Lagged Average Predictions in a Predictability Experiment

Abstract

Lagged average predictions are examined here within the context of an idealized predictability experiment. Lagged predictions contribute to making better forecasts than the forecasts obtained from using only the latest initial state. Analytic models suggest that lagged predictions contribute the greatest amount when the error growth rates are small. Little dependence upon the magnitude of the initial error is found if the growth rates remain constant. It is also shown how lagged average forecasts can be used to predict the error. Discriminating forecasts made only when the error is predicted to be small are shown to have much better than average skill.