{"ID":2849324,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24943","arxiv_id":"2510.24943","title":"Radar DataTree: A FAIR and Cloud-Native Framework for Scalable Weather Radar Archives","abstract":"We introduce Radar DataTree, the first dataset-level framework that extends the WMO FM-301 standard from individual radar volume scans to time-resolved, analysis-ready archives. Weather radar data are among the most scientifically valuable yet structurally underutilized Earth observation datasets. Despite widespread public availability, radar archives remain fragmented, vendor-specific, and poorly aligned with FAIR (Findable, Accessible, Interoperable, Reusable) principles, hindering large-scale research, reproducibility, and cloud-native computation. Radar DataTree addresses these limitations with a scalable, open-source architecture that transforms operational radar archives into FAIR-compliant, cloud-optimized datasets. Built on the FM-301/CfRadial 2.1 standard and implemented using xarray DataTree, Radar DataTree organizes radar volume scans as hierarchical, metadata-rich structures and serializes them to Zarr for scalable analysis. Coupled with Icechunk for ACID-compliant storage and versioning, this architecture enables efficient, parallel computation across thousands of radar scans with minimal preprocessing. We demonstrate significant performance gains in case studies including Quasi-Vertical Profile (QVP) and precipitation accumulation workflows, and release all tools and datasets openly via the Raw2Zarr repository. This work contributes a reproducible and extensible foundation for radar data stewardship, high-performance geoscience, and AI-ready weather infrastructure.","short_abstract":"We introduce Radar DataTree, the first dataset-level framework that extends the WMO FM-301 standard from individual radar volume scans to time-resolved, analysis-ready archives. Weather radar data are among the most scientifically valuable yet structurally underutilized Earth observation datasets. Despite widespread pu...","url_abs":"https://arxiv.org/abs/2510.24943","url_pdf":"https://arxiv.org/pdf/2510.24943v1","authors":"[\"Alfonso Ladino-Rincon\",\"Stephen W. Nesbitt\"]","published":"2025-10-28T20:15:02Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}