Improved atmospheric transport for risk assessment

This is the final report of a two-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). To effectively respond to airborne chemical and biological warfare (CBW) attacks in urban settings, one must understand the vector of the threat, i.e., where is the effluent going and when will it get there. These answers are needed both in real time (who should be evacuated?) and afterwards (where should the cleanup be focused?). Certainly, advanced multiscale models are essential to this task. No model has value, however, until it is normalized and validated by measured data. Experience in atmospheric transport has demonstrated that the quality of model estimations is tightly coupled to the fidelity of the data and the effectiveness of its assimilation. The authors have started to quantify the improvement of accuracy of atmospheric transport and dispersion models that follows from enhanced remote sensing of meteorological parameters. The proper use of remote sensing data requires sophisticated assimilation techniques into advanced models, so the whole project emphasizes the synergy of the newest techniques for remote sensing observation, data analysis, data assimilation, and dynamic modeling. This work quantified the value of various levels of data for improving effluent tracking and prediction, and allows tradeoffs between the cost of data acquisition and its impact on accuracy