I’m interested in utilizing methods from satellite geodesy for Earth system research. Our group in Bonn works on understanding global and regional sea level changes, on mapping 4D thermospheric neutral density and its relation to various drivers, and on assessing long-term and rapid changes in the terrestrial water cycle. In these fields, we develop new retrieval methods and generate data products, confront them with model simulations, and generally seek to generate new inference by combining complementary data sets. The recurring theme is that in these applications, geodetic space techniques provide new and unexplored information which is, however, literally buried in a sea of superimposed signals.
That’s why I’m interested in turning to machine learning methods. In our use case project, we will address precursors of drought, one of the most direct consequences of climate change. We seek to combine satellite-gravimetric data (from the GRACE and GRACE-FO satellites), physical knowledge (from simulation models that represent the terrestrial water cycle), and ML approaches in order to arrive at improved seasonal forecasts of water availability and droughts at the European scale.