Level and source of predictability of seasonal rainfall anomalies in Malaysia using canonical correlation analysis

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Abstract

This study examines the level and origin of seasonal forecast skills of rainfall anomalies in Malaysia. The forecast models are based on an empirical technique known as the canonical correlation analysis (CCA). The CCA technique searches for maximally correlated coupled patterns between sets of predictor and predictand matrices. The predictive skills of five predictor fields, namely station rainfall in preceding seasons (i.e. the predictand itself), local sea surface temperature (SST) over the western Pacific sector, quasi-global SST, sea level pressure, and northern hemisphere 700 hPa geopotential height, are investigated. The sequence of four consecutive 3-month predictor periods is used to capture evolutionary features in the predictor fields. The predictor-predictand setup is designed such that the predictor fields are followed by a lead-time ranging from 0 to 9 months and then by a single predictand period of 3 months' duration. The skills are estimated in hindcast mode using the one-year-out version of the cross-validation technique. Skill estimates are expressed as temporal correlation coefficients between forecasts and observed values. A series of experiments with different predictor combinations reveal that the model with quasi-global SST alone produces most favourable forecast skills. The forecast skills of this model generally outperform the persistence forecast. Moreover, the model also has higher forecast skills in the East Malaysian region compared to those in Peninsular Malaysia. The forecast skill peaks during the late Northern Hemisphere winter season (January-February-March (JFM)) with a secondary maximum during the early summer season (May-June-July, (MJJ)). The average forecast skills are between 0.3-0.5 for up to 5 months' lead-time in the East Malaysian region and this can be considered very useful for the appropriate users. In the Peninsular Malaysia region, the forecast skills are generally weak, although some stations registered modest skills even at a 5-month lead-time. For both prediction periods, the source of predictability originates from anomalous SST conditions associated with the El Niño-Southern Oscillation (ENSO) phenomenon. Generally, during a La Niña (an El Niño) event, regions in northern Borneo experience excess (deficit) rainfall during the JFM period. Similar conditions are experienced during the MJJ period except that the impact tends to be more widespread throughout the country. Interestingly, the origin of predictability during the JFM period can be traced to typical ENSO events, while ENSO events of longer duration are responsible for the MJJ period.

Original languageEnglish
Pages (from-to)1255-1267
Number of pages13
JournalInternational Journal of Climatology
Volume28
Issue number9
DOIs
Publication statusPublished - Jul 2008

Fingerprint

anomaly
rainfall
Southern Oscillation
sea surface temperature
Northern Hemisphere
forecast
analysis
sea level pressure
geopotential
persistence
matrix
winter
summer
prediction
experiment

Keywords

  • Canonical correlation analysis
  • ENSO
  • Indian Ocean dipole
  • Malaysian rainfall
  • Predictability

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

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title = "Level and source of predictability of seasonal rainfall anomalies in Malaysia using canonical correlation analysis",
abstract = "This study examines the level and origin of seasonal forecast skills of rainfall anomalies in Malaysia. The forecast models are based on an empirical technique known as the canonical correlation analysis (CCA). The CCA technique searches for maximally correlated coupled patterns between sets of predictor and predictand matrices. The predictive skills of five predictor fields, namely station rainfall in preceding seasons (i.e. the predictand itself), local sea surface temperature (SST) over the western Pacific sector, quasi-global SST, sea level pressure, and northern hemisphere 700 hPa geopotential height, are investigated. The sequence of four consecutive 3-month predictor periods is used to capture evolutionary features in the predictor fields. The predictor-predictand setup is designed such that the predictor fields are followed by a lead-time ranging from 0 to 9 months and then by a single predictand period of 3 months' duration. The skills are estimated in hindcast mode using the one-year-out version of the cross-validation technique. Skill estimates are expressed as temporal correlation coefficients between forecasts and observed values. A series of experiments with different predictor combinations reveal that the model with quasi-global SST alone produces most favourable forecast skills. The forecast skills of this model generally outperform the persistence forecast. Moreover, the model also has higher forecast skills in the East Malaysian region compared to those in Peninsular Malaysia. The forecast skill peaks during the late Northern Hemisphere winter season (January-February-March (JFM)) with a secondary maximum during the early summer season (May-June-July, (MJJ)). The average forecast skills are between 0.3-0.5 for up to 5 months' lead-time in the East Malaysian region and this can be considered very useful for the appropriate users. In the Peninsular Malaysia region, the forecast skills are generally weak, although some stations registered modest skills even at a 5-month lead-time. For both prediction periods, the source of predictability originates from anomalous SST conditions associated with the El Ni{\~n}o-Southern Oscillation (ENSO) phenomenon. Generally, during a La Ni{\~n}a (an El Ni{\~n}o) event, regions in northern Borneo experience excess (deficit) rainfall during the JFM period. Similar conditions are experienced during the MJJ period except that the impact tends to be more widespread throughout the country. Interestingly, the origin of predictability during the JFM period can be traced to typical ENSO events, while ENSO events of longer duration are responsible for the MJJ period.",
keywords = "Canonical correlation analysis, ENSO, Indian Ocean dipole, Malaysian rainfall, Predictability",
author = "Liew, {Ju Neng} and {Tangang @ Tajudin Mahmud}, Fredolin",
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N2 - This study examines the level and origin of seasonal forecast skills of rainfall anomalies in Malaysia. The forecast models are based on an empirical technique known as the canonical correlation analysis (CCA). The CCA technique searches for maximally correlated coupled patterns between sets of predictor and predictand matrices. The predictive skills of five predictor fields, namely station rainfall in preceding seasons (i.e. the predictand itself), local sea surface temperature (SST) over the western Pacific sector, quasi-global SST, sea level pressure, and northern hemisphere 700 hPa geopotential height, are investigated. The sequence of four consecutive 3-month predictor periods is used to capture evolutionary features in the predictor fields. The predictor-predictand setup is designed such that the predictor fields are followed by a lead-time ranging from 0 to 9 months and then by a single predictand period of 3 months' duration. The skills are estimated in hindcast mode using the one-year-out version of the cross-validation technique. Skill estimates are expressed as temporal correlation coefficients between forecasts and observed values. A series of experiments with different predictor combinations reveal that the model with quasi-global SST alone produces most favourable forecast skills. The forecast skills of this model generally outperform the persistence forecast. Moreover, the model also has higher forecast skills in the East Malaysian region compared to those in Peninsular Malaysia. The forecast skill peaks during the late Northern Hemisphere winter season (January-February-March (JFM)) with a secondary maximum during the early summer season (May-June-July, (MJJ)). The average forecast skills are between 0.3-0.5 for up to 5 months' lead-time in the East Malaysian region and this can be considered very useful for the appropriate users. In the Peninsular Malaysia region, the forecast skills are generally weak, although some stations registered modest skills even at a 5-month lead-time. For both prediction periods, the source of predictability originates from anomalous SST conditions associated with the El Niño-Southern Oscillation (ENSO) phenomenon. Generally, during a La Niña (an El Niño) event, regions in northern Borneo experience excess (deficit) rainfall during the JFM period. Similar conditions are experienced during the MJJ period except that the impact tends to be more widespread throughout the country. Interestingly, the origin of predictability during the JFM period can be traced to typical ENSO events, while ENSO events of longer duration are responsible for the MJJ period.

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