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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
Decent Estimate of CME Arrival time from a Data-assimilated Ensemble in the Alfvén Wave Solar atmosphere Model

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

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

Decent Estimate of CME Arrival time from a Data-assimilated Ensemble in the Alfvén Wave Solar atmosphere Model

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

Decent Estimate of CME Arrival time from a Data-assimilated Ensemble in the Alfvén Wave Solar atmosphere Model

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