Representation of multiscale model error in convective - scale data assimilation
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 KENDA system of the DWD for a two-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 increase in the background ensemble spread during data assimilation enhances the quality of short-term
6-h 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. Furthermore, small-scale additive noise is compared with the other techniques such as warm bubbles and stochasitic boundary layer perturbations and results will be also presented.
Dr. Yuefei Zeng DWD / LMU