The Impact of Background Error on Incomplete Observations for 4D-Var Data Assimilation with the FSU GSM
I. Michael Navon, Dacian N. Daescu, and Zhuo Liu
To assess the impact of incomplete observations on the 4DVar data assimilation, twin experiments were carried out with the dynamical core of the new FSU GSM consisting of a T126L14 global spectral model in a MPI parallel environment. Results and qualitative aspects are presented for incomplete data in the spatial dimension and for incomplete data in time, with and without inclusion of the background term into the cost functional. The importance of the background estimate on the 4D-Var analysis in the presence of small Gaussian errors in incomplete data is also investigated.