@Article{BaņosMaGeSaCaNa:2021: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 
                         convective storms",
              journal = "Geoscientific Model Development Discussions",
                 year = "2021",
               volume = "2021",
               number = "36",
                pages = "1",
             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 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. Results show that a baseline RRFS run without data 
                         assimilation is able to represent the observed convection, but 
                         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 with the supersaturation removal function activated in 
                         GSI. Decreasing the vertical ensemble localization radius in the 
                         first 10 layers of the hybrid analysis results in overall less 
                         skillful forecasts. Convection and precipitation are overforecast 
                         in most forecast hours when using planetary boundary layer 
                         pseudo-observations, but the root mean square error and bias of 
                         the 2 h forecast of 2 m dew point temperature are reduced by 1.6 K 
                         during the afternoon hours. 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 current capabilities of the RRFS data 
                         assimilation system and identify configurations that should be 
                         considered as candidates for the first version of RRFS.",
                 issn = "1991-962X and 1991-9611",
                label = "lattes: 8285827971934692 4 BaņosMaGeSaCaNa:2021:AsDaAs",
             language = "en",
           targetfile = "banos_assessment.pdf",
                  url = "https://gmd.copernicus.org/preprints/gmd-2021-289/",
        urlaccessdate = "19 maio 2024"
}