@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 = "27 abr. 2024"
}