Speaker
Description
Accurate specification and forecasting of Earth’s radiation belt electron environment remain critical for understanding radiation belt dynamics and mitigating space weather hazards to spacecraft. The Versatile Electron Radiation Belt (VERB) code has become a widely used physics-based model for simulating the evolution of relativistic electrons through radial diffusion and wave–particle interactions. In recent years, substantial advances have been made to enhance the physical realism, numerical performance, and data assimilation capability of the VERB framework.
In this work, we present an overview of recent developments in the VERB code, including improvements to diffusion coefficient parameterizations and optimization of the numerical solver for increased computational efficiency. Particular emphasis is placed on the integration of modern data assimilation techniques within VERB. We describe the implementation of ensemble- based and Kalman filter–type approaches to incorporate in situ observations from missions such as Van Allen Probes, GOES, ARASE and other relevant datasets. These upgrades enable more accurate reconstruction of phase space density and improved nowcasting and forecasting capability.
Case and long-term studies are presented demonstrating the impact of assimilation on reproducing observed radiation belt dynamics during geomagnetically active periods. Quantitative validation against independent measurements shows that the updated VERB framework signif icantly reduces model–data discrepancies and improves predictive skill relative to stand-alone simulations. Ongoing work focuses on further coupling with global magnetospheric models, machine-learning-assisted parameter estimation, and real-time applications for space weather operations.
| Numerical model | VERB |
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