arXiv subjects

R. S. Weigel

Publications and source records attributed to R. S. Weigel.

7 recordsLinked to original sources

arXiv2026

Advancing Heliophysics and Space Weather Modeling through Open Science

We present a community-wide effort to develop a strategy and action plan to advance heliophysics and space weather modeling through open science. While open science has the potential to enhance the quality and pace of scientific discovery, its application to scientific modeling requires more careful consideration regarding open data and open software guidelines, as scientific models differ significantly from data analysis software. We gathered feedback from modeling teams worldwide through a living survey and discussion sessions at the 2024 Open Science Workshop in College Park, USA, and at the 2025 COSPAR ISWAT Working Meeting in Cape Canaveral, USA. We complement these findings with lessons learned from almost 25 years of experience at the Community Coordinated Modeling Center in enabling open use of models. We identify key roadblocks in current open science practices and guidelines and offer recommendations for future progress across four overlapping themes: open use of models and simulation results, open validation, open development, and open collaboration. An essential outcome of the discussion is the need for model developers and model users to speak with a united voice and promote the role of models in future open science efforts. We introduce a new cross-domain community initiative called the Heliophysics Open Modeling Environment (HOME), which will be integrated as an overarching activity within COSPAR ISWAT. HOME will serve as a platform for modelers and model users to work together, facilitate community modeling, improve the scientific return on modeling investment, and advance understanding, modeling, and forecasting in heliophysics and space weather.

physics.space-ph↗

arXiv2026

C-SWIM: A Coupled Space Weather Impact Model for Satellite Fleet Vulnerability and Economic Loss Under a 1-in-100-Year Solar Energetic Particle Event

Modern economies depend critically on satellite infrastructure, yet the aggregate economic consequences of extreme solar energetic particle (SEP) events have not been rigorously assessed. This study develops an integrated framework linking SEP hazard characterization, dynamic geomagnetic cutoff rigidity modeling, radiation dose transport, and fleet-wide failure probability estimation to macroeconomic impact analysis. Using extreme-value analysis of 160 SEP events over 27.4 years (1996-2025), failure probability is estimated for ~10,650 US operational satellites under orbital regime-dependent shielding assumptions. The assessment reveals that ~100 satellites (1.0%) are at Critical risk, concentrated in high-altitude low Earth orbit and highly elliptical orbit, while medium Earth orbit and geosynchronous orbit satellites fall in the Negligible class (P_fail < 10^-9) under the assumed radiation-hardened components and shielding. The expected capital loss across the ~$254B fleet totals ~$5.2B. Three failure scenarios, expanding from Critical satellites only (P_fail > 10^-2), to Critical and Elevated (P_fail > 10^-3), and to all satellites with non-negligible risk (P_fail > 10^-6), yield daily economic impacts of ~$70M, ~$270M, and ~$1.3B, respectively. Earth observation suffers up to 95.6% capacity loss in the worst case, while military services experience 16.1-20.4% disruption across scenarios. Results are first-order estimates: hardware failure counts are conservative because only total ionizing dose is modeled, and daily economic impacts represent upper bounds because operator response and recovery are not included.

physics.space-ph↗

arXiv2026

A Comparative Multi-Hazard Risk Assessment of the US High-Voltage Transmission Network

Modern economies depend critically on high-voltage power transmission networks. Yet this infrastructure is routinely disrupted by natural hazards ranging from earthquakes and floods to tornadoes and geomagnetic storms. Risk assessments have historically addressed hazards in isolation, leaving no common basis for comparing economic impacts across the full hazard portfolio. This study addresses this gap by developing an integrated framework linking hazard characterization, fragility modeling, and macroeconomic impact propagation. The framework is applied consistently across nine primary hazards and one compound freezing rain and wind gust hazard. Using national hazard datasets and a US high-voltage transmission network of over 13,000 line segments and 10,000 substations, we derive failure probabilities, expected damage, affected population, and downstream economic output losses. Among individual hazards, tropical cyclone wind produces the largest expected daily damage at $137 M/day, followed by lightning at $87 M/day, earthquake at $47 M/day, flood at $46 M/day, tornado at $42 M/day, and landslide at $34 M/day. Downstream economic output losses are largest for tornado at $4.93 B/day, followed by flood at $3.59 B/day and earthquake at $3.02 B/day. A 250-year geomagnetic storm produces $2.07 B/day, placing space weather within the range of major terrestrial hazards. The compound freezing rain and wind gust scenario produces the largest stress-test disruption, affecting 237.4 M people and yielding a modeled downstream output loss of $85.16 B/day. These results should be interpreted as first-order bounding estimates, with the compound scenario representing an upper-bound stress test. Overall, the framework establishes a consistent baseline for prioritizing investments in transmission network resilience.

econ.EM↗

arXiv2026

Coordinate Systems and Transforms in Space Physics: Terms, Definitions, Implementations, and Recommendations for Reproducibility

In space physics, acronyms for coordinate systems (e.g., \texttt{GEI}, \texttt{GSM}) are commonly used; however, differences in their definitions and implementations can prevent reproducibility. In this work, we compare definitions in online resources, software packages, and frequently cited journal articles and show that implementation differences can lead to transformations between same-named coordinate systems and position values from different data providers to differ significantly. Based on these comparisons and results, and to enable reproducibility, we recommend that (a) a standard for acronyms and definitions for coordinate systems is developed, similar to equivalents in astronomy or earth sciences; (b) a standards body develops a citable database of reference data needed for these transforms. For software that computes coordinate transforms, we also recommend that their developers provide explicit comparisons of their implementations with the results of (b) and documentation on implementation choices. Additionally, we provide recommendations for scientists and metadata developers to ensure that sufficient information is provided to enable reproducibility. Finally, we document that spacecraft positions from data providers can differ both because of differences in how they implemented transforms and because of differences in the original source of the position data, and provide recommendations to improve the documentation of spacecraft positional datasets.

physics.space-ph↗

arXiv2025

GIC--Related Observations During the May 2024 Geomagnetic Storm in the United States

The May 2024 geomagnetic storm was one of the most severe in the past 20~years. Understanding how large geomagnetic disturbances (GMDs) impact geomagnetically induced currents (GICs) within electrical power grid networks is key to ensuring their resilience. We have assembled and synthesized a large and unique set of GMD-related data, compared model predictions with measurements, and identified empirical relationships for GICs in the contiguous United States for this storm. Measurement data include GIC data from $47$ sites and magnetometer data from $17$ sites. Model data include GIC computed by the Tennessee Valley Authority (TVA) power system operators at $4$ sites, GIC computed using a reference model at $47$ sites, and the difference in the surface magnetic field from a baseline ($\Delta \mathbf{B}$) computed at $12$ magnetometer sites from three global magnetospheric models -- the Multiscale Atmosphere-Geospace Environment Model (MAGE), Space Weather Modeling Framework (SWMF), and Open Geospace General Circulation Model (OpenGGCM). GIC measured and computed by TVA had a correlation coefficient $\text{r}>0.8$ and a prediction efficiency between 0.4 and 0.7. The horizontal magnetic field perturbation from a baseline, $\Delta B_H$, computed by MAGE, SWMF, and OpenGGCM had a correlation r from $0.21$ to $0.65$. Two empirical relationships were considered: (1) how the correlation between measured GIC site pairs depended on differences in site separation distance, $\beta$ scaling factor (related to ground conductivity), and geomagnetic latitude; and (2) a regression model for the maximum $\mbox{GIC}$ magnitude at each site given the product of $\alpha$ (related to magnetic latitude) and $\beta$.

physics.space-ph↗

arXiv2010

Autoplot: A browser for scientific data on the web

Autoplot is software developed for the Virtual Observatories in Heliophysics to provide intelligent and automated plotting capabilities for many typical data products that are stored in a variety of file formats or databases. Autoplot has proven to be a flexible tool for exploring, accessing, and viewing data resources as typically found on the web, usually in the form of a directory containing data files with multiple parameters contained in each file. Data from a data source is abstracted into a common internal data model called QDataSet. Autoplot is built from individually useful components, and can be extended and reused to create specialized data handling and analysis applications and is being used in a variety of science visualization and analysis applications. Although originally developed for viewing heliophysics-related time series and spectrograms, its flexible and generic data representation model makes it potentially useful for the Earth sciences.

cs.GR↗

arXiv2010

TSDS: high-performance merge, subset, and filter software for time series-like data

Time Series Data Server (TSDS) is a software package for implementing a server that provides fast super-setting, sub-setting, filtering, and uniform gridding of time series-like data. TSDS was developed to respond quickly to requests for long time spans of data. Data may be served from a fast database, typically created by aggregating granules (e.g., data files) from a remote data source and storing them in a local cache that is optimized for serving time series. The system was designed specifically for time series data, and is optimized for requests where the longest dimension of the requested data structure is time. Scalar, vector, and spectrogram time series types are supported. The user can interact with the server by requesting a time series, a date range, and an optional filter to apply to the data. Available filters include strides, block average/minimum/maximum, exclude, and inequality. Constraint expressions are supported, which allow such operations as a request for data from one time series when a different time series satisfied a specified relationship. TSDS builds upon DAP (Data Access Protocol), NcML (netCDF Mark-up language) and related software libraries. In this work, we describe the current design of this server, as well as planned features and potential implementation strategies.

cs.DB↗