Abstract
This study aimed to analyze the accuracy of daily series of global solar radiation, simulated by the weather generator PGECLIMA_R, in the State of Parana, Brazil. For this purpose, there were used historical series of 30 years from 28 different localities, spatially well distributed, so as to represent the entire State. There were five replications for each localitie, allowing to compare the monthly average observed and simulated data to test the accuracy of the generator PGECLIMA_R through statistical analysis of the coefficients of Pearson correlation index ;;r;;, Willmott agreement index ;;d;;, confidence index ;;c;;, the mean bias error (MBE), the root mean square error (RMSE) and the mean absolute error (MAE). The comparison between data generated by PGECLIMA_R and historical data demonstrated a very satisfactory performance of this weather generator for estimating global solar radiation in almost all studied localities.
Highlights
The weather generators are computational tools, which are mathematical simulation models designed to generate synthetic series of climate data with the same statistical characteristics of the historical series
Weather generators have been important in the modeling and analysis of ecosystems
According to Zanetti (2003) the use of weather generators in the construction of future climate scenarios, aimed at predicting events that might occur at some time in a location of interest, is an alternative of great interest due to the lack of observed data series in the future, allowing the use of simulated data
Summary
The weather generators are computational tools, which are mathematical simulation models designed to generate synthetic series of climate data with the same statistical characteristics of the historical series. Weather generators have been important in the modeling and analysis of ecosystems. According to Zanetti (2003) the use of weather generators in the construction of future climate scenarios, aimed at predicting events that might occur at some time in a location of interest, is an alternative of great interest due to the lack of observed data series in the future, allowing the use of simulated data. There are several weather generators developed, and some better known may be cited as example: CLIGEN – Climate Generator (Nicks et al, 1995), LARS-WG (Semenov & Barrow, 1997), SEDAC_R Stochastic Simulator of Climatic Data (Virgens Filho, 2001), and ClimaBR (Zanetti, 2003)
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