JeongWon Cho

Topology-Preserving State Space Representation for Diagnosing Weather Forecast Models

A trajectory-based diagnostic framework built on a topology-preserving state space for visually and quantitatively comparing forecast behavior across different AI weather forecast models and shows that the proposed representation provides an intuitive way to compare model-dependent forecast evolution, identify regions of relatively good or poor trajectory behavior, and examine state-or season-dependent differences that are not readily summarized by variable-wise RMSE curves alone.