DOI: 10.5194/hess-21-635-2017
Scopus记录号: 2-s2.0-85011290545
论文题名: Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States
作者: Huang C ; , Newman A ; J ; , Clark M ; P ; , Wood A ; W ; , Zheng X
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 1 起始页码: 635
结束页码: 650
语种: 英语
Scopus关键词: Bandpass filters
; Kalman filters
; Snow
; Soil moisture
; Stream flow
; Uncertainty analysis
; Ensemble Kalman Filter
; Pacific Northwest
; Predictive performance
; Snow water equivalent
; Streamflow prediction
; Transformation operators
; Uncertainty estimates
; Western United States
; Forecasting
; data assimilation
; data set
; ensemble forecasting
; hydrological modeling
; Kalman filter
; prediction
; seasonal variation
; snow
; snow water equivalent
; streamflow
; uncertainty analysis
; California
; Pacific Northwest
; Rocky Mountains
; Sacramento
; United States
英文摘要: In this study, we examine the potential of snow water equivalent data assimilation (DA) using the ensemble Kalman filter (EnKF) to improve seasonal streamflow predictions. There are several goals of this study. First, we aim to examine some empirical aspects of the EnKF, namely the observational uncertainty estimates and the observation transformation operator. Second, we use a newly created ensemble forcing dataset to develop ensemble model states that provide an estimate of model state uncertainty. Third, we examine the impact of varying the observation and model state uncertainty on forecast skill. We use basins from the Pacific Northwest, Rocky Mountains, and California in the western United States with the coupled Snow-17 and Sacramento Soil Moisture Accounting (SAC-SMA) models. We find that most EnKF implementation variations result in improved streamflow prediction, but the methodological choices in the examined components impact predictive performance in a non-uniform way across the basins. Finally, basins with relatively higher calibrated model performance (>ĝ€0.80ĝ€NSE) without DA generally have lesser improvement with DA, while basins with poorer historical model performance show greater improvements. © Author(s) 2017. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79275
Appears in Collections: 气候变化事实与影响
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作者单位: College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; National Center for Atmospheric Research, Boulder, CO, United States
Recommended Citation:
Huang C,, Newman A,J,et al. Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States[J]. Hydrology and Earth System Sciences,2017-01-01,21(1)