Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones

Abstract

Four models for predicting Photosynthetically Active Radiation (PAR) were obtained through MultiLinear Regression (MLR) and an Artificial Neural Network (ANN) based on 10 meteorological indices previously selected from a feature selection algorithm. One model was developed for all sky conditions and the other three for clear, partial, and overcast skies, using a sky classification based on the clearness index (kt). The experimental data were recorded in Burgos (Spain) at ten-minute intervals over 23 months between 2019 and 2021. Fits above 0.97 and Root Mean Square Error (RMSE) values below 7.5% were observed. The models developed for clear and overcast sky conditions yielded better results. Application of the models to the seven experimental ground stations that constitute the Surface Radiation Budget Network (SURFRAD) located in different Köppen climatic zones of the USA yielded fitted values higher than 0.98 and RMSE values less than 11% in all cases regardless of the sky type.

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

“…(2020b) obtained r 2 ranging between 0.992 and 0.998 and a rRMSE between 1.86% and 9.97%, while García‐Rodríguez et al. (2022) found values of r 2 from 0.994 to 0.997 and rRMSE values from 4.62% to 6.87%, in addition, López et al. (2001) evaluated a wide range of ANN models with different weather and solar radiation measurements as inputs, as well as PAR Global , obtaining a high range of variation in r 2 (0.337–0.999) and rRMSE (2.0%–44.3%).…”

“…García‐Rodríguez et al. (2022) also proposed two different ways to model PAR from meteorological indices: firstly, by a multilinear regression model, and secondly by using artificial neural networks (ANN). Another very common approach is to consider the PAR‐TSI fraction as a constant when estimating PAR radiation (e.g., Janjai et al., 2015; Yu et al., 2015).…”

“…Notable among these models are those that estimate PAR from TSI from surface measurements, from satellite data averages (e.g., Foyo-Moreno et al, 2017;Hao et al, 2019;Peng et al, 2015;Vindel et al, 2018), or from radiation spectral measurements (Trisolino et al, 2016). García-Rodríguez et al (2022) also proposed two different ways to model PAR from meteorological indices: firstly, by a multilinear regression model, and secondly by using artificial neural networks (ANN). Another very common approach is to consider the PAR-TSI fraction as a constant when estimating PAR radiation (e.g., Janjai et al, 2015;Yu et al, 2015).…”

Solar Ultraviolet Irradiance Characterization under All Sky Conditions in Burgos, Spain

“…The technical specifications of the various measurement instruments are shown in Tables 2-4. A complete description of the experimental facility and its location can be found in previous papers [62,63]. The experimental campaign ran from 14 September 2020 to 6 June 2022.…”

“…Therefore, the CIE standard for sky taxonomy was selected as the reference for atmospheric conditions. A complete description of the CIE standard sky classification model and the procedure for obtaining a CIE standard sky classification from sky scanner measurements can be found in previous works [62,63,81,82].…”