{
  "id": 17075,
  "url": "https://arxiv.org/abs/2608.05981v1",
  "title": "Temporal Bridges for Spatial Resolution: Enhancing Climate Data Super-Resolution with Bidirectional Alignment",
  "summary": "High-resolution climate data is crucial for meteorological predictions and for informing decision support across diverse domains. However, the acquisition of such high-resolution climate information is often prohibitively costly, necessitating the development of data-driven meteorological prediction models. These models aim to generate fine-grained climate data from low-resolution inputs, a process termed climate data super-resolution (SR). Nevertheless, recent advancements in deep learning for",
  "authors": "Yichen Zhang, Yixiong Xiao, Congxi Xiao, Jingbo Zhou",
  "category": "research",
  "topics": "safety-alignment,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T12:58:22.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17075",
  "original_url": "https://arxiv.org/abs/2608.05981v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}