Climate Dyn., sub judice. Revised version
A statistical learning method called random forests is applied to the prediction of transitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric lowfrequency variability. A dataset composed of 55 winters of NH 700-mb geopotential height anomalies is used in the present stud...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.145.7287 2023-05-15T17:33:32+02:00 Climate Dyn., sub judice. Revised version D. Kondrashov J. Shen R. Berk M. Ghil The Pennsylvania State University CiteSeerX Archives 2007 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.7287 http://www.atmos.ucla.edu/tcd/PREPRINTS/transitions_rev_final.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.7287 http://www.atmos.ucla.edu/tcd/PREPRINTS/transitions_rev_final.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.atmos.ucla.edu/tcd/PREPRINTS/transitions_rev_final.pdf text 2007 ftciteseerx 2016-01-07T15:08:04Z A statistical learning method called random forests is applied to the prediction of transitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric lowfrequency variability. A dataset composed of 55 winters of NH 700-mb geopotential height anomalies is used in the present study. A mixture model finds that the three Gaussian components that were statistically significant in earlier work are robust; they are the Pacific– North American (P NA) regime, its approximate reverse (the reverse P NA, or RNA), and the blocked phase of the North Atlantic Oscillation (BNAO). The most significant and robust transitions in the Markov chain generated by these regimes are P NA → BNAO, P NA → RNA and BNAO → P NA. The break of a regime and subsequent onset of another one is forecast for these three transitions. Taking the relative costs of false positives and false negatives into account, the random-forests method shows useful forecasting skill. The calculations are carried out in the phase space spanned by a few leading empirical orthogonal functions of dataset variability. Plots of estimated response functions to a given predictor confirm the crucial influence of the exit angle on a preferred transition path. This result points to the dynamic origin of the transitions. Text North Atlantic North Atlantic oscillation Unknown Pacific |
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English |
description |
A statistical learning method called random forests is applied to the prediction of transitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric lowfrequency variability. A dataset composed of 55 winters of NH 700-mb geopotential height anomalies is used in the present study. A mixture model finds that the three Gaussian components that were statistically significant in earlier work are robust; they are the Pacific– North American (P NA) regime, its approximate reverse (the reverse P NA, or RNA), and the blocked phase of the North Atlantic Oscillation (BNAO). The most significant and robust transitions in the Markov chain generated by these regimes are P NA → BNAO, P NA → RNA and BNAO → P NA. The break of a regime and subsequent onset of another one is forecast for these three transitions. Taking the relative costs of false positives and false negatives into account, the random-forests method shows useful forecasting skill. The calculations are carried out in the phase space spanned by a few leading empirical orthogonal functions of dataset variability. Plots of estimated response functions to a given predictor confirm the crucial influence of the exit angle on a preferred transition path. This result points to the dynamic origin of the transitions. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
D. Kondrashov J. Shen R. Berk M. Ghil |
spellingShingle |
D. Kondrashov J. Shen R. Berk M. Ghil Climate Dyn., sub judice. Revised version |
author_facet |
D. Kondrashov J. Shen R. Berk M. Ghil |
author_sort |
D. Kondrashov |
title |
Climate Dyn., sub judice. Revised version |
title_short |
Climate Dyn., sub judice. Revised version |
title_full |
Climate Dyn., sub judice. Revised version |
title_fullStr |
Climate Dyn., sub judice. Revised version |
title_full_unstemmed |
Climate Dyn., sub judice. Revised version |
title_sort |
climate dyn., sub judice. revised version |
publishDate |
2007 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.7287 http://www.atmos.ucla.edu/tcd/PREPRINTS/transitions_rev_final.pdf |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
http://www.atmos.ucla.edu/tcd/PREPRINTS/transitions_rev_final.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.7287 http://www.atmos.ucla.edu/tcd/PREPRINTS/transitions_rev_final.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766132071540981760 |