Estimation of Low-Level Wind Structures in a Tornado-like Vortex

Radar observations of convective phenomena (such as mesocyclones and tornadoes) are unable to observe the atmosphere in the lowest tens of meters near the surface. In tornadoes, this is the most often the layer where the strongest winds occur. Since measurements cannot be made directly (aside from a few in situ surface instruments), a number of approaches in recent years have attempted estimate the flow based on the observations that are available.

Here a new technique for estimating important parameters relevant to a tornado's structure is introduced using a probabilistic approach and a simplified forward model for the dynamics. We assume that the wind field for the vortex on several vertical levels with be observed by high-resolution Doppler radar(s) (such as those used in VORTEX2). These data are then fit to a parametric model described in Wood and White (2010). The simplified subset of the steady Navier-Stokes equations is then solved to give the radial and vertical components of the velocity field below the level of the observations using the appropriate lower boundary conditions. Since there is uncertainty inherent in the best choice of model parameters, and in the data, the parameter space is sampled and calculate the distribution of possible tornadic wind fields resulting from the simplified dynamics. Using this distribution, we can calculate relevant vortex parameters and the probabilities associated with important thresholds, such as swirl ratio, wind speed, and vortex intensity.

Results will be presented from experiments with two sets of idealized model output. A sample of the type of results is given in Figure 1, which shows the probabilities associated to the event that the wind speed ||v|| is greater than some threshold ||v_o|| below the height z. The velocity data used to fit v in this case comes from an idealized axisymmetric thunderstorm model, described in Davies-Jones (2008). In the future this methodology will be extended to estimate the flow below the observable height from radar data from tornadoes.