Non-uniform downscaling data assimilation algorithm in variational framework

Abstract

To improve the performance of the multiscale assimilation algorithm, we propose a non-uniform downscaling (NUD) data assimilation algorithm in a variational framework based on the relationship between the space structure of the scale decomposition and the space distribution characteristics. The algorithm differs from the traditional uniform downscaling (UD) algorithm in that it enables the distribution of space grid points to be sparse in large scale regions and dense in small scale regions. The non-uniform scale decomposition can better control the propagation range of the observation information. Experiments show that the NUD can reduce the background error by about 5 % relative to the UD. The spatial distribution characteristics of the analysis field obtained by the NUD are also more similar to the true field. In addition, the forecast results show that the non-uniform scale decomposition assimilation algorithm with model integration can produce a stable positive impact and effectively improve the forecast capability for small and medium scale phenomena.