An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation
Agriculture And Forest Meteorology
We present a reformulation of the Bristow–Campbell model for daily solar radiation, developed using daily observations of radiation, temperature, humidity, and precipitation, from 40 stations in contrasting climates. By expanding the original model to include a spatially and temporally variable estimate of clear-sky transmittance, and applying a small number of other minor modifications, the new model produces better results than the original over a wider range of climates. Our method does not require reparameterization on a site-by-site basis, a distinct advantage over the original approach. We do require observations of dewpoint temperature, which the original model does not, but we suggest a method that could eliminate this dependency. Mean absolute error (MAE) for predictions of clear-sky transmittance was improved by 28% compared to the original model formulation. Aerosols and snowcover probably contribute to variation in clear-sky transmittance that remains unexplained by our method. MAE and bias for prediction of daily incident radiation were about 2.4 MJ m−2 day−1 and +0.5 MJ m−2 day−1, respectively. As a percent of the average observed values of incident radiation, MAE and bias are about 15% and +4%, respectively. The lowest errors and smallest biases (percent basis) occurred during the summer. The highest prediction biases were associated with stations having a strong seasonal concentration of precipitation, with underpredictions at summer-precipitation stations, and overpredictions at winter-precipitation stations. Further study is required to characterize the behavior of this method for tropical climates.
Air temperature, Atmospheric transmittance, Daily, Ecosystem process simulation, Humidity, Snowcover, Solar radiation
© 1999 Elsevier
Thornton, P. E., and Running S. W. (1999). An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agriculture and Forest Meteorology: 93(4), 211-228, doi: http://dx.doi.org/10.1016/S0168-1923(98)00126-9
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