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:
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.
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 (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.
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.
Invited talk "Empirical modeling of the plasmasphere dynamics using neural networks", Space Weather: A Multi-Disciplinary Approach Workshop at the Lorentz Center, September 25-29, 2017, Leiden, the Netherlands.
Invited talk "Deriving electron density from electric field measurements on the Van Allen Probes spacecraft and building a global dynamic model of plasma density using neural networks", IAPSO-IAMAS-IAGA 2017, August 27 - September 1, 2017, Cape Town, South Africa.
Award for the best student poster presentation "Empirical modeling of the plasmasphere dynamics using neural networks", GEM 2017 Summer Workshop, June 18-23, 2017, Portsmouth, VA, USA.
Invited talk "Empirical modeling of the plasmasphere with neural networks", POF PT I Workshop: From Atmosphere to Space Weather, May 30, 2017, Potsdam, Germany.
Best EGU poster at GFZ 12th PhD day in April 2017, entitled ”Three-dimensional data assimilation and reanalysis of radiation belt electrons”.
Award for the best student oral presentation "Global dynamic evolution of the cold plasma inferred with neural networks", DGG 2017, March 27-30, 2017, Potsdam, Germany.
Accuracy of space weather prediction depends strongly on the quality of the models. A team led by the GFZ German Research Centre for Geosciences demonstrates how errors in the algorithms can lead to wrong predictions. The authors present a new algorithm for modelling of the electron flux in the geosynchronous orbit which is important for telecommunication and navigation satellites.
A geomagnetic storm on January 17, 2013, provided unique observations that finally resolved a long-standing scientific problem. For decades, scientists had asked how particles hitting the Earth's magnetosphere were lost. A likely mechanism involved certain electromagnetic waves scattering particles into the Earth's atmosphere. More recently, another mechanism was proposed that caused particles to be lost in interplanetary space. Yuri Shprits from the GFZ German Research Centre for Geosciences and the University of Potsdam, together with colleagues from several institutions, recently found that both mechanisms play a role affecting particles at different speeds. “This study resolves some fundamental scientific questions about our space environment and may also help understand fundamental processes that occur elsewhere in space, on the Sun, in outer planets, distant galaxies, and exoplanets,” says Yuri Shprits. He adds: “This study will also help us predict and now-cast the space environment and protect valuable satellites in space.” The study appeared in Nature Communications on Wednesday, September 28.
A geomagnetic storm on January 17, 2013, provided unique observations that finally resolved a long-standing scientific problem.
Applied Mathematician Dr. Tatiana Podladchikova was awarded the International Alexander Chizhevsky Medal at the 12th European Space Weather Week, for major results in space weather.
This structure is pretty close to the Earth, which is important because people want to understand the environment where satellites operate. Usually plasma undergoes a number of different instabilities, and waves tend to move from one region in space to another, so everything you see is noisy, very short-lived, and on smaller scales. But this structure seems to be very persistent, highly coherent in space, and was remarkably organized and structured, which we didn’t know could exist to such high degree.
RBM group scientists have successfully modeled and explained the unprecedented behavior of this third ring, showing that the extremely energetic particles that made up this ring, known as ultra-relativistic electrons, are driven by very different physics than typically observed Van Allen radiation belt particles. The region the belts occupy—ranging from about 1,000 to 50,000 kilometers above the Earth’s surface—is filled with electrons so energetic they move close to the speed of light.
UCLA researchers showed that the missing electrons are swept away from the planet by a tide of solar wind particles during periods of heightened solar activity. The data show that while a small amount of the missing energetic electrons did fall into the atmosphere, the vast majority was pushed away from the planet, stripped away from the radiation belt by the onslaught of solar wind particles during the heightened solar activity that generated the magnetic storm itself.