Twenty-six total climate divisions comprise the three (seven in California, nine in Oregon and ten in Washington). To parse the number of divisions down, keeping a near-coastal focus, and in the process simplying analysis interpretations, the selection is reduced to include those closest to the Pacific (and in the case of Oregon and Washington, essentially those west of the Cascade Mountains). This leaves 12 divisions, three in California, four in Oregon, and five in Washington. The K-Means Clustering methodology is integrated with the V-Fold Cross Validation Algorithm, an iterative training sample type procedure that optimizes the number of clusters created, depending on the choice of statistical distance metric (Euclidean, Squared Euclidean, etc.), percent improvement cutoff threshold (e.g., 5 percent), and other settings. In this study the K-means approach utilizes the Squared Euclidean metric combined with the 5 percent distance improvement cutoff threshold; also the precipitation data are normalized in advance, by division. Seven clusters are resolved.
Then, through referencing and processing of bi-monthly ranked statistics from the MEI ENSO Index Data base back to 1895-96, a Bayesian statistical analysis is performed which addresses the following questions: given the presence of a “Strong” El Nino, “Ordinary” El Nino, Neutral, “Ordinary” La Nina, or “Strong La Nina episode, what are conditional probabilities that each of the seven idealized anomaly modes would be realized for a given July-June rain season. Results are described and interpreted, with the Bayesian probabilities compared among episode types.