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In this study, we theoretically investigate the reconstruction of 2-D crosssections through Gaussian concentration distributions, e.g. emission plumes,from long path DOAS measurements along a limited number of light paths. Thisis done systematically with respect to the extension of the up to four peaksand for six different measurement setups with 2-4 telescopes and 36 lightpaths each. We distinguish between cases with and without additionalbackground concentrations. Our approach parametrises the unknowndistribution by local piecewise constant or linear functions on a regulargrid and solves the resulting discrete, linear system by a least squaresminimum norm principle. We show that the linear parametrisation not onlyallows better representation of the distributions in terms of discretisationerrors, but also better inversion of the system. We calculate area integralsof the concentration field (i.e. total emissions rates for non-vanishingperpendicular wind speed components) and show that reconstruction errors andreconstructed area integrals within the peaks for narrow distributionscrucially depend on the resolution of the reconstruction grid. A recentlysuggested grid translation method for the piecewise constant basisfunctions, combining reconstructions from several shifted grids, is modifiedfor the linear basis functions and proven to reduce overall reconstructionerrors, but not the uncertainty of concentration integrals. We suggest aprocedure to subtract additional background concentration fields beforeinversion. We find large differences in reconstruction quality between thegeometries and conclude that, in general, for a constant number of lightpaths increasing the number of telescopes leads to better reconstructionresults. It appears that geometries that give better results for negligiblemeasurement errors and parts of the geometry that are better resolved arealso less sensitive to increasing measurement errors

atmospheric chemistry and physics

2-D reconstruction of atmospheric concentration peaks from horizontal long path DOAS tomographic measurements: parametrisation and geometry within a discrete approach