Radar DataTree: A FAIR and Cloud-Native Framework for Scalable Weather Radar Archives

TL;DR

Radar DataTree is a scalable, open-source framework that transforms fragmented weather radar archives into FAIR-compliant, cloud-optimized datasets, enabling large-scale analysis and reproducibility.

Contribution

It introduces a novel, hierarchical, metadata-rich data organization for radar archives based on the FM-301 standard, implemented with xarray DataTree and Zarr for cloud-native processing.

Findings

Significant performance improvements in radar data workflows

Enabling efficient, parallel analysis of thousands of radar scans

Open release of tools and datasets for community use

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.