Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation (EI)
文献类型：期刊论文
| 作者 | Chen W.; Huang C.; Shen H.; Li X. |
| 刊名 | Advances in Water Resources
 |
| 出版日期 | 2015 |
| 卷号 | 86页码:425-438 |
| 英文摘要 | Model parameters are a source of uncertainty that can easily cause systematic deviation and significantly affect the accuracy of soil moisture generation in assimilation systems. This study addresses the issue of retrieving model parameters related to soil moisture via the simultaneous estimation of states and parameters based on the Common Land Model (CoLM). The state-parameter estimation algorithms AEnKF (Augmented Ensemble Kalman Filter), DEnKF (Dual Ensemble Kalman Filter) and SODA (Simultaneous optimization and data assimilation) are entirely implemented within an EnKF framework to investigate how the three algorithms can correct model parameters and improve the accuracy of soil moisture estimation. The analysis is illustrated by assimilating the surface soil moisture levels from varying observation intervals using data from Mongolian plateau sites. Furthermore, a radiation transfer model is introduced as an observation operator to analyze the influence of brightness temperature assimilation on states and parameters that are estimated at different microwave signal frequencies. Three cases were analyzed for both soil moisture and brightness temperature assimilation, focusing on the progressive incorporation of parameter uncertainty, forcing data uncertainty and model uncertainty. It has been demonstrated that EnKF is outperformed by all other methods, as it consistently maintains a bias. State-parameter estimation algorithms can provide a more accurate estimation of soil moisture than EnKF. AEnKF is the most robust method, with the lowest RMSE values for retrieving states and parameters dealing only with parameter uncertainty, but it possesses disadvantages related to increasing sources of uncertainty and decreasing numbers of observations. SODA performs well under the complex situations in which DEnKF shows slight disadvantages in terms of statistical indicators; however, the former consumes far more memory and time than the latter. 2015 . |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.casnw.net/handle/362004/27127]  |
| 专题 | 寒区旱区环境与工程研究所_中科院寒区旱区环境与工程研究所（未分类）_期刊论文 |
| 推荐引用方式 GB/T 7714 | Chen W.,Huang C.,Shen H.,et al. Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation (EI)[J]. Advances in Water Resources,2015,86:425-438. |
| APA | Chen W.,Huang C.,Shen H.,&Li X..(2015).Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation (EI).Advances in Water Resources,86,425-438. |
| MLA | Chen W.,et al."Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation (EI)".Advances in Water Resources 86(2015):425-438. |
入库方式： OAI收割
来源：寒区旱区环境与工程研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。