The fluid dynamics of airflow through a city controls the transport and dispersion of airborne contaminants. This is
urban aerodynamics, not meteorology. The average flow, large-scale fluctuations and turbulence are closely coupled to
the building geometry. Buildings create large "rooster-tail" wakes; there are systematic fountain flows up the backs of
tall buildings; and dust in the wind can move perpendicular to or even against the locally prevailing wind. Requirements
for better prediction accuracy demand time-dependent, three-dimensional CFD computations that include solar heating
and buoyancy, complete landscape and building geometry specification including foliage and, realistic wind fluctuations.
This fundamental prediction capability is necessary to assess urban visibility and line-of-sight sensor performance in
street canyons and rugged terrain.
Computing urban aerodynamics accurately is clearly a time-dependent High Performance Computing (HPC) problem. In
an emergency, on the other hand, prediction technology to assess crisis information, sensor performance, and obscured
line-of-sight propagation in the face of industrial spills, transportation accidents, or terrorist attacks has very tight time
requirements that suggest simple approximations which tend to produce inaccurate results. In the past we have had to
choose one or the other: a fast, inaccurate model or a slow accurate model. Using new fluid-dynamic principles, an
urban-oriented emergency assessment system called CT-Analyst® was invented that solves this dilemma. It produces
HPC-quality results for airborne contaminant scenarios nearly instantly and has unique new capabilities suited to sensor
optimization. This presentation treats the design and use of CT-Analyst and discusses the developments needed for
widespread use with advanced sensor and communication systems.