A study on variational quality control of data assimilation over the Qingzang plateau: Parameters optimization and effective assimilation

Changes in regional weather systems in the Qingzang plateau (hereafter referred to as the plateau) have important impacts on numerical weather forecast in its surrounding and downstream regions. However, the low quality and scarcity of observations in the plateau seriously restrict the assimilation analysis and prediction quality. The Variational Quality Control scheme (VarQC) has the potential to make full use of scarce observations to improve the quality of assimilation analysis. In view of this, based on the analysis of the non-Gaussian distribution characteristics of the observation error in the plateau region, this paper constructs a VarQC of "Gaussian distribution plus flat distribution" (Flat-VarQC), which is suitable for the characteristics of the observation error in the plateau region, and optimizes those key parameters, so that it can more accurately adjust the influence weight of the observations on the assimilation analysis based on the innovation vector and thus improve the effective assimilation rate of the scarce observations in the plateau region. The quality of assimilation analysis in plateau region is improved. The results show that the observation error in the plateau region has a more obvious characteristic of "fat-tail distribution", and the simple assumption of Gaussian distribution in the assimilation process will lead to low effective assimilation rate of observations, which reduces the contribution of limited observation data in the plateau region to the assimilation analysis. The key parameter optimization of variational quality control suitable for the characteristics of plateau regional data is crucial for the full play of the Flat-VarQC in plateau region to improve the quality of assimilation analysis. Flat-VarQC can significantly improve the effective assimilation rate of conventional data in the plateau region, absorb more useful data information and eliminate harmful data information, and thus can improve positive contribution of observation data to assimilation analysis. This has a more significant improvement effect on the assimilation analysis of micro- and meso-scale weather systems and heavy rainfall forecast in the plateau region.