@article{HeistermannCollisDixonetal.2015, author = {Heistermann, Maik and Collis, Scott and Dixon, M. J. and Giangrande, S. and Helmus, J. J. and Kelley, B. and Koistinen, J. and Michelson, D. B. and Peura, M. and Pfaff, T. and Wolff, D. B.}, title = {The emergence of open-source software for the weather radar community}, series = {Bulletin of the American Meteorological Society}, volume = {96}, journal = {Bulletin of the American Meteorological Society}, number = {1}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0003-0007}, doi = {10.1175/BAMS-D-13-00240.1}, pages = {117 -- +}, year = {2015}, abstract = {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.}, language = {en} } @article{PhilipsWalzBergneretal.2015, author = {Philips, Andrea and Walz, Ariane and Bergner, Andreas G. N. and Gr{\"a}ff, Thomas and Heistermann, Maik and Kienzler, Sarah and Korup, Oliver and Lipp, Torsten and Schwanghart, Wolfgang and Zeilinger, Gerold}, title = {Immersive 3D geovisualization in higher education}, series = {Journal of geography in higher education}, volume = {39}, journal = {Journal of geography in higher education}, number = {3}, publisher = {Routledge, Taylor \& Francis Group}, address = {Abingdon}, issn = {0309-8265}, doi = {10.1080/03098265.2015.1066314}, pages = {437 -- 449}, year = {2015}, abstract = {In this study, we investigate how immersive 3D geovisualization can be used in higher education. Based on MacEachren and Kraak's geovisualization cube, we examine the usage of immersive 3D geovisualization and its usefulness in a research-based learning module on flood risk, called GEOSimulator. Results of a survey among participating students reveal benefits, such as better orientation in the study area, higher interactivity with the data, improved discourse among students and enhanced motivation through immersive 3D geovisualization. This suggests that immersive 3D visualization can effectively be used in higher education and that 3D CAVE settings enhance interactive learning between students.}, language = {en} } @article{VormoorLawrenceHeistermannetal.2015, author = {Vormoor, Klaus Josef and Lawrence, D. and Heistermann, Maik and Bronstert, Axel}, title = {Climate change impacts on the seasonality and generation processes of floods}, series = {Hydrology and earth system sciences : HESS}, volume = {19}, journal = {Hydrology and earth system sciences : HESS}, number = {2}, publisher = {Copernicus Publications}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-19-913-2015}, pages = {913 -- 931}, year = {2015}, abstract = {Climate change is likely to impact the seasonality and generation processes of floods in the Nordic countries, which has direct implications for flood risk assessment, design flood estimation, and hydropower production management. Using a multi-model/multi-parameter approach to simulate daily discharge for a reference (1961-1990) and a future (2071-2099) period, we analysed the projected changes in flood seasonality and generation processes in six catchments with mixed snowmelt/rainfall regimes under the current climate in Norway. The multi-model/multi-parameter ensemble consists of (i) eight combinations of global and regional climate models, (ii) two methods for adjusting the climate model output to the catchment scale, and (iii) one conceptual hydrological model with 25 calibrated parameter sets. Results indicate that autumn/winter events become more frequent in all catchments considered, which leads to an intensification of the current autumn/winter flood regime for the coastal catchments, a reduction of the dominance of spring/summer flood regimes in a high-mountain catchment, and a possible systematic shift in the current flood regimes from spring/summer to autumn/winter in the two catchments located in northern and south-eastern Norway. The changes in flood regimes result from increasing event magnitudes or frequencies, or a combination of both during autumn and winter. Changes towards more dominant autumn/winter events correspond to an increasing relevance of rainfall as a flood generating process (FGP) which is most pronounced in those catchments with the largest shifts in flood seasonality. Here, rainfall replaces snowmelt as the dominant FGP primarily due to increasing temperature.We further analysed the ensemble components in contributing to overall uncertainty in the projected changes and found that the climate projections and the methods for downscaling or bias correction tend to be the largest contributors. The relative role of hydrological parameter uncertainty, however, is highest for those catchments showing the largest changes in flood seasonality, which confirms the lack of robustness in hydrological model parameterization for simulations under transient hydrometeorological conditions.}, language = {en} } @misc{VormoorLawrenceHeistermannetal.2015, author = {Vormoor, Klaus Josef and Lawrence, D. and Heistermann, Maik and Bronstert, Axel}, title = {Climate change impacts on the seasonality and generation processes of floods}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-84366}, year = {2015}, abstract = {Climate change is likely to impact the seasonality and generation processes of floods in the Nordic countries, which has direct implications for flood risk assessment, design flood estimation, and hydropower production management. Using a multi-model/multi-parameter approach to simulate daily discharge for a reference (1961-1990) and a future (2071-2099) period, we analysed the projected changes in flood seasonality and generation processes in six catchments with mixed snowmelt/rainfall regimes under the current climate in Norway. The multi-model/multi-parameter ensemble consists of (i) eight combinations of global and regional climate models, (ii) two methods for adjusting the climate model output to the catchment scale, and (iii) one conceptual hydrological model with 25 calibrated parameter sets. Results indicate that autumn/winter events become more frequent in all catchments considered, which leads to an intensification of the current autumn/winter flood regime for the coastal catchments, a reduction of the dominance of spring/summer flood regimes in a high-mountain catchment, and a possible systematic shift in the current flood regimes from spring/summer to autumn/winter in the two catchments located in northern and south-eastern Norway. The changes in flood regimes result from increasing event magnitudes or frequencies, or a combination of both during autumn and winter. Changes towards more dominant autumn/winter events correspond to an increasing relevance of rainfall as a flood generating process (FGP) which is most pronounced in those catchments with the largest shifts in flood seasonality. Here, rainfall replaces snowmelt as the dominant FGP primarily due to increasing temperature.We further analysed the ensemble components in contributing to overall uncertainty in the projected changes and found that the climate projections and the methods for downscaling or bias correction tend to be the largest contributors. The relative role of hydrological parameter uncertainty, however, is highest for those catchments showing the largest changes in flood seasonality, which confirms the lack of robustness in hydrological model parameterization for simulations under transient hydrometeorological conditions.}, language = {en} } @article{HeistermannCollisDixonetal.2015, author = {Heistermann, Maik and Collis, Scott and Dixon, M. J. and Helmus, J. J. and Henja, A. and Michelson, D. B. and Pfaff, Thomas}, title = {An Open Virtual Machine for Cross-Platform Weather Radar Science}, series = {Bulletin of the American Meteorological Society : BAMS}, volume = {96}, journal = {Bulletin of the American Meteorological Society : BAMS}, publisher = {American Meteorological Society}, address = {Boston}, issn = {0003-0007}, doi = {10.1175/BAMS-D-14-00220.1}, pages = {1641 -- 1645}, year = {2015}, 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.}, language = {en} } @article{HeistermannCollisDixonetal.2015, author = {Heistermann, Maik and Collis, Scott and Dixon, M. J. and Helmus, Jonathan J. and Henja, A. and Michelson, Daniel B. and Pfaff, Thomas}, title = {An Open Virtual Machine for Cross-Platform Weather Radar Science}, series = {Bulletin of the American Meteorological Society}, volume = {96}, journal = {Bulletin of the American Meteorological Society}, number = {10}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0003-0007}, doi = {10.1175/BAMS-D-14-00220.1}, pages = {6}, year = {2015}, 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.}, language = {en} } @misc{HeistermannCollisDixonetal.2015, author = {Heistermann, Maik and Collis, Scott and Dixon, M. J. and Helmus, J. J. and Henja, A. and Michelson, D. B. and Pfaff, Thomas}, title = {An Open Virtual Machine for Cross-Platform Weather Radar Science}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-96604}, pages = {1641 -- 1645}, year = {2015}, 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.}, language = {en} } @phdthesis{Heistermann2015, author = {Heistermann, Maik}, title = {Advancing weather radar in hydrology}, school = {Universit{\"a}t Potsdam}, pages = {228}, year = {2015}, language = {en} }