Background

Geomagnetic activity indices such as Kp and the higher-resolution Hpo (Hp60 and Hp30) are essential for forecasting, as solar-driven geomagnetic disturbances can significantly impact technological systems and human activities on Earth and in near-Earth space. Our new forecast model (Kervalishvili et al., 2025) improves prediction accuracy by incorporating data from individual Kp-observatories, capturing local physical effects often overlooked by traditional models based on a single global index. By first predicting local indices and then combining them using standard procedures to generate global indices like Kp and Hpo, the model bridges the gap between localized measurements and global forecasts, preserving site-specific physical influences. This localized approach enhances the accuracy of the Kp, Hp60, and Hp30 indices, effectively capturing key trends and overall geomagnetic behaviour even when solar wind data is sparse or incomplete.

Note that unlike the Kp index (Matzka et al., 2021), which is limited to a maximum value of 9 and has a three-hour resolution, the Hpo index (Yamazaki et al., 2022) is open-ended and available at higher temporal resolutions, one hour (Hp60) and half an hour (Hp30), providing a more detailed and scalable representation of geomagnetic storm intensity.

Forecast Model

We have developed a machine learning-based forecast model that leverages solar wind parameters from the high-resolution OMNI dataset and the Sunspot number from the low-resolution OMNI dataset. Minute-by-minute data is used for parameters such as IMF Bt, proton density, and solar wind speed, while hourly data is utilized for the Sunspot number. This model is built on the simplified framework of Kervalishvili et al. (2025), focusing on IMF Bt and a constant Sunspot number (SSN) to generate forecasts.

Movie Description

This dynamic visualization animates the 3-day forecast of geomagnetic activity from the 13 observatories that contribute to the global Kp (Hpo) index. Each frame provides a clear and compelling snapshot of the predicted Kp (Hp60 and Hp30) and Ks (Hs60 and Hs30) values worldwide. At each observatory, the inner circle displays the forecasted Kp (Hpo) index using a colour scale based on the NOAA Space Weather Scales – ranging from quiet conditions to extreme geomagnetic storms. The encircling outer ring reveals the difference between Kp (Hp60 and Hp30) and Ks (Hs60 and Hs30) values:

• Blue indicates Ks (Hso) is lower than Kp (Hpo),
• White shows the values are equal, and
• Red highlights where Ks (Hso) exceeds Kp (Hpo).

This visualization offers an intuitive and immediate grasp of both regional and global geomagnetic variations, providing key insights for researchers, space weather forecasters, and enthusiasts alike.

The data/data products are provided “as-is” without warranty of any kind either expressed or implied, including but not limited to the implied warranties of merchantability, correctness and fitness for a particular purpose. The entire risk as to the quality and performance of the Data/data products is with the Licensee. In no event will GFZ be liable for any damages direct, indirect, incidental, or consequential, including damages for any lost profits, lost savings, or other incidental or consequential damages arising out of the use or inability to use the data/data products.