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    Author(s): Haiganoush K. PreislerShyh-Chin ChenFrancis FujiokaJohn W. Benoit; Anthony L. Westerling
    Date: 2008
    Source: International Journal of Wildland Fire 17(3): 305–316
    Publication Series: Scientific Journal (JRNL)
    PDF: Download Publication  (502.16 KB)


    The National Fire Danger Rating System indices deduced from a regional simulation weather model were used to estimate probabilities and numbers of large fire events on monthly and 1-degree grid scales. The weather model simulations and forecasts are ongoing experimental products from the Experimental Climate Prediction Center at the Scripps Institution of Oceanography. The monthly average Fosberg Fire Weather Index, deduced from the weather simulation, along with the monthly average Keetch–Byram Drought Index and Energy Release Component, were found to be more strongly associated with large fire events on a monthly scale than any of the other stand-alone fire weather or danger indices. These selected indices were used in the spatially explicit probability model to estimate the number of large fire events. Historic probabilities were also estimated using spatially smoothed historic frequencies of large fire events. It was shown that the probability model using four fire danger indices outperformed the historic model, an indication that these indices have some skill. Geographical maps of the estimated monthly wildland fire probabilities, developed using a combination of four indices, were produced for each year and were found to give reasonable matches to actual fire events. This method paves a feasible way to assess the skill of climate forecast outputs, from a dynamical meteorological model, in forecasting the probability of wildland fire severity with known precision.

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    Preisler, Haiganoush K.; Chen, Shyh-Chin; Fujioka, Francis; Benoit, John W.; Westerling, Anthony L. 2008. Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices. International Journal of Wildland Fire 17(3): 305–316.


    FWI, model appraisal, mutual information, NFDRS, semi-parametric logistic regression, spline functions

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