Alaska -Incorporate predictions of large lightning outbreaks (storms that generate hundreds to thousands of lightning strikes over relatively small areas) which ignite multiple fires, overwhelm suppression resources, and lead to a disproportionate number of uncontrolled wildfires -Determine the feasibility of using an alternative, physically based algorithm that includes temperature and moisture from multiple vertical atmospheric layers Specifically, we will obtain lightning strike data for Alaska and Canada (which was not available at the time we started our original JFSP project) to generate risk predictions for these domains. Additionally, we will incorporate a methodology developed by SPC to predict the probability of high numbers of cloud-to-ground flashes. By integrating these two approaches we will be able to create models for large lightning outbreaks occurring without significant rainfall and put into place forecasts available through our website. To address a weakness in the existing algorithm (it considers meteorological variables at only two vertical levels in the atmosphere), we will also develop and test a new geographically independent algorithm for identifying dry thunderstorm days, using moisture and temperature variables throughout a deeper atmospheric layer.

Principal Investigator: Miriam L. Rorig

Agency/Organization: Forest Service

Branch or Dept: PNW-Pacific Northwest Research Station

Other Project Collaborators




Branch or Dept

Co-Principal Investigator

Phillip Bothwell

NOAA-National Oceanic & Atmospheric Administration

NCEP-Storm Prediction Center

Federal Cooperator

Miriam L. Rorig

Forest Service

PNW-Pacific Northwest Research Station

Federal Fiscal Representative

Tamatha S. Verhunc

Forest Service

PNW-Pacific Northwest Research Station

Project Locations

Fire Science Exchange Network


There are no project locations identified for this project.

Project Deliverables

Final Report view or print

("Results presented in JFSP Final Reports may not have been peer-reviewed and should be interpreted as tentative until published in a peer-reviewed source.")

  ID Type Title
view or print   73 Conference/Symposia/Workshop Using the Perfect Prognosis Technique for Predicting Cloud-to-Ground Lightning in Mainland Alaska
view or print   75 Conference/Symposia/Workshop A Climatology and the Intra-Seasonal Variation of Summertime Cloud-to-Ground Lightning in Mainland Alaska
  go to website 2310 Website Experimental automated probabilistic lightning forecasts for Alaska
    5968 Conference/Symposia/Workshop Update on Model-Generated Predictions of Dry Thunderstorm Risk
    6961 Conference/Symposia/Workshop Development, Operational Use, and Evaluation of the Perfect Prognosis National Lightning Prediction System at the Storm Prediction Center

Supporting Documents

The following supporting documents are available for this project.

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