Journal: Space Weather

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Publisher

Wiley

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ISSN

1542-7390

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2020 - 20234
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Publications 1 - 4 of 4
  • Real-Time 3-D Modeling of the Ground Electric Field Due To Space Weather Events. A Concept and Its Validation
    Item type: Journal Article
    Kruglyakov, Mikhail; Kuvshinov, Alexey; Marshalko, Elena (2022)
    Space Weather
    We present a methodology that allows researchers to simulate in real time the spatiotemporal dynamics of the ground electric field (GEF) in a given 3-D conductivity model of the Earth based on continuously augmented data on the spatiotemporal evolution of the inducing source. The formalism relies on the factorization of the source by spatial modes (SM) and time series of respective expansion coefficients and exploits precomputed GEF kernels generated by corresponding SM. To validate the formalism, we invoke a high-resolution 3-D conductivity model of Fennoscandia and consider a realistic source built using the Spherical Elementary Current Systems (SECS) method as applied to magnetic field data from the International Monitor for Auroral Geomagnetic Effect network of observations. The factorization of the SECS-recovered source is then performed using the principal component analysis. Eventually, we show that the GEF computation at a given time instant on a 512 x 512 grid requires less than 0.025 s provided that GEF kernels due to pre-selected SM are computed in advance. Taking the 7-8 September 2017 geomagnetic storm as a space weather event, we show that real-time high-resolution 3-D modeling of the GEF is feasible. This opens a practical opportunity for GEF (and eventually geomagnetically induced currents) nowcasting and forecasting.
  • Uncertainty Quantification for Machine Learning-Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting
    Item type: Journal Article
    Natras, Randa; Soja, Benedikt; Schmidt, Michael (2023)
    Space Weather
    Machine learning (ML) has been increasingly applied to space weather and ionosphere problems in recent years, with the goal of improving modeling and forecasting capabilities through a data-driven modeling approach of nonlinear relationships. However, little work has been done to quantify the uncertainty of the results, lacking an indication of how confident and reliable the results of an ML system are. In this paper, we implement and analyze several uncertainty quantification approaches for an ML-based model to forecast Vertical Total Electron Content (VTEC) 1-day ahead and corresponding uncertainties with 95% confidence intervals (CI): (a) Super-Ensemble of ML-based VTEC models (SE), (b) Gradient Tree Boosting with quantile loss function (Quantile Gradient Boosting, QGB), (c) Bayesian neural network (BNN), and (d) BNN including data uncertainty (BNN + D). Techniques that consider only model parameter uncertainties (a and c) predict narrow CI and over-optimistic results, whereas accounting for both model parameter and data uncertainties with the BNN + D approach leads to a wider CI and the most realistic uncertainties quantification of VTEC forecast. However, the BNN + D approach suffers from a high computational burden, while the QGB approach is the most computationally efficient solution with slightly less realistic uncertainties. The QGB CI are determined to a large extent from space weather indices, as revealed by the feature analysis. They exhibit variations related to daytime/nightime, solar irradiance, geomagnetic activity, and post-sunset low-latitude ionosphere enhancement.
  • A Proper Use of the Adjacent Land-Based Observatory Magnetic Field Data to Account for the Geomagnetic Disturbances During Offshore Directional Drilling
    Item type: Journal Article
    Kruglyakov, Mikhail; Kuvshinov, Alexey; Nair, Manoj (2022)
    Space Weather
    Directional drilling in the oil fields relies particularly on the “on-fly” measurements of the natural magnetic field (measurements while drilling; MWD); the MWD are eventually used to construct the well path. These measurements are the superposition of the signals from the internal, core and crustal, and external, ionospheric and magnetospheric sources and the noise from magnetic elements in the borehole assembly. The internal signals are mostly constant in time and accounted for through the Earth's internal field models. The signals of external origin give rise to diurnal and irregular spatio-temporal magnetic field variations observable in the MWD. One of the common ways to mitigate the effects of these variations in the MWD is to correct readings for the data from an adjacent land-based magnetic observatory/site. This method assumes that the land-based signals are similar to those at the seabed drilling site. In this paper, we show that the sea level and seabed horizontal magnetic fields differ significantly, reaching up to 30% of sea level values in many oceanic regions. We made this inference from the global forward modeling of the magnetic field using realistic models of conducting Earth and time-varying sources. To perform such modeling, we elaborated a numerical approach to efficiently calculate the spatio-temporal evolution of the magnetic field. Finally, we propose and validate a formalism allowing researchers to obtain trustworthy seabed signals using measurements at the adjacent land-based site and exploiting the modeling results, thus without needing additional measurements at the seabed site.
  • Exploring the Influence of Lateral Conductivity Contrasts on the Storm Time Behavior of the Ground Electric Field in the Eastern United States
    Item type: Journal Article
    Marshalko, Elena; Kruglyakov, Mikhail; Kuvshinov, Alexey; et al. (2020)
    Space Weather
    The intensification of the fluctuating geomagnetic field during space weather events leads to generation of a strong electric field in the conducting earth, which drives geomagnetically induced currents (GICs) in grounded technological systems. GICs can severely affect the functioning of such infrastructure. The ability to realistically model the ground electric field (GEF) is important for understanding the space weather impact on technological systems. We present the results of three‐dimensional (3‐D) modeling of the GEF for the eastern United States during a geomagnetic storm of March 2015. The external source responsible for the storm is constructed using a 3‐D magnetohydrodynamic (MHD) simulation of near‐Earth space. We explore effects from conductivity contrasts for various conductivity models of the region, including a 3‐D model obtained from inversion of EarthScope magnetotelluric data. As expected, the GEF in the region is subject to a strong coastal effect. Remarkably, effects from landmass conductivity inhomogeneities are comparable to the coastal effect. These inhomogeneities significantly affect the integrated GEF. This result is of special importance since the computation of GICs relies on integrals of the GEF (voltages), but not on the GEF itself. Finally, we compare the GEF induced by a laterally varying (MHD) source with that calculated using the plane wave approximation and show that the difference is perceptible even in the regions that are commonly considered to be negligibly affected by lateral nonuniformity of the source. Overall, the difference increases toward the north of the model where effects from laterally variable high‐latitude external currents become substantial.
Publications 1 - 4 of 4