气候变化领域集成服务门户(CCPortal): Continuous Multitrack Assimilation of Sentinel-1 Precipitable Water Vapor Maps for Numerical Weather Prediction: How Far Can We Go With Current InSAR Data?

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DOI	10.1029/2020JD034171
	Continuous Multitrack Assimilation of Sentinel-1 Precipitable Water Vapor Maps for Numerical Weather Prediction: How Far Can We Go With Current InSAR Data?
	Mateus P.; Miranda P.M.A.; Nico G.; Catalao J.
发表日期	2021
ISSN	2169897X
卷号	126 期号:3
英文摘要	The present study assesses the viability of including water vapor data from Interferometry Synthetic Aperture Radar (InSAR) in the initialization of numerical weather prediction (NWP) models, using already available Sentinel-1 A and B products. Despite the limitations resulting from the 6-day return period of images produced by the 2-satellite system, it is found that for a sufficiently large domain designed to contain a set of images every 12 h (at varying locations), the impact on model performance is beneficial or at least neutral. The proposed methodology is tested in 24 consecutive 12 h forecasts, covering two cycles of the Sentinel-1 system and 214 images, for a domain containing Iberia. A statistical analysis of the forecast precipitable water vapor (PWV) against independent GNSS observations concluded for relevant improvements in the different scores, especially during a consecutive 3-day period where the standard initial data were less accurate. An analysis of the rain forecasts against gridded remote sensing observations further indicates an overall improvement in the grid-point distribution of different precipitation classes throughout the simulation, even when the mean impact of PWV assimilation was not significant. It is suggested that current InSAR data are already a useful source of NWP data and will only become more relevant as new systems are put into operation. © 2021. American Geophysical Union. All Rights Reserved.
英文关键词	assimilation; InSAR; precipitable water vapor; precipitation; Sentinel-1; three-dimensional variational
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/185515
作者单位	Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, Bari, Italy; Department of Cartography and Geoinformatics, Institute of Earth Sciences, Saint Petersburg State University (SPSU), Saint Petersburg, Russian Federation
推荐引用方式 GB/T 7714	Mateus P.,Miranda P.M.A.,Nico G.,et al. Continuous Multitrack Assimilation of Sentinel-1 Precipitable Water Vapor Maps for Numerical Weather Prediction: How Far Can We Go With Current InSAR Data?[J],2021,126(3).
APA	Mateus P.,Miranda P.M.A.,Nico G.,&Catalao J..(2021).Continuous Multitrack Assimilation of Sentinel-1 Precipitable Water Vapor Maps for Numerical Weather Prediction: How Far Can We Go With Current InSAR Data?.Journal of Geophysical Research: Atmospheres,126(3).
MLA	Mateus P.,et al."Continuous Multitrack Assimilation of Sentinel-1 Precipitable Water Vapor Maps for Numerical Weather Prediction: How Far Can We Go With Current InSAR Data?".Journal of Geophysical Research: Atmospheres 126.3(2021).