Parameterizations and parameter estimation

Parameterization schemes are a major source of uncertainty and systematic errors in numerical weather predictions (NWP). They are used in NWP models to account for the impact of sub-grid-scale processes, that is, meteorological phenomena that cannot be explicitly represented on a model grid because their characteristic length scales are too small. Parameterized processes include, among others, turbulent exchange in the planetary boundary layer and the formation and growth of precipitating particles (water drops and ice crystals) in clouds.

Parameter estimation with ensemble methods
Parameterization schemes typically combine simplified theoretical arguments with semi-empirical relationships, which are determined on the basis of laboratory experiments, field measurements or idealized high-resolution numerical simulations. Research in our group aims at providing better constraints for the uncertain parameters of parameterization schemes, using ensemble-based data assimilation methods. In the state-and-parameter estimation approach, uncertain model parameters are treated in the same way as model state variables, and are therefore determined in a statistically optimal sense by minimizing analysis errors. By reducing random forecast errors and model biases, improved parameterization schemes help increasing the weight of observational information in the data assimilation process.

Representation of the planetary boundary layer over complex orography
Ongoing research deals with boundary-layer models and focuses on the optimal estimation of mixing lengths, entrainment ratios and non-local transport coefficients. Of particular interest is the representation of turbulent flow fields that evolve over complex terrain, with large spatial variability in orography and land-use as found in major mountain ranges (e.g., the Alps).