Open AccessEnsemble Statistics for Diagnosing Dynamics: Tropical Cyclone Track Forecast Sensitivities Revealed by Ensemble Regression
Author(s) -
Daniel Gombos,
Ross N. Hoffman,
James A. Hansen
Publication year - 2012
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr-d-11-00002.1
Subject(s) - ensemble forecasting , geopotential height , anomaly (physics) , south atlantic anomaly , ensemble learning , multivariate statistics , meteorology , ensemble kalman filter , forecast skill , principal component analysis , statistics , climatology , computer science , mathematics , kalman filter , precipitation , geography , geology , artificial intelligence , physics , magnetosphere , van allen radiation belt , plasma , quantum mechanics , condensed matter physics , extended kalman filter
Ensemble 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.