%0 Journal article
%A Zeng, Yuefei
%A Janjic, Tijana
%A Sommer, Matthias
%A de Lozar, Alberto
%A Blahak, Ulrich
%A Seifert, Axel
%T Representation of Model Error in Convective-Scale Data Assimilation: Additive Noise Based on Model Truncation Error
%R 10.1029/2018MS001546
%R 10.23689/fidgeo-4964
%J Journal of Advances in Modeling Earth Systems
%V 11
%N 3

%X To account for model error on multiple scales in convective-scale data assimilation, we incorporate the small-scale additive noise based on random samples of model truncation error and combine it with the large-scale additive noise based on random samples from global climatological atmospheric background error covariance. A series of experiments have been executed in the framework of the operational Kilometre-scale ENsemble Data Assimilation system of the Deutscher Wetterdienst for a 2-week period with different types of synoptic forcing of convection (i.e., strong or weak forcing). It is shown that the combination of large- and small-scale additive noise is better than the application of large-scale noise only. The specific increase in the background ensemble spread during data assimilation enhances the quality of short-term 6-hr precipitation forecasts. The improvement is especially significant during the weak forcing period, since the small-scale additive noise increases the small-scale variability which may favor occurrence of convection. It is also shown that additional perturbation of vertical velocity can further advance the performance of combination.
%U http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9310
%~  FID GEO-LEO e-docs