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Sensitivity of Supercell Tornado Simulations to Variations in Microphysical Parameters Nathan Snook and Ming Xue School of Meteorology, University of Oklahoma, Norman OK, U.S.A. Introduction

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Sensitivity of Supercell Tornado Simulations to Variations in Microphysical Parameters

Nathan Snook and Ming Xue

School of Meteorology, University of Oklahoma, Norman OK, U.S.A.

Introduction

  • Tornadoes spawned by supercell thunderstorms are a major severe weather hazard in the central United States, causing multiple fatalities and millions of dollars in damage each year.
  • Accurate numerical simulation of tornadic supercells remains a challenge, as the solution is affected by grid resolution and model parameters.
  • Commonly used microphysical schemes in NWP models assume a dropsize distribution based on that observed by Marshall and Palmer (1948) for some or all hydrometeor species explicitly predicted.
  • Observational studies of Marshall-Palmer intercept parameters for rain, snow, and hail have yielded values that vary by several orders of magnitude (Gilmore et al., 2004).

Results

Table of 100 m Experiments

  • Coarse resolution simulations revealed that cold pool intensity was most sensitive to rain and hail intercept parameters, and less sensitive to snow intercept parameter and hail density, as seen in Fig. 1.
  • Simulations with large raindrops and hailstones produced weak cold pools, while small raindrops and hailstones produced strong cold pools due to enhanced evaporational cooling.

Fig. 1) Plot of cold pool intensity for 1 km resolution simulations. Explanation of experiments not conducted at 100 m resolution: s7 and s8 have an increased snow intercept parameter, d400 has decreased hail density. Kessler uses warm rain microphysics.

Large Raindrops (r5)

Maximum intensity:f2

Duration: 9 min.

Control (CON)

Maximum intensity:f2

Duration: 4 min.

  • Simulations favoring large hydrometeors (weak cold pools) were observed to be most favorable for formation of long-lived tornadoes.
  • Tornadic spinups in simulations favoring small hydrometeors (strong cold pools) were weaker and more short-lived than those in simulations favoring large hydrometeors (weak cold pools). Long-lived surface tornadic vortices are noted in Fig. 2.

Conclusions

  • Of the variables studied, the rain intercept parameter appears to have the most influence on supercell dynamics, followed by the hail intercept parameter.
  • Organizational mode, storm propagation, gust front location, and tornado potential were all strongly influenced by variation in microphysical parameters.
  • Simulations with weaker cold pools produced more vertically oriented updrafts, while simulations with strong cold pools tended to produce updrafts that tilted westward with height.
  • Dropsize distributions favoring large raindrops and large hailstones (small rain and hail intercept parameters respectively) result in weaker cold pools and greater potential for long-lived tornadoes due to more favorable updraft orientation and vertical alignment of low- and mid-level vorticity maxima.
  • Varying intercept parameters alone is enough to affect whether or not tornadoes form.

Fig. 2) Timeseries of maximum vertical vorticity in the lowest 2 km of the atmosphere for high-resolution simulations. Long-lived surface tornadic circulations are noted, along with their f-scale intensity and duration.

Objectives

  • Investigate the sensitivity of supercell storm dynamics to variation in Marshall-Palmer intercept parameters for rain, hail, and snow dropsize distributions, and hail density.
        • Cold Pool Intensity
        • Organizational Mode
        • Precipitation Distribution and Intensity
  • Explore the impacts of these effects on tornado potential and tornado formation.
  • In simulations with stronger cold pools, the gust front was stronger and propagated eastward more quickly, often advancing several kilometers ahead of the storm.
  • A more linear storm mode was favored in the simulation with the strongest cold pool (h6r7, pictured on the right of Fig. 3a).

Fig. 3b) Zoomed-in view of the tornadic vortex circulation in r5. Plotted are radar reflectivity (color-fill), vertical vorticity (contour), and surface wind vectors.

Fig. 3a) Plots of cold pool strength (shaded), vertical vorticity (color-fill), radar reflectivity (contour) and wind vectors for a simulation favoring large raindrops (r5, left) and one favoring small raindrops and hailstones (h6r7, right). The black box in the left plot indicates the area plotted in Fig. 3b.

Data and Methods

  • The Advanced Regional Prediction System (ARPS) was used to numerically simulate supercell storms initialized using a thermal perturbation superimposed on a horizontally homogeneous base state derived from a sounding associated with the May 20, 1977 tornadic supercell near Del City, Oklahoma.
  • 18 runs were conducted at coarse (1 km) horizontal grid resolution to determine which parameters were most influential in supercell dynamics.
  • 7 runs varying the most influential parameters within the range of published observations were conducted at uniform 100 m horizontal grid resolution.
  • The positioning of the gust front in simulations favoring large hydrometeors (weak cold pools) allowed for stronger updrafts with a more vertical orientation than in simulations favoring small hydrometeors (strong cold pools).
  • Simulations with strong cold pools exhibited more pulse-like updraft behavior and fewer supercell characteristics, as seen on the right of Fig. 4.

Acknowledgement

This research was conducted as

part of the Center for

Collaborative Adaptive Sensing

of the Atmosphere (CASA),

and wasfunded in part by NSF

grant EEC-0313747 of the

Engineering Research Center Program.

For further information, contact Nathan Snook at nsnook@ou.edu

Fig. 4) Vertical cross-sections of radar reflectivity (color-fill), cold pool intensity (shaded), and wind vectors for a simulation favoring large raindrops (r5, left) and one favoring small raindrops and hailstones (h6r7, right).

Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 2610-2626.

Marshall, J. S., and W. M. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165-166.

References: