Abstract
Sequential data assimilation schemes approaching true optimality for sizable atmospheric models are becoming a reality. The behavior of the Kalman filter (KF) under difficult conditions needs therefore to be understood. In this two-part paper the authors implemented a KF for a two-dimensional shallow-water model with one or two layers. The model is linearized about a basic flow that depends on latitude; this permits the one-layer (1-L) case to be barotropically unstable. Constant vertical shear in the two-layer (2-L) case induces baroclinic instability.
The stable and unstable 1-L cases were studied in Part I. In the unstable case, even a very small number of observations can keep the forecast and analysis errors from the exponential growth induced by the flow's instability. In Part II, the authors now consider the 2-L, baroclinically stable and unstable cases. Simple experiments show that both cases are, quite similar to their barotropic counterparts. Once again, the KF is shown to keep the estimated flow's error bars bounded, even when a small number of observations—taken with realistic frequency—is utilized.
