Fog, a weather phenomenon near the Earth’s surface, poses significant visibility challenges, critically impacting all modes of transportation. Accurate fog prediction models are crucial for ensuring safety and reducing delays. Forecasting meteorological phenomena involves complex five-dimensional data structures, including geographical coordinates, atmospheric conditions, altitude layers, and time-series data. The challenge is to effectively learn from this data to improve visibility predictions. Recent advancements in vision transformers have revolutionized deep learning, particularly in image analysis, opening new avenues for interpreting complex spatio-temporal data in atmospheric science. This paper focuses on coastal fog forecasting with a 24-hour prediction window. We introduce and compare innovative tokenization strategies for vision transformer models aimed at enhancing the accuracy and interpretability of fog predictions. The study evaluates various sampling methods, comparing traditional 2D approaches (Vanilla Vision Transformer and Unified Variable Transformer) with more sophisticated 3D and 4D techniques (Spatio-Temporal Transformer, Spatio-Variable Transformer, and Physics-Informed Transformer). FogNet-v2.0, a PIT model, emerges as the front-runner, outperforming other models and benchmarks, including the 3D CNN-based FogNet. FogNet-v2.0 improves prediction accuracy across most metrics except for the Critical Success Index (CSI). Key innovations of this research include correctly forecasting fog events, improved skill scores, reduced miss cases, and the development of an explainable, physics-informed vision transformer. This paper highlights the integration of physical principles with machine learning for precise, interpretable weather prediction models, showcasing the efficacy of advanced tokenization and physics-informed methodologies in addressing the complexities of atmospheric phenomena.

Geoscience applications have been using sophisticated machine learning methods to model complex phenomena. These models are described as black boxes since it is unclear what relationships are learned. Models may exploit spurious associations that exist in the data. The lack of transparency may limit user’s trust, causing them to avoid high performance models since they cannot verify that it has learned realistic strategies. EXplainable Artificial Intelligence (XAI) is a developing research area for investigating how models make their decisions. However, XAI methods are sensitive to feature correlations. This makes XAI challenging for high-dimensional models whose input rasters may have extensive spatial-temporal autocorrelation. Since many geospatial applications rely on complex models for target performance, a recommendation is to combine raster elements into semantically meaningful feature groups. However, it is challenging to determine how best to combine raster elements. Here, we explore the explanation sensitivity to grouping scheme. Experiments are performed on FogNet, a complex deep learning model that uses 3D Convolutional Neural Networks (CNN) for coastal fog prediction. We demonstrate that explanations can be combined with domain knowledge to generate hypotheses about the model. Meteorological analysis of the XAI output reveal FogNet’s use of channels that capture relationships related to fog development, contributing to good overall model performance. However, analyses also reveal several deficiencies, including the reliance on channels and channel spatial patterns that correlate to the predominate fog type in the dataset, to make predictions of all fog types. Strategies to improve FogNet performance and trustworthiness are presented.