A multimodel data assimilation framework via the ensemble Kalman filter

doi:10.1002/2013WR014525

CORC > 北京大学 > 工学院

	A multimodel data assimilation framework via the ensemble Kalman filter
	Xue, Liang ; Zhang, Dongxiao
刊名	water resources research
	2014
关键词	INCORPORATING PRIOR INFORMATION ATMOSPHERIC DATA ASSIMILATION HYDROLOGIC DATA ASSIMILATION RESERVOIR SIMULATION-MODELS STEADY-STATE CONDITIONS GROUNDWATER-FLOW GLUE METHODOLOGY UNCERTAINTY ESTIMATION PARAMETER-ESTIMATION BAYESIAN-ESTIMATION
DOI	10.1002/2013WR014525
英文摘要	The ensemble Kalman filter (EnKF) is a widely used data assimilation method that has the capacity to sequentially update system parameters and states as new observations become available. One noticeable feature of the EnKF is that it not only can provide real-time updates of model parameters and state variables, but also can give the uncertainty associated with them in each assimilation step. The natural system is open and complex, rendering it prone to multiple interpretations and mathematical descriptions. In this paper, a multimodel data assimilation method is proposed by embedding the EnKF into the Bayesian model averaging framework to account for the uncertainty stemming from the model itself. An illustrative example, considering both hydrogeological and groundwater flow uncertainties, is employed to demonstrate the proposed multimodel data assimilation approach via the EnKF. Results show that statistical bias and uncertainty underestimation can occur when the data assimilation process relies on a single postulated model. The posterior model weight can adjust itself dynamically in time according to its consistency with observations. The performances of log conductivity estimation and head prediction are compared to the standard EnKF method based on the postulated single model and the proposed multimodel EnKF method. Comparisons show that the multimodel EnKF performs better in terms of statistical measures, such as log score and coverage, when sufficient observations have been assimilated in this case.; Environmental Sciences; Limnology; Water Resources; SCI(E); EI; 1; ARTICLE; xueliang@pku.edu.cn; 5; 4197-4219; 50
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/154265]
专题	工学院
推荐引用方式 GB/T 7714	Xue, Liang,Zhang, Dongxiao. A multimodel data assimilation framework via the ensemble Kalman filter[J]. water resources research,2014.
APA	Xue, Liang,&Zhang, Dongxiao.(2014).A multimodel data assimilation framework via the ensemble Kalman filter.water resources research.
MLA	Xue, Liang,et al."A multimodel data assimilation framework via the ensemble Kalman filter".water resources research (2014).