A Penalized 4-D Var data assimilation method for reducing forecast error

Four dimensional variational (4D-Var) Data Assimilation (DA) method is used to find the optimal initial conditions by minimizing cost function in which background information and observations are provided as the input of the cost function. The corrected initial condition based on background error covariance matrix and observations improve the forecast. The targeted observations determined by using a targeting method, for instance adjoint sensitivity, observation sensitivity or singular vector may further improve the forecast . In this paper, we are proposing a new technique–a penalized 4D-Var DA method that is able to reduce the forecast error significantly. Here we are penalizing the cost function by the forecast aspect defined over the verification domain at the verification time. The result shows that the initial condition is optimally estimated, thus resulting in a better forecast by significantly reducing the forecast error . ∗Corresponding author: Department of Scientific Computing, Florida State University, Tallahassee, FL 32306-4120