Abstract
Abstract. A new integrated mass-flux adjustment filter is introduced, which uses the analyzed integrated mass-flux divergence field to correct the analyzed wind field. The filter has been examined in twin experiments with rapid update cycling using an idealized setup for convective-scale radar data assimilation. It is found that the new filter slightly reduces the accuracy of background and analysis states; however, it preserves the main structure of cold pools and primary mesocyclone properties of supercells. More importantly, it considerably diminishes spurious mass-flux divergence and the high surface pressure tendency, and it thus results in more dynamically balanced analysis states. For the ensuing 3 h forecasts, the experiment that employs the filter becomes more skillful after 1 h. These preliminary results show that the filter is a promising tool to alleviate the imbalance problem caused by data assimilation, especially for convective-scale applications.
Highlights
The performance of convective-scale data assimilation has been considerably enhanced in the last decades, greatly due to the usage of Doppler radar observations
A similar approach is developed, but the increment of the horizontal wind field from the analyzed integrated mass-flux divergence and vorticity is calculated analytically. We name this new method “the integrated mass-flux adjustment filter” and examine it in a rapid update cycling of convective-scale data assimilation using an idealized setup of the KENDA system for the COSMO model with the data assimilation scheme of the local ensemble transform Kalman filter (LETKF; Hunt et al, 2007)
It is noticed that the patterns of integrated mass-flux divergence and the surface pressure tendency are comparable in the non-convective regions, and the former one has much stronger signals within the convective regions
Summary
The performance of convective-scale data assimilation has been considerably enhanced in the last decades, greatly due to the usage of Doppler radar observations. Hydrostatic balancing of analysis increments (Rhodin et al, 2013) is implemented in the Kilometre-scale ENsemble Data Assimilation (KENDA; Schraff et al, 2016) system for the model of COSMO (COnsortium for Small-scale MOdelling; Baldlauf et al, 2011) at the DWD to suppress the noise that is visible in the surface pressure tendency These temporal filtering and spatial balancing methods are derived for synoptic-scale processes and work successfully for synopticscale data assimilation. A similar approach is developed, but the increment of the horizontal wind field from the analyzed integrated mass-flux divergence and vorticity is calculated analytically We name this new method “the integrated mass-flux adjustment filter” and examine it in a rapid update cycling of convective-scale data assimilation using an idealized setup of the KENDA system for the COSMO model with the data assimilation scheme of the local ensemble transform Kalman filter (LETKF; Hunt et al, 2007).
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