Improved algorithm with adaptive regularization for tomographic reconstruction of gas distributions using DOAS measurements.

Abstract

Differential optical absorption spectroscopy (DOAS) is notably well suited for the retrieval of UV-absorbing trace gases present in the atmosphere. We combine multi-axis DOAS observations to perform a tomographic reconstruction of the distribution of gases emitted from different sources. We use a new algorithm based on a regularized minimization approach embedding key physical aspects of the solution to constrain the inversion. In this work, we take into account that the spatial sampling of the plume being scanned by the instruments is not homogeneous. Therefore, we introduce an adaptive approach with a locally tuned regularization weight according to the uncertainty levels introduced by the sampling scheme. We tested our approach on reconstructions of simulated gas distributions and different configurations applicable to multi-axis DOAS. Finally, our approach is applied to experimental data for the retrieval of the distribution of ${\rm NO}_2$NO2 within a plume cross section emitted from a group of stacks.