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 configuration
• radclss.util — column-processing utilities

Development

Run tests and pre-commit hooks before committing:

pytest tests/
pre-commit run --all-files

License

MIT — see LICENSE.