基于守恒重映射的全球非结构网格模式同化及预报初步应用

Preliminary application of assimilation and prediction of global unstructured grid model based on conservative remapping

全球大气模式在发展过程中不断获得改进,并逐渐采用非结构计算网格,如以球面重心Voronoi网格为特点的MPAS-A模式。为改进MPAS-A模式初值,相关的资料同化研究同步在积极开展。本文为实现利用变分方法快速同化多源观测资料的需求,以美国NCEP业务上使用的GSI系统作为同化模块,基于守恒重映射方法进行非结构与结构化球面网格转换,构建了GSI-MPAS同化及预报框架,并进行了网格转换测试和同化预报试验。网格转换检验测试表明,模式物理量的转换误差与其分布特征密切相关,二阶精度守恒重映射转换结果优于一阶精度转换结果。连续一周的滚动循环同化及预报试验表明,基于守恒重映射方法的GSI-MPAS同化及预报框架能够有效同化多源观测资料,改善了初值场的质量并使得MPAS-A预报得到的各个变量更加准确,且对降水预报具有正面效果。进一步分析表明,由于在北半球同化了更多观测资料,所以北半球地区的改进明显优于南半球及赤道地区。

Global atmospheric models have been continuously improved in the development process,and have gradually adopted unstructured computing grids,such as the atmospheric component of Model for Prediction Across Scales(MPAS-A)characterized by the spherical centroid Voronoi mesh.In order to improve the initial value of MPAS-A model,relevant data assimilation research is actively carried out simultaneously.In order to meet the needs of rapid assimilation of multi-source observation data by using variational method,this paper takes the Gridpoint Statistical Interpolation(GSI)system used in NCEP business in the United States as the assimilation module,handles transformations between unstructured and structured spherical grids based on conservation remapping method,constructs the GSI-MPAS assimilation and prediction framework,and carries out grid transformation experiments and assimilation prediction experiments.The grid transformation tests show that the transformation errors of model physical quantities are closely related to their distribution patterns,and the second-order precision conservative remapping transformation results are better than the first-order precision transformation results.The continuous rolling cycle assimilation and prediction experiment for one week shows that the GSI-MPAS assimilation and prediction framework based on the conservation remapping method can effectively assimilate multi-source observation data,improve the quality of initial value field,make each variable obtained by MPAS-A prediction more accurate,and have a positive effect on precipitation prediction.Further analysis shows that the improvements in the Northern Hemisphere are obviously better than those in the Southern Hemisphere and the equatorial region due to the assimilation of more observation data in the Northern Hemisphere.