A Method to Improve Prediction of Atmospheric Flow Transitions

Abstract

Ensemble prediction has become an indispensable tool in weather forecasting. One of the issues in ensemble prediction is that, regardless of the method, the prediction error does not map well to the underlying physics (i.e., error estimates do not project strongly onto physical structures). This paper is driven by the hypothesis that prediction error includes a deterministic component, which can be isolated and then removed, and that removing the error would enable researchers and forecasters to better map the error to the physics and improve prediction of atmospheric transitions. Here, preliminary experimental evidence is provided that supports this hypothesis. This evidence is provided from results obtained from two low-order but highly chaotic systems, one of which incorporates atmospheric flow transitions. Using neural networks to probe the deterministic component of forecast error, it is shown that the error recovery relates to the underlying type of flow and that it can be used to better forecast transitions in the atmospheric flow using ensemble data. A discussion of methods to extend these ideas to more realistic forecast settings is provided.