@Article{MartinsSilvPere:2008:InClCo,
author = "Martins, Fernando Ramos and Silva, Sheila de Araujo Bandeira and
Pereira, Enio Bueno",
affiliation = "Instituto Nacional de Pesquisas Espaciais (INPE), Centro de
Previs{\~a}o de Tempo e Estudos Clim{\'a}ticos (CPTEC) and
Instituto Nacional de Pesquisas Espaciais (INPE), Centro de
Previs{\~a}o de Tempo e Estudos Clim{\'a}ticos (CPTEC) and
Instituto Nacional de Pesquisas Espaciais (INPE), Centro de
Previs{\~a}o de Tempo e Estudos Clim{\'a}ticos (CPTEC)",
title = "The influence of cloud cover index on the accuracy of solar
irradiance model estimates",
journal = "Meteorology and Atmospheric Physics",
year = "2008",
volume = "99",
number = "3-4",
pages = "169--180",
month = "Apr.",
note = "{DOI 10.1007/s00703-007-0272-5}",
keywords = "satellite data, physical model, surface, radiation, land.",
abstract = "Cloud cover index (CCI) obtained from satellite images contains
information on cloud amount and their optical thickness. It is the
chief climate data for the assessment of solar energy resources in
most radiative transfer models, particularly for the model
BRASIL-SR that is currently operational at CPTEC. The wide range
of climate environments in Brazil turns CCI determination into a
challenging activity and great effort has been directed to develop
new methods and procedures to improve the accuracy of these
estimations from satellite images (Martins 2001; Martins et al.
2003a; Ceballos et al. 2004). This work demonstrates the influence
of CCI determination methods on estimates of surface solar
irradiances obtained by the model BRASIL-SR comparing deviations
among ground data and model results. Three techniques using
visible and=or thermal infrared images of GOES-8 were employed to
generate the CCI for input into the model BRASIL-SR. The
groundtruth data was provided by the solar radiation station
located at Caico=PE, in Brazilian Northeast region, which is part
of the UNEP=GEF project SWERA (Solar and Wind Energy Resources
Assessment). Results have shown that the application of the
bi-spectral techniques have reduced mean bias error up to 66% and
root mean square error up to 50% when compared to the usual
technique for CCI determination based on the straightforward
determination of month-by-month extremes for maximum and minimum
cloud states.",
doi = "10.1007/s00703-007-0272-5",
url = "http://dx.doi.org/10.1007/s00703-007-0272-5",
issn = "0177-7971",
language = "en",
targetfile = "martins_influence.pdf",
urlaccessdate = "16 jun. 2024"
}