arXiv subjects

Wolfgang Schulz

Publications and source records attributed to Wolfgang Schulz.

5 recordsLinked to original sources

arXiv2025

Open-source FDTD solvers: The applicability of Elecode, gprMax and MEEP for simulations of lightning EM fields

In this study, the open-source finite-difference time-domain (FDTD) solvers gprMax, Elecode and MEEP are investigated for their suitability to compute lightning electromagnetic field propagation. Several simulations are performed to reproduce the results of typical field propagation scenarios that can be found in the literature. The results of the presented solvers are validated through comparison with reference field results corresponding to propagation over perfectly conducting and lossy ground. In most of the tested scenarios, all solvers reproduce the reference fields with satisfactory accuracy. However, close attention must be paid to the proper choice of the spatial discretization to avoid artificial numerical dispersion, and the application of the simulation cell boundaries, which can cause significant impairment of the results due to undesired reflections. Some cases of inaccurate FDTD results due to improper choices of parameters are demonstrated. Further, the features, the performance and limitations, and the advantages and drawbacks of the presented solvers are highlighted. For familiarization with the solvers' programmatical interfaces to initialize and run the simulations, the developed scripts are made available to the community in an openly accessible repository.

cs.CE↗

arXiv2024

Spatio-seasonal risk assessment of upward lightning at tall objects using meteorological reanalysis data

This study investigates lightning at tall objects and evaluates the risk of upward lightning (UL) over the eastern Alps and its surrounding areas. While uncommon, UL poses a threat, especially to wind turbines, as the long-duration current of UL can cause significant damage. Current risk assessment methods overlook the impact of meteorological conditions, potentially underestimating UL risks. Therefore, this study employs random forests, a machine learning technique, to analyze the relationship between UL measured at Gaisberg Tower (Austria) and $35$ larger-scale meteorological variables. Of these, the larger-scale upward velocity, wind speed and direction at 10 meters and cloud physics variables contribute most information. The random forests predict the risk of UL across the study area at a 1 km$^2$ resolution. Strong near-surface winds combined with upward deflection by elevated terrain increase UL risk. The diurnal cycle of the UL risk as well as high-risk areas shift seasonally. They are concentrated north/northeast of the Alps in winter due to prevailing northerly winds, and expanding southward, impacting northern Italy in the transitional and summer months. The model performs best in winter, with the highest predicted UL risk coinciding with observed peaks in measured lightning at tall objects. The highest concentration is north of the Alps, where most wind turbines are located, leading to an increase in overall lightning activity. Comprehensive meteorological information is essential for UL risk assessment, as lightning densities are a poor indicator of lightning at tall objects.

physics.soc-ph↗

arXiv2023

Upward lightning at wind turbines: Risk assessment from larger-scale meteorology

Upward lightning (UL) has become an increasingly important threat to wind turbines as ever more of them are being installed for renewably producing electricity. The taller the wind turbine the higher the risk that the type of lightning striking the man-made structure is UL. UL can be much more destructive than downward lightning due to its long lasting initial continuous current leading to a large charge transfer within the lightning discharge process. Current standards for the risk assessment of lightning at wind turbines mainly take the summer lightning activity into account, which is inferred from LLS. Ground truth lightning current measurements reveal that less than 50% of UL might be detected by lightning location systems (LLS). This leads to a large underestimation of the proportion of LLS-non-detectable UL at wind turbines, which is the dominant lightning type in the cold season. This study aims to assess the risk of LLS-detectable and LLS-non-detectable UL at wind turbines using direct UL measurements at the Gaisberg Tower (Austria) and Säntis Tower (Switzerland). Direct UL observations are linked to meteorological reanalysis data and joined by random forests, a powerful machine learning technique. The meteorological drivers for the non-/occurrence of LLS-detectable and LLS-non-detectable UL, respectively, are found from the random forest models trained at the towers and have large predictive skill on independent data. In a second step the results from the tower-trained models are extended to a larger study domain (Central and Northern Germany). The tower-trained models for LLS-detectable lightning is independently verified at wind turbine locations in that domain and found to reliably diagnose that type of UL. Risk maps based on case study events show that high diagnosed probabilities in the study domain coincide with actual UL events.

stat.ML↗

arXiv2022

Upward Lightning at the Gaisberg Tower: Initiation Mechanism and Flash Type and the Atmospheric Influence

Upward lightning is much rarer than downward lightning and requires tall ($100+$~m) structures to initiate. It may be either triggered by other lightning discharges or completely self-initiated. While conventional lightning location systems reliably detect downward lightning, they miss a specific flash type of upward lightning that consists only of a continuous current. Globally, only few specially instrumented towers can detect this flash type. The proliferation of wind turbines in combination with large damage from upward lightning necessitates an improved understanding under which conditions the self-initiated and the undetected subtype of upward lightning occur. To find larger-scale meteorological conditions favorable for self-initiated and undetectable upward lightning, this study uses a random forest machine learning model. It combines direct measurements at the specially instrumented tower at Gaisberg mountain in Austria with explanatory variables from larger-scale atmospheric reanalysis data (ERA5). Atmospheric variables reliably explain whether upward lightning is self-initiated by the tower or triggered by other lightning discharges. The most important variable is the height of the $-10~^\circ$C isotherm above the tall structure: the closer it is the higher is the probability of self-initiated upward lightning. Two-meter temperature and the amount of CAPE are also important. For the occurrence of upward lightning undetectable by lightning location systems, this study finds a strong relationship to the absence of lightning in the vicinity.

physics.ao-ph↗

arXiv2021

Diversity in News Recommendations

News diversity in the media has for a long time been a foundational and uncontested basis for ensuring that the communicative needs of individuals and society at large are met. Today, people increasingly rely on online content and recommender systems to consume information challenging the traditional concept of news diversity. In addition, the very concept of diversity, which differs between disciplines, will need to be re-evaluated requiring a interdisciplinary investigation, which requires a new level of mutual cooperation between computer scientists, social scientists, and legal scholars. Based on the outcome of a multidisciplinary workshop, we have the following recommendations, directed at researchers, funders, legislators, regulators, and the media industry: 1. Do more research on news recommenders and diversity. 2. Create a safe harbor for academic research with industry data. 3. Optimize the role of public values in news recommenders. 4. Create a meaningful governance framework. 5. Fund a joint lab to spearhead the needed interdisciplinary research, boost practical innovation, develop. reference solutions, and transfer insights into practice.

cs.CY↗