An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation
文献类型：期刊论文
| 作者 | Han, Xujun; Li, Xin |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2008-04-15 |
| 卷号 | 112期号:4页码:1434-1449 |
| 关键词 | Bayesian filtering nonlinear/non-Gaussian sequential data assimilation Kalman filter particle filter Lorenz model Monte Carlo methods land surface model microwave remote sensing |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2007.07.008 |
| 通讯作者 | Han, Xujun(hanxj@lzb.ac.cn) |
| 英文摘要 | This paper aims to investigate several new nonlinear/non-Gaussian filters in the context of the sequential data assimilation. The unscented Kalman filter (UKF), the ensemble Kalman filter (EnKF), the sampling importance resampling particle filter (SIR-PF) and the unscented particle filter (UPF) are described in the state-space model framework in the Bayesian filtering background. We first evaluated those methods with a simple highly nonlinear Lorenz model and a scalar nonlinear non-Gaussian model to investigate the filter stability and the error sensitivity, and then their abilities in the one-dimensional estimation of the soil moisture content with the synthetic microwave brightness temperature assimilation experiment in the land surface model VIC-3L. All the results are compared with the EnKF. The advantages and disadvantages of each filter are discussed. The results in the Lorenz model showed that the particle filters are suitable for the large measurement interval assimilation and that the Kalman filters were suitable for the frequent measurement assimilation as well as small measurement uncertainties. The EnKF also showed its feasibility for the non-Gaussian noise. The performance of the SIR-PF was actually not as good as that of the UKF or the EnKF regarding a very small observation noise level compared with the uncertainties in the system. In the one-dimensional brightness temperature assimilation experiment, the UKF, the EnKF and the SIR-PF all proved to be flexible and reliable nonlinear filter algorithms for the low dimensional sequential land data assimilation application. For the high dimensional land surface system that takes the horizontal error correlations into account, the UKF is restricted by its computational demand in the covariance propagation; we must use the EnKF, the SIR-PF and other covariance reduction algorithms. The large computational cost prevents the UPF from being applied in practice. (C) 2007 Elsevier Inc. All rights reserved. |
| 收录类别 | SCI |
| WOS关键词 | ENSEMBLE KALMAN FILTER ; MAXIMUM-LIKELIHOOD-ESTIMATION ; ERROR COVARIANCE PARAMETERS ; SURFACE DATA ASSIMILATION ; MONTE-CARLO METHODS ; SOIL-MOISTURE ; PARTICLE FILTER ; MODEL ; SYSTEMS ; FORECAST |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS类目 | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000254961500015 |
| 出版者 | ELSEVIER SCIENCE INC |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2556059 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Han, Xujun |
| 作者单位 | Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Han, Xujun,Li, Xin. An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation[J]. REMOTE SENSING OF ENVIRONMENT,2008,112(4):1434-1449. |
| APA | Han, Xujun,&Li, Xin.(2008).An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation.REMOTE SENSING OF ENVIRONMENT,112(4),1434-1449. |
| MLA | Han, Xujun,et al."An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation".REMOTE SENSING OF ENVIRONMENT 112.4(2008):1434-1449. |
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来源：寒区旱区环境与工程研究所
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