Premium
Evaluation of Solar Radiation Prediction Models in North America
Author(s) -
Ball Rosalind A.,
Purcell Larry C.,
Carey Sean K.
Publication year - 2004
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2004.3910
Subject(s) - longitude , empirical modelling , coefficient of determination , latitude , mathematics , mean squared error , regression analysis , atmospheric sciences , range (aeronautics) , statistics , physics , geology , geodesy , materials science , computer science , composite material , programming language
Solar radiation data at the earth's surface ( R s , MJ m −2 d −1 ) are not typically recorded at weather stations, but they may be predicted from other meteorological measurements. For one location, Keiser, AR, we developed an empirical equation for predicting R s . The mechanistic models of Hargreaves–Samani (HS) and two forms of the Bristow–Campbell model, described by Thornton and Running (TR) and Weiss et al. (WS), were also evaluated for predicting R s at 13 sites, covering a 23° range in latitude and a 42° range in longitude. For the HS, TR, and WS models, we used coefficients as they were originally published, and for the HS model, a site‐specific coefficient (HS‐SS) was derived and evaluated for each site. Regression of predicted vs. observed R s values using the empirical equation for Keiser gave r 2 values (0.77) similar to the best of the mechanistic models. The HS‐SS model had the lowest root mean square error of 3.50 MJ m −2 d −1 , followed by the TR (3.56), the HS (3.86), and the WS (4.33) models. Predicted vs. observed values gave r 2 values ranging from 0.72 (TR model) to 0.56 (WS model). There was a slight superiority of the TR model over the HS‐SS and HS models. Similar fits ( r 2 > 0.87) and errors were found among the TR, HS‐SS, and HS models when R s values were averaged over a 7‐d period, and it was concluded that these three models provided accurate and precise R s estimations for our sites without further model modification.