Information Flow in Ensemble Weather Predictions

Abstract

In a weather prediction, information flows from the initial conditions to a later prediction. The uncertainty in the initial conditions implies that such a flow should be quantified with tools from probability theory. Using several recent developments in information theory, this flow is explored using a moderate-resolution primitive equation atmospheric model with simplified physics. Consistent with operational experience and other methodologies explored in the literature, such as singular vectors, it is found that the midlatitude flow is mainly in an easterly direction. At upper levels, the flow is primarily steered by advection of the jet stream; however, at low levels there is clear evidence that synoptic dynamics are important and this makes the direction of flow more complex. Horizontal rather than vertical flow is generally found to be more important, although there was evidence for propagation from the mid- to upper troposphere of zonal velocity. As expected, as the length of the prediction increases, more remote areas become important to local predictions. To obtain reliable/stable results, rather large ensembles are used; however, it is found that the basic qualitative results can be obtained with ensembles within present practical reach. The present method has the advantage that it makes no assumptions concerning linearity or ensemble Gaussianicity.