Main Research Interest
- Atmospheric downscaling
- Intermediate complexity downscaling
- Numerical modelling
- Model evaluation and evaluation strategies
I’m a trained physicist that works in Atmospheric and Cryospheric Sciences, more specifically with downscaling techniques at the intersection between dynamic and statistical downscaling.
- In December 2015 I started my PhD in Atmospheric and Cryospheric sciences. I currently work with ICAR, an atmospheric model based on linear theory, to downscale atmospheric fields for data sparse regions
- Starting in 2013, I worked for two years at the Fraunhofer Ernst Mach Institute in Freiburg (Germany) investigating the interaction of high power CW lasers with iron and steel plates.
- Between 2012 and 2013 I finished my masters degree in physics at the University of Vienna. My thesis focused on Quantum Nanophysics, studying LIAD as a potential molecular beam source for matter wave interferometry.
- From 2008 to 2010 I was a project staff member at the University of Veterinary Medicine in Vienna. We developed a model calculating the cabin air temperature in vehicles based on only three meteorological input parameters (global radiation, two meter air temperature and wind speed) available at standard weather stations.
Additionally, we investigated drug storage and transport in passive, insulated boxes and presented some best practices on how to most efficiently do just that. The paper also describes a simple do-it-yourself experiment to determine the relevant thermal property of a box.
A more detailed CV may be downloaded here.
In my spare time I write a popular science blog called timaios where I explain things that pique my curiosity (german).
You may also find me on
DoG – Atmospheric Downscaling for Glacierized Mountain Environments
Understanding and quantifying the response of mountain glaciers to changing climate and weather conditions is a pressing task for climate scientists and glaciologists all over the world. To date, models that relate glacier mass and dynamics to atmospheric variations are well established. It has remained as a major challenge, however, to accurately describe the past, present and future atmospheric states that affect the glaciers. DoG systematically investigates the following research hypothesis: “The uncertainty in global and regional glacier simulations can be reduced significantly by reducing the uncertainty in the atmospheric drivers of the glacier simulations.”