摘要
从理论上论证了借助于同伦方法构造的适定空间边界条件确保有限区域上伴随模式产生的超定边界条件问题得到解决,同时又能维持伴随模式中边界处理的优化特征。从某种意义上讲,伴随模式超定空间边界条件的存在是不可避免的,这是因为数据同化过程必须引进和采用给定的观测资料,而它们在模式空间边界上的定义往往是超定的。我们提出的空间边界条件的算法构架事实上是在数据同化过程中综合运用了张弛滤波、考虑外部强迫的辐射边界条件以及与观测相容的狄里希利边界条件。显然,对于该理论构架所涉及到的具体数值处理方法在中尺度模式中都十分成熟易行。
Theoretical argumentation for so-called suitable spatial condition is conducted by the aid of homotopy framework to demonstrate that the proposed boundary condition does guarantee that the over-specification boundary condition resulting from an adjoint model on a limited-area is no longer an issue, and yet preserve its well-poseness and optimal character in the boundary setting. The ill-poseness of over-specified spatial boundary condition is in a sense, inevitable from an adjoint model since data assimilation processes have to adapt prescribed observations that used to be over-specified at the spatial boundaries of the modeling domain.
In the view of pragmatic implement, the theoretical framework of our proposed condition for spatial boundaries indeed can be reduced to the hybrid formulation of nudging filter, radiation condition taking account of ambient forcing, together with Dirichlet kind of compatible boundary condition to the observations prescribed in data assimilation procedure. All of these treatments, no doubt, are very familiar to mesoscale modelers.
Key words Variational data assimilation - Adjoint model - Over-specified partial boundary condition
This research work is sponsored by the National Key Programme for Developing Basic Sciences (G1998040907), the Project of Natural Science Foundation of Jiangsu Province (BK99020), the President Foundation of Nanjing University (985) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.
基金
the National Key Programme for Developing Basic Sciences(G1998040907)
the Project of Natural Science Foundation of Jiangsu Pr
关键词
变分数据同化
伴随模式
超定局地边界条件
Variational data assimilation
Adjoint model
Over-specified partial boundary condition