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An Integrated Framework for Mapping Nationwide Daily Temperature in China
Zhong, Shaobo1; Ye, Xinlan2; Wang, Mingxing2; Mei, Xin2; Song, Dunjiang3; Wang, Wenhui2
发表期刊ADVANCES IN METEOROLOGY
摘要Air temperature (T-a) is an essential parameter for science research and engineering practice. While the traditional site-based approach is only able to obtain observations in limited and discrete locations, satellite remote sensing is promising to retrieve some environmental variables with spatially continuous coverage. Nowadays, land surface temperature (T-s) measurements can be obtained from some satellite sensors (e.g., MODIS), further enabling us to estimate T-a in view of the relationship between T-a and T-s. In this article, we proposed a two-phase integrated framework to estimate daily mean T-a nationwide. In the first phase, multivariate linear regression models were fitted between site-based observations of daily mean air temperature (Ta-mean) and MODIS land surface temperature products (including Terra day: TMOD-day, Terra night: TMOD-night, Aqua day: TMYD-day, and Aqua night: TMYD-night) conditional on some covariates of environmental factors. The fitted models were then used to predict Ta-mean from those covariates at unobserved locations. The predicted Ta-mean were looked on as stochastic variables, and their distributions were also obtained. In the second phase, Bayesian maximum entropy (BME) methods were used to produce spatially continuous maps of Ta-mean taking the meteorological station observations as hard data and the predicted Ta-mean in the first phase as soft data. It is shown that the proposed approach is promising to improve the interpolation accuracy significantly, comprehensively considering the prior knowledge and the context of space variability and correlation, which will enable it to compile spatially continuous air temperature products with higher accuracy.
2022
卷号2022
ISSN1687-9309
文章类型Article
DOI10.1155/2022/9895576
关键词[WOS]YUN-GUI-GUANG ; AIR-TEMPERATURE ; DAILY MAXIMUM ; PRECIPITATION ; PRODUCTS ; DROUGHT
语种英语
WOS研究方向Meteorology & Atmospheric Sciences
WOS类目Meteorology & Atmospheric Sciences
WOS记录号WOS:000800285700002
引用统计
文献类型期刊论文
条目标识符http://ir.casisd.cn/handle/190111/12055
专题可持续发展战略研究所
作者单位1.Beijing City Univ, Urban Construct Sch, Beijing, Peoples R China
2.Beijing Acad Sci & Technol, Inst Urban Syst Engn, Beijing 100035, Peoples R China
3.Hubei Univ, Fac Resources & Environm Sci, Wuhan, Peoples R China
4.Chinese Acad Sci, Inst Sci & Dev, Beijing 100190, Peoples R China
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Zhong, Shaobo,Ye, Xinlan,Wang, Mingxing,et al. An Integrated Framework for Mapping Nationwide Daily Temperature in China[J]. ADVANCES IN METEOROLOGY,2022,2022.
APA Zhong, Shaobo,Ye, Xinlan,Wang, Mingxing,Mei, Xin,Song, Dunjiang,&Wang, Wenhui.(2022).An Integrated Framework for Mapping Nationwide Daily Temperature in China.ADVANCES IN METEOROLOGY,2022.
MLA Zhong, Shaobo,et al."An Integrated Framework for Mapping Nationwide Daily Temperature in China".ADVANCES IN METEOROLOGY 2022(2022).
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