With weak-wind nocturnal conditions, turbulent mixing is weak and concentrations of contaminants remain high.  Submeso motions lead to unpredictable changes of wind direction.  The videos below reveal the time-space structure of nocturnal winds based on networks of data.  The animations of the wind field are based on interpolating observed winds to a finer regular grid. Simulated particles are released into the interpolated observed flow.  Except for one cross-section, the animations are shown in plan view. (Animations by Chris Mills)

This animation of the inner part of the Utah domain shows the large spatial variability of the wind direction with only weak coherence between stations.

This Iowa animation begins with a one-time release of particles throughout the domain. The x-y axes are labeled are in km.  Background wave motions are evident from the animation as well as an eventual convergence of particles in the southeast part of the domain. The animations reveal that convergence zones are common

This animation is based on the Iowa network using four points of particles releases. On this scale, the particle trajectories show vertical vorticity (some rotation in the horizontal plane).

This cross-section animation of temperature on a slope is based on five 32-meter towers. Drainage flow begins in the early evening as indicated by the wind vectors . As the evening proceeds, cold air from the basin (blue) surges up the slope. The flow mechanism behind this surge is not known.

In this animation based on the CASES99 network, the blue particles reveal the instantaneous distribution of particles while the smaller white specks show the accumulated particle count since the beginning of the animation. Due to relatively sudden shifts in the wind direction, the accumulated particle distribution tends to be concentrated in three streaks.

 

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