Can Topic Modeling Shed Light on Climate Extremes?

Cheng Tang, Claire Monteleoni

Abstract: Understanding changes in climate extremes is an urgent challenge. Topic modeling techniques from natural language processing can help scientists learn climate patterns from data. The authors' work extracts global climate patterns from multivariate climate data, modeling relations between variables via latent topics and discovering the probability of each climate topic appearing at different geographical locations.