Long-range probabilistic fire-weather forecasting based on Ensemble Model Output Statistics and Ensemble Copula Coupling

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 less than seven days, with most products only providing day 1-3 outlooks. 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) out to two weeks ahead. ERA-Interim reanalysis data over the continental US (CONUS) are used to correct biases in twenty years of ECMWF reforecasts. The univariate post-processing fits the truncated normal distribution to data transformed with a flexible selection of power exponents. Forecast trajectories 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. Skill scores show postprocessing is beneficial during all seasons over CONUS out to two weeks. Improvement gained over 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. Aggregating forecast days increases the skill of the forecasts even further and highlights future time periods of potentially hazardous fire weather.