Seasonal-to-interannual variability of ethiopia/horn of Africa monsoon. Part II: Statistical multimodel ensemble rainfall predictions

Segele, ZT; Richman, MB; Leslie, LM; Lamb, PJ

Seasonal-to-interannual variability of ethiopia/horn of Africa monsoon. Part II: Statistical multimodel ensemble rainfall predictions

Segele, ZT Richman, MB Leslie, LM

Lamb, PJ

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Publication Type:: Journal Article
Citation:: Journal of Climate, 2015, 28 (9), pp. 3511 - 3536
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Segele, ZT	en_US
dc.contributor.author	Richman, MB	en_US
dc.contributor.author	Leslie, LM https://orcid.org/0000-0001-5616-2330	en_US
dc.contributor.author	Lamb, PJ	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Journal of Climate, 2015, 28 (9), pp. 3511 - 3536	en_US
dc.identifier.issn	0894-8755	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119413
dc.description.abstract	© 2015 American Meteorological Society. An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June-September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble. For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed-predicted correlation r (SSClim) being +0.81 (65%) for 1970-2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society.	en_US
dc.relation.ispartof	Journal of Climate	en_US
dc.relation.isbasedon	10.1175/JCLI-D-14-00476.1	en_US
dc.subject.classification	Meteorology & Atmospheric Sciences	en_US
dc.title	Seasonal-to-interannual variability of ethiopia/horn of Africa monsoon. Part II: Statistical multimodel ensemble rainfall predictions	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	28	en_US
utslib.for	0909 Geomatic Engineering	en_US
utslib.for	0401 Atmospheric Sciences	en_US
utslib.for	0405 Oceanography	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

© 2015 American Meteorological Society. An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June-September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble. For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed-predicted correlation r (SSClim) being +0.81 (65%) for 1970-2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society.

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http://hdl.handle.net/10453/119413