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We consider the inverse problem of fitting atmospheric dispersion parametersbased on time-resolved back-scattered differential absorption Lidar (DIAL)measurements. The obvious advantage of light-based remote sensing modalities istheir extended spatial range which makes them less sensitive to strictly localperturbations/modelling errors or the distance to the plume source. In contrastto other state-of-the-art DIAL methods, we do not make a single scatteringassumption but rather propose a new type modality which includes the collectionof multiply scattered photons from wider/multiple fields-of-view and argue thatthis data, paired with a time dependent radiative transfer model, is beneficialfor the reconstruction of certain image features. The resulting inverse problemis solved by means of a semi-parametric approach in which the image is reducedto a small number of dispersion related parameters and high-dimensional butcomputationally convenient nuisance component. This not only allows us toeffectively avoid a high-dimensional inverse problem but simultaneouslyprovides a natural regularisation mechanism along with parameters which aredirectly related to the dispersion model. These can be associated withmeaningful physical units while spatial concentration profiles can be obtainedby means of forward evaluation of the dispersion process.

Imaging of atmospheric dispersion processes with Differential Absorption Lidar