NCEP–ECPC monthly to seasonal US fire danger forecasts
J. Roads A , P. Tripp A , H. Juang B , J. Wang B , F. Fujioka C and S. Chen C DA Experimental Climate Prediction Center, Scripps Institution of Oceanography, University of California – San Diego, 0224, La Jolla, CA 92093, USA.
B National Centers for Environmental Prediction, Camp Springs, MD 20746, USA.
C USDA Forest Service, Riverside Fire Laboratory, Riverside, CA 92507, USA.
D Corresponding author. Email: schen@fs.fed.us
International Journal of Wildland Fire 19(4) 399-414 https://doi.org/10.1071/WF07079
Submitted: 7 June 2007 Accepted: 26 March 2010 Published: 24 June 2010
Abstract
Five National Fire Danger Rating System indices (including the Ignition Component, Energy Release Component, Burning Index, Spread Component, and the Keetch–Byram Drought Index) and the Fosberg Fire Weather Index are used to characterise US fire danger. These fire danger indices and input meteorological variables, including temperature, relative humidity, precipitation, cloud cover and wind speed, can be skilfully predicted at weekly to seasonal time scales by a global to regional dynamical prediction system modified from the National Centers for Environmental Prediction’s Coupled Forecast System. The System generates global and regional spectral model ensemble forecasts, which in turn provide required input meteorological variables for fire danger. Seven-month US regional forecasts were generated every month from 1982 to 2007. This study shows that coarse-scale global predictions were more skilful than persistence, and fine-scale regional model predictions were more skilful than global predictions. The fire indices were better related to fire counts and area burned than meteorological variables, although relative humidity and temperature were useful predictors of fire characteristics.
Additional keywords: climate models, fire climatology.
Acknowledgments
This research was funded by a cooperative agreement from NOAA-NA17RJ1231 and USDA FS USDA 02-CA-11272166–056. The views expressed herein are those of the authors and do not necessarily reflect the views of NOAA or USFS. We are grateful to A. L. Westerling for providing his gridded dataset of fire characteristics to the community.
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