Abstract
Understanding of the characteristics of variation in surface solar irradiance on time scales shorter than several hours has been limited because ground-based observation stations are located coarsely. However, satellite observation data can be used to bridge this gap. We propose an approach for predicting characteristics of a time series of surface solar irradiance in a 121-min time window for areas without ground-based measurement systems. Time series features—mean, standard deviation, and sample entropy—are used to represent the characteristics of variation in surface solar irradiance quantitatively. We examine cloud properties over the area to design prediction models of these time series features. Cloud properties averaged over the defined domain and texture features that represent characteristics of the spatial distribution of clouds are used as measures of cloud features. Predictors for time series features, where explanatory variables are cloud features, are constructed employing the random-forest regression method. The performance test for predictions indicates that the mean and standard deviation can be predicted with higher prediction skill, whereas the predictor for sample entropy has lower prediction skill. The importance of cloud features for predictors and partial dependence of the predictors on explanatory variables are also analyzed. Cloud optical thickness (COT) and cloud fraction (CFR) were important for predicting the mean. Two texture features—contrast and local homogeneity (LHM)—and COT were important for predicting the standard deviation, and COT, LHM, and CFR were important for predicting the sample entropy. These results indicate which satellite-derived cloud field properties are useful for predicting time series features of surface solar irradiance.
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