Representation of Model Error in Convective-Scale Data Assimilation: Additive Noise Based on Model Truncation Error

Janjic, Tijana

Sommer, Matthias
de Lozar, Alberto
Blahak, Ulrich
Seifert, Axel

DOI: https://doi.org/10.1029/2018MS001546
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9310
Abstract
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.
Subjects
additive noisemodel truncation error
multiscale
radar data assimilation
probabilistic forecasts