  The coupling of models for the different components of the
Soil-Vegetation-Atmosphere-System is required to investigate component
interactions and feedback processes. However, the component models for
atmosphere, land-surface and subsurface are usually operated at different
resolutions in space and time owing to the dominant processes. The
computationally often more expensive atmospheric models, for instance, are
typically employed at a coarser resolution than land-surface and subsurface
models. Thus up- and downscaling procedures are required at the interface
between the atmospheric model and the land-surface/subsurface models. We apply
multi-objective Genetic Programming (GP) to a training data set of
high-resolution atmospheric model runs to learn equations or short programs
that reconstruct the fine-scale fields (e.g., 400 m resolution) of the
near-surface atmospheric state variables from the coarse atmospheric model
output (e.g., 2.8 km resolution). Like artificial neural networks, GP can
flexibly incorporate multivariate and nonlinear relations, but offers the
advantage that the solutions are human readable and thus can be checked for
physical consistency. Using the Strength Pareto Approach for multi-objective
fitness assignment allows us to consider multiple characteristics of the
fine-scale fields during the learning procedure.
