Evaluation of MM5 model resolution when applied to prediction of National Fire Danger Rating indexesJeanne L. Hoadley A F , Miriam L. Rorig A , Larry Bradshaw B , Sue A. Ferguson A , Kenneth J. Westrick C , Scott L. Goodrick D and Paul Werth E
A USDA Forest Service, Pacific Northwest Research Station, 400 N 34th Street #201, Seattle, WA 98103, USA.
B USDA Forest Service, Pacific Northwest Research Station Fire Sciences Laboratory, 5775 US W Highway 10, Missoula, MT 59808-9361, USA.
C 3Tier Environmental Forecast Group, 2825 Eastlake Avenue E #301, Seattle, WA 98102, USA.
D USDA Forest Service, Southeast Research Station, 320 Green Street, Athens, GA 30602-2044, USA.
E US Department of Interior, Northwest Interagency Coordination Center, 5420 NE Marine Drive, Portland, OR 97218-1007, USA.
F Corresponding author. Email: firstname.lastname@example.org
International Journal of Wildland Fire 15(2) 147-154 https://doi.org/10.1071/WF05015
Submitted: 20 January 2005 Accepted: 25 October 2005 Published: 31 May 2006
Weather predictions from the MM5 mesoscale model were used to compute gridded predictions of National Fire Danger Rating System (NFDRS) indexes. The model output was applied to a case study of the 2000 fire season in Northern Idaho and Western Montana to simulate an extreme event. To determine the preferred resolution for automating NFDRS predictions, model performance was evaluated at 36, 12, and 4 km. For those indexes evaluated, the best results were consistently obtained for the 4-km domain, whereas the 36-km domain had the largest mean absolute errors. Although model predictions of fire danger indexes are consistently lower than observed, analysis of time series results indicates that the model does well in capturing trends and extreme changes in NFDRS indexes.
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Hoadley JL, Westrick KJ, Ferguson SA, Goodrick SL, Bradshaw L , Werth PA (2004) The effect of increased model resolution in predicting meteorological parameters used in fire danger rating. Journal of Applied Meteorology 43, 1333–1347.
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