Function and Responsibilities:

Since 2023:    TERENO-NE (Coordinator)

Career:

2020 - 2023:    Science Coordinator, Section 2.7 "Space Physics and Space Weather", GFZ Potsdam

            Project coordinator of the EU Horizon2020-project PAGER (Prediction of Adverse effects of Geomagnetic storms and Energetic Radiation)

            Project coordinator of the Helmholtz-pilot project MAP (MAchine learning based Plasma density model)

2019 - 2020:   Scientific Coordinator Assistant, Dept. Molecular Ecology, Max Planck Institute for Chemical Ecology, Jena

2016 - 2017:   Field Coordinator, Charité Universitätsmedizin, Berlin - at Smithsonian Tropical Research Institute, Panama

                       Project: Ecology and species barriers in emerging viral diseases

Projects:

Project manager of the EU Horizon2020-project PAGER (Prediction of Adverse effects of Geomagnetic storms and Energetic Radiation)

Stakeholders – such as satellite operators and manufacturers - require space weather predictions to have long lead times and confidence levels and that they should be tailored to particular engineering systems.

To utilize available measurements and to address the space weather needs, we will combine state-of-the-art models covering all the way from the Solar surface to the Earth’s inner magnetosphere. We will also run ensembles of physics-based and machine-learning models to make predictions of the space weather conditions 1-2 days in advance. This innovative approach will allow us to not only make predictions, but also to provide the relevant confidence levels. On top of that, predictive models will be blended with data by means of data assimilation.

The team includes the leading academic experts in space weather, while the advisory board consists of the heads of the space weather prediction centers of ESA, NASA and NOAA.

www.spacepager.eu

Satellite technology and in particular GPS- or GNSS-based systems are becoming vital for our society. Plasma density structures in the near-Earth space can significantly influence the propagation of GPS signals and hence influence the accuracy of GPS navigation. Moreover, space plasmas can also damage satellites. To carefully evaluate these effects of the space environment, it is important to develop an accurate model of the plasma density based on a variety of direct and indirect measurements.
In this initial project we will demonstrate how machine learning tools can be used to produce a real-time global empirical model of the near-Earth plasma density based on a variety of measurements. The model that will be developed as a follow-up to this current project will then utilize all available data and will be used by a broad range of stakeholders for GPS navigation and satellite operations.