期刊
JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER
卷 240, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jqsrt.2019.106672
关键词
Surface shortwave radiation; Artificial neural network; Geostationary satellite; Empirical orthogonal function
资金
- National Natural Science Foundation of China (NSFC) [91837204]
- National Key Research and Development Plan [2017YFA0603502]
- NSFC [41905023, 41771395, 41590875]
In this study, we use an artificial neural network (ANN) method to estimate the downward surface shortwave radiation (DSSR) over China from Himawari-8 geostationary satellite data. As the training data of the DSSR estimation algorithm, ground-observed DSSR (GOS) data is compiled to complete the ANN method. GOS data from 89 stations over mainland China in 2016 are divided into training, testing and validation samples with a proportion of 3:1:1, in order to perform the DSSR estimation and accuracy validation. As a result, estimated DSSR from Himawari-8 data in 2016 shows good consistency with validation samples of ground observed DSSR, holding the determination coefficient and root mean square error of 0.90 and 88.86W m(-2) for the hourly mean DSSR, and 0.96 and 24.46W m(-2) for the daily mean DSSR, respectively. To investigate the spatio-temporal variation of daily DSSR in China, we performed the first empirical orthogonal function (EOF) analysis based on the ANN-derived DSSR estimates. The spatial pattern of the first EOF mode reveals a larger DSSR variation in northeast and northwest China, in contrast to the smallest variation appeared in the southwest. The hourly ANN-derived DSSR analysis shows a peak point at noon (local time) in each region, particularly in the Tibetan Plateau. In terms of the monthly and annual DSSR spatial distribution, the regions with higher elevations, such as Northwest, Tibetan Plateau and Inner Mongolia Plateau in China, have larger DSSR than other regions. In contrast, relatively low DSSR appears in the southwest and the northeast of China through the year. (C) 2019 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据