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identified by the rsPCA in the 500-mb GPH at that same frequency of 15 days. Therefore, this mode is interpreted as the signature of an atmospheric Rossby wave on the SST and reveals ocean–atmosphere coupling. This shows the ability of the spectral PCA to extract coherent modes of low amplitude from random variability and noise. Fig . 17. First unrotated eigenvector (magnitude and phase) at the 15-day period of the sPCA applied to daily SST anomalies. The eastward propagating signal is interpreted as the
identified by the rsPCA in the 500-mb GPH at that same frequency of 15 days. Therefore, this mode is interpreted as the signature of an atmospheric Rossby wave on the SST and reveals ocean–atmosphere coupling. This shows the ability of the spectral PCA to extract coherent modes of low amplitude from random variability and noise. Fig . 17. First unrotated eigenvector (magnitude and phase) at the 15-day period of the sPCA applied to daily SST anomalies. The eastward propagating signal is interpreted as the
importance of Atlantic Ocean temperatures as drivers of SWUS precipitation as well ( Enfield et al. 2001 ; McCabe et al. 2004 ), our focus here is only on the Pacific Ocean, as a first step. We cast the prediction problem as an estimation problem in which predictors are not specified in advance, but rather emerge from the data by minimizing an appropriate loss function. We first demonstrate the increased predictive skill of the proposed GTV model when the covariance matrix that defines the GTV
importance of Atlantic Ocean temperatures as drivers of SWUS precipitation as well ( Enfield et al. 2001 ; McCabe et al. 2004 ), our focus here is only on the Pacific Ocean, as a first step. We cast the prediction problem as an estimation problem in which predictors are not specified in advance, but rather emerge from the data by minimizing an appropriate loss function. We first demonstrate the increased predictive skill of the proposed GTV model when the covariance matrix that defines the GTV