Overview
The near-Earth space environment is hazardous and poses a significant risk for satellites and humans in space. Currently, there are hundreds of operational commercial satellites with a revenue stream of tens of billions of dollars per year. There are also a number of other satellites that assist in navigation, weather prediction, and telecommunication. Frequent satellite failures caused by space weather have fueled a surge in interest with specification and prediction of space weather during the last decade.
Our section is working on understanding of the dynamical evolution of the hazardous space radiation environment and developing the tools for specification and prediction of the adverse effects of space environment using models and data assimilation. We study fundamental processes in the near-Earth environment and focus on understanding fundamental processes responsible for the evolution of space radiation. Our research will help safely design and operate satellites and maintain ground networks. In our research we try to bridge our theoretical studies with high performance computing to develop tools that can be used by engineers.
Below you can find more details on the main areas of research in our section:
Nachfolgend finden Sie weitere Details zu den Hauptforschungsgebieten unserer Abteilung:
Radiation Belt Modelling
Earth’s radiation belts consist of highly energetic protons and electrons trapped by Earth’s magnetic field in the region of 1.2~8 Re (Earth radii) away from Earth’s center, which can be hazardous for satellite equipment. Our group uses modelling approaches to better understand the dynamic evolution of the outer radiation belts. Specifically, we have developed physics-based 3D and 4D Versatile Electron Radiation Belt (VERB) codes to help us understand important mechanisms controlling the dynamic evolution of radiation belts, such as radial diffusion, local acceleration, local loss, magnetopause shadowing and electric convection.
Data Assimilation
Analysis of radiation belt observations present a major challenge, as satellite observations are often incomplete, inaccurate and have only limited spatial coverage. Nevertheless, through data assimilation observations can be blended with information from physics-based models, in order to fill gaps and lead to a better understanding of the underlying dynamical processes. We have developed a scheme that enables efficient data assimilation from multiple satellite missions into the state-of-the-art partial differential equation-based model of the inner magnetosphere Versatile Electron Radiation Belt (VERB-3D).
Machine Learning
Machine learning (ML) methods and algorithms can be applied to space weather related problems in order to develop new data-driven models of different physical phenomena in space and to enhance existing physics-based models. In our group, we use ML algorithms to develop predictive models of electron density in the plasmasphere and Kp index, and use these models to enhance our radiation belt forecasts.
Ring Current Modelling
The ring current is an electric current encircling the Earth at the distances between ~3 and ~5 Earth’s radii from the center of the Earth in the equatorial plane. It is a crucial component in our understanding of the magnetosphere dynamics and geomagnetic storms, and it can also affect human infrastructures such as high-latitude power grids or currently operating communication or navigation satellites. In our group, we use the four-dimensional Versatile Electron Radiation Belt (VERB-4D) code to model the dynamics of the ring current.
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