Volume 11 Issue 3
Jul.  2018
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Article Navigation >  Water Science and Engineering  > 2018  >  11(3): 196-204
Xiao-lei Fu, Zhong-bo Yu, Yong-jian Ding, Ying Tang, Hai-shen Lü, Xiao-lei Jiang, Qin Ju. 2018: Analysis of influence of observation operator on sequential data assimilation through soil temperature simulation with common land model. Water Science and Engineering, 11(3): 196-204. doi: 10.1016/j.wse.2018.09.003
Citation: Xiao-lei Fu, Zhong-bo Yu, Yong-jian Ding, Ying Tang, Hai-shen Lü, Xiao-lei Jiang, Qin Ju. 2018: Analysis of influence of observation operator on sequential data assimilation through soil temperature simulation with common land model. Water Science and Engineering, 11(3): 196-204. doi: 10.1016/j.wse.2018.09.003
shu

Analysis of influence of observation operator on sequential data assimilation through soil temperature simulation with common land model

doi: 10.1016/j.wse.2018.09.003

  • a College of Civil Engineering, Fuzhou University, Fuzhou 350116, China

  • b State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China

  • c State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

  • d Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI 48824, USA

Funds:  This work was supported by the National Key Research and Development Program of China (Grants No. 2016YFC0402706 and 2016YFC0402710), the National Natural Science Foundation of China (Grants No. 51709046 and 41323001), and the Open Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University (Grant No. 2015490311).
More Information
  • Corresponding author: Zhong-bo Yu
  • Received Date: 2017-10-07
  • Rev Recd Date: 2018-06-24
    Abstract
  • An observation operator is a bridge linking the system state vector and observations in a data assimilation system. Despite its importance, the degree to which an observation operator influences the performance of data assimilation methods is still poorly understood. This study aimed to analyze the influences of linear and nonlinear observation operators on the sequential data assimilation through soil temperature simulation using the unscented particle filter (UPF) and the common land model. The linear observation operator between unprocessed simulations and observations was first established. To improve the correlation between simulations and observations, both were processed based on a series of equations. This processing essentially resulted in a nonlinear observation operator. The linear and nonlinear observation operators were then used along with the UPF in three assimilation experiments: an hourly in situ soil surface temperature assimilation, a daily in situ soil surface temperature assimilation, and a moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) assimilation. The results show that the filter improved the soil temperature simulations significantly with the linear and nonlinear observation operators. The nonlinear observation operator improved the UPF’s performance more significantly for the hourly and daily in situ observation assimilations than the linear observation operator did, while the situation was opposite for the MODIS LST assimilation. Because of the high assimilation frequency and data quality, the simulation accuracy was significantly improved in all soil layers for hourly in situ soil surface temperature assimilation, while the significant improvements of the simulation accuracy were limited to the lower soil layers for the assimilation experiments with low assimilation frequency or low data quality.

     

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