radar datatree: Cloud-native, time-aware weather radar datasets

radar datatree: Cloud-native, time-aware weather radar datasets#

You want to look at how a Chicago-area storm evolved on a given date. The traditional path: download multiple gigabytes of NEXRAD Level II files, decode them, georeference each sweep, and stitch them together. This notebook does the same end-to-end, streaming straight from the cloud — connect, load, slice, georeference, plot.

Why a DataTree?#

Weather radar volumes are organized hierarchically: a Volume Coverage Pattern (VCP) cycles through several 360° sweeps at increasing elevation angles, repeating every 4–10 minutes. Different VCPs have different shapes, which is exactly what xarray.DataTree is built for.

Hierarchical — every VCP × sweep combination is a node you navigate (dt["VCP-12/sweep_0"]), not a separate file you parse.
Time-indexed — all scans share a vcp_time dimension, so .sel(vcp_time=...) replaces the file-iteration loop.
Lazy — opening the full archive fetches metadata only (~MB). Variables stream from object storage on demand.

For architecture and scaling benchmarks, see Ladino-Rincón et al. (2026, in preparation). Unfamiliar acronyms (VCP, polarimetric, ZDR, RHOHV, …) are all defined in the glossary.

Where to next#

Want to reproduce a published figure? → Notebook 2 — QVP comparison reproduces Ryzhkov et al. (2016) Fig. 4 and asserts numerical equivalence between the traditional file-based path and the ARCO streaming path.

Want to estimate rainfall accumulation? → Notebook 3 — QPE scaling applies the Marshall–Palmer Z–R relation live for one day, with cluster-recommended templates for 7-day / 30-day / 6-month windows.

Want to query a different radar or event? → The quickstart shows the 5-line connection pattern. Swap "KLOT" for "KVNX" (Oklahoma) to point at a different archive — more radars are added to nexrad-arco as they’re processed.

Want to understand the data model? → About covers the DataTree / Icechunk / Zarr stack and the AtmoScale parent platform. Glossary defines every radar acronym in one place.

Cite this work: Ladino-Rincón, A., et al. (2026). Radar DataTree: A FAIR and Cloud-Native Framework for Scalable Weather Radar Archives. (Manuscript in preparation.) Earlier preprint: arXiv:2510.24943, doi:10.48550/arXiv.2510.24943.

radar datatree: Cloud-native, time-aware weather radar datasets

Contents

radar datatree: Cloud-native, time-aware weather radar datasets#

Why a DataTree?#

radar datatree in practice#

Opening only what you need#

Plot a polarimetric snapshot#

Where to next#