The variational method of data assimilation is used to solve an inverse problem in the transport of sediment in river, which plays an important role in the change of natural environment. The cost function is defined t...The variational method of data assimilation is used to solve an inverse problem in the transport of sediment in river, which plays an important role in the change of natural environment. The cost function is defined to measure the error between model predictions and field observations. The adjoint model of IAP river sedimentation model is created to obtain the gradient of the cost function with respect to control variables. The initial conditions are taken as the control variables; their optimal values can be retrieved by minimizing the cost function with limited memory quasi Newton method (LMQN). The results show that the adjoint method approach can successfully make the model prediction well fit the simulated observations. And it is expected to use this method to solve other inverse problems of river sedimentation. But some numerical problems need to be discussed before applying to real river data.展开更多
文摘The variational method of data assimilation is used to solve an inverse problem in the transport of sediment in river, which plays an important role in the change of natural environment. The cost function is defined to measure the error between model predictions and field observations. The adjoint model of IAP river sedimentation model is created to obtain the gradient of the cost function with respect to control variables. The initial conditions are taken as the control variables; their optimal values can be retrieved by minimizing the cost function with limited memory quasi Newton method (LMQN). The results show that the adjoint method approach can successfully make the model prediction well fit the simulated observations. And it is expected to use this method to solve other inverse problems of river sedimentation. But some numerical problems need to be discussed before applying to real river data.
