Articles | Volume 15, issue 10
https://doi.org/10.5194/essd-15-4519-2023
https://doi.org/10.5194/essd-15-4519-2023
Data description paper
 | 
06 Oct 2023
Data description paper |  | 06 Oct 2023

An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach

Boyang Jiao, Yucheng Su, Qingxiang Li, Veronica Manara, and Martin Wild

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Cited articles

Aguiar, L. M., Pereira, B., David, M., Díaz, F., and Lauret, P.: Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks, Sol. Energy, 122, 1309–1324, https://doi.org/10.1016/j.solener.2015.10.041, 2015. 
Alexandersson, H.: A homogeneity test applied to precipitation data, J. Climatol., 6, 661–675, https://doi.org/10.1002/joc.3370060607, 1986. 
Bookstein, F. L.: Principal warps: Thin-plate splines and the decomposition of deformations, IEEE T. Pattern Anal., 11, 567–585, https://doi.org/10.1109/34.24792, 1989. 
Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. B., and Jones, P. D.: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850, J. Geophys. Res.-Atmos., 111, D12106m https://doi.org/10.1029/2005JD006548, 2006. 
Collins, F. C.: A comparison of spatial interpolation techniques in temperature estimation, The 3rd International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Barbara, Santa Fe, NM; Santa Barbara, CA, 21–26 January 1996. 
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This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
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