Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR024670 |
On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation | |
Shokri, Ashkan1; Walker, Jeffrey P.1; van Dijk, Albert I. J. M.2; Pauwels, Valentijn R. N.1 | |
2019-09-05 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:9页码:7622-7637 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia |
英文摘要 | The ensemble Kalman filter (EnKF) has been proved as a useful algorithm to merge coarse-resolution Gravity Recovery and Climate Experiment (GRACE) data with hydrologic model results. However, in order for the EnKF to perform optimally, a correct forecast error covariance is needed. The EnKF estimates this error covariance through an ensemble of model simulations with perturbed forcing data. Consequently, a correct specification of perturbation magnitude is essential for the EnKF to work optimally. To this end, an adaptive EnKF (AEnKF), a variant of the EnKF with an additional component that dynamically detects and corrects error misspecifications during the filtering process, has been applied. Due to the low spatial and temporal resolutions of GRACE data, the efficiency of this method could be different than for other hydrologic applications. Therefore, instead of spatially or temporally averaging the internal diagnostic (normalized innovations) to detect the misspecifications, spatiotemporal averaging was used. First, sensitivity of the estimation accuracy to the degree of error in forcing perturbations was investigated. Second, efficiency of the AEnKF for GRACE assimilation was explored using two synthetic and one real data experiment. Results show that there is considerable benefit in using this method to estimate the forcing error magnitude and that the AEnKF can efficiently estimate this magnitude. |
英文关键词 | adaptive EnKF GRACE data assimilation model error misspecification error correction |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000487415000001 |
WOS关键词 | HYDROLOGICAL DATA ASSIMILATION ; SEQUENTIAL DATA ASSIMILATION ; SNOW DATA ASSIMILATION ; SENSED SOIL-MOISTURE ; LAND-SURFACE MODEL ; WATER STORAGE ; STREAMFLOW ; UNCERTAINTIES ; SIMULATIONS ; PREDICTION |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/186944 |
专题 | 资源环境科学 |
作者单位 | 1.Monash Univ, Dept Civil Engn, Clayton, Vic, Australia; 2.Australian Natl Univ, Fenner Sch Environm & Soc, Clayton, ACT, Australia |
推荐引用方式 GB/T 7714 | Shokri, Ashkan,Walker, Jeffrey P.,van Dijk, Albert I. J. M.,et al. On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation[J]. WATER RESOURCES RESEARCH,2019,55(9):7622-7637. |
APA | Shokri, Ashkan,Walker, Jeffrey P.,van Dijk, Albert I. J. M.,&Pauwels, Valentijn R. N..(2019).On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation.WATER RESOURCES RESEARCH,55(9),7622-7637. |
MLA | Shokri, Ashkan,et al."On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation".WATER RESOURCES RESEARCH 55.9(2019):7622-7637. |
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