气候变化领域集成服务门户(CCPortal): High-resolution reconstruction and correction of FY-4A precipitable water vapor in China using back propagation neural network

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DOI	10.1016/j.atmosres.2024.107357
	High-resolution reconstruction and correction of FY-4A precipitable water vapor in China using back propagation neural network
发表日期	2024
ISSN	0169-8095
EISSN	1873-2895
起始页码	304
卷号	304
英文摘要	Precipitable water vapor (PWV) with high spatial and temporal resolution is essential for a deeper understanding of the study area ' s hydrological cycle and climate change. This study uses the European Center for MediumRange Weather Forecasts Reanalysis 5 (ERA5) PWV as auxiliary verification data and proposes a reconstruction method based on back propagation neural network (BPNN) downscaling. Combining with high temporal resolution meteorological elements, digital elevation model (DEM), location, and time, reconstructed the Fengyun-4A (FY -4A) PWV data set with high spatial and temporal resolution in China was obtained. Subsequently, using the characteristics of FY -4A PWV with DEM, location, and time, a BPNN-based correction method is proposed to correct the reconstructed FY -4A PWV using the high -precision global navigation satellite system (GNSS) PWV. The results showed that the root mean square error (RMSE), Bias, and Pearson correlation coefficient (R) of the reconstructed model with respect to the FY -4A PWV are 1.32 mm, 0 mm, and 0.99, respectively. Compared with the auxiliary verification data ERA5 PWV, the RMSE, Bias, and R of the reconstructed FY -4A PWV are 0.85 mm, 0.18 mm, and 0.99, respectively. Compared with the GNSS PWV, the RMSE of the corrected FY -4A PWV is 2.44 mm, which is 28.86% lower than that of the reconstructed FY -4A PWV, the Bias is improved from -1.46 mm to 0 mm, and the R is enhanced from 0.98 to 0.99. The reconstruction and correction method of FY -4A PWV with high spatial and temporal resolution proposed in this study dramatically improves the completeness and accuracy of FY -4A PWV in China. It lays the foundation for studying rapid changes in China ' s hydrological cycle and climate.
英文关键词	FY-4A PWV; BPNN; ERA5; GNSS; High spatiotemporal resolution
语种	英语
WOS研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
WOS记录号	WOS:001223433700001
来源期刊	ATMOSPHERIC RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/303040
作者单位	Xi'an University of Science & Technology; Chinese Academy of Sciences; Innovation Academy for Precision Measurement Science & Technology, CAS; Wuhan University; Wuhan University
推荐引用方式 GB/T 7714	. High-resolution reconstruction and correction of FY-4A precipitable water vapor in China using back propagation neural network[J],2024,304.
APA	(2024).High-resolution reconstruction and correction of FY-4A precipitable water vapor in China using back propagation neural network.ATMOSPHERIC RESEARCH,304.
MLA	"High-resolution reconstruction and correction of FY-4A precipitable water vapor in China using back propagation neural network".ATMOSPHERIC RESEARCH 304(2024).

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