Improved tropical cyclone track predictions using error recycling

Abstract

Errors produced by a nonlinear predictive scheme contain information about both the observations and the prediction system. Therefore, its error history would be expected to contribute to increasing the skill of the predictions if it is included in the forecast. In this study an error recycling procedure is developed for tropical cyclone track prediction. Errors are defined here as differences between the model forecast and the best track position. Error histories are incorporated into a nonlinear analogue, or simplex, forecast scheme and applied to tropical cyclone track prediction, using the archives of observed position data associated with the forecast errors. Various forecast experiments of the cyclone tracks are performed: standard simplex predictions using observed positions only; simplex predictions improved by error forecasts based on libraries of both observations and the recycled forecast errors; and, finally, predictions that include NWP-model forecasts and their errors as predictors. The resulting gains in skill of predictions out to 72 hours ahead are found to be substantial.