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

Abstract

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.