Predicting Weather Regime Transitions in Northern Hemisphere Datasets
A statistical learning method called random forests is applied to the prediction of transitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric low frequency variability. A dataset composed of 55 winters of NH 700-mb geopotential height anomalies is used in the present stu...
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ftcdlib:qt4d7003v7 2023-05-15T17:33:48+02:00 Predicting Weather Regime Transitions in Northern Hemisphere Datasets D. Kondrashov J. Shen R. Berk F. D 2011-10-25 application/pdf http://www.escholarship.org/uc/item/4d7003v7 english eng eScholarship, University of California qt4d7003v7 http://www.escholarship.org/uc/item/4d7003v7 public D. Kondrashov; J. Shen; R. Berk; & F. D. (2011). Predicting Weather Regime Transitions in Northern Hemisphere Datasets. Department of Statistics, UCLA. UCLA: Department of Statistics, UCLA. Retrieved from: http://www.escholarship.org/uc/item/4d7003v7 Physical Sciences and Mathematics article 2011 ftcdlib 2016-04-02T18:56:55Z A statistical learning method called random forests is applied to the prediction of transitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric low frequency 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 America (P N A) regime, its approximate reverse (the reverse P N A, or RN A), and the blocked phase of the North Atlantic Oscillation (BN AO). The most significant and robust transitions in the Markov chain generated by these regimes are P N A -> BN AO, P N A -> RN A and BN AO -> P N A. 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. Article in Journal/Newspaper North Atlantic North Atlantic oscillation University of California: eScholarship Pacific |
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Open Polar |
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University of California: eScholarship |
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language |
English |
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Physical Sciences and Mathematics |
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Physical Sciences and Mathematics D. Kondrashov J. Shen R. Berk F. D Predicting Weather Regime Transitions in Northern Hemisphere Datasets |
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Physical Sciences and Mathematics |
description |
A statistical learning method called random forests is applied to the prediction of transitions between weather regimes of wintertime Northern Hemisphere (NH) atmospheric low frequency 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 America (P N A) regime, its approximate reverse (the reverse P N A, or RN A), and the blocked phase of the North Atlantic Oscillation (BN AO). The most significant and robust transitions in the Markov chain generated by these regimes are P N A -> BN AO, P N A -> RN A and BN AO -> P N A. 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. |
format |
Article in Journal/Newspaper |
author |
D. Kondrashov J. Shen R. Berk F. D |
author_facet |
D. Kondrashov J. Shen R. Berk F. D |
author_sort |
D. Kondrashov |
title |
Predicting Weather Regime Transitions in Northern Hemisphere Datasets |
title_short |
Predicting Weather Regime Transitions in Northern Hemisphere Datasets |
title_full |
Predicting Weather Regime Transitions in Northern Hemisphere Datasets |
title_fullStr |
Predicting Weather Regime Transitions in Northern Hemisphere Datasets |
title_full_unstemmed |
Predicting Weather Regime Transitions in Northern Hemisphere Datasets |
title_sort |
predicting weather regime transitions in northern hemisphere datasets |
publisher |
eScholarship, University of California |
publishDate |
2011 |
url |
http://www.escholarship.org/uc/item/4d7003v7 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
D. Kondrashov; J. Shen; R. Berk; & F. D. (2011). Predicting Weather Regime Transitions in Northern Hemisphere Datasets. Department of Statistics, UCLA. UCLA: Department of Statistics, UCLA. Retrieved from: http://www.escholarship.org/uc/item/4d7003v7 |
op_relation |
qt4d7003v7 http://www.escholarship.org/uc/item/4d7003v7 |
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public |
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1766132416297041920 |