气候变化领域集成服务门户(CCPortal): Observation operators for assimilation of satellite observations in fluvial inundation forecasting

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DOI	10.5194/hess-23-2541-2019
	Observation operators for assimilation of satellite observations in fluvial inundation forecasting
	Cooper E.S.; Dance S.L.; García-Pintado J.; Nichols N.K.; Smith P.J.
发表日期	2019
ISSN	1027-5606
起始页码	2541
结束页码	2559
卷号	23 期号:6
英文摘要	Images from satellite-based synthetic aperture radar (SAR) instruments contain large amounts of information about the position of floodwater during a river flood event. This observational information typically covers a large spatial area but is only relevant for a short time if water levels are changing rapidly. Data assimilation allows us to combine valuable SAR-derived observed information with continuous predictions from a computational hydrodynamic model and thus to produce a better forecast than using the model alone. In order to use observations in this way, a suitable observation operator is required. In this paper we show that different types of observation operators can produce very different corrections to predicted water levels; this impacts the quality of the forecast produced. We discuss the physical mechanisms by which different observation operators update modelled water levels and introduce a novel observation operator for inundation forecasting. The performance of the new operator is compared in synthetic experiments with that of two more conventional approaches. The conventional approaches both use observations of water levels derived from SAR to correct model predictions. Our new operator is instead designed to use backscatter values from SAR instruments as observations; such an approach has not been used before in an ensemble Kalman filtering framework. Direct use of backscatter observations opens up the possibility of using more information from each SAR image and could potentially speed up the time taken to produce observations needed to update model predictions. We compare the strengths and weaknesses of the three different approaches with reference to the physical mechanisms with which each of the observation operators allow data assimilation to update water levels in synthetic twin experiments in an idealised domain. © Author(s) 2019.
语种	英语
scopus关键词	Backscattering; Floods; Forecasting; Radar imaging; Space-based radar; Water levels; Computational hydrodynamics; Conventional approach; Data assimilation; Ensemble Kalman filtering; Observation operator; Physical mechanism; Satellite observations; Synthetic experiments; Synthetic aperture radar; backscatter; data assimilation; flood; flood forecasting; forecasting method; hydrodynamics; instrumentation; Kalman filter; observational method; satellite imagery; synthetic aperture radar
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159670
作者单位	Cooper, E.S., Department of Meteorology, University of Reading, Reading, United Kingdom; Dance, S.L., Department of Meteorology, University of Reading, Reading, United Kingdom, Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom; García-Pintado, J., MARUM Center for Marine Environmental Sciences, Department of Geosciences, University of Bremen, Bremen, Germany; Nichols, N.K., Department of Meteorology, University of Reading, Reading, United Kingdom, Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom; Smith, P.J., Department of Meteorology, University of Reading, Reading, United Kingdom
推荐引用方式 GB/T 7714	Cooper E.S.,Dance S.L.,García-Pintado J.,et al. Observation operators for assimilation of satellite observations in fluvial inundation forecasting[J],2019,23(6).
APA	Cooper E.S.,Dance S.L.,García-Pintado J.,Nichols N.K.,&Smith P.J..(2019).Observation operators for assimilation of satellite observations in fluvial inundation forecasting.Hydrology and Earth System Sciences,23(6).
MLA	Cooper E.S.,et al."Observation operators for assimilation of satellite observations in fluvial inundation forecasting".Hydrology and Earth System Sciences 23.6(2019).