RadCLss
Extracted Radar Columns and In-Situ Sensors
radclss extracts vertical radar columns over user-specified site locations and merges them with co-located in-situ sensor data (sondes, met stations, disdrometers, pluviometers, etc.) into a single xarray dataset / netCDF file. It supports multiple radar systems (e.g. CSAPR2, KASACR, XSACR, NEXRAD) and parallel processing via Dask.
Installation
From PyPI:
pip install radclss
From source:
git clone https://github.com/ARM-Development/radclss.git
cd radclss
pip install -e .
Quick Start
import radclss
volumes = {
"date": "20250520",
"radar_csapr2cmac": [...], # list of radar files
"sonde": [...],
"met_M1": [...],
# ... additional radar / in-situ inputs
}
input_site_dict = {
"M1": (34.34525, -87.33842, 293), # (lat, lon, alt_m)
"S4": (34.46451, -87.23598, 197),
}
columns = radclss.core.radclss(
volumes,
input_site_dict,
"radar_csapr2cmac",
serial=False,
verbose=True,
nexrad=True,
)
radclss.io.write_radclss_output(columns, "radclss_example.nc", "radclss.c2")
fig, ax = radclss.vis.create_radclss_columns("radclss_example.nc")
See examples/bnf_example.py for a full end-to-end script using the BNF (Bankhead National Forest) site, including Dask LocalCluster setup and multi-radar / multi-instrument inputs.
Package Layout
radclss.core— column extraction (radclss.core.radclss)radclss.io— netCDF output (radclss.io.write_radclss_output)radclss.vis— quicklook plots (radclss.vis.create_radclss_columns)radclss.config— default and output configurationradclss.util— column-processing utilities
Development
Run tests and pre-commit hooks before committing:
pytest tests/
pre-commit run --all-files
License
MIT — see LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for radclss 2026.9.16.1
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| radclss-2026.9.16.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.3 MB
Release files / radclss-2026.9.16.1.tar.gz
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