The Institute for Solar-Terrestrial Physics focusses on the observation, investigation and modelling of the near-Earth space weather. Space weather has a decisive influence on the propagation of radio waves, which is used for terrestrial and satellite-based communication as well as for satellite-based navigation. In view of society's growing dependence on the accuracy, availability and reliability of satellite-based radio systems, preventive measures are needed to protect human life and technical systems from the effects of space weather. The observation and modeling of the state of the ionosphere, which are crucial components of these measures, have been carried out at DLR's Neustrelitz site for many years. Predicting the state of the ionosphere is a major challenge because it is based on complex interactions between the atmosphere, ionosphere and magnetosphere, which need to be scientifically investigated and described. Especially in the altitude range between 80 and 120 km complex electro-dynamic processes take place, which have an influence on the state of the entire ionosphere- magnetosphere system.

Your mission is to enhance future space weather operations by developing a research ensemble of upper atmosphere models to improve the forecast accuracy of thermosphere-ionosphere key parameters and provide the associated forecast uncertainty estimates. The study involves the use of different thermosphere-ionosphere physics-based models and associated data science techniques like machine learning or data assimilation, as well as the use of satellite and ground-based observations.

Your work performance will be 19.25 hours per week. The remuneration is 75% of German TVöD group 13.

Your tasks will be:

• Validation of forecast from numerical thermosphere-ionosphere models
• Analysis of the capabilities of ensemble modeling to estimate model/forecast uncertainties
• Development of methods for determination of uncertainties of thermosphere ionosphere modeling
• Planning and execution of a long-term modelling campaign