| description abstract | This research begins the process of creating an ensemble-based forecast system for smoke aerosols generated from wildfires using a modified version of the National Severe Storms Laboratory (NSSL) Warn-on-Forecast System (WoFS). The existing WoFS has proven effective in generating short-term (0–3 h) probabilistic forecasts of high-impact weather events such as storm rotation, hail, severe winds, and heavy rainfall. However, it does not include any information on large smoke plumes generated from wildfires that impact air quality and the surrounding environment. The prototype WoFS-Smoke system is based on the deterministic High-Resolution Rapid Refresh-Smoke (HRRR-Smoke) model. HRRR-Smoke runs over a continental United States (CONUS) domain with a 3-km horizontal grid spacing, with hourly forecasts out to 48 h. The smoke plume injection algorithm in HRRR-Smoke is integrated into the WoFS forming WOFS-Smoke so that the advantages of the rapidly cycling, ensemble-based WoFS can be used to generate short-term (0–3 h) probabilistic forecasts of smoke. WoFS-Smoke forecasts from three wildfire cases during 2020 show that the system generates a realistic representation of wildfire smoke when compared against satellite observations. Comparison of smoke forecasts with radar data show that forecast smoke reaches higher levels than radar-detected debris, but exceptions to this are noted. The radiative effect of smoke on surface temperature forecasts is evident, which reduces forecast errors compared to experiments that do not include smoke. | |
