## 1 Introduction  
Energy generation from renewable sources has become a significant issue for the social and economic development of any community that aims to be sustainable. Many hopes are linked to the photovoltaic (PV) conversion of solar energy. Since solar energy is the cheapest fuel and a solar plant has a very low maintenance cost, PV plants have become widely spread across the globe as non-polluting power sources. This statement is argued by the weight of solar electricity in the energy mix, which, in the last years, experienced a sharp increase and this trend is expected to continue [1]. A PV plant project goes through two stages: development and exploitation. In the development stage, reliable solar resource statistics is required for the feasibility study and PV system design [2]. Solar energy collected on tilted surfaces is the physical quantity of main interest. Since only a very small number of radiometric stations record solar irradiance on tilted surfaces, numerical estimates are used in practice as substitute for measurements [3]. Generally, the solar irradiance components measured on a horizontal surface are transferred on the tilted surfaces by means of the so-called transposition models [4]. When only the global horizontal irradiance is measured, before applying a transposition model, it is broken down into its fundamental components: beam and diffuse. The operation is carried out by applying a separation model [5]. Following the pioneering work of the 1960s by [PERSON] and [PERSON] [6], which reports an empirical equation that linearly connect daily diffuse and global solar irradiation, an abundance of separation models has been proposed. Almost all the separation models have an empirical basis of different deepness levels, going from purely empirical models [7]to the very rare models encapsulating features from the physics of the radiative transfer through the atmosphere [8]. Many empirical models are fitted on data collected from a limited geographical area (most of times a single location, e.g. [9]), which may dramatically limit their applicability only around the origin location. Other equations are fitted on data collected from a larger area, claiming a general applicability on a climate zone or even at a global level [10; 11]. Aiming for increasing the separation accuracy, the very simple linear equation of [PERSON] and [PERSON] [6], was replaced with more and more complicated equations: quadric [12], third order polynomial [13], exponential [14], separate equation per season [15], and so on. As far as the predictors are concerned, there is also a large diversity of separation models. Most of the empirical separation models express the diffuse fraction (see Section 2 for a definition) as a function of clearness index [16] and, sometimes, other atmospheric parameters (e.g. [17] considers 23 predictors, including some that account for the effect of clouds). From the timescale perspective, there are separation models that operate on data series of global solar irradiance ranging from 1-min sampling [7] to daily [18] or even monthly [19] sums. For more information about the separation models categories, we point the reader to Ref. [20] where a documented cross-classification is discussed. The current state-of-the-art of the separation models is well captured by the recent work of [PERSON] [11]. The [PERSON]'s paper evaluates the separation models developed after 2016, which claim a better performance than the Engerer2 model [21], which was found to be the best until 2016 [22]. The comprehensive review [22] compares the performance of 140 separation models using data from 54 locations from all climate zones. Whereas none of the models was found to be able to outperform the others in all locations, the Engerer2 model was found to be quasi-universal, based on statistical results averaged over different climate zones.