Coupling meteorological variables with Moderate Resolution Imaging Spectroradiometer atmospheric products for estimating global solar radiation

Chen, J.-L., He, L., Yang, H. ORCID: https://orcid.org/0000-0001-9940-8273, Chen, Q., Mao, M.-H., Wang, X.-X. and Xiao, Z.-L. (2020) Coupling meteorological variables with Moderate Resolution Imaging Spectroradiometer atmospheric products for estimating global solar radiation. Energy Conversion and Management, 205. 112383. ISSN 0196-8904 doi: 10.1016/j.enconman.2019.112383

Abstract/Summary

Global solar radiation is a crucial variable for scientific researches and solar energy application, while it is measured at very few sites mainly due to the technical and fiscal obstacles. Developing robust and accurate models for estimating global solar radiation had been being a focus for many studies. This study was conducted to develop integrated models combining Moderate Resolution Imaging Spectroradiometer atmospheric products and meteorological variables. 43 empirical models based on the meteorological variables were collected. A total of 645 integrated models incorporating atmospheric constituents into the empirical models were developed. The researched models were evaluated and compared at Chongqing in Three Gorges Reservoir Area, China. The results showed that the integrated models outperformed the empirical models. The best integrated model had the root mean square error of 0.817 MJ m−2 and relative root mean square error of 8.11%. On average, the integrated models had the root mean square error of 1.071 MJ m−2, 15.6% smaller than the empirical models. The results suggest that coupling Moderate Resolution Imaging Spectroradiometer atmospheric products with meteorological variables can enhance the performance of the conventional empirical models, which may provide a promising alternative to generate global solar radiation data with better accuracy.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/88506
Identification Number/DOI	10.1016/j.enconman.2019.112383
Refereed	Yes
Divisions	Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Publisher	Elsevier
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