Comparação de produtos de radiação solar incidente à superfície para a América do Sul

Abstract

RESUMOPara algumas aplicações meteorológicas, o conhecimento da radiação solar incidente à superfície (S in ) é muito importante. Métodos convencionais de medida deste fluxo são pontuais e, portanto, representativos de pequenas áreas. Por outro lado, os satélites meteorológicos possibilitam uma cobertura regular de todo o globo terrestre, proporcionando estimativas de fluxos radiativos sobre grandes áreas com resolução espacial do sensor (apenas alguns quilômetros). Além disso, produtos de reanálise fornecem estimativas alternativas de S in , que precisam ser adequadamente validadas. Este trabalho compara quatro bancos de dados de radiação solar incidente para a América do Sul: três produtos provenientes de reanálise (NCEP/NCAR, ERA-40 e Eta/CPTEC) e o produto gerado pelo algoritmo GL1.2, que produz estimativas da S in a partir do conjunto de imagens do satélite GOES. Os resultados mostram que os campos de radiação solar incidente dos produtos de reanálise do NCEP/NCAR, ERA-40 e as estimativas do satélite GOES apresentam valores médios mensais bem próximos para todos os biomas e bacias estudados. Já a reanálise do Eta/CPTEC apresentou valores bem mais elevados para à radiação sobre a América do Sul, tanto para a média anual como nas médias mensais analisadas. Palavras-Chave: Radiação solar incidente, Reanálise, Série de dados ABSTRACT: PRODUCT COMPARISON OF SOLAR RADIATION INCIDENT SURFACE FOR SOUTH AMERICAFor some meteorological applications, knowledge of incoming solar radiation at surface level (S in ) is very important. Conventional measurements of this flux by individual sensors are representative of small areas only. On the other hand, meteorological satellites allow a regular cover of the entire globe, providing estimates of radiative fluxes over wide areas, within the spatial resolution of the sensor (a few km). In addition, reanalysis products provide alternative S in estimates that must be adequately validated. This work compares four long-term S in databases for South America: three reanalysis products (NCEP/NCAR, ERA-40 e Eta/CPTEC) and the database produced by the algorithm GL1.2, which estimates S in from a set of GOES images. The results indicate that the monthly fields of the NCEP/NCAR and the ERA-40 and the GOES-based estimates are very similar for all studied regions. On the other hand, Eta/CPTEC reanalysis presents much higher radiation values, for both the long-term annual and monthly means over South America.

A Successful Practical Experience with Dedicated Geostationary Operational Environmental Satellites GOES-10 and -12 Supporting Brazil

“…Currently a solar radiation time series of 19 years is available covering South America and neighboring oceanic areas (Ceballos 2008). This product has been used in a broad range of applications, such as atmospheric circulation model evaluation and data assimilation (Tarasova et al 2006), climatological studies (Pinto et al 2010), renewable energy (Ortega et al 2010), and agricultural management.…”

Evaluation of Global Solar Irradiance Estimates from GL1.2 Satellite-Based Model over Brazil Using an Extended Radiometric Network

“…The authors reported daily mean errors generally within ±10 W m −2 with standard deviations lower than 20 W m −2 . The GL1.2 database has contributed to the validation of numerical weather prediction models and data assimilation systems [29,30], to climatological and environmental studies [31][32][33], to hydrological modeling [34], to potential evapotranspiration estimation [35] and, last but not least, to solar resource assessment [36].…”

Parametrization of Models and Use of Estimated Global Solar Radiation Data in the Irrigated Rice Yield Simulation

“…To fill this gap, several empirical models based on air temperature were developed to estimate the GSR (Salima and Chavula, 2012;Silva et al, 2012;Jerszurki and Souza, 2013), given that air temperature is more commonly recorded and generally can be spatialized with higher accuracy. Alternatively, the GSR can be estimated by meteorological satellite sensors, which regularly cover the entire earth (Ceballos et al, 2004;Pinto et al, 2010, Van Wart et al, 2013. However, for the correct use of empirical estimate models of GSR based on air temperature, it is necessary to calibrate it locally, due to the presence of terms, which are implicitly through coefficients.…”