Source Characterization is a contemporary issue in dispersion modeling. It is becoming evident that it is often necessary to also back-calculate the meteorological forcing parameters in order to accurately predict subsequent transport and dispersion of the contaminant. One important aspect of the problem that has not yet been addressed is ascertaining the depth of the Atmospheric Boundary Layer (ABL), which is a critical meteorological parameter for modeling of transport and dispersion. The lid of the ABL is important because it limits the upward dispersion of contaminants away from the surface. This crucial parameter has historically been hard to determine since the depth of the ABL varies with stability, time of day, horizontal and vertical wind field, yet is measured sparsely at best. Given these factors, it is essential that boundary layer depth be determined as part of the meteorology retrieval and source characterization process. In our Gaussian puff model a 'lid' is added to the top of the boundary layer to reflect contaminants downward or trap them above. Using time-dependent concentration observations from the puff as it is advected across the domain we use a Genetic Algorithm to characterize the source, boundary layer depth, as well as boundary layer wind speed and direction, and other meteorological parameters. We show that the Genetic Algorithm can characterize both the source and the meteorological parameters while working solely from concentration observations.