AI RESEARCH
Contrastive Learning Boosts Deterministic and Generative Models for Weather Data
arXiv CS.LG
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ArXi:2603.24744v1 Announce Type: new 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.