Do we model what we measure?

In an ideal world, both numerical models and measurements would provide high quality data with high resolution on the spatial and temporal scale. However, this is often not feasible due to practical constraints such as limited resources and logistical challenges: model results suffer from increased uncertainties due to the numerous sub-grid processes that need to be represented by parameterizations in numerical models; and the representativeness of point measurements in field experiments is often questionable due to the large surface heterogeneity in the surrounding area. In addition, research sometimes suffers from a lack of communication between modelers and field scientists, which often leads to difficulties in validating model results against measurements and vice-versa. The discussion of such difficulties during this session can raise awareness about the unique challenges each group faces and the uncertainties associated with every dataset. By sharing insights, we aim to improve collaborations between modelers and field scientists and therefore strengthen the reliability and robustness of future research.