[2603.24744] Contrastive Learning Boosts Deterministic and Generative Models for Weather Data

Computer Science > Machine Learning arXiv:2603.24744 (cs) [Submitted on 25 Mar 2026] Title:Contrastive Learning Boosts Deterministic and Generative Models for Weather Data Authors:Nathan Bailey View a PDF of the paper titled Contrastive Learning Boosts Deterministic and Generative Models for Weather Data, by Nathan Bailey View PDF HTML (experimental) Abstract:Weather data, comprising multiple variables, poses significant challenges due to its high dimensionality and multimodal nature. Creating low-dimensional embeddings requires compressing this data into a compact, shared latent space. This compression is required to improve the efficiency and performance of downstream tasks, such as forecasting or extreme-weather detection. Self-supervised learning, particularly contrastive learning, offers a way to generate low-dimensional, robust embeddings from unlabelled data, enabling downstream tasks when labelled data is scarce. Despite initial exploration of contrastive learning in weather data, particularly with the ERA5 dataset, the current literature does not extensively examine its benefits relative to alternative compression methods, notably autoencoders. Moreover, current work on contrastive learning does not investigate how these models can incorporate sparse data, which is more common in real-world data collection. It is critical to explore and understand how contrastive learning contributes to creating more robust embeddings for sparse weather data, thereby improving per...