Characterizing preferred local and synoptic-scale conditions for prescribed fires in Northern California

Rochelle Worsnop

NOAA Physical Sciences Laboratory

Tuesday, Apr 22, 2025, 2:00 pm MT
DSRC Room GC402

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Abstract

The United States Forest Service plans to treat over 50 million additional hectares of hazardous fuels through the early 2030s, including through the use of prescribed fires. These controlled burns create more wildfire-resilient communities and restore forests’ natural fire regimes. Successful prescribed burns require specific ranges of fire-weather, fire-indicator, and vegetation conditions that enable fuels to ignite and sustain fire yet minimize the chance of uncontrollable spread.

To inform future prescribed fire planning, we identify the preferred local and synoptic-scale conditions associated with past prescribed fires and compare those to conditions during large wildfire growth. We base our analysis on more than 10,000 prescribed fires in Northern California from 2011 to 2022 using a novel burn-permit dataset from The Nature Conservancy. We identify large wildfires (> 1000 ha) that exhibited above-normal daily growth using the 2001–2020 Fire Event Delineation (FIRED) dataset, which was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Burned Area Product.

This presentation will show the spatial and temporal distribution of past prescribed fires across Northern California as well as the range of environmental conditions that they were conducted under. We also quantify the expected number of days each season that conditions should be conducive to prescribed fires or large wildfire growth with an examination of factors that limit even more days. Finally, we will show composites of large-scale weather patterns associated with prescribed and large growth wildfires to help identify differences in their synoptic setups as their unique patterns may be useful to predict burn-windows-of-opportunity weeks in advance.

Bio: Rochelle Worsnop is a Research Physical Scientist in NOAA's Physical Sciences Laboratory. She earned a B.S. in Meteorology from Florida State University and a M.S. and Ph.D. in Atmospheric and Oceanic Sciences from the University of Colorado in Boulder. Her research focuses on the understanding and probabilistic prediction of fire-weather variables and wildfire indicators at medium-to-subseasonal timescales. For recent projects, she developed experimental fire-forecasting tools by leveraging large hindcast datasets in combination with traditional and neural-network-based statistical post-processing techniques to improve the skill and reliability of forecasts output from NOAA's Global Ensemble Forecast System. Her current research focuses on reducing risk from wildfires by characterizing the environmental conditions needed to successfully implement prescribed fires, a critical wildfire mitigation tool.