A new model to estimate daytime net surface radiation under all sky conditions Foyo Moreno, Inmaculada Lozano, Ismael L. Alados, Inmaculada Guerrero Rascado, Juan Luis This work was supported by the Spanish Ministry of Economy and Competitiveness through projects CGL2017-90884-REDT, PID2020-120015RB-I00, PID2020-117825GB-C21, PID2020-117825GB-C22 and PID2021-128008OB-I00, by the Andalusia Regional Government, University of Granada and FEDER funds through project C-EXP-363-UGR23. This research was partially supported by the Scientific Units of Excellence Program (grant no. UCE-PP2017-02). Net surface radiation is a crucial parameter across various fields, as it represents the available energy for the energy exchange between the surface and the atmosphere. This work presents a new model for estimating instantaneous daytime net surface radiation (Rn) under all sky conditions, using solar position via cos ϴz and the clearness index (kt) as predictors. Global solar radiation (G↓) is the primary factor influencing Rn and is exten sively measured at numerous radiometric stations. Consequently, this model takes advantage of using a single input (G↓). The model was validated against other empirical models at various sites with diverse climatological characteristics. Two types of models were evaluated, one including reflected global solar irradiance (G↑) as an additional input variable alongside G↓. The best results were obtained when incorporating G↑. However, this poses a challenge as G↑ is not measured at most radiometric stations. Nevertheless, in both types, the simplest model consistently outperformed the others, revealing no significant improvements with the addition of extra variables. Overall, the proposed model demonstrated good fit with the experimental data, although with some overestimation. The coefficient of determination (R2) is over 0,94, except at sites with extreme surface albedo conditions (α >0,55). Mean bias error values ranged from 4 Wm2 to 44 Wm2, while root mean square error values varied from 25 Wm2 to 62 Wm2. Additional assessments across different seasons and sky conditions revealed improved performance during colder seasons and under cloudy conditions. Finally, the statistical analysis of the proposed model falls within the range of other more sophisticated models that involve additional input variables. 2025-05-12T09:30:44Z 2025-05-12T09:30:44Z 2025-04-01 journal article I. Foyo-Moreno et al. Atmospheric Research 315 (2025) 107886. https://doi.org/10.1016/j.atmosres.2024.107886 https://hdl.handle.net/10481/104058 10.1016/j.atmosres.2024.107886 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ open access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Elsevier