Improving the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating <Emphasis Type="Italic">in situ</Emphasis> observations and SSM/I data |
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| Authors: | Rui Jin Xin Li |
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| Institution: | (1) George Mason University, Fairfax, VA, USA;(2) Ghent University, B-9000 Ghent, Belgium;(3) Department of Civil and Environmental Engineering, The University of Melbourne, Victoria, Australia |
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| Abstract: | 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°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 m3·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°C, while the soil moisture RMSE of each soil layer during the summer decreased by 0.018 m3·m−3. |
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