ENVIRONMENTAL SCIENCES
Tornado
fluid dynamics
Dr Richard Rotunno discusses his efforts to improve tornado prediction. He details the challenges
of understanding the fluid dynamics of these violent storms, and how he bridges the gap from
theory to reality
What drives your work on tornadoes? How
did you become interested in the subject
and what are you trying to achieve through
your work?
Tornadoes remain one of the last frontiers in
weather prediction, and given their effects on
lives and property, they are a very important
one. I don’t remember one particular moment
when I said to myself that I was going to work
on tornadoes, but it was sometime in 1976
after I completed my doctorate at Princeton
and before I came to the US National Center
for Atmospheric Research (NCAR) as a
postdoctoral fellow. I found there were several
researchers at NCAR who had exciting ideas
about tornadoes, in particular Dr Douglas K
Lilly. My goal is to discover enough about the
workings of the tornado to enable forecasts
with greater lead times than are currently
available based on observations.
Much of what is understood of the
fluid dynamics of tornadoes comes
from laboratory and numerical model
idealisations. How do you aim to improve the
accuracy of tornado models?
In my opinion, the accuracy of tornadostructure models is limited by our
knowledge of atmospheric fluid turbulence.
All laboratory studies and numerical
models are constrained to be in regimes
where the effects of fluid viscosity are,
relative to the atmosphere, large in
comparison to fluid inertia. I am currently
involved in a project that aims to gain a
better understanding of the effects of
turbulence in actual tornadoes.
How do you translate simulations to tornadoes
in real time?
The transition to real-time forecasting requires
an understanding of tornado formation
regimes. In the laboratory and/or numerical
model setting it is enough to specify external
parameters, such as updraft velocity and
rotation rate of the low-level air entering the
updraft, and then record the resulting tornadolike structure. If the atmosphere were as simple
as the laboratory, it would be enough to predict
the thunderstorm updraft and low-level rotation
rate on the scale of the supercell thunderstorm,
and deduce by analogy the strength and
structure of the expected tornado. However,
at a quantitative level, there are a number of
obstacles that prevent this. First, the role of
cold air in producing low-level rotation is not
explicitly represented in laboratory studies
and remains the subject of active research
in numerical modelling studies of supercell
storms. Second, the role of ground friction and
fluid turbulence is poorly understood in the
context of supercell thunderstorms. Inadequate
observations are another important limitation
to forecasting.
Why are scientists unable to distinguish
which supercell thunderstorms will produce
a tornado?
With the advent of a US national network of
Doppler radars (known as NEXRAD) in the
1980s, there was great hope that routine
observations of supercells would reveal some
unique features indicating when a tornado
was present or, ideally, about to occur.
Unfortunately that has not turned out to be
the case, as routine observations have not yet
been shown to distinguish tornado-bearing
from non-tornado-bearing supercells.
However, it’s important to remember
that NEXRAD lacks spatial and temporal
resolution and does not see low levels
very well. The lowest measurements from
NEXRAD radars are only below 1 km for about
30 per cent of the US, so shorter distances
(more radars) are needed to observe changes
in near-surface winds. NEXRAD radars
also require about 5 minutes to obtain a
volume of measurements, which is generally
inadequate to observe rapid changes leading
to tornado formation. Future technologies
could reduce measurement times to less
than 1 minute (phased-array radars). Several
major field programmes have been carried
out over the years to make measurements
of variables that are not routinely available,
such as the thermal structure of the air
below the cloud base. There are some
interesting leads, but so far no conclusive
results.
Do you collaborate with any other researchers
or laboratories?
Yes, the effort to understand and predict
tornadoes would not be possible without
multiple areas of expertise. I carry
out theoretical modelling along with
collaborators who have good knowledge of
numerical modelling techniques as well
as experience in software engineering. The
observational side of the enterprise requires
ingenious and innovative work on remote
sensing. I am in frequent contact with the
scientists who make the measurements.
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The final frontier in
weather prediction
Scientists at the US National Center for Atmospheric Research
are studying the dynamics of air flow in tornadoes. In doing so,
they are closing the gap between model and observation, and
contributing to more accurate short-term predictions
TORNADOES ARE THE most violent storms
nature has to offer. Under certain conditions,
warm, moist air is drawn upwards into a
thunderstorm, and begins to rotate as a result
of the difference between the lower- and
upper-level winds. As this rotating column
gains force, it creates the perfect conditions for
a collision below. The rotating column throws
rain off to one side of the storm. The rain then
falls and creates rain-cooled downdrafts of
air that spread along the ground, lifting warm
air that continues to fuel the thunderstorm.
The subsequent formation of a low-hanging
rotating cloud-form, called a ‘wall cloud’,
often heralds the development of a tornado.
This flow configuration, marked by a rotating
updraft aloft and rain-cooled outflow of air
near the ground, is called a ‘supercell’, as it
may exist in a nearly steady state for hours,
causing devastation to the surrounding areas
as it moves.
Although it is impossible to prevent a tornado,
accurate forecasting could save lives. However,
current observation-based forecasts only
provide around 9-15 minutes warning of such
an event – clearly insufficient time to properly
mitigate damage.
Dr Richard Rotunno, Senior Scientist at the
US National Center for Atmospheric Research
(NCAR), has dedicated the past 40 years of his
career to studying these violent storms. Rotunno
aims to understand how and why liquids and
gases move (their fluid dynamics), in order to
better predict when tornadoes might occur.
structure, which is characterised by its velocity
field – the variation of the velocity as a function
of height above ground and distance from the
tornado’s centre. In the earliest studies of
tornado structure, the velocity field could only
be deduced using particle-tracking analysis
of motion pictures. This method, called
photogrammetry, required painstaking frameby-frame analysis.
RADAR DATA
It was not until the 1990s that
photogrammetry was replaced by a more
automated method: Doppler radars. These
specialised radars use the phenomenon
known as the Doppler effect to deduce
velocity data from airborne objects at a
distance, and enable an understanding of not
only the exterior but also the interior flow
of tornadoes. Comparisons of radar data
with laboratory- and numerically simulated
tornadoes showed that the latter could
provide a good overall picture of the fluid
dynamics of tornadoes. On the strength of
these favourable comparisons, the laboratory
and numerical studies offered ideas and
predictions that could be tested in new
observational studies.
These studies yielded a number of novel
insights. They showed that most intense
tornado winds are formed at near-ground
level, which is also where most human
populations reside. They also revealed that the
PREDICTING VELOCITY
It is extremely difficult, not to mention dangerous,
to take measurements of an active tornado.
It is also impossible to perform repeatable
controlled experiments in the atmosphere, so
most knowledge of tornado fluid dynamics comes
from laboratory experiments. More recently,
numerical simulation has become an important
tool. “The close visual correspondence between
the types of vortices produced in lab experiments
and those observed in nature motivated
deeper study through numerical modelling of
lab experiments,” explains Rotunno. These
numerical experiments allowed scientists to go
beyond flow visualisation and assess the forces
behind the structures seen in the lab.
Rotunno’s research efforts have used both
of these methods to understand tornado
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INTERNATIONAL INNOVATION
Figure 1. Smoke visualisation of a laboratory vortex (left panel) with a schematic diagram
of the vortex flow (right panel) based on numerical and theoretical models.
THE FLUID DYNAMICS OF TORNADOES
OBJECTIVE
HOW IS A TORNADO FORMED?
To investigate the theory behind, and modelling of,
Dr Richard Rotunno uses an ice skater analogy to explain the physics of
tornado formation
“Imagine a spinning ice skater with arms extended who then pulls her arms inwards
and spins faster. The ice skater is taking advantage of the conservation of angular
momentum by which the radius, defined by the distance from her body centre to her
fingertips, multiplied by her initial spinning velocity, remains constant. Hence, the
contraction of her arms, which is a decrease in radius, requires an increase in her
spinning velocity to maintain a constant angular momentum.
The same principle applies to air near the ground that is drawn into a powerful
thunderstorm updraft. If the air happens to be rotating as it is pulled towards the
thunderstorm-updraft centre, by the conservation of angular momentum, the rotation
of that air will increase in inverse proportion to the distance it moves inward.”
meteorological phenomena including tornadoes and
supercell thunderstorms.
KEY COLLABORATORS
Dr Howard B Bluestein; Dr Brian H Fiedler, University
of Oklahoma, USA
Dr David J Bodine; Dr George H Bryan; Dr Joseph
B Klemp; Dr Morris L Weisman, National Center for
Atmospheric Research, USA
Dr Nathan A Dahl; Dr David S Nolan, University of
Miami, USA
PARTNERS
University of Miami, USA; University of
Oklahoma, USA
FUNDING
National Science Foundation
CONTACT
median tornado diameter is 300 m, over
which velocity can vary by more than
80 m/s, lasting from minutes to around
an hour. The observations also
revealed peak wind speeds over
130 m/s, often in association with
multiple vortices.
MODEL LIMITATIONS
Although advanced technologies
have shed new light on tornado
formation and structure, there are
still many knowledge gaps. One
example is low-level rotation – the
phenomenon that gives rise to a
tornado – which remains something of a
mystery, despite intense research efforts.
Many additional problems are encountered
when translating simulations to actual
tornadoes. Under lab conditions, equilibrium
is formed between updraft velocity and
the swirling, tornado velocity beneath it.
Using this simple model of thunderstorm
updraft and source of rotation, it is possible
to deduce estimates of tornado velocity.
However, the typical tornado has several
features that complicate this model of
prediction, as Rotunno discovered during
some of the earliest numerical simulations
of supercell thunderstorms, which give rise
to the most intense varieties of tornado.
Numerical models of supercells struggle
to correctly represent thermal boundaryassociated rotation, which is critical to
tornado formation. “These models generally
neglect or poorly represent surface
processes, and routine observations do not
capture the structure of low-level thermal
boundaries in supercells,” Rotunno explains.
As a result, uncertainties remain regarding
vortex dynamics in the vicinity of a tornado.
TIME TO PREPARE
Laboratory and numerical experiments can
successfully reproduce vortices very similar
to those observed in a real-life tornado,
and have advanced understanding of the
fluid dynamical interactions that
produce vortices.
While progress has clearly been made,
scientists are still unable to determine which
supercell storms will produce a tornado.
Important pieces of information are missing,
but Rotunno believes that further progress can
be made through a better understanding of
the connection between laboratory flows and
natural vortices.
Rotunno is working hard to bridge this
gap by studying tornado fluid dynamics in
unprecedented detail. By identifying the
most important features of tornadoes and
incorporating them into a model, he ultimately
hopes to provide more accurate short-term
prediction. This could save many lives in the
US, where the vast majority of tornadoes occur.
Dr Richard Rotunno
Senior Scientist
National Center for Atmospheric Research
3090 Center Green Drive
Boulder
Colorado
80301
USA
T +1 303 497 8904
E [email protected]
RICHARD ROTUNNO is Interim
Director of the Advanced Study
Program at the National Center
for Atmospheric Research (NCAR)
in Boulder, Colorado. He is Past
Director of NCAR’s Mesoscale and Microscale
Meteorology Division (2010-15) in which he has been
a senior scientist since 1989. He received a PhD in
1976 in Geophysical Fluid Dynamics from Princeton
University. Over the past four decades he has
contributed to a wide range of topics in mesoscale
dynamical meteorology including: tornadoes,
rotating thunderstorms, squall lines, hurricanes,
polar lows, midlatitude cyclones, fronts, mountainvalley and sea-breeze circulations, and coastally
trapped disturbances. He has also studied a variety
of related problems such as the dynamics of density
currents, vortex stability, convection and atmospheric
predictability. Through a combination of theory
and numerical modelling, his work is directed at
the understanding needed to make progress in the
forecasting of mesoscale weather phenomena. He is
Fellow of the American Meteorological Society and
two-time recipient of the American Meteorological
Society’s Banner I. Miller Award (1991 with K Emanuel
and 2010 with G Bryan) and in 2004 he was the
recipient of the American Meteorological Society’s
Figure 2. Types of vortices produced in the laboratory
Jule G Charney Award.
for various parameter settings. Panels (a)-(c) show
a horizontal-vertical scan through the vortex centre,
illustrating the flow in the plane that varies between
(a) single-cell (updraft everywhere) and (c) two-celled
(downdraft in the vortex core) structure. Panel (b) is an
intermediate case in which there is optimal amplification
of the background rotation (see Figure 1). Panel (d) is a 3D
schematic of multiple vortices. All of these vortex forms
are routinely observed in actual tornadoes.
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