Unsupervised concept discovery for deep weather forecast models with high-resolution radar data

The global climate crisis is creating increasingly complex rainfall patterns, leading to a rising demand for data-driven artificial intelligence (AI) in short-term weather forecasting. However, the black-box nature of AI models acts as a critical obstacle against their integration into the existing forecasting operations. This study addresses this issue by developing an explainable AI framework that extracts precipitation mechanisms from the model’s internal activation patterns when it predicts rainfall intensity in the future. The primary objective of this study is to enable the semi-automatic knowledge discovery of the weather mechanisms embedded in the nonlinear AI model by developing the unsupervised concept explanation method. A key challenge is the inherent fuzziness and the complexity of precipitation systems. We propose a probabilistic multi-label self-supervised clustering approach within the explainable framework to address this. Our algorithm refines an insufficient embedding space into perceptually meaningful representations. It improves the clustering performance over existing baselines, achieving an increase of 0.5358 in terms of a Silhouette Coefficient metric, which measures the similarity of intra-clusters and the dissimilarity of inter-clusters. We extract and characterize primary meteorological mechanisms through comprehensive case studies: convectional, frontal, orographic, and cyclonic precipitations. These findings are further validated by a user study involving forecasters at the Korea Meteorological Administration. We assess the distinguishability of the extracted rainfall patterns by conducting a user survey regarding the homogeneity of the extracted rainfall patterns. The results indicate comparable accuracies between existing human-annotated label-based examples (80%) and the unsupervised model-based ones (92%). Furthermore, the proposed method can effectively identify between polar low and typhoon cases, successfully capturing the different mechanisms while their cyclonic shapes are analogous. Our structured methodology can provide a pathway for detecting extreme weather events–such as heavy rainfall and isolated thunderstorms–in near real-time, thereby supporting operational forecasting or posthoc analysis tasks.