@Article{JonesAhJaPeFrGr:2022:PrWaSy,
               author = "Jones, Thomas and Ahmadov, Ravan and James, Eric and Pereira, 
                         Gabriel and Freitas, Saulo Ribeiro de and Grell, Georg",
          affiliation = "{University of Oklahoma} and {University of Colorado} and 
                         {University of Colorado} and {Universidade Federal de S{\~a}o 
                         Jo{\~a}o del Rei (UFSJ)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {NOAA/OAR/Global Systems Laboratory}",
                title = "Prototype of a Warn-on-Forecast System for Smoke (WoFS-Smoke)",
              journal = "Weather And Forecasting",
                 year = "2022",
               volume = "1",
                pages = "1",
             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 hour) 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 hours. 
                         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 hour) probabilistic forecasts of smoke. WoFS-Smoke 
                         forecasts from 3 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.",
                  doi = "10.1175/WAF-D-21-0143.1",
                  url = "http://dx.doi.org/10.1175/WAF-D-21-0143.1",
                 issn = "0882-8156",
                label = "lattes: 9873289111461387 5 JonesAhJaPeFrGr:2022:PrWaSy",
             language = "pt",
        urlaccessdate = "25 jun. 2024"
}