MPAS动力框架和伴随模式简介以及未来扩展和应用

Introduction to MPAS Dynamic Core,Adjoint Model and Their Future Extension and Applications

跨尺度大气预报模式(MPAS-A)的动力学框架是非结构化的球面质心Voronoi网格,它代表了过去几十年来数值天气预报最重要进展之一。MPAS-A具有开放性、计算机程序和文件规范性、科学性及先进性等特点,因此可选其作为未来全球四维变分资料同化系统模式,目前已经发展了MPAS-A的切线线性模式和伴随模式。选择MPAS-A为全球四维变分资料同化系统模式,不仅可以避免MPAS-A预报模式网格与资料同化网格之间的来回插值所产生的误差,减少每次极小化迭代时MPAS-A模式变量与资料同化分析变量之间的转换所需计算时间,同时也为非结构化球面质心网格的跨尺度全球资料同化研究提供新机遇。

The dynamic framework of the Model for Prediction Across Scales-Atmosphere (MPAS-A) has unstructured Spherical Centroidal Voronoi Tesselation (SCVT) meshes with C-staggering,it represents one of the most important advances in numerical weather prediction (NWP) over the past few decades.Because of the openness,normative computer program and documents,scientific nature and progressiveness,the MPAS-A is chosen for developing an advanced global four-dimensional variational (4D-Var) data assimilation (DA) system.As the first step,the tangent linear model and adjoint model of MPAS-A are developed.The advanced global MPAS-A 4D-Var DA system not only avoids the errors arising from back-and-forth interpolations between MPAS-A forecasting analysis grids and non MPAS-A DA grids as well as reduces computational cost arising from conversions between MPAS-A model variables and non MPAS-A DA analysis variables at each iteration of minimization,but also provides new opportunities for across-scales global DA with unstructured spherical centroidal meshes for weather and climate studies.