Improving the Estimation of the Diffuse Component of Photosynthetically Active Radiation (PAR)

Most weather forecasting models are not able to accurately reproduce the great variability existing in the measurements of the diffuse component of photosynthetically active radiation (PAR; 400–700 nm) under all sky conditions. Based on the well‐known relationship between the diffuse fraction (k) and the clearness index (kt), this study addresses improvements in estimations by proposing adaptations of previous models, which were previously applied only to the total solar irradiance (TSI; 280–3,000 nm). In order to reproduce this variability, additional parameters were introduced. The models were tested employing a multisite database gathered at the Mediterranean basin. Since Artificial Neural Network (ANN) models are not limited to fixed coefficients to predict the diffuse fraction of PAR (kPAR), these types of models are more accurate than empirical ones, reaching determination coefficients (r2) up to 0.998. However, the simpler linear model proposed by Foyo‐Moreno et al. (2018), https://doi.org/10.1016/j.atmosres.2017.12.012 shows a similar performance to the ANN models, directly predicting the diffuse component of PAR (PARDiffuse) from TSIDiffuse, with a r2 up to 0.997. Results obtained here also determine that the most important variables for estimating PARDiffuse are kt or kt,PAR, and the apparent solar time (AST). Therefore, PARDiffuse can be modeled using TSI measured in most radiometric stations, reaching r2 up to 0.858 for empirical models and 0.970 for ANN models. This modified approach will allow for the very accurate construction of long‐term data series of PARDiffuse in regions where continuous measurements of PAR are not available. Plain Language Summary Increasing and deepening the knowledge of photosynthetically active radiation (PAR; 400–700 nm wavelength) is important since it plays a key role as one of Earth's climate drivers, since PAR represents approximately 50% of sun's energy. PAR also plays a main role modulating emissions of greenhouse gases (GHGs) in biomass production (agriculture), among other issues. The knowledge of the diffuse component of PAR is especially crucial since it can be directly related to the light use efficiency (LUE) of plants, explaining somehow its effects over the aforementioned issues. However, at most measurement stations direct PAR measurements are not available. In fact, in those where PAR is measured it is rare to find diffuse PAR measurements, which limits the understanding of PAR effects. Modeling the