Plant growth is directly related to levels of photosynthetic photon flux density, $Q_p$. The improvement of plant-growth models therefore requires accurate estimations of the $Q_p$ parameter that is often indirectly calculated on the basis of its relationship with solar irradiation, $R_S$, due to the scarcity of ground measurements of photosynthetic photon flux density. In this experimental campaign in Burgos, Spain, between April 2019 and January 2020, an average value of the $Q_p/R_s$ ratio is determined on the basis of measurements at ten-minute intervals. The most influential factor in the $Q_p/R_s$ ratio, over and above any daily or seasonal pattern, is the existence of overcast sky conditions. The CIE standard sky classification can be used to establish an unequivocal characterization of the cloudiness conditions of homogeneous skies. In this study, the relation between the CIE standard sky type and $Q_p/R_s$ is investigated. Its conclusions were that the $Q_p/R_s$ values, the average of which was $1.93\pm 0.15$ μmol·J⁻¹, presented statistically significant differences for each CIE standard sky type. The overcast sky types presented the highest values of the ratio, while the clear sky categories presented the lowest and most dispersed values. During the experimental campaign, only two exceptions were noted for covered and partial covered sky-type categories, respectively, sky types 5 and 9. Their values were closer to those of categories classified as clear sky according to the CIE standard. Both categories presented high uniformity in terms of illumination.

In the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (${\mathrm{I}}_{\mathrm{UV}}$), on the basis of data collected at Burgos (Spain) during an experimental campaign between March 2020 and May 2022. The aim is to explore the effectiveness of a range of variables for modelling horizontal ultraviolet irradiance through a comparison of supervised artificial neural network (ANN) and regression model results. A preliminary feature selection process using the Pearson correlation coefficient was sufficient to determine the variables for use in the models. The following variables and their influence on horizontal ultraviolet irradiance were analyzed: horizontal global irradiance $({\mathrm{I}}_{\mathrm{GH}}$), clearness index $({\mathrm{k}}_{\mathrm{t}}$), solar altitude angle $(\mathsf{\alpha })$, horizontal beam irradiance $\left({\mathrm{I}}_{\mathrm{BH}}\right)$, diffuse fraction ($\mathrm{D}$), temperature $\left(\mathrm{T}\right)$, sky clearness $(\mathsf{\epsilon })$, cloud cover $(\mathrm{Cc}$), horizontal diffuse irradiance $\left({\mathrm{I}}_{\mathrm{DH}}\right)$, and sky brightness (Δ). The ANN models yielded results of greater accuracy than the regression models.

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 (k_t). 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.

Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image‐processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty‐two image‐processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image‐processing methods are applied. While the use of the traditional RGB color space for image‐processing yielded good results (ANN accuracy equal to 86.6%), other color spaces, such as Hue Saturation Value (HSV), which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image‐processing methods could improve ANN‐algorithm accuracy, depending on the image information required for the classification problem.

Solar Ultraviolet Radiation (UVR), which is identified as a major environmental health hazard, is responsible for a variety of photochemical reactions with direct effects on urban and aquatic ecosystems, human health, plant growth, and the deterioration of industrial systems. Ground measurements of total solar UVR are scarce, with low spatial and temporal coverage around the world, which is mainly due to measurement equipment maintenance costs and the complexities of equipment calibration routines; however, models designed to estimate ultraviolet rays from global radiation measurements are frequently used alternatives. In an experimental campaign in Burgos, Spain, between September 2020 and June 2022, average values of the ratio between horizontal global ultraviolet irradiance (GHUV) and global horizontal irradiance (GHI) were determined, based on measurements at ten-minute intervals. Sky cloudiness was the most influential factor in the ratio, more so than any daily, monthly, or seasonal pattern. Both the CIE standard sky classification and the clearness index were used to characterize the cloudiness conditions of homogeneous skies. Overcast sky types presented the highest values of the ratio, whereas the clear sky categories presented the lowest and most dispersed values, regardless of the criteria used for sky classification. The main conclusion, for practical purposes, was that the ratio between GHUV and GHI can be used to model GHUV.