Global radiation by simplified models for the state of Mato Grosso, Brazil
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Data
2017-04-01
Autores
Souza, Adilson Pacheco de
Silva, Andrea Carvalho da
Tanaka, Adriana Aki
Uliana, Eduardo Morgan
Almeida, Frederico Terra de
Klar, Antonio Evaldo [UNESP]
Almeida Gomes, Anthony Wellington
Título da Revista
ISSN da Revista
Título de Volume
Editor
Empresa Brasil Pesq Agropec
Resumo
The objective of this work was to estimate the global radiation by simplified models for the state of Mato Grosso, Brazil. The parameterized coefficients of 15 simplified models were regionally calibrated to estimate the daily global radiation, based only on air temperature, using data from 28 automatic weather stations (AWS) of the network of the Brazilian Meteorology Institute, distributed throughout the different biomes of the state of Mato Grosso. The simplified models are mostly derived from the Hargreaves and Bristow & Campbell methods, with different parameterized coefficients to be calibrated. The coefficient of determination (R-2), the mean bias error (MBE), the root mean square error (RMSE), and Willmott's d index were used to evaluate statistical performance. For the recommendation of models per station and/or biome, the models were rated numerically (position values) according to their specific performance in each statistical indicator. The simplified models derived from Bristow & Campbell showed better statistical performances for estimating daily global radiation. The values of the calibrated coefficients of the same model varied greatly among the AWS and biomes. The R-2 values ranged from 0.60 to 0.75, indicating a satisfactory result for the obtained calibrations. The Bristow & Campbell model for the Amazon and the Cerrado and the Goodin model for the Cerrado are recommended, with scattering varying between 1.52 and 4.33 MJ m(-2) per day and adjustments greater than 65%.
Descrição
Palavras-chave
air temperature, Amazon, Cerrado, parameterized coefficients, solar radiation
Como citar
Pesquisa Agropecuaria Brasileira. Brasilia Df: Empresa Brasil Pesq Agropec, v. 52, n. 4, p. 215-227, 2017.