Improving the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating in situ observations and SSM/I data
文献类型：期刊论文
| 作者 | Jin Rui; Li Xin |
| 刊名 | SCIENCE IN CHINA SERIES D-EARTH SCIENCES
 |
| 出版日期 | 2009-11-01 |
| 卷号 | 52期号:11页码:1732-1745 |
| 关键词 | land data assimilation active layer of frozen ground soil moisture soil temperature passive remote sensing |
| ISSN号 | 1006-9313 |
| DOI | 10.1007/s11430-009-0174-0 |
| 通讯作者 | Li Xin(lixin@lzb.ac.cn) |
| 英文摘要 | The active layer Of frozen ground data assimilation system adopts the SHAW (Simulteneous Heat and Water) model as the model operator. It employs an ensemble kalman filter to fuse state variables predicted by the SHAW model with in situ observation and the SSM/I 19 GHz brightness temperature for the purpose of optimizing model hydrothermal state variables. When there is little water movement in the frozen soil during the winter season, the unfrozen water content depends primarily on soil temperature. Thus, soil temperature is the crucial state variable to be improved. In contrast, soil moisture is heavily influenced by precipitation during the summer season. The simulation accuracy of soil moisture has a strong and direct impact on the soil temperature. In this case, the crucial state variable to be improved is soil moisture. One-dimensional assimilation experiments that have been carried out at AMDO station show that land data assimilation method can improve the estimation of hydrothermal state variables in the soil by fusing model information and observation information. The reasonable model error covariance matrix plays a key role in transferring the optimized surface state information to the deep soil, and it provides improved estimations of whole soil state profiles. After assimilating the 4-cm soil temperature by in situ observation, the soil temperature RMSE (Root Mean Square Error) of each soil layer decreased by 0.96 degrees C on average relative to the SHAW simulation. After assimilating the 4-cm soil moisture in situ observation, the soil moisture RMSE of each soil layer decreased by 0.020 m(3).m(-3). When assimilating the SSM/I 19 GHz brightness temperature, the soil temperature RMSE of each soil layer during the winter decreased by 0.76 degrees C, while the soil moisture RMSE of each soil layer during the summer decreased by 0.018 m(3).m(-3). |
| 收录类别 | SCI |
| WOS关键词 | SOIL-MOISTURE PROFILE ; ENSEMBLE KALMAN FILTER ; SEQUENTIAL DATA ASSIMILATION ; NEAR-SURFACE OBSERVATIONS ; LAND DATA ASSIMILATION ; TEMPERATURE PROFILES ; SIMULTANEOUS HEAT ; ROUGH SURFACES ; CLIMATE MODEL ; SYSTEM |
| WOS研究方向 | Geology |
| WOS类目 | Geosciences, Multidisciplinary |
| 语种 | 英语 |
| WOS记录号 | WOS:000272033200006 |
| 出版者 | SCIENCE PRESS |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2556262 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Li Xin |
| 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 | Jin Rui,Li Xin. Improving the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating in situ observations and SSM/I data[J]. SCIENCE IN CHINA SERIES D-EARTH SCIENCES,2009,52(11):1732-1745. |
| APA | Jin Rui,&Li Xin.(2009).Improving the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating in situ observations and SSM/I data.SCIENCE IN CHINA SERIES D-EARTH SCIENCES,52(11),1732-1745. |
| MLA | Jin Rui,et al."Improving the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating in situ observations and SSM/I data".SCIENCE IN CHINA SERIES D-EARTH SCIENCES 52.11(2009):1732-1745. |
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来源：寒区旱区环境与工程研究所
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