A Generalization of Using an Adjoint Model in Intermittent Data Assimilation Systems

Abstract

A generalized setup is proposed for the poor man?s 4D variational data assimilation system (PMV) of Huang et al. The new scheme is referred to as a generalization of PMV (GPV) and has the same basic idea as that of PMV, that is, to use an adjoint model to improve an optimum interpolation (OI)?based assimilation system. In addition, GPV includes the possibility of using different forecast models in the original OI-based assimilation system and in the variational component of the scheme. This generalization leads to three advantages over the original setup: 1) a wider application of an adjoint model developed for a particular forecast model; 2) an implementation flexibility due to its incremental nature; 3) considerable CPU savings when the variational component is run on low resolutions. A detailed comparison is made between GPV and PMV. The steps of a practical implementation are also given. A 5-day period characterized by intense cyclone development is chosen for testing different data assimilation schemes. Experiments with GPV using a low-resolution variational component based on different model formulations indicate that the proposed scheme GPV, as its predecessor PMV, also leads to better first guess fields, smaller analysis increments, modified baroclinic structures in the final analyses, and improved forecasts. The differences between the GPV analyses and the original OI-based analyses are mainly in the data-sparse area and are related to baroclinic processes.