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DOI | 10.1016/j.apenergy.2020.115178 |
Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data | |
Jiang, Hou; Lu, Ning; Huang, Guanghui; Yao, Ling; Qin, Jun; Liu, Hengzi | |
通讯作者 | Lu, N (通讯作者) |
发表日期 | 2020 |
ISSN | 0306-2619 |
EISSN | 1872-9118 |
卷号 | 270 |
英文摘要 | The presence of nonhomogeneous clouds and their induced radiation interactions result in significant horizontal photon transport and spatially adjacent effects on surface solar radiation (SSR), making spatial estimation scale-dependent. Overlooking scale effects during SSR retrieval from satellite data is responsible for variations in retrieval accuracy and deviations in associated applications. In this paper, the spatial scale effects on SSR retrieval accuracy are investigated using multivariate linear regression and artificial neural network and convolutional neural network models. Scale effects are quantified through changes in retrieval accuracy under varying satellite data input size and compared among different models to reveal the merits and defects of classic linear, ordinary nonlinear, and spatially nonlinear models. The results show that scale effects have considerable impacts on retrieval accuracy in each of the three models for both site-specific and general conditions. The maximum improvement in terms of the root mean square error can reach up to 9% after involving scale information. The performance of site-specific models is continually enhanced with the expansion of spatial scale, while that of general models will drop to some extent beyond a particular threshold. Approximate distances of 20 km and 40 km from the central location are identified as the optimal scale for artificial neural and convolutional neural networks, respectively. This study also concludes that the robustness of general models is relevant to various atmospheric factors, providing perspectives for further improvements including the fusion of time series images, integration with physical modules, and the combination of multi-resolution data. |
关键词 | SUNSHINE DURATIONENERGY-PRODUCTIONQUALITY-CONTROLAIR-POLLUTIONIRRADIANCENETWORKMODELSPARAMETERIZATIONIMPACT |
英文关键词 | Scale effect; Surface solar radiation; Convolutional neural network; Artificial neural network; Multivariate linear regression |
语种 | 英语 |
WOS研究方向 | Energy & Fuels ; Engineering |
WOS类目 | Energy & Fuels ; Engineering, Chemical |
WOS记录号 | WOS:000540433000031 |
来源期刊 | APPLIED ENERGY |
来源机构 | 中国科学院青藏高原研究所 |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/259877 |
推荐引用方式 GB/T 7714 | Jiang, Hou,Lu, Ning,Huang, Guanghui,et al. Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data[J]. 中国科学院青藏高原研究所,2020,270. |
APA | Jiang, Hou,Lu, Ning,Huang, Guanghui,Yao, Ling,Qin, Jun,&Liu, Hengzi.(2020).Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data.APPLIED ENERGY,270. |
MLA | Jiang, Hou,et al."Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data".APPLIED ENERGY 270(2020). |
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