Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec, Canada. Issue 2 (14th March 2016)

• Record Type: Journal Article
• Authors: Jeong, D. I.; St-Hilaire, A.; Gratton, Y.; Bélanger, C.; Saad, C.
• Is Part Of: Atmosphere-ocean. Volume 54:Issue 2(2016)
• Page Start: 117
• Page End: 130
• Publication Date: 2016-03-14
• Subjects: global solar radiation -- regionalization -- stochastic model -- temperature -- relative humidity; Ocean-atmosphere interaction -- Canada -- Periodicals; Ocean-atmosphere interaction -- Periodicals; Oceanography -- Canada -- Periodicals; Oceanography -- Periodicals; Meteorology -- Canada -- Periodicals; Meteorology -- Periodicals; 551.5246
• DOI: 10.1080/07055900.2016.1151766
• Languages: English
• ISSNs: 0705-5900

Abstract

Abstract: Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54 MJ m −2 d −1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46 MJ m −2 d −1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.