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Hourly photosynthetically active radiation estimation in Midwestern United States from artificial neural networks and conventional regressions models

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Abstract

The relationship between hourly photosynthetically active radiation (PAR) and the global solar radiation (R s ) was analyzed from data gathered over 3 years at Bondville, IL, and Sioux Falls, SD, Midwestern USA. These data were used to determine temporal variability of the PAR fraction and its dependence on different sky conditions, which were defined by the clearness index. Meanwhile, models based on artificial neural networks (ANNs) were established for predicting hourly PAR. The performance of the proposed models was compared with four existing conventional regression models in terms of the normalized root mean square error (NRMSE), the coefficient of determination (r 2), the mean percentage error (MPE), and the relative standard error (RSE). From the overall analysis, it shows that the ANN model can predict PAR accurately, especially for overcast sky and clear sky conditions. Meanwhile, the parameters related to water vapor do not improve the prediction result significantly.

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Acknowledgments

We sincerely thank the researchers affiliated with the SURFRAD network. We are grateful to the Earth System Research Laboratory, Global Monitoring Division from National Oceanic and Atmospheric Administration (NOAA) for providing the radiation data at Bondville and Sioux Falls sites. This study was funded by Natural Sciences and Engineering Research Council (NSERC) of Canada awarded to Professor Xulin Guo and the Chinese Scholarship Council (CSC) awarded to Xiaolei Yu.

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  1. Department of Geography and Planning, University of Saskatchewan, Kirk Hall 117 Science Place, Saskatoon, SK, S7N 5C8, Canada

    Xiaolei Yu & Xulin Guo

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  1. Xiaolei Yu
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  2. Xulin Guo
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Correspondence to Xiaolei Yu.

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Yu, X., Guo, X. Hourly photosynthetically active radiation estimation in Midwestern United States from artificial neural networks and conventional regressions models. Int J Biometeorol 60, 1247–1259 (2016). https://doi.org/10.1007/s00484-015-1120-9

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