An increasing number of satellites are being launched to observe the atmospheric concentrations of a variety of trace species. They cover a wide area at once and are expected to provide more extensive information than the rare ground-based concentration measurements. The paper introduces an adjoint technique to retrieve the emissions based on a recent concept of renormalization. This technique is used with a set of synthetic column-averaged measurements for an idealized satellite corresponding to a prescribed ground-level source. The Indian region is considered with two contrast meteorological conditions in the months of January and July, corresponding to winter and monsoon season. Since it is not feasible to handle a large volume of satellite data in the inversion due to the time involved in the computation of the matrices, a preprocessing is suggested to extract the manageable data set as a representative of the whole data. Considering a limited number of observations, it is shown that the emissions are underestimated without and with the renormalization procedure. The degree of underestimation is relatively more with non-renormalized estimates. The non-renormalized estimate is degraded further by a refined resolution of the model, whereas the renormalized estimate is not altered appreciably. The preprocessing based on aggregation of data is found to retrieve the prescribed emissions up to 75% in the month of January and 90% in the month of July. The relatively computationally expensive renormalization may be avoided except in the case of partial visibility of the area of interest, due to cloud cover or a technical constraint. A simple criterion for the optimum design of a monitoring network is suggested.

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