Characterization of the Long-Time and Short-Time Predictability of Low-Order Models of the Atmosphere

Abstract

Methods to quantify predictability properties of atmospheric flows are proposed. The ?extended self-similarity? technique, recently employed in turbulence data analysis, is used to characterize predictability properties at both short and long times. These methods are applied to the low-order atmospheric model of Lorenz. Also investigated is how initialization procedures that eliminate gravity waves from the model dynamics influence predictability properties.