TY - JOUR A1 - Heistermann, Maik A1 - Collis, Scott A1 - Dixon, M. J. A1 - Giangrande, S. A1 - Helmus, J. J. A1 - Kelley, B. A1 - Koistinen, J. A1 - Michelson, D. B. A1 - Peura, M. A1 - Pfaff, T. A1 - Wolff, D. B. T1 - The emergence of open-source software for the weather radar community JF - Bulletin of the American Meteorological Society N2 - Weather radar analysis has become increasingly sophisticated over the past 50 years, and efforts to keep software up to date have generally lagged behind the needs of the users. We argue that progress has been impeded by the fact that software has not been developed and shared as a community. Recently, the situation has been changing. In this paper, the developers of a number of open-source software (OSS) projects highlight the potential of OSS to advance radar-related research. We argue that the community-based development of OSS holds the potential to reduce duplication of efforts and to create transparency in implemented algorithms while improving the quality and scope of the software. We also conclude that there is sufficiently mature technology to support collaboration across different software projects. This could allow for consolidation toward a set of interoperable software platforms, each designed to accommodate very specific user requirements. Y1 - 2015 U6 - https://doi.org/10.1175/BAMS-D-13-00240.1 SN - 0003-0007 SN - 1520-0477 VL - 96 IS - 1 SP - 117 EP - + PB - American Meteorological Soc. CY - Boston ER - TY - JOUR A1 - Heistermann, Maik A1 - Collis, Scott A1 - Dixon, M. J. A1 - Helmus, J. J. A1 - Henja, A. A1 - Michelson, D. B. A1 - Pfaff, Thomas T1 - An Open Virtual Machine for Cross-Platform Weather Radar Science JF - Bulletin of the American Meteorological Society : BAMS N2 - 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. Y1 - 2015 U6 - https://doi.org/10.1175/BAMS-D-14-00220.1 SN - 0003-0007 SN - 1520-0477 VL - 96 SP - 1641 EP - 1645 PB - American Meteorological Society CY - Boston ER - TY - GEN A1 - Heistermann, Maik A1 - Collis, Scott A1 - Dixon, M. J. A1 - Helmus, J. J. A1 - Henja, A. A1 - Michelson, D. B. A1 - Pfaff, Thomas T1 - An Open Virtual Machine for Cross-Platform Weather Radar Science N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 262 Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-96604 SP - 1641 EP - 1645 ER - TY - JOUR A1 - Heistermann, Maik A1 - Collis, Scott A1 - Dixon, M. J. A1 - Helmus, Jonathan J. A1 - Henja, A. A1 - Michelson, Daniel B. A1 - Pfaff, Thomas T1 - An Open Virtual Machine for Cross-Platform Weather Radar Science JF - Bulletin of the American Meteorological Society N2 - 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. Y1 - 2015 U6 - https://doi.org/10.1175/BAMS-D-14-00220.1 SN - 0003-0007 SN - 1520-0477 VL - 96 IS - 10 PB - American Meteorological Soc. CY - Boston ER -