A penalized four-dimensional variational data assimilation method for reducing forecast error related to adaptive observations
Hossen, Jakir; Navon, I. M.; Fang, F.
Four-dimensional variational (4D-Var) data assimilation method is used to find the optimal initial conditions by minimizing a cost function in which background information and observations are provided as the input of the cost function. The optimized initial conditions based on background error covariance matrix and observations improve the forecast. The targeted observations determined by using methods such as adjoint sensitivity, observation sensitivity, or singular vectors may further...[Show more]
|Collections||ANU Research Publications|
|Source:||International Journal for Numerical Methods in Fluids|
|01_Hossen_A_penalized_four-dimensional_2012.pdf||3.64 MB||Adobe PDF||Request a copy|
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