## 7 Conclusions  
The study validated and evaluated the performance of clear sky models, namely Bird, Simplified SOLIS, Ineichen and Perez, including simple clear sky models Adnot-Bourges-Campana-Gicquel (ABCG), Berger-Duffie, and Haurwitz as well as the SOLCASTsatellite-based dataset against the McClear clear sky model (see also Equations (1)-(7)). The study period ranged from 2014 to 2021 at five sites with different climates, namely Marrakech, Agadir, Fes, Tangier, and Ouarzzate. ERA5 was used to filter out cloudy days. ERA5 and SOLCAST satellite-based data sets can be used as a clear sky model tool in the study area and in regions where there are no inputs to calibrate complex clear sky models. The models were configured using pvlib python, and different input parameters were gathered per the requirements of each model, ranging from those of simple models, which require simple input parameters, to those of complex models, which require more input parameters. Statistical metrics have been calculated, namely the relative mean bias error (rMBE), the relative root mean square error (rRMSE), the relative mean absolute error (rMAE), and the coefficient of determination (R\({}^{2}\)). The models were ranked according to how well they performed in the study area. The Bird clear sky model was the overall most feasible model in the study area. ABCG showed overall poor performance, but it was the best performer at Tantan station, a site with low latitude 28.437 (\({}^{\circ}\) N). The SOLCAST satellite-based dataset performed better than simple clear sky models, but it struggled in very low (45 m) and very high (1140 m) altitude sites. In the absence of meteorological inputs to calibrate complex clear sky models, SOLCAST and ERA5 cloud data can be used to filter clear-sky days. The availability of observation data, particularly GHI data measured in situ, was a challenge in this study; as a result, the McClear clear sky model was used as a reference. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app13010320/s1](https://www.mdpi.com/article/10.3390/app13010320/s1)]([https://www.mdpi.com/article/10.3390/app13010320/s1](https://www.mdpi.com/article/10.3390/app13010320/s1)). Table S1-Table S6, which show clear sky model-estimated GHI versus McClear model prediction at all six stations studied with validation statistical metrics. The colours in the tables are used to indicate the clear sky model's skill in estimating GHI-based statistical metrics, with blue representing excellent performance, green representing good performance, yellow representing average performance, and pink representing poor performance. Figure S1-Figure S7 show the scatter plots of McClear-modelled hourly GHI against other clear sky model-modelled hourly GHI at all six stations under study. Conceptualization, A.M. and T.W. ; methodology, A.M. and B.M. ; software, A.M. and B.M. ; validation, A.M. and T.W. ; formal analysis, A.M. and T.W. ; investigation, A.M. and H.B. ; resources, A.M.; writing--original draft preparation, A.M.; writing--review and editing, H.B. and T.W. ; visualization, A.M. and B.M. ; supervision, T.W. ; project administration, A.M. and T.W. All authors have read and agreed to the published version of the manuscript. This research was funded by National Research, Development, and Innovation Office Fund grant number K138176. This research is also supported by the Stipendium Hungaricum Doctoral Scholarship of the Tempus Public Foundation where the first author is the beneficiary. Not applicable.