@Article{BaņosMaGeSaCaNa:2022:AsDaAs,
               author = "Baņos, Ivette Hernandes and Mayfield, Will D. and Ge, Guoqing and 
                         Sapucci, Luiz Fernando and Carley, Jacob R. and Nance, Louisa",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {National 
                         Center for Atmospheric Research} and {NOAA Global Systems 
                         Laboratory} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {NOAA/NCEP Environmental Modeling Center} and {National Center 
                         for Atmospheric Research}",
                title = "Assessment of the data assimilation framework for the Rapid 
                         Refresh Forecast System v0.1 and impacts on forecasts of a 
                         convective storm case study",
              journal = "Geoscientific Model Development",
                 year = "2022",
               volume = "15",
               number = "17",
                pages = "6891--6917",
             keywords = "Data assimilation, Convective process, Rapid Refresh Forecast 
                         System.",
             abstract = "The Rapid Refresh Forecast System (RRFS) is currently under 
                         development and aims to replace the National Centers for 
                         Environmental Prediction (NCEP) operational suite of regional- and 
                         convective-scale modeling systems in the next upgrade. In order to 
                         achieve skillful forecasts comparable to the current operational 
                         suite, each component of the RRFS needs to be configured through 
                         exhaustive testing and evaluation. The current data assimilation 
                         component uses the hybrid three-dimensional ensemble-variational 
                         data assimilation (3DEnVar) algorithm in the Gridpoint Statistical 
                         Interpolation (GSI) system. In this study, various data 
                         assimilation algorithms and configurations in GSI are assessed for 
                         their impacts on RRFS analyses and forecasts of a squall line over 
                         Oklahoma on 4 May 2020. A domain of 3 km horizontal grid spacing 
                         is configured, and hourly update cycles are performed using 
                         initial and lateral boundary conditions from the 3 km grid 
                         High-Resolution Rapid Refresh (HRRR). Results show that a baseline 
                         RRFS run is able to represent the observed convection, although 
                         with stronger cells and large location errors. With data 
                         assimilation, these errors are reduced, especially in the 4 and 6 
                         h forecasts using 75 % of the ensemble background error covariance 
                         (BEC) and 25 % of the static BEC with the supersaturation removal 
                         function activated in GSI. Decreasing the vertical ensemble 
                         localization radius from 3 layers to 1 layer in the first 10 
                         layers of the hybrid analysis results in overall less skillful 
                         forecasts. Convection is greatly improved when using planetary 
                         boundary layer pseudo-observations, especially at 4h forecast, and 
                         the bias of the 2 h forecast of temperature is reduced below 800 
                         hPa. Lighter hourly accumulated precipitation is predicted better 
                         when using 100 % ensemble BEC in the first 4 h forecast, but 
                         heavier hourly accumulated precipitation is better predicted with 
                         75 % ensemble BEC. Our results provide insight into the current 
                         capabilities of the RRFS data assimilation system and identify 
                         configurations that should be considered as candidates for the 
                         first version of RRFS.",
                  doi = "10.5194/gmd-15-6891-2022",
                  url = "http://dx.doi.org/10.5194/gmd-15-6891-2022",
                 issn = "1991-959X",
                label = "lattes: 8285827971934692 4 BaņosMaGeSaCaNa:2021:AsDaAs",
             language = "en",
           targetfile = "gmd-15-6891-2022.pdf",
        urlaccessdate = "19 maio 2024"
}