## 1 Introduction  
The knowledge of the local solar radiation profile is essential to properly design solar energy systems. Solar energy is used in many applications that seek to optimize the capture of solar energy, so that it is possible to achieve energy savings and carry out sustainable energy consumption. Furthermore, solar radiation data are important for applications in meteorology, agriculture, and crop modeling as well as in the health sector [1]. The most frequent solar radiation variable registered by weather stations is global irradiance measured on a horizontal surface. Additionally, solar radiation fractions, such as direct and diffuse fractions, are required for several applications. For example, the direct component is a basic input to predict the performances and design for concentrating solar plants. Since global solar irradiance is more commonly measured at radiometric stations, the components of direct and diffuse must be modeled. Different models can be found in the literature to estimate these components [2; 3; 4; 5]. However, because of the limited spatial coverage and representativeness, site-based radiation observations have drawbacks when used in many regional and global applications. Therefore, remote sensing techniques are used [1, 6, 7, 8]. Solar radiation (SR) is considered to be one of the most important parameters of climate elements. The amount of solar radiation received at the ground level is the main source of energy for the heating of the ground and the atmosphere, evapotranspiration, and photosynthesis. Therefore, the World Meteorological Organization (WMO) has defined the surface radiation budget as an essential climate variable (ECV) that affects the physical, chemical, and biological systems which characterize the Earth's climate [9]. It affects other climate variables and is crucial for climate change, renewable energy, agriculture, architecture, and hydrology [10]. Global irradiance (\(G\)) estimation and the prediction of its components, i.e., direct (\(B\)), and diffuse (\(D\)) radiation are widely discussed in the literature. Considerable work has been done since the pioneer contribution by [PERSON] and [PERSON] (1960) [11]. Since then, several models and comparative analyses have been presented and discussed. An extensive review of the measurements and modeling of all shortwave radiation components was given by [PERSON] (2004) [2], [PERSON] (2008) [3], [PERSON] (2013) [4] and more recently by [PERSON] (2020) [5]. In general, different models exist to predict solar irradiance. Two categories of solar radiation models can be distinguished: (a) models that are based on meteorological data, such as cloudiness, sunshine duration, turbidity of the atmosphere and (b) models that use global horizontal irradiance [12]. Examples from the first category are the Bird model (1981) [13, 14], METSTAT [15], the REST2 [16] and Ineichen model [17]. Details about the computation procedure of the said models are given by [PERSON] [4]. [PERSON] [12] found that these models are consistently capable of predicting solar irradiance. In the second group, we can classify the models which use the decomposition method by which global irradiance is split into its direct and diffuse components and are basically based on the clearness index [12]. In this category we could classify the work of [PERSON] and [PERSON] (1977) [18], [PERSON] et al. (1982) [19], [PERSON] et al. (1998) [20], [PERSON] et al. (2008) [21] and [PERSON] et al. (2020) [22], to mention a few of them. The separation models are still highly popular. They are often described as site-dependent in the literature, since they are essentially empirical in nature. The Boland model was found to perform satisfactorily at different sites. The first objective of this work is to assess the measurements obtained by pyranometers and pyrhelometer through an extensive quality control procedure and perform statistical analysis on the measured and derived parameters.