Irregular (in time and space) network observations are often applied to reconstruct an optimized 3D field on a defined grid, e.g. for the initialization of weather forecast models. Thus, questions arise: how many observations of a certain type are reasonable, what benefit do I obtain by including additional observations or what observations are actually redundant? Sometimes even: is it worth investing in experimental observation technology?

We conduct research on optimal instrument configurations, in collaboration with the German Weather Service (DWD), within the Hans-Ertel-Centre for Weather research (HErZ) and the University of Vienna. To quantify the impact of observations we use data assimilation methods employing AI, statistical interpolation and ensemble variance reduction techniques.

To find out more contact: Tanya Nomokonova (ensemble methods), Maria Toporov (inverse methods/statistical interpolation) and Ulrich Löhnert