Spatial or temporal deficiencies often exist in solar radiation datasets that are derived from satellite remote sensing, which can seriously affect the accuracy of application models of land-surface energy balance. The goal of reconstructing radiation data is to interpolate the missing observations and optimize the whole time-series dataset.

Researchers from Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (IGSNRR-CAS) proposed a Kalman filter based assimilation algorithm, the retrieved radiation values are corrected through the continuous introduction of local in situ global solar radiation provided by China Meteorological Data Sharing Service System.

The means and standard deviations of the solar radiation data after reconstruction were significantly improved. Most of the differences of the pairs were near the line of zero and the extent is lower than 10 W/m2 in winter and 20 W/m2 in other days. This finding indicates that the time-series reconstruction method established in this study is effective for applied research.

The results show that the mean value and standard deviation of the reconstructed solar radiation data series are significantly improved, with greater consistency with ground-based observations than the series before reconstruction. The method proposed in this study provides a new solution for the time-series reconstruction of surface energy parameters, which can provide more reliable data for scientific research and regional renewable-energy planning.

The project is supported and funded by the Chinese Academy of Sciences (Grant No. KZZD-EW-08), Chinese Earthquake Administration (Grant No. 201208018-3) and State Key Laboratory of Resources and Environmental Information Systems, IGSNRR. The result of the research has been published in the Energies (2013, 6, 2804-2818; doi:10.3390/en6062804).

CONTACT:
JIANG Dong
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. Beijing, China.
E-mail: jiangd@igsnrr.ac.cn