This paper summarizes the challenges encountered with our ongoing development and use of a software system designed to facilitate exploration of computational optimizations and strategies for Data Assimilation (DA). The software system is designed and constructed from scratch using modern software development methods and tools, though it incorporates components of pre-existing systems where appropriate. We present results of experiments that employ this system to test approaches for assimilation of observations using a four-dimensional variational (4D Var) scheme. We propose a modular DA system software architecture and demonstrate its utility using a set of models of varying realism and complexity. The software system design and implementation was initially tested and validated using a simple chaotic atmospheric model. A Quasi-Geostrophic (QG) atmospheric model was used to conduct DA experiments of increased difficulty and to validate the software design at larger scales of model complexity. Our QG DA study focused on 2016 winter weather data where a Nature run was used to represent the “true” state of the atmosphere and observations, whereas observation error covariance and observation operator were adapted from pre-existing DA systems. To increase performance, a parallel-in-time algorithm was applied to solve the proposed 4D Var data assimilation problem. That is, the assimilation window was divided into multiple sub-intervals, which allowed for parallelization of the cost function and gradient computations. Continuity equations of the solution were added as constraints across interval boundaries. This approach produced a different formulation of the variational data assimilation problem than weakly constrained 4D Var. We explored a combination of serial and parallel 4D Var algorithms to increase performance.
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