@Article{HerdiesSGSGCLRKSMM:2023:EvSuDa,
               author = "Herdies, Dirceu Luis and Silva, Fabricio Daniel dos Santos and 
                         Gomes, Helber Barros and Silva, Maria Cristina Lemos da and Gomes, 
                         Heliofabio Barros and Costa, Rafaela Lisboa and Lins, Mayara 
                         Christine Correia and Reis, Jean Souza dos and Kubota, Paulo 
                         Yoshio and Souza, Dayana Castilho de and Melo, Maria Luciene Dias 
                         de and Mariano, Glauber Lopes",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Alagoas (UFAL)} and {Universidade Federal 
                         de Alagoas (UFAL)} and {Universidade Federal de Alagoas (UFAL)} 
                         and {Universidade Federal de Alagoas (UFAL)} and {Universidade 
                         Federal de Alagoas (UFAL)} and {Universidade Federal de Alagoas 
                         (UFAL)} and {Universidade Federal do Rio Grande do Norte (UFRN)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Alagoas (UFAL)} and {Universidade Federal 
                         de Alagoas (UFAL)}",
                title = "Evaluation of surface data simulation performance with the 
                         Brazilian Global Atmospheric Model (BAM)",
              journal = "Atmosphere",
                 year = "2023",
               volume = "14",
               number = "1",
                pages = "e125",
                month = "Jan.",
             keywords = "climate model evaluation, BAM-v2, 2, 1, solar radiation, 
                         temperature, wind speed.",
             abstract = "In this study, we evaluated the performance of the Brazilian 
                         Global Atmospheric Model (BAM), in its version 2.2.1, in the 
                         representation of the surface variables solar radiation, 
                         temperature (maximum, minimum, and average), and wind speed. Three 
                         experiments were carried out for the period from 2016 to 2022 
                         under three different aerosol conditions (constant (CTE), 
                         climatological (CLIM), and equal to zero (ZERO)), discarding the 
                         first year as a spin-up period. The observations came from a 
                         high-resolution gridded analysis that provides Brazil with robust 
                         data based on observations from surface stations on a daily scale 
                         from 1961 to 2020; therefore, combining the BAM outputs with the 
                         observations, our intercomparison period took place from 2017 to 
                         2020, for three timescales: daily, 10-day average, and monthly, 
                         targeting different applications. In its different simulations, 
                         BAM overestimated solar radiation throughout Brazil, especially in 
                         the Amazon; underestimated temperature in most of the northeast, 
                         southeast, and south regions; and overestimated in parts of the 
                         north and mid-west; while wind speed was only not overestimated in 
                         the Amazon region. In relative terms, the simulations with 
                         constant aerosol showed better performance than the others, 
                         followed by climatological conditions and zero aerosol. The 
                         dexterity indices applied in the intercomparison between BAM and 
                         observations indicate that BAM needs adjustments and calibration 
                         to better represent these surface variables. Where model 
                         deficiencies have been identified, these can be used to drive 
                         model development and further improve the predictive 
                         capabilities.",
                  doi = "10.3390/atmos14010125",
                  url = "http://dx.doi.org/10.3390/atmos14010125",
                 issn = "2073-4433",
             language = "en",
           targetfile = "atmosphere-14-00125.pdf",
        urlaccessdate = "23 maio 2024"
}