Accurately predicting the horizontal component of the ground magnetic field perturbation (dBH), which can be used to calculate the Geomagnetically Induced Currents (GICs), is crucial for estimating the space weather impact of geomagnetic disturbances. In this work, we develop a new data-driven model GeoDGP using deep Gaussian process (DGP), which is a Bayesian non-parametric approach. The model provides global probabilistic forecasts of dBH at 1-minute time cadence and with arbitrary spatial resolutions. We evaluate the model comprehensively on a wide range of geomagnetic storms, including the 2024 Gannon extreme storm. The results show that GeoDGP significantly outperforms the state-of-the-art physics-based first-principles Space Weather Modeling Framework (SWMF) Michigan Geospace model and the data-driven DAGGER model.
In this study, we show that coronal mass ejection (CME) simulations conducted with the Space Weather Modeling Framework (SWMF) can be assimilated with SOHO LASCO white-light (WL) coronagraph observations and solar wind observations at L1 prior to the CME eruption to improve the prediction of CME arrival time. L1 observations are used to constrain the background solar wind, while LASCO coronagraph observations filter the initial ensemble simulations by constraining the simulated CME propagation speed. We then construct probabilistic predictions for CME arrival time using the data-assimilated ensemble. Scripts in this work are written in R, Python and Julia.
The Space Weather Modeling Framework (SWMF) was run in the Michigan Geospace configuration, as well as in the MHD-AEPIC configuration, for two extreme space weather events. Output is provided in the standard SWMF format, as well as in text files for easy accessibility.
Data used in the paper "Theory of Magnetic Switchbacks Fully Supported by Parker Solar Probe Observations" by G. Toth, M. Velli and B. van der Holst, ApJ 2023.
The Observations directory contains the PSP observations as simple text files that can be easily read by the IDL macros in the BATSRUS/share/IDL/General/ or any other plotting software.
The Simulations directory contains BATSRUS simulations including input and output files. The runlog files show the Git references. The output files are in binary format that can be read by the IDL macros in the BATSRUS/share/IDL/General/ or with the SpacePy software.
The BATSRUS directory contains the source code that can be used to reproduce the simulations.
G. Toth, M. Velli, B. van der Holst, 2023, Theory of Magnetic Switchbacks Fully Supported by Parker Solar Probe Observations, The Astrophysical Journal, in press
In this work, we perform Global Sensitivity Analysis (GSA) for the background solar wind in order to quantify contributions from uncertainty of different model parameters to the variability of in-situ solar wind speed and density at 1au, both of which have a major impact on CME propagation and strength. Scripts written in the Julia language are used to build the PCE and calculate the sensitivity results. Data is available in csv, NetCDF and JLD files. A `Project.toml` file is included to activate and install all required dependencies (See README for details).
In this work, we trained gradient boosted trees using XGBoost to predict the SYM-H forecasting using different combinations of solar wind and interplanetary magnetic field (IMF) parameters. Data are in csv and Python pickle formats.
Iong, D., Y. Chen, G. Toth, S. Zou, T. I. Pulkkinen, J. Ren, E. Camporeale, and T. I. Gombosi, New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines, Space Weather,11, accepted, 2022. https://doi.org/10.1002/essoar.10508063.3
