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.
Highlights
Active Radiation (PAR) is a key factor for photosynthesis, vegetation growth, and climate change
Yu et al [23] studied the relationship between hourly Photosynthetically Active Radiation (PAR) and r15">15] used Global Horizontal Irradiance (RaGH) from data collected over three years at the Bondville, IL, and Sioux Falls, SD, ground weather stations (United States)
The dataset was distributed into three categories of sky conditions based on the clearness index, kt, [28] and the values adapted by Suarez-García [34] considering clear [0.65, 1), partial (0.35, 0.65), and overcast
Summary
Active Radiation (PAR) is a key factor for photosynthesis, vegetation growth, and climate change. Pankaew et al [21] developed an ANN model for estimating hourly PAR data using seven atmospheric parameters (cosine of solar zenith angle, cloud index, precipitable water content, and aerosol optical depth) as the input collected from satellite data. Yu et al [23] studied the relationship between hourly PAR and RaGH from data collected over three years at the Bondville, IL, and Sioux Falls, SD, ground weather stations (United States) From these data, they determined the temporal variability of the PAR fraction and its dependence on different sky conditions (defined by the clearness index (kt)). Zs is the angle between the sky zenith and sun. δ, φ, ω are the respective declination, hour angle, and geographic latitude of the specific location
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