Access 1 Day of Radar Data in 5 Seconds

Access 1 Day of Radar Data in 5 Seconds#

Stop waiting. Stop downloading. Start analyzing.

Radar DataTree Architecture

What is Radar DataTree?#

Radar DataTree is a FAIR and cloud-native framework that transforms fragmented weather radar archives into analysis-ready datasets. Built on the WMO FM-301/CfRadial 2.1 standard, it provides a dataset-level abstraction that aggregates millions of individual radar files into a single hierarchical structure optimized for scientific analysis.

Instead of downloading and parsing thousands of binary files, you get direct access to time-indexed, multidimensional arrays—right from your Python session.

Try It Now#

import xarray as xr
import icechunk as ic

# Connect to NEXRAD KLOT (Chicago) — no credentials needed
storage = ic.s3_storage(
    bucket='nexrad-arco', prefix='KLOT-RT',
    endpoint_url='https://umn1.osn.mghpcc.org',
    anonymous=True, force_path_style=True, region='us-east-1',
)
repo = ic.Repository.open(storage)
session = repo.readonly_session("main")

# Load the entire archive (lazy — only metadata is fetched)
dtree = xr.open_datatree(session.store, zarr_format=3, consolidated=False, chunks={}, engine="zarr")

# Plot reflectivity
dtree["VCP-34/sweep_0"].DBZH.sel(vcp_time="2025-12-13 15:36", method="nearest").plot(
    x="x", y="y", cmap="ChaseSpectral", vmin=-10, vmax=70
)

What just happened? You opened an entire day of radar data in seconds. Only metadata was fetched—actual data loads on demand when you compute or plot.

Learning Path#

Follow this 3-notebook journey from beginner to advanced:

1. Your First Weather Radar

Start here. Access 92 GB of radar data in 5 seconds. Learn the fundamentals of cloud-native radar with plain-English explanations of what radar actually measures.

  • Connect to cloud storage

  • Visualize 5 polarimetric variables

  • Explore Git-like version control

15-20 min | Beginner

Your First Weather Radar in 60 Seconds

2. Scientific Showcase

Reproduce published science. Recreate Figure 4 from Ryzhkov et al. (2016) in under a minute. Learn how QVPs reveal precipitation microphysics.

  • 36x speedup demonstration

  • QVP science explained

  • Scientific interpretation guide

25-30 min | Intermediate

Scientific Showcase: Reproducing Published Radar Science in 60 Seconds

3. Real-World Application

Compute snow accumulation. Analyze the December 2025 Illinois winter storm. Apply Z-R relationships and create geographic accumulation maps.

  • Z-R relationships for snow

  • Multi-VCP handling

  • Uncertainty discussion

30-40 min | Intermediate-Advanced

Computing Snow Accumulation from the December 2025 Illinois Winter Storm

Run Locally#

git clone https://github.com/AtmoScale/radar-datatree.git
cd radar-datatree
conda env create -f environment.yml
conda activate radar-datatree
jupyter lab notebooks/

git clone https://github.com/AtmoScale/radar-datatree.git
cd radar-datatree
uv sync
uv run jupyter lab notebooks/

No multi-gigabyte downloads required—data streams directly from the cloud.

Why Radar DataTree?#

The Problem#

Traditional weather radar archives present fundamental challenges:

  • Millions of standalone binary files in proprietary formats

  • No temporal indexing — each scan must be downloaded and parsed independently

  • Hours to days required for multi-week analyses

  • Fundamentally misaligned with FAIR data principles

The Solution#

A dataset-level abstraction that aggregates individual radar files into a single hierarchical dataset. Query by time, elevation, or variable—and load only what you need.

Performance Impact#

Task

Traditional Workflow

Radar DataTree

Speedup

Data Loading

38.5 minutes

1.5 seconds

100x faster

QVP Computation

38.7 minutes

23 seconds

100x faster

Total Time (1 week)

77.2 minutes

24.5 seconds

189x faster

Benchmark: 1 week of NEXRAD KLOT data (92 GB, 3,888 files). See Workflow Comparison notebook for details.

Technology Stack#

Built on proven open-source technologies

Component

Purpose

WMO FM-301 / CfRadial 2.1

Standardized radar data model ensuring interoperability

xarray.DataTree

Hierarchical data structures for multi-dimensional arrays

Zarr v3

Cloud-optimized storage with chunked compression

Icechunk

ACID-compliant transactional storage with version control

xradar

Radar-specific I/O, QC, and processing utilities

The stack ensures compatibility with existing tools while enabling cloud-native workflows.

Citation#

Ladino-Rincón, A., & Nesbitt, S. W. (2025). Radar DataTree: A FAIR and Cloud-Native Framework for Scalable Weather Radar Archives. arXiv:2510.24943. doi:10.48550/arXiv.2510.24943

Contents