Seasonal prediction of winter extreme precipitation over Canada by support vector regression

For forecasting the maximum 5-day accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR) (nonlinear and linear versions), no...

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Published in:Hydrology and Earth System Sciences
Main Authors: Zeng, Z., Hsieh, W. W., Shabbar, A., Burrows, W. R.
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2011
Subjects:
Online Access:https://doi.org/10.5194/hess-15-65-2011
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spelling ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00027978 2023-05-15T14:58:32+02:00 Seasonal prediction of winter extreme precipitation over Canada by support vector regression Zeng, Z. Hsieh, W. W. Shabbar, A. Burrows, W. R. 2011-01 electronic https://doi.org/10.5194/hess-15-65-2011 https://noa.gwlb.de/receive/cop_mods_00027978 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00027933/hess-15-65-2011.pdf https://hess.copernicus.org/articles/15/65/2011/hess-15-65-2011.pdf eng eng Copernicus Publications Hydrology and Earth System Sciences -- http://www.bibliothek.uni-regensburg.de/ezeit/?2100610 -- http://www.hydrol-earth-syst-sci.net/volumes_and_issues.html -- 1607-7938 https://doi.org/10.5194/hess-15-65-2011 https://noa.gwlb.de/receive/cop_mods_00027978 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00027933/hess-15-65-2011.pdf https://hess.copernicus.org/articles/15/65/2011/hess-15-65-2011.pdf uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2011 ftnonlinearchiv https://doi.org/10.5194/hess-15-65-2011 2022-02-08T22:48:26Z For forecasting the maximum 5-day accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR) (nonlinear and linear versions), nonlinear Bayesian neural network (BNN) and multiple linear regression (MLR). The 118 stations were grouped into six geographic regions by K-means clustering. For each region, the leading principal components of the winter maximum 5-d accumulated precipitation anomalies were the predictands. Potential predictors included quasi-global sea surface temperature anomalies and 500 hPa geopotential height anomalies over the Northern Hemisphere, as well as six climate indices (the Niño-3.4 region sea surface temperature, the North Atlantic Oscillation, the Pacific-North American teleconnection, the Pacific Decadal Oscillation, the Scandinavia pattern, and the East Atlantic pattern). The results showed that in general the two robust SVR models tended to have better forecast skills than the two non-robust models (MLR and BNN), and the nonlinear SVR model tended to forecast slightly better than the linear SVR model. Among the six regions, the Prairies region displayed the highest forecast skills, and the Arctic region the second highest. The strongest nonlinearity was manifested over the Prairies and the weakest nonlinearity over the Arctic. Article in Journal/Newspaper Arctic North Atlantic North Atlantic oscillation Niedersächsisches Online-Archiv NOA Arctic Canada Pacific Hydrology and Earth System Sciences 15 1 65 74
institution Open Polar
collection Niedersächsisches Online-Archiv NOA
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language English
topic article
Verlagsveröffentlichung
spellingShingle article
Verlagsveröffentlichung
Zeng, Z.
Hsieh, W. W.
Shabbar, A.
Burrows, W. R.
Seasonal prediction of winter extreme precipitation over Canada by support vector regression
topic_facet article
Verlagsveröffentlichung
description For forecasting the maximum 5-day accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR) (nonlinear and linear versions), nonlinear Bayesian neural network (BNN) and multiple linear regression (MLR). The 118 stations were grouped into six geographic regions by K-means clustering. For each region, the leading principal components of the winter maximum 5-d accumulated precipitation anomalies were the predictands. Potential predictors included quasi-global sea surface temperature anomalies and 500 hPa geopotential height anomalies over the Northern Hemisphere, as well as six climate indices (the Niño-3.4 region sea surface temperature, the North Atlantic Oscillation, the Pacific-North American teleconnection, the Pacific Decadal Oscillation, the Scandinavia pattern, and the East Atlantic pattern). The results showed that in general the two robust SVR models tended to have better forecast skills than the two non-robust models (MLR and BNN), and the nonlinear SVR model tended to forecast slightly better than the linear SVR model. Among the six regions, the Prairies region displayed the highest forecast skills, and the Arctic region the second highest. The strongest nonlinearity was manifested over the Prairies and the weakest nonlinearity over the Arctic.
format Article in Journal/Newspaper
author Zeng, Z.
Hsieh, W. W.
Shabbar, A.
Burrows, W. R.
author_facet Zeng, Z.
Hsieh, W. W.
Shabbar, A.
Burrows, W. R.
author_sort Zeng, Z.
title Seasonal prediction of winter extreme precipitation over Canada by support vector regression
title_short Seasonal prediction of winter extreme precipitation over Canada by support vector regression
title_full Seasonal prediction of winter extreme precipitation over Canada by support vector regression
title_fullStr Seasonal prediction of winter extreme precipitation over Canada by support vector regression
title_full_unstemmed Seasonal prediction of winter extreme precipitation over Canada by support vector regression
title_sort seasonal prediction of winter extreme precipitation over canada by support vector regression
publisher Copernicus Publications
publishDate 2011
url https://doi.org/10.5194/hess-15-65-2011
https://noa.gwlb.de/receive/cop_mods_00027978
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00027933/hess-15-65-2011.pdf
https://hess.copernicus.org/articles/15/65/2011/hess-15-65-2011.pdf
geographic Arctic
Canada
Pacific
geographic_facet Arctic
Canada
Pacific
genre Arctic
North Atlantic
North Atlantic oscillation
genre_facet Arctic
North Atlantic
North Atlantic oscillation
op_relation Hydrology and Earth System Sciences -- http://www.bibliothek.uni-regensburg.de/ezeit/?2100610 -- http://www.hydrol-earth-syst-sci.net/volumes_and_issues.html -- 1607-7938
https://doi.org/10.5194/hess-15-65-2011
https://noa.gwlb.de/receive/cop_mods_00027978
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00027933/hess-15-65-2011.pdf
https://hess.copernicus.org/articles/15/65/2011/hess-15-65-2011.pdf
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container_title Hydrology and Earth System Sciences
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