Improving Seasonal Prediction of California Winter Precipitation Using Canonical Correlation Analysis

Abstract

AbstractWe have developed a canonical correlation analysis (CCA) model for improving seasonal winter rainfall prediction. It uses the anomalies of sea surface temperature (SST), vertically integrated vapor transport (IVT), and geopotential height at 250 hPa (Z250) in October and November, respectively, as the predictors for winter rainfall prediction. These predictors represent the processes that influence winter rainfall over California as documented in the literature, but their potential for improving predictability was previously unclear. This statistical model shows prediction skills higher than those of the baseline autoregressive model, the CCA‐based prediction model using only the SST anomalies, and the dynamic predictions by the North American Multi‐Model Ensemble (NMME). Averaged over California, the Pearson correlation (R) is 0.64, root mean squared error (RMSE) is 0.65, and Heidke skill score (HSS) is 0.42 when the CCA‐based model is initialized by the three predictor fields (SST, IVT, and Z250) in November. These skills are higher than those of the NMME predictions initialized in November (R, RMSE, and HSS are 0.30, 0.83, and 0.15, respectively) and those of the autoregressive baseline (R, RMSE, and HSS are 0.10, 0.79, and 0.08, respectively). Hindcasts of winter rainfall initialized by October observations show R, RMSE, and HSS of 0.53, 0.81, and 0.39, respectively, also higher than those of the NMME seasonal prediction initialized in October (0.32, 0.79, and 0.22 for R, RMSE, and HSS, respectively) and the autoregressive model (0.30, 0.75, and 0.16 for R, RMSE, and HSS, respectively).