AI RESEARCH
Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting
arXiv CS.LG
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ArXi:2510.02050v3 Announce Type: replace-cross Improving statistical forecasts of tropical cyclone (TC) intensity is limited by complex nonlinear interactions and difficulty in identifying relevant predictors. Conventional methods prioritize correlation or fit, often overlooking confounding variables and limiting generalizability to unseen TCs. To address this, we leverage a multidata causal discovery framework with a replicated dataset based on Statistical Hurricane Intensity Prediction Scheme (SHIPS) using ERA5 meteorological reanalysis.