Digestible Pieces: comparing three options for partitioning the Northeast Pacific Coast for S2S sea surface height prediction

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 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 gridpoints 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 tasks' 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 task, the Cluster task represents all gridpoints in the Northeast Pacific Coast with minimal tunable parameters. Throughout this exercise we learned that clustering gridpoints as a pre-processing step is the preferred approach between the three for making S2S predictions of coastal sea level.