1,2,1,2,1- 1GFZ Helmholtz Centre for Geosciences, Sektion 1.5, Potsdam, Germany (bhaas@gfz-potsdam.de)
- 2Institute for Physics and Astronomy, University of Potsdam, Potsdam, Germany
- 3Department of Earth Planetary and Space Sciences, University of California, Los Angeles, Los Angeles, USA
Data assimilation has been applied to study the radiation belts for many years, and more recently, to the ring current and the plasmasphere. While data assimilation shows significant potential as an efficient tool for nowcasting and post-event analysis, validating data-assimilative simulations of particles in the inner magnetosphere remains a significant challenge due to the scarcity of scientific data.
In this work, we utilize data from both Arase and Van Allen Probes to validate data-assimilative simulations of the plasmasphere, ring current, and radiation belts. All simulations are performed by using a variant of the VERB (Versatile near-Earth environment of Radiation Belts and ring current) code tailored to each particle population, combined with an extended Kalman Filter. By assimilating measurements from the Van Allen Probes and comparing the results with independent Arase measurements, we aim to evaluate the performance of our data assimilation model.
Before assimilating the data, both data sets are processed and harmonized using the newly open-sourced processing framework: ELaborative Particle Analysis from Satellite Observations (EL PASO, available on GitHub). EL PASO allows the user to download, process, and harmonize space physics data, producing the output in a standardized format, to support practical multi-mission studies. In addition, metadata saved alongside the data ensures that the output follows the FAIR principles.
In this study, we show that data assimilation helps to reproduce the dynamics of all three particle populations: plasmasphere, ring current, and radiation belts. Even when the measurements are assimilated only in a limited magnetic local time sector, the accuracy of the predictions is improved in a global manner. Therefore, data assimilation proves to be an invaluable tool for nowcasting and post-event analysis, especially in cases when measurements are sparse.
How to cite: Haas, B., Shprits, Y., Garcia Penaranda, M., Drozdov, A., Wang, D., Lyu, X., and Jhawar, S.: Validation of Data-assimilative Plasmasphere, Ring Current, and Radiation Belts Simulations Powered by the Open-Source Data Processing Framework EL-PASO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6291, https://doi.org/10.5194/egusphere-egu26-6291, 2026.
