1 Product

The webpage products includes:

The ground magnetic perturbation maps assess regional space weather impacts, and the Dst index measures the intensity of a geomagnetic storm. These products are useful for managing the risk of natural hazards caused by space weather events, such as geomagnetically induced currents (GICs).

2 Model Overview

GeoDGP is a data-driven model that provides global probabilistic forecasts of geomagnetic perturbations at a 1-minute temporal resolution and an arbitrary spatial resolution. GeoDGP is trained on 30 years (1995-2022) of NASA/GSFC’s OMNI dataset and SuperMAG ground magnetometer measurements. The model framework consists of a 4-layer deep Gaussian process (DGP). Please see the reference paper for details. The same framework is used to train a separate model for predicting the Dst index based on the Kyoto Dst dataset.

3 Data

3.1 Input

  • Solar wind observations from the National Oceanic and Atmospheric Administration (NOAA) Space Weather Prediction center (SWPC) Data Service.
  • The location input (for geomagnetic perturbations only).
Variable Name Description
\(B_x, B_y, B_z\) Interplanetary Magnetic Field (IMF)
\(V_x\) Solar wind velocity
\(N_p\) Proton number density
\(T\) Plasma temperature
Dst Disturbance storm time index
\(\theta\) Dipole tilt angle
\(\lambda\) Geomagnetic latitude
\(\phi\) Geomagnetic longitude in Solar Magnetic (SM) co-ordinates

3.2 Output

  • The north, east, and horizontal components of geomagnetic perturbations (\(dBH\), \(dBN\), and \(dBE\)) with a lead time corresponding to the solar wind propagation time from the first Lagrangian point (L1).
  • The Dst index for the next hour.

4 Reference

Hongfan Chen, Gabor Toth, Yang Chen, et al. GeoDGP: One-Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting using Deep Gaussian Process. ESS Open Archive. December 23, 2024. DOI: 10.22541/essoar.173499121.15272711/v1

5 Contacts

Please contact Hongfan Chen () for any questions.

6 Acknowledgement

This work is supported by the National Science Foundation (NSF) under Grant No. 2027555. NextGen Space Weather Modeling Framework Using Data, Physics and Uncertainty Quantification. We acknowledge use of NASA/GSFC’s Space Physics Data Facility’s OMNIWeb and CDAWeb service, and OMNI data. We gratefully acknowledge the SuperMAG collaborators. The Dst data are provided by the WDC for Geomagnetism, Kyoto.