Active remote sensors have various applications, from air pollution studies to defense uses. However, investing in new sensors can be expensive especially if the success of the instrument is not guaranteed. A relatively inexpensive alternative to field testing is numerical simulation. Our numeric... Show moreActive remote sensors have various applications, from air pollution studies to defense uses. However, investing in new sensors can be expensive especially if the success of the instrument is not guaranteed. A relatively inexpensive alternative to field testing is numerical simulation. Our numerical simulations will model tracer releases in the atmospheric boundary layer (ABL); modeling tracer atmospheric transport and dispersion (AT&D) is difficult in the ABL because this layer of the atmosphere is subject to turbulence. There are two types of numerical models which can resolve the effects of turbulence, each with its own set of advantages and disadvantages: ensemble average and single realization models. Ensemble average models are computationally efficient, however, they lack resolution, while single realization models maintain a high level of resolution, yet become computationally expensive. This study focuses on comparing these two atmospheric dispersion models via diagnosing detection statistics of NCAR's Raman shifted Eye safe Aerosol LIDAR (REAL). Specifically, we are looking to answer if model type impacts conclusions on performance of the REAL. The process involved generating scenarios with the dispersion models, adding perturbations to mimic turbulence or creating multiple representations, depending on the model, and running through code representing the REAL in order to obtain detection statistics. Results are presented in terms of time, as in many defense uses, time is of the essence. It is shown that the ensemble average model has much less scatter than the single realization model, which exemplifies the effects of averaging. It also answers the question that model type does impact sensor performance. Show less
