Land surface models are often highly nonlinear with model physics that contain parameterized discontinuities. These model attributes severely limit the application of advanced variational data assimilation methods int...Land surface models are often highly nonlinear with model physics that contain parameterized discontinuities. These model attributes severely limit the application of advanced variational data assimilation methods into land data assimilation. The ensemble Kalman filter (EnKF) has been widely employed for land data assimilation because of its simple conceptual formulation and relative ease of implementation. An updated ensemble-based three-dimensional variational assimilation (En3-DVar) method is proposed for land data assimilation.This new method incorporates Monte Carlo sampling strategies into the 3-D variational data assimilation framework. The proper orthogonal decomposition (POD) technique is used to efficiently approximate a forecast ensemble produced by the Monte Carlo method in a 3-D space that uses a set of base vectors that span the ensemble. The data assimilation process is thus significantly simplified. Our assimilation experiments indicate that this new En3-DVar method considerably outperforms the EnKF method by increasing assimilation precision. Furthermore, computational costs for the new En3-DVar method are much lower than for the EnKF method.展开更多
This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly, based on the ensemble Kalman filter and the shuffled complex evolution method (SCE-UA). It use...This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly, based on the ensemble Kalman filter and the shuffled complex evolution method (SCE-UA). It uses the soil water model of the land surface model CLM3.0 as the forecast operator, and a radiative transfer model (RTM) as the observation operator in the assimilation system. The assimilation scheme is implemented in two phases: the parameter calibration phase and the pure soil moisture assimilation phase. The vegetation optical thickness and surface roughness parameters in the RTM are calibrated by SCE-UA method and the optimal parameters are used as the final model parameters of the observation operator in the assimilation phase. The ideal experiments with synthetic data indicate that this scheme could significantly improve the simulation of soil moisture at the surface layer. Further- more, the estimation of soil moisture in the deeper layers could also be improved to a certain extent. The real assimilation experiments with AMSR-E brightness temperature at 10.65 GHz (vertical polariza- tion) show that the root mean square error (RMSE) of soil moisture in the top layer (0―10 cm) by as- similation is 0.03355 m3·m-3, which is reduced by 33.6% compared with that by simulation (0.05052 m3·m-3). The mean RMSE by assimilation for the deeper layers (10―50 cm) is also reduced by 20.9%. All these experiments demonstrate the reasonability of the assimilation scheme developed in this study.展开更多
Water table over an arid region can be elevated to a critical level to sustain terrestrial ecosystem along the natural channel by the stream water conveyance. Estimation of water table depth and soil moisture on river...Water table over an arid region can be elevated to a critical level to sustain terrestrial ecosystem along the natural channel by the stream water conveyance. Estimation of water table depth and soil moisture on river channel profile may be reduced to a two-dimensional moving boundary problem with soil water-groundwater interaction. The two-dimensional soil water flow with stream water transferred is divided into an unsaturated vertical soil water flow and a horizontal groundwater flow. Therefore, a prediction model scheme for water table depths under the interaction between soil water and groundwater with stream water transferred is presented, which includes a vertical soil water movement model, a horizontal groundwater movement model, and an interface model. The synthetic experiments are conducted to test the sensitivities of the river elevation, horizontal conductivity, and surface flux, and the results from the experiments show the robustness of the proposed scheme under different conditions. The groundwater horizontal conductivity of the proposed scheme is also calibrated by SCE-UA method and validated by data collected at the Yingsu section in the lower reaches of the Tarim River, which shows that the model can reasonably simulate the water table depths.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 40705035)the National High Technology Research and Development Program of China (863 Program) (Grant Nos. 2009AA12Z129 and 2007AA12Z144)
文摘Land surface models are often highly nonlinear with model physics that contain parameterized discontinuities. These model attributes severely limit the application of advanced variational data assimilation methods into land data assimilation. The ensemble Kalman filter (EnKF) has been widely employed for land data assimilation because of its simple conceptual formulation and relative ease of implementation. An updated ensemble-based three-dimensional variational assimilation (En3-DVar) method is proposed for land data assimilation.This new method incorporates Monte Carlo sampling strategies into the 3-D variational data assimilation framework. The proper orthogonal decomposition (POD) technique is used to efficiently approximate a forecast ensemble produced by the Monte Carlo method in a 3-D space that uses a set of base vectors that span the ensemble. The data assimilation process is thus significantly simplified. Our assimilation experiments indicate that this new En3-DVar method considerably outperforms the EnKF method by increasing assimilation precision. Furthermore, computational costs for the new En3-DVar method are much lower than for the EnKF method.
基金Supported by National Basic Research Program of China (Grant Nos. 2009CB421407 and 2005CB321703)National High Technology Research and Development Program of China (Grant Nos. 2007AA12Z144 and 2009AA12Z129)Chinese COPES Project (Grant No. GYHY200706005)
文摘This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly, based on the ensemble Kalman filter and the shuffled complex evolution method (SCE-UA). It uses the soil water model of the land surface model CLM3.0 as the forecast operator, and a radiative transfer model (RTM) as the observation operator in the assimilation system. The assimilation scheme is implemented in two phases: the parameter calibration phase and the pure soil moisture assimilation phase. The vegetation optical thickness and surface roughness parameters in the RTM are calibrated by SCE-UA method and the optimal parameters are used as the final model parameters of the observation operator in the assimilation phase. The ideal experiments with synthetic data indicate that this scheme could significantly improve the simulation of soil moisture at the surface layer. Further- more, the estimation of soil moisture in the deeper layers could also be improved to a certain extent. The real assimilation experiments with AMSR-E brightness temperature at 10.65 GHz (vertical polariza- tion) show that the root mean square error (RMSE) of soil moisture in the top layer (0―10 cm) by as- similation is 0.03355 m3·m-3, which is reduced by 33.6% compared with that by simulation (0.05052 m3·m-3). The mean RMSE by assimilation for the deeper layers (10―50 cm) is also reduced by 20.9%. All these experiments demonstrate the reasonability of the assimilation scheme developed in this study.
基金supported by National Basic Research Program (Grant Nos. 2010CB428403, 2010CB951001)Chinese COPES Project (Grant No. GYHY200706005)National High Technology Research and Development Program of China (Grant No. 2009AA12Z129)
文摘Water table over an arid region can be elevated to a critical level to sustain terrestrial ecosystem along the natural channel by the stream water conveyance. Estimation of water table depth and soil moisture on river channel profile may be reduced to a two-dimensional moving boundary problem with soil water-groundwater interaction. The two-dimensional soil water flow with stream water transferred is divided into an unsaturated vertical soil water flow and a horizontal groundwater flow. Therefore, a prediction model scheme for water table depths under the interaction between soil water and groundwater with stream water transferred is presented, which includes a vertical soil water movement model, a horizontal groundwater movement model, and an interface model. The synthetic experiments are conducted to test the sensitivities of the river elevation, horizontal conductivity, and surface flux, and the results from the experiments show the robustness of the proposed scheme under different conditions. The groundwater horizontal conductivity of the proposed scheme is also calibrated by SCE-UA method and validated by data collected at the Yingsu section in the lower reaches of the Tarim River, which shows that the model can reasonably simulate the water table depths.
