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Diffuse solar radiation plays important role in carbon exchange between forest ecosystems and the atmosphere. Based on a 2-year (2016–2017) dataset of global and diffuse solar radiation, the annual mean diffuse fraction (the proportion of diffuse solar radiation to global solar radiation, DF) was approximately 0.45 over the plantation canopy. A proposed model (BRL-1), including six predictors (half-hourly clearness index CI, solar elevation angle, apparent solar time, daily CI, a persistence of global solar radiation and relative humidity), was developed using the ground-based measurements data of 2016. The DF values estimated by the proposed model were in good agreement with the measured ones. The values of the coefficient of determination (R2), the root mean squared error (RMSE), the mean bias error (MBE) and the t stat for the proposed model were 0.82, 0.12, 0.002 and 1.16, respectively, lower than the results derived from other models. Therefore, the performance of the proposed model estimating half-hourly DF was better than the other models. Moreover, half-hourly diffuse solar radiation in cloudy skies estimated by the proposed model was more consistent with the measured one than that under clear skies. This study aims at helping us understand the change of diffuse solar radiation with clouds. Relationship between clearness index (CI) and solar elevation angle (β). Scatter plots of diffuse fraction (DF) and CI, half-hourly measured diffuse solar radiation and estimated values. Image 10927 • Diffuse fraction was correlated with relative humidity and clearness index. • The performance of the proposed model was better than other eight models. • Estimated diffuse radiation was consistent with measured values in cloudy skies. [ABSTRACT FROM AUTHOR]

Diffuse radiation is vital for climatology, sustainable energy, agriculture and biological activities. It is often estimated based on some meteorological factors but seldom takes total cloud cover into consideration. In this study, cloud cover data are used to establish models, compared with other regular models for estimating diffuse radiation in different climatic zones of China during 1992–2015. The results showed that the performance of the new model based on sunshine fraction (n/N), clearness index (k t), total cloud cover (CI), air temperature (T a), relative humidity (RH), wind speed and the day of the year outperformed other models and further improved the predictive accuracy. Generally, the k d models based on the single k t was generally more precise compared with single n/N-based or single CI-based models. And models on the basis of multiple factors performed better than the single factor-based model. Comparisons between these models indicated that neural network models (with the largest R and smallest RMSD values) provided better overall accuracy than other models. The parameter of CI as the co-factor can improve the prediction of diffuse radiation. The models developed and evaluated in this study can contribute to developing and utilizing solar energy in China, especially in areas without diffuse radiation records. [Display omitted] • The work can estimate diffuse radiation accurately, especially in some areas without diffuse radiation measurement. • Applicability of 21 empirical and 2 artificial neural network models were investigated in five climatic zones of China. • The models considering diffuse fraction, sunshine fraction and cloud cover and four parameters were proposed. • The parameter of total cover cloud as the co-factor can improve the prediction of diffuse radiation. [ABSTRACT FROM AUTHOR]