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This study aimed to assess monthly average daily global solar irradiation ( H g m) estimates in Southeastern Brazil from empirical models and artificial intelligence (AI) techniques using extreme air temperatures (maximum—Tx and minimum—Tn) and extraterrestrial solar irradiation ( H 0 m ) and evaluate the geographic and climate factors in the performance of the models or the AI techniques. The H g m , Tx and Tn data series from 95 automatic weather stations of the Brazilian National Institute of Meteorology (INMET) were used. The Hargreaves-Samani (HS) and Bristow-Campbell (BC) models were fitted, and AI techniques (artificial neural network—ANN and support vector machines—SVM) were trained, and assessed according to local climate conditions. Three input schemes in the AI techniques were assessed: (i) H 0 m , Tx, and Tn; (ii) H 0 m and air temperature amplitude (ΔT); and (iii) H 0 m , Tx, Tn, and ΔT. The coefficients of the HS (0.134 ≤ kr ≤ 0.262) and BC (0.539 ≤ β0 ≤ 0.796; 0.004 ≤ β1 ≤ 0.843; 0.35 ≤ β2 ≤ 2.60) models and their performance were influenced by climate, continentality/maritime effects, altitude and weather systems. The performance of the HS model was inferior to that of BC model and the SVM and ANN techniques. Overall, the SVN and ANN techniques performed better than the BC method. However, BC had similar or superior precision and accuracy to SVM and ANN for some climatic conditions and combinations of input variables. The quality of the H g m series, the combination of input variables, and climate influenced the estimates of the SVM and ANN techniques. [ABSTRACT FROM AUTHOR]

Diffuse solar irradiation (H D) data are essential for the design and management of photovoltaic solar systems, biosphere-atmosphere modeling, and other applications. However, H D observations are scarce in several locations, especially in tropical regions. Employing hourly diffuse solar irradiation (H D h) and global solar irradiation (H G h) data collected between 2002─2003 and 2007─2008 in Alagoas State, Northeast Brazil, this study assesses various modeling techniques. Empirical models, including third-degree polynomial, logistic, sigmoidal, and rational functions, were compared with AI methods such as artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Additionally, it explores how solarimetric and meteorological variables impact the performance of these models. The empirical models showed similar performance in estimating K D h (= H D h / H G h) (r2 > 0.726, modified Willmott – d m > 0.704, and RMSD < 0.103), with the third-degree polynomial model standing out. The empirical models had difficulty estimating K D h for hourly atmospheric transmittance (K T h) > 0.80, which indicated that they are not able to adequately simulate clear sky conditions, mostly due to surface reflections and clouds at the end of the day. ANN (r2 > 0.718, d m > 0.702, and RMSD < 0.105) showed better precision and accuracy of estimates for a greater number of schemes in relation to SVM and ANFIS (r2 > 0.704, d m > 0.699, RMSD < 0.108) and to empirical models. AI methods were able to represent the complexity of these conditions, with overall performance in estimating K D h superior or equivalent to empirical models. This study underscores the significance of exploring diverse methods for H D estimation, demonstrating promising potential for accurate and reliable estimation of hourly diffuse solar irradiation. • Hourly diffuse solar irradiation fraction (K D h) was estimated using atmospheric transmittance(K t h). • Empirical models and artificial intelligence (AI) methods were assessed. • Solarimetric and, or meteorological variables were considered on AI methods. • The empirical models and AI methods had similar overall performance. • The AI methos estimating better the complexity of K D h for K T h > 0.80 [ABSTRACT FROM AUTHOR]

This present study aims to evaluate the stratospheric ozone that was continuously measured during 2016 over Campo Grande, the capital of South Mato Grosso state, Brazil. To determine the best-adjusted distribution describing the ozone (O3) co-generation data in Campo Grande, 15 functions were used while modeling the numerical results. Five sets of data were used: the entire year, spring (September to December), summer (December to March, high solar radiation data), autumn (March to June), and winter (June to September, low solar radiation data) to study the seasonal variation in the statistical behavior of the probability distribution functions. The distribution performances are evaluated using three tests of quality, namely Kolmogorov–Smirnov (K-S), Anderson–Darling (A-D), and Chi-square tests. Finally, all the results of the fitted quality tests have been compared. It has been observed that the generalized extreme value distribution provides a good fit all along the year, while for specific seasons the best distributions vary. The best distributions, according to the seasons, are Gamma 3P for the winter, lognormal 3P for spring, Weibull for summer and Gamma 3P for autumn, respectively. There was a coincidence in the probability distribution function adjustment in winter and autumn, period with lower O3 concentrations, kurtosis, and skewness. In the summer and spring, it was observed higher concentrations of O3, kurtosis, and asymmetry and different probability distribution functions. [ABSTRACT FROM AUTHOR]