A new model to estimate daytime net surface radiation under all sky conditions
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Fecha
2025-04-01Referencia bibliográfica
I. Foyo-Moreno et al. Atmospheric Research 315 (2025) 107886. https://doi.org/10.1016/j.atmosres.2024.107886
Patrocinador
Spanish Ministry of Economy and Competitiveness CGL2017-90884-REDT, PID2020-120015RB-I00, PID2020-117825GB-C21, PID2020-117825GB-C22 and PID2021-128008OB-I00; Andalusia Regional Government; University of Granada; FEDER C-EXP-363-UGR23; Scientific Units of Excellence Program (UCE-PP2017-02)Resumen
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.