Abstract
Accurate and complete global solar radiation (Hs) data at a specific region are crucial for regional climate assessment, crop growth modeling, and all operations that use solar energy. However, in the Minas Gerais state, Southeastern Brazil (SEB), the number of weather stations that measure global solar radiation is scarce, and when it is available, it presents gaps in the time series. An attractive alternative to solve the data gap problem is to estimate global solar radiation using empirical models. In this study, thirteen models based on maximum and minimum air temperatures, precipitation, sunshine duration, and extraterrestrial solar radiation were compared in the daily estimation ofHs.Data from 10 weather stations, from 1999 to 2017, located in Minas Gerais were used. Also, cluster analysis was used to group the localities (weather stations) with similar patterns of model performance, climatic classification (Köppen–Geiger and Thornthwaite), and seasonal data variability, considering minimum and maximum air temperatures, precipitation, sunshine duration, and global solar radiation. Although it is apparently simple, studies on this subject are scarce and the few existing ones in Minas Gerais have flaws, which justifies this study. The models were evaluated by root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (BIAS), Willmott’s index of agreement (d), and performance index (c-index). Models based on sunshine duration, such as those proposed by Ertekin and Yaldiz and by Newland, showed the best performance (averagec-index = 0.71). Models based on temperature and precipitation showed the worst results (averagec-index = 0.41). Cluster analysis showed that there is a similar pattern between the performance of the models, climatic classification, and seasonal variability of data among the localities of Minas Gerais. In general, models that presented extremely poor performance were formed with weather stations located in the dry zone, but with different climate classification, and models that presented very good (and good) performance were composed by weather stations located in the humid zone (dry subhumid) with the same climate classification and similar seasonal variability. Furthermore, the models based on temperature have a tendency to overestimate radiation values below 10 MJ·m−2 day−1and to underestimate values higher than 25 MJ·m−2 day−1. This point is a limitation of the model for estimating global solar radiation below and above these levels, showing the influence of atmospheric systems and atmospheric attenuation mechanisms of global solar radiation.
Highlights
Global solar radiation (Hs) directly influences the physical, chemical, and biological processes that occur in the biosphere atmosphere [1]
Cold fronts, mainly from the south of Minas Gerais (MG), reduce temperatures and affect rainfall in the region, especially in the winter months [65]. e highest air temperatures were found in the northern MG state (Araçuaı, Montes Claros, Paracatu, and Pirapora), while the lowest were found in the southern MG state (Lavras and Machado) (Figure 2), corroborating [41]. is shows that the variables used in the empirical relationships vary temporally and spatially in MG, and these factors are crucial for determining the goodness of fit for empirical models (EMs) of Hs [5, 8]
E calibrated coefficients for the same EM may vary considerably between locations, i.e., Gd (b1: 0.3110 to 17.2790), and between other EMs themselves (S1). ese results and their discrepancies are expected as have been discussed in the literature [1] and were presented in the limited studies conducted in MG in [3, 16, 32] that were fit for the city of Lavras, the northwestern part of MG, and the metropolitan, Zona da Mata, and Vale do Rio Doce regions. is further emphasizes that EMs must be calibrated to each particular location [9, 11] as this is crucial for improving EM performance [1, 66]
Summary
Global solar radiation (Hs) directly influences the physical, chemical, and biological processes that occur in the biosphere atmosphere [1]. Ese data are insufficient to carry out studies surrounding this particular variable. According to the Integrated Environmental Data System (Sistema Integrado de Dados Ambientais (SINDA)), of the 61 AWSs in operation in the Minas Gerais (MG) state (Figure 1), only 32 have Hs measurement capabilities, and of these 32, only 10 have Hs records available (Figure 1). For this reason, in the absence of measured Hs data, it is fundamental to opt for its estimation, mainly by using empirical models (EMs) or artificial neural networks (ANNs), and even by using recent techniques such as satellite-based data and mesoscale data [6, 7, 12]. EMs use different functional relationships and include measurable variables, such as air temperature, precipitation, and insolation [2, 8, 13,14,15]. is makes EMs attractive since these variables are frequently recorded in weather stations
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