Index Catalog // Deep Blue Data

Data and Analysis for Global Probabilistic Geomagnetic Perturbation Forecasting Using the Data-Driven Model GeoDGP

Creator:: Chen, Hongfan, Chen, Yang, Huang, Zhenguang, Zou, Shasha, Huan, Xun, and Toth, Gabor
Description:: 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.
Keyword:: Space Weather, Uncertainty Quantification, Machine Learning, and Bayesian Inference
Citation to related publication:: Chen, H., et al. (2024). GeoDGP: One-Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting using Deep Gaussian Process.
Discipline:: Science and Engineering

Creator:: Chen, Hongfan, Sachdeva, Nishtha, Huang, Zhenguang, van der Holst, Bart, Manchester, Ward, Jivani, Aniket, Zou, Shasha, Chen, Yang, Huan, Xun, and Toth, Gabor
Description:: 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.
Keyword:: Data Assimilation, Uncertainty Quantification, and Space Weather
Citation to related publication:: https://doi.org/10.1029/2024SW004165
Discipline:: Engineering

Creator:: Jivani, Aniket, Sachdeva, Nishtha, Huang, Zhenguang, Chen, Yang, van der Holst, Bart, Manchester, Ward, Iong, Daniel, Chen, Hongfan, Zou, Shasha, Huan, Xun, and Toth, Gabor
Description:: 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).
Keyword:: Uncertainty Quantification, Space Weather, and Global Sensitivity Analysis
Discipline:: Engineering