Utilizing Synthetic, Single Realization, Atmospheric Transport and Dispersion Datasets for Sensor Testing and Evaluation

It is often unclear at the early stages of development of an air sampling technology how that system will perform across a broad range of environmental and operational scenarios. This knowledge is critical to making informed technology development and acquisition decisions. In this presentation we will illustrate a methodology to address this need for air sampling technologies used for short-range atmospheric transport and dispersion (AT&D) applications. The first step of this methodology involves generating a large ensemble of “single realization” AT&D datasets using the Large Eddy Simulation (LES) capability within the Weather Research and Forecasting (WRF) model for an array of environmental and operation scenarios. Next a sensor emulation tool that describes the air sampler is placed within the synthetic AT&D environments. This emulation capability is used to generate a series of sampler responses to the ensemble of AT&D scenarios. The final step of this process is a methodology for collapsing the sampler response data into a single, easy to interpret metric that describes technology performance.

This methodology is demonstrated for a set of air sampling detection technologies utilized for chemical and biological (CB) defense applications. For this application we are able to collapse sensor detection statistics for all operational and environmental scenarios into a single metric by considering relevant contaminant travel times as the independent variables. This allowed us to provide insight on detection technology performance, as well as how to improve technology performance by varying sensor specific trades.