Regional four-dimensional variational data assimilation in a quasi- operational forecasting environment

Abstract

Three different types of assimilation experiments are performed: a) adjustment of initial conditions only (standard "adjoint' approach); b) adjustment of a correction to the model equations only (variational continuous assimilation); and c) simultaneous or sequential adjustment of both initial conditions and the correction term. Results indicate significantly better results when the correction term is included in the assimilation. It is shown, for a single case, that the new technique [experiment (c)] is able to produce a forecast better than the current conventional OI assimilation. It is very important to note that these results are obtained with an approximate gradient, calculated from a simplified adjoint model. Thus, it may be possible to perform an operational four-dimensional variational data assimilation of realistic forecast models, even before more complex adjoint models are developed. It may be possible to reduce the large computational cost of assimilation by using only a few iterations of the minimization algorithm. -from Author