Deep Clustering for Climate: Analyzing Teleconnections through Learned Categorical States

ArXi:2604.22909v1 Announce Type: new Understanding and representing complex climate variability is essential for both scientific analysis and predictive modeling. However, identifying meaningful climate regimes from raw variables is challenging, as they exhibit high noise and nonlinear dependencies. In this work, we explore the use of Masked Siamese Networks to discretize climate time series into semantically rich clusters.