Adjoint models are widely applied in numerical weather prediction.For instance,in four-dimensional variational data assimilation (4DVar),they are the best method to efficiently determine optimal initial con-ditions.The minimization of the 4DVar cost function is solved with an iterative algorithm and is computa-tionally demanding.Though the minimization is usually performed with a much lower resolution than in forecast model,obtaining the optimal model state requires dozens of iterations,and the model parallel effi-ciency must be fast enough.However,the parallel efficiency of GRAPES global tangent linear model and adjoint model version 1.0 based on GRAPES global non-linear model 1.0 is so low that it seriously impacts the development of GRAPES 4DVar.In order to reduce the computational cost,a new tangent linear mod-el and adjoint model version 2.0 are re-designed and re-developed based on GRAPES global model version 2.0.By optimizing the program structure of tangent linear model,the calculating time of GRAPES tangent linear model can be best controlled within 1.2 times of GRAPES non-linear model’s consumption with only dynamic framework.And by methods transferring the model base state and trajectory to the adjoint model, the calculating time of GRAPES adjoint model can be best controlled within 1.5 times of GRAPES non-lin-ear model’s consumption.Therefore,the new GRAPES tangent linear model and adjoint model version 2.0 are very successful in terms of computational efficiency to speed up the development of GRAPES 4DVar. In practical applications,the tangent linear model and adjoint model is run at a lower resolution than the non-linear model,since the dynamics is already simplified through the reduction in horizontal resolu-tion,the linearized physics doesn’t necessarily need to be exactly tangent to the full physics.In principle, physical parameterizations can already behave differently between non-linear and tangent-linear models due to the change in resolution.In order to reduce computational cost,it is often necessary to select different set of simplified linearized parameterizations with the full physical processes of GRAPES forecast model. By decoupling base states calculation in GRAPES and the perturbation calculation in the tangent linear and adjoint model,the computational cost of GRAPES tangent and adjoint model with simplified physical pa-rameterizations increases only a little than no physical parameterizations versions,and the computational efficiency is higher than GRAPES forecast model with full physical parameterizations.%伴随技术是四维变分同化（4DVar）系统中计算代价函数梯度的最佳办法，切线性和伴随模式的效果和效率直接影响着4DVar 系统的发展。基于 GRAPES（Global and Regional Assimilation PrEdiction System）全球切线性和伴随模式1．0版本，利用 GRAPES 全球模式2．0版本在并行框架和性能等方面的改善，重新优化和设计了GRAPES 全球切线性伴随模式2．0版本，提高了 GRAPES 全球切线性和伴随模式的效果和效率，优化了切线性模式程序结构，使其计算时间最优可控制在非线性模式的1．2倍以内；采用在切线性模式中保存基态的方法，重构了伴随模式的程序结构，使其计算时间最优控制在非线性模式的1．5倍以内；在 GRAPES 全球切线性物理过程的设计中，将线性物理过程的轨迹基态计算和切线性扰动计算解耦，提高了 GRAPES 全球切线性和伴随模式的计算效果和效率。
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