arXiv subjects

Michael Wiltberger

Publications and source records attributed to Michael Wiltberger.

5 recordsLinked to original sources

arXiv2024

A Reproducible Method for Mapping Electricity Transmission Infrastructure for Space Weather Risk Assessment

Space weather risk assessment is constrained by the lack of available asset information needed to model geomagnetically induced currents (GICs) in electricity transmission infrastructure. We propose a systematic method that enables risk analysts to collect their own open-source substation data. Using a web browser platform for annotation, we convert OpenStreetMap (OSM) substation locations into high-resolution, component-level mappings of electricity transmission assets. We convert an initial 1,313 high-voltage (>=230 kV) substations to 52,273 components using low-altitude, satellite, and Street View imagery accessed through Google Earth, identifying 7,949 transformers. Compared to the OSM baseline, this approach provides detailed insights on voltage levels and substation configurations. We then construct a geospatial GIC network for the Tennessee Valley Authority (TVA) region, comparing May 2024 results with the University of Illinois Urbana-Champaign 150-bus (UIUC150) synthetic network and with measured ground GICs at 13 monitoring devices. The transformer types at unannotated substations and the grounding resistances are unknown, so we sample both across a Monte Carlo ensemble. This gives a median TVA 95th-percentile peak ground GIC of 29.1 A, with a 90 percent confidence interval of 23.9-36.1 A. The UIUC150 network yields a 95th-percentile peak ground GIC of 35.8 A under the same forcing, falling within this interval, and the modeled time series broadly capture the temporal morphology of the geomagnetic storm at the monitoring sites. This method shows promise for spatially explicit, screening-level GIC assessment without requiring access to operator data.

physics.geo-ph↗

arXiv2024

Major Space Weather Risks Identified via Coupled Physics-Engineering-Economic Modeling

Space weather poses an important but under-quantified threat to society. While severe geomagnetic storms are recognized as potential global catastrophes, their socio-economic impacts remain poorly quantified. We present a novel physics-engineering-economic framework that links geophysical drivers to power grid geoelectric fields, transformer vulnerability, and macroeconomic consequences. Using the United States as an example, we estimate daily U.S. economic losses for a 250-year geomagnetic storm from transformer thermal heating of 2.04 billion USD (95 percent confidence interval: 1.86 to 2.22 billion USD), disrupting power for approximately 5.7 million people and 150,000 businesses. These estimates are conservative lower bounds, reflecting only transformer thermal heating effects and excluding voltage collapse, cascading failures, and restoration costs. The true societal risk is likely substantially higher. Nonetheless, the contribution is in providing the first nationwide end-to-end coupling from space physics to potential macroeconomic loss, with quantified uncertainties. Our results demonstrate that coupled socio-economic modeling of space weather is both feasible and essential, and the framework is scalable and transferable, offering a template for assessing space weather risk to critical infrastructure in other countries.

physics.geo-ph↗

arXiv2023

Reimagining Heliophysics: A bold new vision for the next decade and beyond

The field of Heliophysics has a branding problem. We need an answer to the question: ``What is Heliophysics\?'', the answer to which should clearly and succinctly defines our science in a compelling way that simultaneously introduces a sense of wonder and exploration into our science and our missions. Unfortunately, recent over-reliance on space weather to define our field, as opposed to simply using it as a practical and relatable example of applied Heliophysics science, narrows the scope of what solar and space physics is and diminishes its fundamental importance. Moving forward, our community needs to be bold and unabashed in our definition of Heliophysics and its big questions. We should emphasize the general and fundamental importance and excitement of our science with a new mindset that generalizes and expands the definition of Heliophysics to include new ``frontiers'' of increasing interest to the community. Heliophysics should be unbound from its current confinement to the Sun-Earth connection and expanded to studies of the fundamental nature of space plasma physics across the solar system and greater cosmos. Finally, we need to come together as a community to advance our science by envisioning, prioritizing, and supporting -- with a unified voice -- a set of bold new missions that target compelling science questions - even if they do not explore the traditional Sun- and Earth-centric aspects of Heliophysics science. Such new, large missions to expand the frontiers and scope of Heliophysics science large missions can be the key to galvanizing the public and policymakers to support the overall Heliophysics program.

physics.space-ph↗

arXiv2019

GAMERA: A three-dimensional finite-volume MHD solver for non-orthogonal curvilinear geometries

Efficient simulation of plasmas in various contexts often involves the use of meshes that conform to the intrinsic geometry of the system under consideration. We present here a description of a new magnetohydrodynamic code, Gamera (Grid Agnostic MHD for Extended Research Applications), designed to combine geometric flexibility with high-order spatial reconstruction and constrained transport to maintain the divergence-free magnetic field. Gamera carries on the legacy of its predecessor, the LFM (Lyon-Fedder-Mobarry), a research code whose use in space physics has spanned three decades. At the time of its initial development the LFM code had a number of novel features: eighth-order centered spatial differencing, the Partial Donor Cell Method limiter for shock capturing, a non-orthogonal staggered mesh with constrained transport, and conservative averaging-reconstruction for axis singularities. A capability to handle multiple ion species was also added later. Gamera preserves the core numerical philosophy of LFM while also incorporating numerous algorithmic and computational improvements. The upgrades in the numerical schemes include accurate grid metric calculations using high-order Gaussian quadrature techniques, high-order upwind reconstruction, non-clipping options for interface values, and improved treatment of axis singularities. The improvements in the code implementation include the use of data structures and memory access patterns conducive to aligned vector operations and the implementation of hybrid parallelism, using MPI and OMP. Gamera is designed to be a portable and easy-to-use code that implements multi-dimensional MHD simulations in arbitrary non-orthogonal curvilinear geometries on modern supercomputer architectures.

physics.comp-ph↗

arXiv2013

Parameter tuning for a multi-fidelity dynamical model of the magnetosphere

Geomagnetic storms play a critical role in space weather physics with the potential for far reaching economic impacts including power grid outages, air traffic rerouting, satellite damage and GPS disruption. The LFM-MIX is a state-of-the-art coupled magnetospheric-ionospheric model capable of simulating geomagnetic storms. Imbedded in this model are physical equations for turning the magnetohydrodynamic state parameters into energy and flux of electrons entering the ionosphere, involving a set of input parameters. The exact values of these input parameters in the model are unknown, and we seek to quantify the uncertainty about these parameters when model output is compared to observations. The model is available at different fidelities: a lower fidelity which is faster to run, and a higher fidelity but more computationally intense version. Model output and observational data are large spatiotemporal systems; the traditional design and analysis of computer experiments is unable to cope with such large data sets that involve multiple fidelities of model output. We develop an approach to this inverse problem for large spatiotemporal data sets that incorporates two different versions of the physical model. After an initial design, we propose a sequential design based on expected improvement. For the LFM-MIX, the additional run suggested by expected improvement diminishes posterior uncertainty by ruling out a posterior mode and shrinking the width of the posterior distribution. We also illustrate our approach using the Lorenz `96 system of equations for a simplified atmosphere, using known input parameters. For the Lorenz `96 system, after performing sequential runs based on expected improvement, the posterior mode converges to the true value and the posterior variability is reduced.

stat.AP↗