Predictive Skills of Seasonal to Annual Rainfall Variations in the U.S. Affiliated Pacific Islands: Canonical Correlation Analysis and Multivariate Principal Component Regression Approaches

Zhi-Ping Yu Pacific ENSO Applications Center, University of Hawaii at Manoa, Honolulu, Hawaii

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Pao-Shin Chu Department of Meteorology, School of Ocean and Earth Science and Technology, University of Hawaii at Manoa, Honolulu, Hawaii

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Thomas Schroeder Department of Meteorology, School of Ocean and Earth Science and Technology, University of Hawaii at Manoa, Honolulu, Hawaii

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Abstract

Drought and flooding are recurrent and serious problems in the U.S. Affiliated Pacific Islands (USAPI). Given the agricultural and water-dependent characteristics of the USAPI economies, accurate forecasts of seasonal to interseasonal rainfall variations have the potential to provide important information for decision makers involved in resource management issues and response strategies related to drought and flood events.

Climatology of rainfall and outgoing longwave radiation (OLR) cycle in the USAPI and the response of OLR to the El Niño–Southern Oscillation (ENSO) are addressed. Boxplot and harmonic analyses indicate that the annual cycles in rainfall and OLR are generally strong in USAPI except those stations close to the equator. Northern USAPI have positive (negative) OLR anomalies during El Niño (La Niña) winters.

Two statistical models, canonical correlation analysis (CCA) and a relatively new method called multivariate Principal Component Regression (PCR), are employed to forecast rainfall variations in 10 USAPI stations. Sea surface temperatures (SSTs) in the Pacific Ocean are used as predictors for both models. The results of this study indicate that both models are potentially useful in predicting seasonal rainfall variations in the USAPI region, especially in winter (DJF) and spring (MAM). CCA cross validation shows that at one and two seasons lead JFM is the most accurately forecast period in the northern USAPI stations, with average skills of 0.53 and 0.41, respectively. However, the authors’ analysis indicates a problem of lower predictive skill in summer (JJA) and fall (SON). One reason might be associated with the so-called spring barrier in predictive skill in the tropical ocean–atmosphere system. Another reason might be associated with the tropical cyclone activity during these seasons. Predictions using the PCR model yield similar predictive skill. Though simpler than He and Barnston’s model in term of the number of predictor variables used, the authors’ CCA and PCR provide comparable skills.

Corresponding author address: Mr. Zhi-Ping Yu, Jr. Researcher, Pacific ENSO Applications Center, University of Hawaii at Manoa, 2525 Correa Rd., HIG 331, Honolulu, HI 96822.

Email: yu@soest.hawaii.edu

Abstract

Drought and flooding are recurrent and serious problems in the U.S. Affiliated Pacific Islands (USAPI). Given the agricultural and water-dependent characteristics of the USAPI economies, accurate forecasts of seasonal to interseasonal rainfall variations have the potential to provide important information for decision makers involved in resource management issues and response strategies related to drought and flood events.

Climatology of rainfall and outgoing longwave radiation (OLR) cycle in the USAPI and the response of OLR to the El Niño–Southern Oscillation (ENSO) are addressed. Boxplot and harmonic analyses indicate that the annual cycles in rainfall and OLR are generally strong in USAPI except those stations close to the equator. Northern USAPI have positive (negative) OLR anomalies during El Niño (La Niña) winters.

Two statistical models, canonical correlation analysis (CCA) and a relatively new method called multivariate Principal Component Regression (PCR), are employed to forecast rainfall variations in 10 USAPI stations. Sea surface temperatures (SSTs) in the Pacific Ocean are used as predictors for both models. The results of this study indicate that both models are potentially useful in predicting seasonal rainfall variations in the USAPI region, especially in winter (DJF) and spring (MAM). CCA cross validation shows that at one and two seasons lead JFM is the most accurately forecast period in the northern USAPI stations, with average skills of 0.53 and 0.41, respectively. However, the authors’ analysis indicates a problem of lower predictive skill in summer (JJA) and fall (SON). One reason might be associated with the so-called spring barrier in predictive skill in the tropical ocean–atmosphere system. Another reason might be associated with the tropical cyclone activity during these seasons. Predictions using the PCR model yield similar predictive skill. Though simpler than He and Barnston’s model in term of the number of predictor variables used, the authors’ CCA and PCR provide comparable skills.

Corresponding author address: Mr. Zhi-Ping Yu, Jr. Researcher, Pacific ENSO Applications Center, University of Hawaii at Manoa, 2525 Correa Rd., HIG 331, Honolulu, HI 96822.

Email: yu@soest.hawaii.edu

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