During data assimilation, observations are optimally combined with a (numerical) model to obtain the best estimate of the system's state taking all observations into account. As observations are often noisy, incomplete and inconsistent, substantial observation pre-processing is required. In this session, we provide insight into various data pre-processing and visualization methods and introduce the concept of data assimilation using the example of numerical weather prediction. The practical exercises based on Jupyter notebooks will illustrate different methods for data processing and data assimilation.

The workshop will run from September 11 - 12 on-site at KIT Campus South in Karlsruhe and is open to (doctoral) researchers from KIT as well as other universities and research centers.

Find more information and apply to join the workshop