Evaluation and Postprocessing of Ensemble Fire Weather Predictions over the Northeast United States

Abstract

AbstractThe Short-Range Ensemble Forecast (SREF) system is verified and bias corrected for fire weather days (FWDs) defined as having an elevated probability of wildfire occurrence using a statistical Fire Weather Index (FWI) over a subdomain of the northeastern United States (NEUS) between 2007 and 2014. The SREF is compared to the Rapid Update Cycle and Rapid Refresh analyses for temperature, relative humidity, specific humidity, and the FWI. An additive bias correction is employed using the most recent previous 14 days [sequential bias correction (SBC)] and the most recent previous 14 FWDs [conditional bias correction (CBC)]. Synoptic weather regimes on FWDs are established using cluster analysis (CA) on North American Regional Reanalysis sea level pressure, 850-hPa temperature, 500-hPa temperature, and 500-hPa geopotential height. SREF severely underpredicts FWI (by two indices at FWI = 3) on FWDs, which is partially corrected using SBC and largely corrected with CBC. FWI underprediction is associated with a cool (ensemble mean error of −1.8 K) and wet near-surface model bias (ensemble mean error of 0.46 g kg−1) that decreases to near zero above 800 hPa. Although CBC improves reliability and Brier skill scores on FWDs, ensemble FWI values exhibit underdispersion. CA reveals three synoptic weather regimes on FWDs, with the largest cool and wet biases associated with a departing surface low pressure system. These results suggest the potential benefit of an operational analog bias correction on FWDs. Furthermore, CA may help elucidate model error during certain synoptic weather regimes.