Extended-Range Probabilistic Fire-Weather Forecasting Based on Ensemble Model Output Statistics and Ensemble Copula Coupling

Abstract

Abstract Probabilistic fire-weather forecasts provide pertinent information to assess fire behavior and danger of current or potential fires. Operational fire-weather guidance is provided for lead times fewer than seven days, with most products only providing day 1–3 outlooks. Extended-range forecasts can aid in decisions regarding placement of in- and out-of-state resources, prescribed burns, and overall preparedness levels. We demonstrate how ensemble model output statistics and ensemble copula coupling (ECC) postprocessing methods can be used to provide locally calibrated and spatially coherent probabilistic forecasts of the hot–dry–windy index (and its components). The univariate postprocessing fits the truncated normal distribution to data transformed with a flexible selection of power exponents. Forecast scenarios are generated via the ECC-Q variation, which maintains their spatial and temporal coherence by reordering samples from the univariate distributions according to ranks of the raw ensemble. A total of 20 years of ECMWF reforecasts and ERA-Interim reanalysis data over the continental United States are used. Skill of the forecasts is quantified with the continuous ranked probability score using benchmarks of raw and climatological forecasts. Results show postprocessing is beneficial during all seasons over CONUS out to two weeks. Forecast skill relative to climatological forecasts depends on the atmospheric variable, season, location, and lead time, where winter (summer) generally provides the most (least) skill at the longest lead times. Additional improvements of forecast skill can be achieved by aggregating forecast days. Illustrations of these postprocessed forecasts are explored for a past fire event.