@article{VoitHeistermann2022, author = {Voit, Paul and Heistermann, Maik}, title = {A new index to quantify the extremeness of precipitation across scales}, series = {NHESS - Natural Hazards and Earth System Sciences}, volume = {22}, journal = {NHESS - Natural Hazards and Earth System Sciences}, edition = {8}, publisher = {Copernicus}, address = {Katlenburg-Lindau}, issn = {1684-9981}, doi = {10.5194/nhess-22-2791-2022}, pages = {2791 -- 2805}, year = {2022}, abstract = {Quantifying the extremeness of heavy precipitation allows for the comparison of events. Conventional quantitative indices, however, typically neglect the spatial extent or the duration, while both are important to understand potential impacts. In 2014, the weather extremity index (WEI) was suggested to quantify the extremeness of an event and to identify the spatial and temporal scale at which the event was most extreme. However, the WEI does not account for the fact that one event can be extreme at various spatial and temporal scales. To better understand and detect the compound nature of precipitation events, we suggest complementing the original WEI with a "cross-scale weather extremity index" (xWEI), which integrates extremeness over relevant scales instead of determining its maximum. Based on a set of 101 extreme precipitation events in Germany, we outline and demonstrate the computation of both WEI and xWEI. We find that the choice of the index can lead to considerable differences in the assessment of past events but that the most extreme events are ranked consistently, independently of the index. Even then, the xWEI can reveal cross-scale properties which would otherwise remain hidden. This also applies to the disastrous event from July 2021, which clearly outranks all other analyzed events with regard to both WEI and xWEI. While demonstrating the added value of xWEI, we also identify various methodological challenges along the required computational workflow: these include the parameter estimation for the extreme value distributions, the definition of maximum spatial extent and temporal duration, and the weighting of extremeness at different scales. These challenges, however, also represent opportunities to adjust the retrieval of WEI and xWEI to specific user requirements and application scenarios.}, language = {en} } @misc{VoitHeistermann2022, author = {Voit, Paul and Heistermann, Maik}, title = {A new index to quantify the extremeness of precipitation across scales}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1283}, issn = {1866-8372}, doi = {10.25932/publishup-57089}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-570893}, pages = {2791 -- 2805}, year = {2022}, abstract = {Quantifying the extremeness of heavy precipitation allows for the comparison of events. Conventional quantitative indices, however, typically neglect the spatial extent or the duration, while both are important to understand potential impacts. In 2014, the weather extremity index (WEI) was suggested to quantify the extremeness of an event and to identify the spatial and temporal scale at which the event was most extreme. However, the WEI does not account for the fact that one event can be extreme at various spatial and temporal scales. To better understand and detect the compound nature of precipitation events, we suggest complementing the original WEI with a "cross-scale weather extremity index" (xWEI), which integrates extremeness over relevant scales instead of determining its maximum. Based on a set of 101 extreme precipitation events in Germany, we outline and demonstrate the computation of both WEI and xWEI. We find that the choice of the index can lead to considerable differences in the assessment of past events but that the most extreme events are ranked consistently, independently of the index. Even then, the xWEI can reveal cross-scale properties which would otherwise remain hidden. This also applies to the disastrous event from July 2021, which clearly outranks all other analyzed events with regard to both WEI and xWEI. While demonstrating the added value of xWEI, we also identify various methodological challenges along the required computational workflow: these include the parameter estimation for the extreme value distributions, the definition of maximum spatial extent and temporal duration, and the weighting of extremeness at different scales. These challenges, however, also represent opportunities to adjust the retrieval of WEI and xWEI to specific user requirements and application scenarios.}, language = {en} } @phdthesis{Heistermann2015, author = {Heistermann, Maik}, title = {Advancing weather radar in hydrology}, school = {Universit{\"a}t Potsdam}, pages = {228}, year = {2015}, 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} } @article{SaltikoffFriedrichSoderholmetal.2019, author = {Saltikoff, Elena and Friedrich, Katja and Soderholm, Joshua and Lengfeld, Katharina and Nelson, Brian and Becker, Andreas and Hollmann, Rainer and Urban, Bernard and Heistermann, Maik and Tassone, Caterina}, title = {An Overview of Using Weather Radar for Climatological Studies: Successes, Challenges, and Potential}, series = {Bulletin of the American Meteorological Society}, volume = {100}, journal = {Bulletin of the American Meteorological Society}, number = {9}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0003-0007}, doi = {10.1175/BAMS-D-18-0166.1}, pages = {1739 -- 1751}, year = {2019}, abstract = {Weather radars have been widely used to detect and quantify precipitation and nowcast severe weather for more than 50 years. Operational weather radars generate huge three-dimensional datasets that can accumulate to terabytes per day. So it is essential to review what can be done with existing vast amounts of data, and how we should manage the present datasets for the future climatologists. All weather radars provide the reflectivity factor, and this is the main parameter to be archived. Saving reflectivity as volumetric data in the original spherical coordinates allows for studies of the three-dimensional structure of precipitation, which can be applied to understand a number of processes, for example, analyzing hail or thunderstorm modes. Doppler velocity and polarimetric moments also have numerous applications for climate studies, for example, quality improvement of reflectivity and rain rate retrievals, and for interrogating microphysical and dynamical processes. However, observational data alone are not useful if they are not accompanied by sufficient metadata. Since the lifetime of a radar ranges between 10 and 20 years, instruments are typically replaced or upgraded during climatologically relevant time periods. As a result, present metadata often do not apply to past data. This paper outlines the work of the Radar Task Team set by the Atmospheric Observation Panel for Climate (AOPC) and summarizes results from a recent survey on the existence and availability of long time series. We also provide recommendations for archiving current and future data and examples of climatological studies in which radar data have already been used.}, language = {en} } @misc{PetrowHeistermannBronstert2017, author = {Petrow, Theresia and Heistermann, Maik and Bronstert, Axel}, title = {Analysis of Flash Floods in Germany}, series = {Hydrologie und Wasserbewirtschaftung}, volume = {61}, journal = {Hydrologie und Wasserbewirtschaftung}, publisher = {Bundesanst. f{\"u}r Gew{\"a}sserkunde}, address = {Koblenz}, issn = {1439-1783}, pages = {212 -- 212}, year = {2017}, language = {en} } @article{FranckeHeistermannKoehlietal.2022, author = {Francke, Till and Heistermann, Maik and K{\"o}hli, Markus and Budach, Christian and Schr{\"o}n, Martin and Oswald, Sascha}, title = {Assessing the feasibility of a directional cosmic-ray neutron sensing sensor for estimating soil moisture}, series = {Geoscientific Instrumentation, Methods and Data Systems}, volume = {11}, journal = {Geoscientific Instrumentation, Methods and Data Systems}, publisher = {Copernicus Publ.}, address = {G{\"o}ttingen}, issn = {2193-0864}, doi = {10.5194/gi-11-75-2022}, pages = {75 -- 92}, year = {2022}, abstract = {Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools such as soil moisture, snow or vegetation. The intrinsic integration over a radial hectare-scale footprint is a clear advantage for averaging out small-scale heterogeneity, but on the other hand the data may become hard to interpret in complex terrain with patchy land use. This study presents a directional shielding approach to prevent neutrons from certain angles from being counted while counting neutrons entering the detector from other angles and explores its potential to gain a sharper horizontal view on the surrounding soil moisture distribution. Using the Monte Carlo code URANOS (Ultra Rapid Neutron-Only Simulation), we modelled the effect of additional polyethylene shields on the horizontal field of view and assessed its impact on the epithermal count rate, propagated uncertainties and aggregation time. The results demonstrate that directional CRNS measurements are strongly dominated by isotropic neutron transport, which dilutes the signal of the targeted direction especially from the far field. For typical count rates of customary CRNS stations, directional shielding of half-spaces could not lead to acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates should be feasible.}, language = {en} } @misc{FranckeHeistermannKoehlietal.2022, author = {Francke, Till and Heistermann, Maik and K{\"o}hli, Markus and Budach, Christian and Schr{\"o}n, Martin and Oswald, Sascha}, title = {Assessing the feasibility of a directional cosmic-ray neutron sensing sensor for estimating soil moisture}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, issn = {1866-8372}, doi = {10.25932/publishup-54422}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-544229}, pages = {75 -- 92}, year = {2022}, abstract = {Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools such as soil moisture, snow or vegetation. The intrinsic integration over a radial hectare-scale footprint is a clear advantage for averaging out small-scale heterogeneity, but on the other hand the data may become hard to interpret in complex terrain with patchy land use. This study presents a directional shielding approach to prevent neutrons from certain angles from being counted while counting neutrons entering the detector from other angles and explores its potential to gain a sharper horizontal view on the surrounding soil moisture distribution. Using the Monte Carlo code URANOS (Ultra Rapid Neutron-Only Simulation), we modelled the effect of additional polyethylene shields on the horizontal field of view and assessed its impact on the epithermal count rate, propagated uncertainties and aggregation time. The results demonstrate that directional CRNS measurements are strongly dominated by isotropic neutron transport, which dilutes the signal of the targeted direction especially from the far field. For typical count rates of customary CRNS stations, directional shielding of half-spaces could not lead to acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates should be feasible.}, language = {en} }