Weather and climate modeling is an interdisciplinary endeavour involving not only atmospheric science, but also applied mathematics and computer science. Atmospheric models cover a wide range of spatial and temporal scales that require robust multi-scale numerical schemes.
In Climate & Space, we investigate modern numerical techniques that are suitable for weather and climate models, remote sensing and statistical applications. We develop and improve numerical schemes for partial differential equations on the sphere (the so-called dynamical cores) and collaborate closely with US modeling centers like the National Center for Atmospheric Research (NCAR), NASA or NOAA laboratories like the Geophysical Fluid Dynamics Laboratory (GFDL). Examples of some particular research areas are Adaptive Mesh Refinement (AMR grids), Cubed-Sphere grids and dynamical core intercomparisons. In addition, our research addresses high-performance and parallel computing aspects, as well as software and data management for atmospheric models.