Head(s): Prof. Dr.-Ing. Sebastian Reich (U Potsdam), Prof. Dr.-Ing. Rupert Klein (FU Berlin)
Project member(s): Ray Chew, Gottfried Hastermann, Dr. Nikolas Nüsken
Participating institution(s): FU Berlin, U Potsdam

Project Summary

Computational flow models can only resolve part of the vast range of spatio-temporal scales found in the atmosphere. Consequently, their numerical discretisations modify scale interactions through associated truncation errors, and parameterisations of the net effects of unresolved scales introduce further model errors. At the same time, insight into the current state of the atmosphere is limited by the sparsity of meteorological observations. To cope with the resulting uncertainties, data assimilation (DA) enables controlled adjustments of model-based forward simulations using incoming observational data by minimizing the model-to-data distances in suitable norms. DA algorithms require explicit use of the multi-scale nature of atmospheric flows to be applicable in the presence of limited data and poor statistical resolution.
This project aims at DA methods connecting scale analysis, computational fluid dynamics, and advanced data filtering. Methodologically speaking, we address the predictive modelling of weather systems whose root model is known but computationally inaccessible due to a cascade of partially unresolvable scales.
More specifically, we will exploit observational data and asymptotic characterisations of both the root model and the DA procedures, to (i) derive efficient and robust data assimilation techniques, to (ii) extend the DA approach from the first funding period for providing physically consistent analysis fields to meteorological applications, and to (iii) provide a mathematical and computational framework for multi-level DA applicable to model hierarchies involving moist atmospheric processes.

Publications A02