论文标题
在4D变化数据同化中，用于求解大规模非线性逆差问题的可扩展时空域分解方法
A scalable space-time domain decomposition approach for solving large-scale nonlinear regularized inverse ill-posed problems in 4D variational data assimilation
论文作者
论文摘要
我们开发了用于解决大规模应用中四维变分数据同化的强构造公式的创新算法。我们提出了一种时空分解方法，该方法在重叠案例中采用域分解沿空间和时间方向进行分解，并涉及解决方案和操作员的分配。从整个域上定义的全局功能开始，我们在子域的集合中获得了一种正规化的本地功能，从而提供了预测性和数据同化模型的顺序降低。我们在降低时间复杂性和算法可伸缩性方面分析了算法收敛及其性能。数值实验是根据汉堡大学提供的海洋合成/重新分析目录在球体上的浅水方程上进行的。
We develop innovative algorithms for solving the strong-constraint formulation of four-dimensional variational data assimilation in large-scale applications. We present a space-time decomposition approach that employs domain decomposition along both the spatial and temporal directions in the overlapping case and involves partitioning of both the solution and the operators. Starting from the global functional defined on the entire domain, we obtain a type of regularized local functionals on the set of subdomains providing the order reduction of both the predictive and the data assimilation models. We analyze the algorithm convergence and its performance in terms of reduction of time complexity and algorithmic scalability. The numerical experiments are carried out on the shallow water equation on the sphere according to the setup available at the Ocean Synthesis/Reanalysis Directory provided by Hamburg University.
