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@Article{PorfirioCebaBritCoel:2020:EvGlSo,
               author = "Porfirio, Anthony Carlos Silva and Ceballos, Juan Carlos and 
                         Britto, Jos{\'e} M{\'a}rcio da Silva and Coelho, Simone Marilene 
                         Sievert da Costa",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Evaluation of global solar irradiance estimates from GL1.2 
                         satellite-based model over Brazil using an extended radiometric 
                         network",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "8",
                pages = "e1331",
                month = "apr.",
             keywords = "global solar radiation, satellite-based product, GL model, 
                         geostationary satellite, validation.",
             abstract = "The GL (GLobal radiation) physical model was developed to compute 
                         global solar irradiance at ground level from (VIS) visible channel 
                         imagery of geostationary satellites. Currently, its version 1.2 
                         (GL1.2) runs at Brazilian Center for Weather Forecast and Climate 
                         Studies/National Institute for Space Research (CPTEC/INPE) based 
                         on GOES-East VIS imagery. This study presents an extensive 
                         validation of GL1.2 global solar irradiance estimates using 
                         ground-based measurements from 409 stations belonging to the 
                         Brazilian National Institute of Meteorology (INMET) over Brazil 
                         for the year 2016. The INMET reasonably dense network allows 
                         characterizing the spatial distribution of GL1.2 data 
                         uncertainties. It is found that the GL1.2 estimates have a 
                         tendency to overestimate the ground data, but the magnitude varies 
                         according to region. On a daily basis, the best performances are 
                         observed for the Northeast, Southeast, and South regions, with a 
                         mean bias error (MBE) between 2.5 and 4.9 W m\−2 (1.2% and 
                         2.1%) and a root mean square error (RMSE) between 21.1 and 26.7 W 
                         m\−2 (10.8% and 11.8%). However, larger differences occur 
                         in the North and Midwest regions, with MBE between 12.7 and 23.5 W 
                         m\−2 (5.9% and 11.7%) and RMSE between 27 and 33.4 W 
                         m\−2 (12.7% and 16.7%). These errors are most likely due to 
                         the simplified assumptions adopted by the GL1.2 algorithm for 
                         clear sky reflectance (Rmin) and aerosols as well as the 
                         uncertainty of the water vapor data. Further improvements in 
                         determining these parameters are needed. Additionally, the results 
                         also indicate that the GL1.2 operational product can help to 
                         improve the quality control of radiometric data from a large 
                         network, such as INMET's. Overall, the GL1.2 data are suitable for 
                         use in various regional applications.",
                  doi = "10.3390/RS12081331",
                  url = "http://dx.doi.org/10.3390/RS12081331",
                 issn = "2072-4292",
             language = "en",
           targetfile = "porfirio_2020.pdf",
        urlaccessdate = "18 jan. 2021"
}


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