Digestible Pieces: Comparing Three Options for Partitioning the Northeast Pacific Coast for S2S Sea Surface Height Prediction

Abstract We discuss the utility of applying clustering as a preprocessing step for identifying subseasonal to seasonal forecasts of opportunity of coastal sea level using convolutional neural networks (CNNs). Clustering leverages potential covariance due to modes of climate variability among points along the same coastline or in the same ocean basin. To evaluate the utility of clustering for reliably identifying forecasts of opportunity, we compare CNNs trained to predict sea level probability distributions in three ways: over the whole northeast Pacific Coast simultaneously, over predetermined clusters within this coastline, and at individual grid points near tide gauges. All CNN prediction tasks (Whole Coast, Cluster, Point) outperform climatology by a similar margin at week 3, when the entire test set is used to evaluate CNN skill. However, when comparing the skill of each task’s 20% most confident predictions, we find the skill of the Cluster and Point tasks to be on par with each other and substantially more skillful than the Whole Coast task. Of the Cluster and Point tasks, the Cluster task represents all grid points in the northeast Pacific Coast with minimal tunable parameters. Throughout this exercise, we learned that clustering grid points as a preprocessing step is the preferred approach between the three for making S2S predictions of coastal sea level. Significance Statement When building CNNs to make predictions for geophysical variables, researchers need to decide how to partition the domain of interest. This paper explores three ways to partition the northeast Pacific Coast to make subseasonal to seasonal predictions of sea level. We find that preclustering the points and training CNNs to predict those clusters yield skillful predictions which can cover the entire domain of interest with a minimal number of CNNs. This work underscores the importance of clustering as a method for dimensionality reduction in geophysical systems and implies that clustering based on spatial or temporal covariability can lead to increased predictive skill.