Extension of locally adapted models of photosynthetically active radiation for all sky conditions

Abstract

Photosynthetically Active Radiation (PAR, 400-700 nm) is the energy source to trigger photosynthesis. This process makes food and biomass production and forest productivity possible, so it becomes essential for determining the impact of deforestation and climate change on agriculture. Due to the scarcity of PAR data from direct measurements at ground meteorological stations, empirical models based on linear regressions have been developed for estimate PAR data, using other meteorological and climatic variables. In recent years, machine learning algorithms have been discovered as a useful tool for modelling meteorological and climatic data. Thus, Artificial Neural Networks (ANN) have been used for modelling PAR, with different meteorological variables as input. Both procedures, multilinear regressions and ANN’s, have been used in this work for modelling PAR in Burgos (Spain) under all sky conditions attending to the sky clearness classification and in an hourly basis. The performance of the resulting models has been tested for PAR estimates at other locations. To this end, he experimental data obtained from the Surface Radiation Budget Network (SURFRAD) in the USA was used. This proves the good fit of the models developed in Burgos to the SURFRAD weather stations.