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Fire Weather Prediction Tool Modernizes Science Behind Forecasts

A Hot-Dry-Windy (HDW) analysis using historical weather data for the Pagami Creek Fire (Minnesota, 2011) showing very high HDW values for the day when the fire spread was greatest.  . Photo by Joseph Charney, USDA Forest Service

Fire weather forecasters need accurate and proven tools to help them anticipate when weather conditions can make wildfires dangerous for fire managers. USDA Forest Service scientists are expanding the options with the development of the Hot-Dry-Windy Index (HDW), a new fire-weather prediction tool based on the key atmospheric variables that affect wildland fire: temperature, moisture, and wind.

For 30 years, the Haines Index has been the standard measure used by fire weather forecasters to assess how weather conditions above the ground might contribute to large growth days for wildfire. While some forecasters knew it had shortcomings, they had no research to support their concerns, and use of the Haines Index became further entrenched every year. Northern Research Station scientists and colleagues in the Southern Research Station and the Pacific Northwest Research Station have developed the Hot-Dry-Windy Index (HDW), a physically based and easily understood new fire weather index named after the key atmospheric variables that affect wildland fire. When tested against several past wildfires, HDW appears to correspond with conditions recorded in those large fire events. National Weather Service meteorologists across the United States have access to an HDW forecast website for use in their work, and they are providing scientists with feedback on its performance that will be considered as evaluation of HDW continues. In Washington, Oregon, and Idaho, forecasters are citing it in their daily fire weather briefings. The research team demonstrated that the Haines Index is fundamentally flawed and performs poorly; the National Wildfire Coordinating Group's Fire Weather Subcommittee has formally recommended that the Haines Index be dropped from training curricula and from use.



Research Partners

External Partners

  • Alan Srock, St. Cloud State University