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An Open Virtual Machine for Cross-Platform Weather Radar Science

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  • 1 University of Potsdam, Institute of Earth and Environmental Sciences, Potsdam, Germany
  • | 2 Argonne National Laboratory, Argonne, Illinois
  • | 3 National Center for Atmospheric Research (NCAR), Boulder, Colorado
  • | 4 Argonne National Laboratory, Argonne, Illinois
  • | 5 HENJAB, Växjö, Sweden
  • | 6 Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
  • | 7 Blue Yonder, Karlsruhe, Germany
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Abstract

In a recent BAMS article, it is argued that community-based Open Source Software (OSS) could foster scientific progress in weather radar research, and make weather radar software more affordable, flexible, transparent, sustainable, and interoperable.

Nevertheless, it can be challenging for potential developers and users to realize these benefits: tools are often cumbersome to install; different operating systems may have particular issues, or may not be supported at all; and many tools have steep learning curves.

To overcome some of these barriers, we present an open, community-based virtual machine (VM). This VM can be run on any operating system, and guarantees reproducibility of results across platforms. It contains a suite of independent OSS weather radar tools (BALTRAD, Py-ART, wradlib, RSL, and Radx), and a scientific Python stack. Furthermore, it features a suite of recipes that work out of the box and provide guidance on how to use the different OSS tools alone and together. The code to build the VM from source is hosted on GitHub, which allows the VM to grow with its community.

We argue that the VM presents another step toward Open (Weather Radar) Science. It can be used as a quick way to get started, for teaching, or for benchmarking and combining different tools. It can foster the idea of reproducible research in scientific publishing. Being scalable and extendable, it might even allow for real-time data processing.

We expect the VM to catalyze progress toward interoperability, and to lower the barrier for new users and developers, thus extending the weather radar community and user base.

CORRESPONDING AUTHOR: Maik Heistermann, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany, E-mail: maik.heistermann@uni-potsdam.de

Abstract

In a recent BAMS article, it is argued that community-based Open Source Software (OSS) could foster scientific progress in weather radar research, and make weather radar software more affordable, flexible, transparent, sustainable, and interoperable.

Nevertheless, it can be challenging for potential developers and users to realize these benefits: tools are often cumbersome to install; different operating systems may have particular issues, or may not be supported at all; and many tools have steep learning curves.

To overcome some of these barriers, we present an open, community-based virtual machine (VM). This VM can be run on any operating system, and guarantees reproducibility of results across platforms. It contains a suite of independent OSS weather radar tools (BALTRAD, Py-ART, wradlib, RSL, and Radx), and a scientific Python stack. Furthermore, it features a suite of recipes that work out of the box and provide guidance on how to use the different OSS tools alone and together. The code to build the VM from source is hosted on GitHub, which allows the VM to grow with its community.

We argue that the VM presents another step toward Open (Weather Radar) Science. It can be used as a quick way to get started, for teaching, or for benchmarking and combining different tools. It can foster the idea of reproducible research in scientific publishing. Being scalable and extendable, it might even allow for real-time data processing.

We expect the VM to catalyze progress toward interoperability, and to lower the barrier for new users and developers, thus extending the weather radar community and user base.

CORRESPONDING AUTHOR: Maik Heistermann, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany, E-mail: maik.heistermann@uni-potsdam.de
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