气候变化领域集成服务门户(CCPortal): Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method

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DOI	10.1016/j.atmosenv.2021.118850
	Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method
	Ma X.; Yao Y.; Zhang B.; Yang M.; Liu H.
发表日期	2022
ISSN	1352-2310
卷号	269
英文摘要	Precipitable water vapor (PWV) is a crucial variable in water and energy transfers between the surface and atmosphere, and it is sensitive to climate and environmental changes. Among various PWV monitoring techniques, the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5)-derived PWV, with a spatial resolution of 0.25 × 0.25°, has excellent spatiotemporal continuity. However, its accuracy has relatively large uncertainties, and its spatial resolution is inadequate for small regions such as the Tibetan Plateau (TP). Therefore, the aim of this study was to propose machine learning-based modification and downscaling methods to improve the accuracy and spatial resolution of ERA5-derived PWV. First, a modification model based on a back propagation neural network (BPNN) was proposed to improve the accuracy of ERA5-derived PWV. The results showed that the root mean square error (RMSE) of ERA5-derived PWV decreased from 2.83 mm to 2.24 mm in China, i.e., an improvement of 20.8%. In the TP region, the RMSE decreased from 2.92 mm to 1.96 mm, and the improvement was 32.9%. Subsequently, a BPNN-based downscaling model was established using the modified ERA5-derived PWV to generate PWV with a 6-hourly, 0.1° × 0.1° spatiotemporal resolution in the TP region. Compared with global navigation satellite system-derived PWV, the RMSE of the generated PWV was 2.13 mm. The spatial distribution of BPNN-derived PWV based on the downscaling method exhibited suitable stability in the TP region, indicating that the proposed method could significantly improve the accuracy and spatial resolution of ERA5-derived PWV in the TP region. © 2021 Elsevier Ltd
关键词	Back propagation neural network Downscaling High resolution Precipitable water vapor
语种	英语
scopus关键词	Backpropagation; Energy transfer; Image resolution; Mean square error; Neural networks; Water vapor; Weather forecasting; Back-propagation neural networks; Down-scaling; Downscaling methods; High resolution; Network-based; Plateau region; Precipitable water vapour; Root mean square errors; Spatial resolution; Tibetan Plateau; Torsional stress; accuracy assessment; artificial neural network; back propagation; data set; downscaling; precipitable water; satellite altimetry; spatial resolution; water vapor; weather forecasting; article; back propagation neural network; China; water vapor; China; Qinghai-Xizang Plateau
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248128
作者单位	School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China; Geodetic Data Processing Center of Ministry of Natural Resource, Xi'an, 710054, China
推荐引用方式 GB/T 7714	Ma X.,Yao Y.,Zhang B.,et al. Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method[J],2022,269.
APA	Ma X.,Yao Y.,Zhang B.,Yang M.,&Liu H..(2022).Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method.ATMOSPHERIC ENVIRONMENT,269.
MLA	Ma X.,et al."Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method".ATMOSPHERIC ENVIRONMENT 269(2022).