Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices
Haiganoush K. Preisler A D , Shyh-Chin Chen B , Francis Fujioka B , John W. Benoit B and Anthony L. Westerling C
A USDA Forest Service, Pacific Southwest Research Station, 800 Buchanan St, West Annex, Albany, CA 94710, USA.
B USDA Forest Service, Pacific Southwest Research Station, Riverside, CA 92507, USA.
C Sierra Nevada Research Institute, PO Box 2039, Merced, CA 95344, USA.
D Corresponding author. Email: firstname.lastname@example.org
International Journal of Wildland Fire 17(3) 305-316 http://dx.doi.org/10.1071/WF06162
Submitted: 9 December 2006 Accepted: 11 December 2007 Published: 23 June 2008
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.
Additional keywords: FWI, model appraisal, mutual information, NFDRS, semi-parametric logistic regression, spline functions.
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