In our work, we consider a class of complex interaction-transport systems of atmospheric chemistry in the context of SVD-based model reduction. Many tasks of simulation, optimization and control can be performed more efficiently, if the intermediate complexity of the chemical model is reduced. We use an SVD-based approach ("method of snapshots") to extract information from a set of full model observations and project the model equations onto a reduced order space so that the full dynamics are preserved with only a moderate error.

We examine and improve many features of the method. In particular, we show how to measure sensitivities of the model reduction process, and use the results to select the placement and weighting of observations to best reproduce specific events in the full model behavior. We develop techniques for reduced space basis selection that allow us to take into account multiple events. We show how to construct reduced models to replace the full model in iterative parameter optimization procedures so that fewer steps and lower computational cost is needed for the search to converge.

The overall result of our study is a more complete understanding of how to perform factor analysis, simulation and optimization of nonlinear models using model reduction tools.