BER launches Environmental System Science Program. Visit our new website under construction!

U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights

The Python ARM Radar Toolkit: Leading Interactive Radar Toolkit
Published: July 18, 2016
Posted: October 04, 2016

Scientists develop an open-source radar toolkit to advance the analysis of research radar data.

The Science
Cloud and precipitation radars provide abundant data for the weather and climate communities, but considerable effort is required to extract scientifically meaningful parameters. Converting raw voltage measurements to geophysical parameters requires a combination of signal processing techniques, quality control and correction routines, and application of scientific algorithms. The Python ARM Radar Toolkit (Py-ART) enables scientists to work effectively with radar data and extract scientific insights from these large datasets.

The Impact
Development of a flexible, open-source software package for working with radar data has enabled a wide range of users to analyze radar data from the Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) program. Py-ART provides an environment for researchers to visualize, correct, and analyze data from all types of radars used by the weather and climate communities. Py-ART has over 100 users who participate in mailing list discussions and has been downloaded over 6,000 times. The library is used to create visualization for a number of websites and has been used for analysis in multiple peer-reviewed publications.

Originally started by ARM scientists, a community of scientists, software engineers, and open-source enthusiasts has come together to develop Py-ART, a library for reading, visualizing, correcting, and analyzing data from weather and climate radars. The toolkit provides a platform for scientists to examine data from cloud and precipitation radars operated by DOE’s ARM program as well as radars operated by other groups.

Py-ART comes with a number of built-in, computationally efficient processing and analysis routines that can be used to process radar data and retrieve meaningful geophysical parameters from the moments collected by the radar. Specific processing routines include modules that convert between azimuth and Cartesian coordinates, unfold radar Doppler velocities, correct attenuation using polarimetric variables, and process differential phase using a linear programming method. Additionally, the toolkit can be used as a platform to rapidly design and test new techniques for analyzing radar data. The toolkit is written in the Python programming language and is built on top of libraries in the Scientific Python ecosystem including NumPy, SciPy, and matplotlib. The package is open-source software and can be used, modified, and extended by anyone free of charge. Development is coordinated on the social coding website, GitHub, where others are encouraged to contribute and participate.

Contacts (BER PM)
Sally McFarlane
ARM Program Manager
(PI Contact)
Jonathan Helmus
Argonne National Laboratory

Funding for this work was provided by the Department of Energy’s Atmospheric Radiation Measurement program.

Helmus, J. J., and S. M. Collis. 2016. “The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language,” Journal of Open Research Software 4(1), e25. DOI: 10.5334/jors.119. (Reference link)

Topic Areas:

  • Facility: DOE ARM User Facility

Division: SC-33.1 Earth and Environmental Sciences Division, BER


BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

Mar 23, 2021
Molecular Connections from Plants to Fungi to Ants
Lipids transfer energy and serve as an inter-kingdom communication tool in leaf-cutter ants&rsqu [more...]

Mar 19, 2021
Microbes Use Ancient Metabolism to Cycle Phosphorus
Microbial cycling of phosphorus through reduction-oxidation reactions is older and more widespre [more...]

Feb 22, 2021
Warming Soil Means Stronger Microbe Networks
Soil warming leads to more complex, larger, and more connected networks of microbes in those soi [more...]

Jan 27, 2021
Labeling the Thale Cress Metabolites
New data pipeline identifies metabolites following heavy isotope labeling.

Analysis [more...]

Aug 31, 2020
Novel Bacterial Clade Reveals Origin of Form I Rubisco

  • All plant biomass is sourced from the carbon-fixing enzyme Rub [more...]

List all highlights (possible long download time)