To improve the trustworthiness of an artificial intelligence (AI) model, finding consistent, understandable representations of its inference process is essential. This understanding is particularly important in high-stakes operations such as weather forecasting, where the identification of underlying meteorological mechanisms is as critical as the accuracy of the predictions. Despite the growing literature that addresses this issue through explainable AI, the applicability of their solutions is often limited due to their AI-centric development. To fill this gap, we follow a user-centric process to develop an example-based concept analysis framework, which identifies cases that follow a similar inference process as the target instance in a target model and presents them in a user-comprehensible format. Our framework provides the users with visually and conceptually analogous examples, including the probability of concept assignment to resolve ambiguities in weather mechanisms. To bridge the gap between vector representations identified from models and human-understandable explanations, we compile a human-annotated concept dataset and implement a user interface to assist domain experts involved in the framework development. Significance Statement: This study investigates deep neural networks’ (DNNs) ability to encode semantic patterns of precipitation mechanisms and aims to provide a ready-to-deploy explainable artificial intelligence (XAI) tool. Key findings reveal that DNNs can extract nonlinear precipitation mechanisms and represent semantically meaningful meteorological attributes. The concept explanations align with expert perceptions, enhancing the interpretability and trustworthiness of model predictions. These findings demonstrate DNNs’ potential to provide insightful, explainable predictions in meteorology, improving the trustworthiness of DNNs for practitioners. Follow-up research could involve refining the XAI framework, exploring its application for other meteorological phenomena, regions or scales, and integrating it with operational systems to assess the strengths and limitations in real-world scenarios.

Deep generative models are proficient in generating realistic data but struggle with producing rare samples in low density regions due to their scarcity of training datasets and the mode collapse problem. While recent methods aim to improve the fidelity of generated samples, they often reduce diversity and coverage by ignoring rare and novel samples. This study proposes a novel approach for generating diverse rare samples from high-resolution image datasets with pretrained GANs. Our method employs gradient-based optimization of latent vectors within a multi-objective framework and utilizes normalizing flows for density estimation on the feature space. This enables the generation of diverse rare images, with controllable parameters for rarity, diversity, and similarity to a reference image. We demonstrate the effectiveness of our approach both qualitatively and quantitatively across various datasets and GANs without retraining or fine-tuning the pretrained GANs.

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.

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 act as a critical obstacle against their integration into existing forecasting operations. This study addresses this issue by implementing an explainable AI framework that translates model behavior into human-understandable information. Our proposed framework integrates example-based explanations, which provide interpretations in the form of user-familiar materials such as radar data, and unsupervised concept vector analysis, which identifies semantic concepts captured by the internal vector space of AI models, to interpret a forecasting model's behavior in terms of human-understandable weather concepts. We develop a multi-label self-supervised deep clustering algorithm to derive perceptually meaningful representations from an insufficient embedding space. Our method improves clustering performance over baseline methods, achieving an increase of 0.5358 in terms of silhouette coefficients. We assess the interpretability of the extracted concepts by performing a survey with five forecasters regarding the homogeneity of selected rainfall patterns. The results indicate comparable accuracies between human label-based (80%) and model-based (92%) examples. Furthermore, the proposed method can effectively distinguish between polar low and typhoon cases, successfully capturing the nonlinear weather patterns represented by data-driven models. Our explanation framework may be extended to explore the internal decision behaviors of state-of-the-art multivariable models by extracting nonlinear rainfall development and dissipation mechanisms in a human-interpretable manner.