KEYWORDS: Data modeling, Inverse problems, Atmospheric modeling, Atmospheric chemistry, Algorithm development, Reconstruction algorithms, Chemical elements, Chemical species
Data assimilation algorithms are an important part of modern air quality modeling techniques. To study the real-time operation mode features of the data assimilation algorithms we numerically compare its performance to the solution in the “inverse problem mode”, when the same set of data is available “at once”. The objective of the paper is to compare the gradient-based (variational) and derivative-free solvers in the data assimilation mode to the solution of the reference inverse problem of reconstructing unobservable chemical species concentrations for the atmospheric chemistry model with a derivative-free solver.
KEYWORDS: Data modeling, Inverse problems, Atmospheric chemistry, Atmospheric modeling, Algorithm development, Chemical elements, Data acquisition, Process modeling, Mathematical modeling
The development of efficient data assimilation algorithms for atmospheric chemistry models is an important part of modern air quality studies. In the data assimilation framework considered, the identification of the chosen model parameters is used to continue the model state function to the unobservable part of the domain. This continuation problem is solved sequentially on the set of time intervals called the data assimilation windows. The framework is illustrated on a low-dimensional atmospheric chemistry model.
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