Ensemble Statistics for Diagnosing Dynamics: Tropical Cyclone Track Forecast Sensitivities Revealed by Ensemble Regression

Abstract

nsemble regression (ER) is a simple linear inverse technique that uses correlations from ensemble model output to make inferences about dynamics, models, and forecasts. ER defines a multivariate regression operator in the principal component subspaces of ensemble forecasts and analyses of atmospheric fields. ER uses the ensemble members of a predictor and a predictand field as training samples to compute the ensemble anomaly (with respect to the ensemble mean of the predictand field) with which a dynamically relevant ensemble anomaly (with respect to the ensemble mean of the predictor field) is linearly related. Specifically, an ER operator defined by the Japan Meteorological Agency?s ensemble forecast 500-hPa geopotential height and 1000-hPa potential vorticity is used to show that Supertyphoon Sepat?s (2007) track strongly covaried with the position and strength of the antecedent steering subtropical high to its northeast and the trough to its northwest. The case study illustrates how ER can identify, in real time, the dynamical processes that are particularly relevant for operational forecasters to make specific forecasting decisions and can help researchers to infer physical relationships from multivariate statistical sensitivities.