THE DETECTION OF
MESOSCALE CYCLONES USING
DOPPLER WEATHER RADAR
By
Paul McCrone
ATS 615
RADAR and Severe Storms
Prof: Dr. Richard Penc
Date:
December 11, 1998
Dept. of Atmospheric Sciences
TABLE OF
FIGURES
Figure
1: Developed by the Author.
Figures
2 through 7: Taken from Rotunno, R., 1993: Supercell Thunderstorm Modeling and
Theory. Geophysics Monograph 79, The Tornado: Its Structure, Dynamics,
Prediction, and Hazards , 57-73.
Figures
8 and 9: Finley, C. 1990: Technical
Training: Doppler Weather Radar: Thunderstorm Morphology and Dynamics, 27
August 1990,
Figure
10: Taken from the National Severe Storms Laboratory (NSSL) Web Page, http://www.nssl.noaa.gov/
Figure
11: Taken from Lemon, L. R.,
D. W. Burgess, and R. A Brown, 1978: Tornadic Storm Airflow and
Morphology Derived from Single-Doppler Radar Measurements. Mon. Wea. Rev., 106,
48-61.
Figures
12 and 13: Taken from the National Severe Storms Laboratory (NSSL) Web Page, http://www.nssl.noaa.gov/
Figures
14 through 20: Taken from (Author Unknown) 1998: WSR-88D Build 10 Training,
National Weather Service, NEXRAD Operational Support Facility, Norman Oklahoma,
available at http://www.osf.noaa.gov/otb/otb.html
THE DETECTION
OF MESOSCALE CYCLONES USING
DOPPLER
WEATHER RADAR
ABSTRACT
The focus of this paper will be a discussion of the
current state of the art related to
the
detection of mesoscale cyclones (or ‘mesocyclones’) using Doppler Weather
RADAR. A brief discussion of the
theory on mesocyclone development will be followed
by
some specific applications, which will focus on the WSR-88D (Next Generation
Weather
RADAR – or NEXRAD). Particular attention will be paid to the use of
computer based algorithms in the automated detection of mesocyclones, in
addition to a
discussion of pattern recognition in both reflectivity and radial velocity
fields. In addition
to
this background, we will look at two different scenarios that demonstrate the
detection
of mesocyclones.
I.
INTRODUCTION
Mesoscale cyclones (or ‘mesocyclones’) are indeed significant meteorological
phenomena to study. They often precede tornadoes, which obviously cause significant
damage and loss of life. Since there is often little time between the initial development of
the parent mesocyclone and the spawned tornado, it becomes critical to rapidly identify
these patterns and issue warnings.
Using Doppler Weather RADAR, it is possible to
detect these features in near
real-time. However, in order to be able
to detect these storms, we must have good
knowledge of exactly what we are looking for on the RADAR display. A key to this
is
mastery over the theory of mesocyclone development. This paper will present the latest
accepted theory on this subject. Because
this is such a potentially complicated topic, only
a
brief overview of the theory will be provided.
It is essential to have as complete an
understanding as possible, in order to interpret the phenomena on the RADAR display
with optimum efficiency.
Our focus will be on the WSR-88D (Next Generation
Weather RADAR – or
NEXRAD). This is due to the fact that
this S-band RADAR is well situated across the
continental US in a fairly dense network of mostly overlapping RADAR sites. Further,
the
NEXRAD system (3 GHz , 10 cm, PRF 318 to 1000 Hz) possesses good
characteristics to detect these phenomena.
Pattern recognition of mesocyclones - in both
reflectivity and radial velocity
fields - will be discussed. Reflectivity and radial velocity are the two main
parameters
that most meteorologists will consider first on the NEXRAD. The use of reflectivity is
well documented, and as such only a brief period of time will be spent
here, summarizing
main points. Radial velocity data
will be studied closer, since this data gives keen, quick
insight into the development of rotation in a thunderstorm.
After this is complete, our attention will shift to
two different cases that
demonstrate the detection of mesocyclones.
Having looked at the pattern recognition aspects of
mesocyclones in NEXRAD,
we
will then pay particular attention to the use of computer based algorithms in
the
NEXRAD system to aid in the automated detection of mesocyclones. As a new version
of
NEXRAD software is now coming out (called “Build 10” – a new NEXRAD
software/data processing series- from the NEXRAD Operational Support Facility, or
OSF,
in Norman Oklahoma), it becomes even more important that ever to understand
what the NEXRAD is telling us. I
will demonstrate that there are significant changes in
the
algorithms for tornado detection from the NEXRAD OSF in Build 10
II. MESOCYCLONE DESCRIPTION:
CURRENT THEORY
To start this discussion, let us
first obtain an initial definition of the mesoscale
cyclone, or “mesocyclone”. Rogers and
Yau [1976] defined it as a “…horizontal
circulation about 10 km across with values of vertical vorticity in the order of 10-2 /sec”.
The
mesocyclone is a mesoscale feature of the supercell thunderstorm. Glass and
Przybylinski [1994] provided the following
background on mesocyclones:
From 1971 through 1977, the National Severe Storms
Laboratory (NSSL) conducted an extensive study on the evolution of mesocyclones
across
Glass
and Przybylinski [1994]
also indicated that follow on studies (conducted since the
earlier work from the 1970’s began) show that an average of 50% of all
mesocyclones
tend to produce tornadoes, and that 90% produce some type of severe weather
(tornadoes,
high winds, or hail). Almost all
tornadoes, on the other hand, are produced from parent
mesocyclones. In one of the studies
conducted by NSSL [Brown, Lemon, and Burgess,
1977], indications were
made that in one vigorous case, it took 25 minutes for a clear
mesocyclone - detectable by a Doppler RADAR-
to actually produce a tornado.
This has
clear implications for the operational forecaster that is attempting to
provide warnings to
the
general public: detect the parent mesocyclone, and you have a good chance of at
least
getting severe weather, if not a tornado.
This overview is a good first look
at the mesocyclone, but we will need a
somewhat better physical description of the mesocyclone (and the supercell
thunderstorm, for that matter) if we hope to actually be able to recognize such a
feature
on
our NEXRAD PPI. Therefore, a short
overview of the supercell and its evolution will
follow.
The first step in this process is
the establishment of a thermodynamically unstable
environment with strong vertical wind shear.
Figure 1 below demonstrates the shear
Figure 1: Shear Environment for a Supercell
environment. This figure starts our
consideration. As the shear environment
develops,
regions of horizontal vorticity are induced.
The line of overall horizontal vorticity is
shown by the line connecting the two plus signs in the figure. As these lines of vorticity
are
advected into regions of upward vertical motion, these horizontal lines of
vorticity are
deformed. See figure 2 for a visual
demonstration of this effect. In figure
2, the lines of
horizontal vorticity come under the influence of a ‘bump’ – this is the region of
instability. This unstable air rises,
causing the entire vortical line to be raised and
deformed to match the rising air.
This deformation causes the positive horizontal
vorticity lines to tilt into
two
vertical lines of vorticity – one line is positive, one is negative. As the unstable air
continues to ascend, and the cumulonimbus cell develops, the pair of vertically
oriented
Figure 2: Deformation (tilting) of lines
with horizontal vorticity into vertical vorticity. [figure
from Rotunno, 1993]
vorticity lines form two rotating cells.
One is cyclonic, and the other is anti-cyclonic. See
figure 3 to see this graphically.
These cells will both contribute to the vertical instability
Figure 3: Formation of mesoscale
cyclonically and anticyclonically rotating cells. [figure
from Rotunno, 1993]
of
the overall cloud system. As the cloud
continues to grow, the moisture in the cloud
will inevitably condense and fall out as rain. As the rain continues, it will drag down air
with it, causing another deformation in the line identified by the (a)
in figure 3. This will
lead to the birth of two new cells of positive and negative vertical
vorticity, as shown in
figure 4. As the thunderstorm system
continues to move (in the overall direction of the
upper level wind field), the storm will continue to draw in moisture (which
will help
continue the rain), and it will encounter (or run into) additional lines of
horizontal
vorticity, which will keep the rotational energy going in the vertical vorticity
cells.
Eventually,
these two factors will cause the storm to split in the middle, giving rise to
two
new
thunderstorms, similar to the ones seen in figure 3 previously. This could, in theory,
could simply continue on, with new cells forming and splitting. However, we will see
that this would only be possible if the vertical shear pattern remains as
shown in figure 1.
Figure 4: Formation of the new set of
mesoscale cyclonic and anticyclonic cells due to rainfall activity. [figure from Rotunno, 1993]
Note
also the following feature associated with the vertical profile: as the wind blows on
the
windward side of the thunderstorm, a relative maximum of pressure is induced,
whereas a minimum of pressure is induced on the leeward side of the cell. This is
portrayed in figure 5 below. The nature
of the mean shear is repeated and emphasized by
the
hodograph in the bottom right of this figure. Note that this situation is, in
fact, no
different from figure 3 at all – it is the same situation. This serves to continue the process
that we have already discussed.
Figure 5: Formation of the microscale
relative high pressure on the windward side of the storm, and the microscale
low pressure on the leeward side. [figure from
Rotunno, 1993]
This above situation will change
dramatically when we introduce a new vertical
wind distribution. This is depicted
below in figure 6. Note the new
hodograph – we now
have a very different pressure gradient situation acting on the cloud at
different levels.
Note
that a new feedback mechanism takes place.
Take a closer look at figure 6: there is
Figure 6: Formation of the microscale
relative high and low pressure regions with a cyclonically curved hodograph. [figure from Rotunno, 1993]
a
new feature here: on the left side of
the storm (this is on your right as you look at the
figure) a relative microscale high pressure is built up in the upper portion
of the storm,
whereas a relative low pressure region is developed below. This will discourage upward
vertical motion on the left side of the storm.
In the previous case, this was the side with
the
anticyclonically rotating cell. On the
right side, the exact opposite takes place: a
relative microscale high pressure region lies at the lower level, whereas
relative
lower pressure is above. This
encourages additional upward vertical motion.
This is
taking place on the side with the cyclonically rotating vorticity cell. As this process
continues, the cyclonically rotating cell is strengthened, whereas the
anticyclonically
rotating cell is suppressed. Note that
this is the case for a cyclonically curved
hodograph. If we were to have the reverse
case (an anticyclonically rotating hodograph),
then we would see the anticyclonically rotating cell become dominant.
The net effect of all this is shown
below in figure 7, which depicts our
thunderstorm system. This is otherwise known
as the classic Supercell. The
cyclonically
rotating vertically oriented vortical circulation is the classic mid-level
mesocyclone. It
normally forms in the middle levels of the storm, somewhere between 4-6 km on
average.
It
is reasoned by many that the mesocyclone will continue to build both upward and
downward through the supercell. As it builds downward, it will, at least 30-50%
of the
time, develop into a tornado. The
development of the mesocyclone is further aided by
Figure 7: The classic tornadic supercell.
[figure from Rotunno, 1993]
unstable air rising in the cell. As the
air rises vigorously, the rotational column is
stretched in the vertical. This will
serve to decrease the moment of inertia of the column
of
rotating air. Of course, from physics,
we will remember the conservation of angular
momentum, which requires that L=I . w remain constant throughout (L is the angular momentum, I is
the moment of inertia, and w is the angular velocity). In short, given
the
L must remain constant (let us ignore viscous effects for the short term), and
that the
moment of inertia is decreasing (due to the vertical stretching process
induced by the
upward vertical motion), we must then see an increase in the angular
velocity. This is
what leads to increasing mesocyclone intensity, and ultimate formation of
the tornado.
The upward stretched mesocyclone can reach far above
and below the 4-6 km
level of its initial formation. If it
is, in fact, tornadic, then you will see the mesocyclone
reach from nearly the surface to 10 km or more. Initially, the mesocyclone can be very
wide in diameter. Typically, these
diameters vary from 1-10 km, depending on the size
and
strength of the cyclone [Stumpf et al, 1997].
This concludes our discussion of the
supercell and mesocyclone. We now shift
the
focus onto the aspects of RADAR detection of the mesocyclone.
III. PATTERNS AND SIGNATURES FROM
THE RADAR
We will now look at the mesocyclone of the RADAR’s perspective. What will
the
RADAR see? Let’s look at the supercell
from the top down, as in figure 8. The
image has a north arrow pointing upward on the paper, and the storm is
moving to the
east-northeast. The yellow region generally
corresponds to a region of precipitation.
In
the
forward part of the region of rain, we see the forward flank downdraft , or FFD
(a
Figure 8: A plan view of the tornadic
supercell. [figure from Finley, 1990]
feature we would have expected if we looked again at figure 1). To the rear is the rear
flank downdraft, or RFD – a feature that is consistent with the microscale
high pressure
we
addressed earlier. The arrows are the
near surface streamlines that will clue us into
the
expected nature of our isodops (lines of equal Doppler velocity on the RADAR
display) at low RADAR antenna elevations.
A further inspection of the precipitation area
(see figure 9) gives us some good hope as to the expected
reflectivities we will expect to
observe in the supercell.
Figure 9: A plan view of hydrometer type
and precipitation distribution in the tornadic supercell. [figure
from Finley, 1990]
A.
REFLECTIVITY
Let’s take another look at the yellow region in figure 8. Here, we have a small,
cyclonically curved region in the southwestern part of the yellow precipitation band. This
is called the HOOK ECHO, and it locates our mesocyclone very nicely. A real world
example is provided below in figure 10.
Figure 10: A PPI view of an actual
tornadic supercell with classic hook pattern. The red triangle denotes a
tornado as detected by Doppler RADAR - This storm did
have a confirmed tornado [information and figure from the National Severe
Storms Laboratory home page, 1997]
Note the reflectivities
start high in red (55 dbZ +) and decrease through orange, yellow,
green, blue, and finally black
for the lowest return. This pattern of
reflectivity is
consistent with the pattern of
precipitation and hydrometeors as seen in figure 9.
B. RADIAL VELOCITY
The problem with the hook echo is that tornadoes –
especially F0 / F1 strength –
are
not always going to exhibit the classic hook echo pattern. The radial Doppler velocity
product can, on the other hand, tell us
a great deal about the internal working of the
supercell, even giving us a quicker detection time for mesocyclones. In general, with the
mid-level mesocyclone, we would expect the following radial wind distribution,
as seen
in
figure 11. This is the RANKINE
COMBINED VORTEX, and it represents the
Figure 11: The Rankine Combined Vortex. [figure
from Lemon et al, 1978]
theoretical distribution of winds in the horizontal plane, associated with the mesocyclone
(note that this is a similar wind model for the hurricane). The Rankine Combined Vortex
(or RCV) is made up of two regimes - one is the solidly rotating core, where the winds
are given by the equations below:
V =C1 C1=Vmax/R
r
where R is the radius of the vortex core, r
is less than R, V
is tangential velocity, and
Vmax is the maximum tangential velocity (C1 is obviously meant to be a
constant,
which means that the solidly rotating core increases linearly in absolute
tangential
velocity with distance from the center of the core.).
Outside the solidly rotating core,
there is another flow region, which is referred to as the potential vortex
flow. This is
given by the equations below:
Vr =C2 C2=VmaxR
The
variables are the same as above, with C2 also being a constant [These equations
come from the work of Lemon at al, 1978].
Lemon et
al [1978] also listed four initial
criteria for identifying a signature as
a true mesocyclone:
“1) There
is significant tangential shear of radial velocity in a quasi-horizontal plane
where the sum of the angular diameter and elevation angle of observation is
less than 30 degrees
2) Tangential shear persists
for at least half the period required for one vortex revolution.
3) Vertical extent of the
shear pattern exceed horizontal diameter
4) Qualitative shear pattern
is invariant during a viewing angle change approaching or exceeding 45
degrees.”
[Lemon et al, 1978]
This
represents an initial set of identifying conditions needed to identify a
velocity pattern
as
a mesocyclone. These actually do not
represent a list of sufficient conditions, but they
are
necessary conditions.
An example from a real case is given
in Figure 12. Here, the reflectivity
data is
the
left, and velocity data is on the right.
Look at the velocity data near
1.8
degrees) and even more so on the velocity panel on the lower right (0.4
degrees),
again near
clear cut example of the tornadic mesocyclone.
Figure 12: A four panel PPI view of an
actual tornadic supercell with classic mesocyclone velocity pattern. The
velocity data is on the right. Note the
contrast of inbound winds (green) and outbound winds (red) – a sure sign of a
mesocyclone. The red triangle (left two
panels) denotes a tornado as detected by NEXRAD Doppler RADAR algorithms - This
storm did have a confirmed tornado on 13 Jun 1998 [information and figure from
the National Severe Storms Laboratory home page, 1997]
Figure 13: A four panel PPI view of
another actual tornadic supercell with weaker mesocyclone velocity pattern. The RADAR site is due west of this location.
The velocity data is on the right. Note
the contrast of inbound winds (green) and outbound winds (red) due west of the
Isle of Wight (near
Another example from a real case is
given in Figure 13. Here, the
reflectivity
data doesn’t seem to give a clear hook pattern, yet there is a contrast of
velocity near the
Isle of Wight (west of
mesocyclone, like figure 12, and an F1 tornado was associated with this
event. This
example shows that reflectivity alone is insufficient to detect tornadic
cells.
We have paid much attention to the
mesocyclone in the horizontal, but let us take
a
moment to remember that the mesocyclone is also a three dimensional
feature. The
circulation may appear impressive initially at the middle levels, but unless the
feature
builds vertically (up and down) then the feature has not attained full
mesocyclone status.
The
only way to do this with the RADAR is to scan the feature at different
elevations,
and
observe the circulation at more than one level.
Both figures 12 and 13 have this –
both features show two different elevation scans, and the mesocyclone is
evident in both
scans in each case. Doing this will
increase our confidence that a mesocyclone is
forming (or already exists). Keep in
mind that many transitory features (shears and
circulations) have been observed in shallow layers – often in the boundary
layer. Again,
as
mentioned earlier, we will need to look for significant vertical depth to this
signature.
What is ‘significant vertical depth’?
The proposed answer was given by
Keighton and Medlin [1995] as follows: …
“….[the] depth of the rotational signature must be at least
comparable to the diameter of the
mesocyclone core. In other words, a broad rotational
core signature (say 5 nm) would need to be roughly 5 nm deep (approx. 30,000
ft) for confident identification as a bona fide mesocyclone. The depth criteria were further modified for
operational use ... in the late 1970’s …to allow for a minimum of about 50% of
the core diameter, as long as it was not less than 3 km (10,000 ft) in depth.
Keep in mind that this particular depth criterion was established from a data
set consisting primarily of Plains supercells.” [Keighton and Medlin 1995]
Another note is that the mesocyclone should have some time continuity. The
rule of thumb suggested by Keighton and Medlin [1995] was that the mesocyclone
should be evident for two volume scans for initial mesocyclone detection. However,
Keighton and Medlin [1995] also pointed out that …..
“…an exception
to the time continuity criterion occurs in the event that the signature is
already strong and deep when discovered.
In this situation, immediate action is justified! Remember, those mesocyclone
cores that are characterized by the highest rotational velocities and exhibit a
tendency to build downward with time pose the highest tornadic threat!”
[Keighton and Medlin 1995]
A final note concerns a limitation of the RADAR. Since it samples at discrete
time intervals and discrete volume sizes, there may be situations where the
signature
changes rapidly from volume scan to volume scan. This can be a very significant factor
at
longer ranges. In reality, the feature
may be steady state, but this can become
challenging to the analyst without experience.
Caution is the key word here!
IV. WSR-88D (NEXRAD) AUTOMATED DETECTION ALGORITMS
The WSR-88D, otherwise known as the Next Generation Weather RADAR
(NEXRAD)
is a valuable tool for meteorologists, since the RADAR Product Generator
(or RPG) within the NEXRAD system can digitally run numerical
algorithms needed to
objectively estimate severe thunderstorm activity.
One such application is called the
Mesocyclone
Detection Algorithm (or MDA), which is currently used in the RPG’s
existing software. The current software
version is called ‘Build 9’; at the time this paper
is
being written, the National Weather Service is preparing to implement a new
software
version called ‘Build 10’. It is
significant to note that no change in the MDA is expected
for
Build 10, although the method of detecting tornadoes is changing quite a bit.
We will
focus on the MDA here, and briefly mention both the Tornado Vortex Signature
(TVS in
Build
9) and the Tornado Detection Algorithm (TDA) in Build 10. Please note that
almost all this section of this paper, section 4, is obtained from the
document from the
NEXRAD
Operational Support Facility (OSF) in Norman Oklahoma, entitled “WSR-88D
Build 10 Training” including all figures.
In Builds 9 and 10, the MDA begins
with a careful inspection of Doppler radial
velocities in a given RADAR antenna angle, and looks at adjacent range bins, or
range
gates. The RPG, a specialized digital
computer system, determines groups of lines at a
constant range from the RADAR that exhibit increasing Doppler velocity. This
means
that the processor looks for a group of velocities that go from negative
values of velocity
to
positive values of velocity with increasing azimuth (the RPG doesn’t look for
the
opposite case as a mesocyclone, but the opposite case will be identified as a
significant
region of shear). What this all means
is that the NEXRAD only looks for cyclonically
rotating mesocyclones. Once the RPG
identifies a region as having a positive gradient of
radial velocity from negative to positive, it calls such a grouping a ‘run’
of increasing
velocities with increasing azimuth. This
is then called a ‘pattern vector’. The
MDA will
attempt to identify all the pattern vectors it can manage, as shown below in
figure 14.
Figure 14: A plan view of the pattern
vectors defined by the MDA in the NEXRAD RPG are highlighted in
blue. Note the location of the RADAR
Antenna (the RDA) below [figure and information from the NEXRAD OSF ‘WSR-88D
Build 10 Training’ Document ]
Once
the pattern vectors are identified, calculations are made to determine if
certain
minimum shear and momentum criteria are met.
If these criteria are not met, then the
pattern vector in question is discarded. See figure 15 for an example of this.
Figure 15: Rejected pattern vectors are
highlighted in green. [figure and information from the
NEXRAD OSF ‘WSR-88D Build 10 Training Document ]
Once
this takes place, another algorithm is run. Called the ‘Threshold Pattern
Vector
(TPV)’
in NEXRAD, it is user definable. This
algorithm ensures that a minimum number
of
correlated pattern vectors are present (normally this is set to 10) to have the
system
lock in a feature as a two – dimensionally correlated feature. See figure 16 below for an
example.
Figure 16: Correlated pattern vectors are
identified as correlated 2-D features, highlighted by the thick black line. [figure and information from the NEXRAD OSF ‘WSR-88D Build 10
Training’ Document ]
Once
this process in complete, this feature must be correlated with other features
in the
vertical. NEXRAD scans in other
elevations, for example, and looks to see if there are
other 2-D features that correspond to this feature you see above in figure
16. See figure
Figure 17:
Vertically correlated 2-D features are
identified and correlated as 3-D features, highlighted by the grey areas. [figure and information from the NEXRAD OSF ‘WSR-88D
Build 10 Training’ Document ]
17
to get a feel for what the NEXRAD is scanning.
It
is worthwhile to note that NEXRAD does allow for one missing 2-D feature in one
of
the
elevation angles to allow for the possibility of range/velocity aliasing.
Once the 3-D correlated feature is defined, it is
labeled a ‘mesocyclone
circulation’ with a circle on the NEXRAD PUP (Principal User Processor) display,
as
long as minimum horizontal to vertical aspect ratio is maintained (see the
previous
section for a discussion on this).
For the Tornado Vortex Signature
(TVS in NEXRAD), this basically takes
advantage of all the work done by the MDA. In order to have a TVS, there must be
two
elevation slices that meet a certain threshold
shear value. This shear value is
determined
within a given slice by taking the maximum inbound and maximum outbound
radial
winds, as shown in figure 18.
Figure 18: Maximum inbound and outbound winds are
highlighted in green and red for the TVS calculation. [figure
and information from the NEXRAD OSF ‘WSR-88D Build 10 Training’ Document ]
The actual value of this shear is
definable by the user in the TVS Threshold Shear
(TTS) algorithm in NEXRAD. If the
minimum shear is met, a TVS is located on the
PUP
display with either a red triangle (tornado) or a yellow triangle (ETVS –
elevated
TVS,
or funnel cloud). The key thing to remember is that the MDA MUST DETECT A
MESOCYCLONE BEFORE THIS WILL
EXECUTE AT ALL. If there is no
mesocyclone identified by the MDA, the TVS (Build 9) will not execute.
The new algorithm for Build 10, the
Tornado Detection Algorithm (TDA) works
differently. First, it is a totally independent algorithm – it does not need the
MDA to
identify a mesocyclone. There are three
steps to the TDA.
In the first step, the TDA
identifies pattern vectors in a manner similar to the
MDA. The difference is that the
shear and momentum thresholds are calculated as the
pattern vector is defined. This is seen in Figure 19. One other item to note
here is that the
TDA
has a high shear/momentum threshold.
Only CYCLONIC shears are identified.
Figure 19: Maximum inbound and outbound winds are highlighted
in pink for the TDA calculation. The additional pattern vectors (in blue)
represent the MDA features, for comparison.
[figure and information from the NEXRAD OSF
‘WSR-88D Build 10 Training’ Document ]
In the second step, the pattern
vectors are correlated into 2-D features.
Two
things are done here: One, there must be a minimum of three (3) pattern
vectors in
order to define a 2-D feature.
Second, TDA runs a symmetry test to ensure the feature
maintains a certain range of length to width ratio. If this is accomplished, then a 2-D
feature is identified. See figure 20
for an example of this 2-D depiction.
Finally, the two dimensional
features are vertically correlated, like the MDA
Figure 20: The 2-D feature in the TDA is
highlighted by the heavy black line. The additional pattern vectors (in blue,
red, and green) represent the MDA/TVS features, for comparison. [figure and
information from the NEXRAD OSF ‘WSR-88D Build 10 Training’ Document ]
algorithm. There needs to be a minimum of
three (3) vertically correlated 2-D features in
order for the TDA to identify the overall feature as a 3-D feature (or, as
either a tornado
or
ETVS). Also, there may be a gap of one elevation where a 2-D feature is
missing, in
order to account for range/velocity considerations.
Comparisons are underway for the TVS
vs. the TDA. Recent commentary (from
the
October 1998 National Weather Association Meeting in
author attended ) seems to indicate that the new TDA algorithm has, in many
cases, been
identifying phenomena in severe thunderstorms as tornadoes, when no tornadic
activity
was
actually observed. If this pattern holds true, then one would expect a higher
false
alarm rate for tornado warnings in the near future. One well identified advantage of the
TDA,
though, was noted. Apparently, when
tornadoes DO occur, the TDA tends to alert
forecasters to the possibility of tornadoes faster that the older TVS did. Therefore, there
are
new strengths and weaknesses that must be further studied with regard to these
algorithms. In the interim, forecasters
should be cautious!
V. CONCLUSIONS
The WSR-88D has a tremendous capability to detect
the many different features
of
the supercell thunderstorm, using both reflectivity and radial velocity
information
displayed on the Principal User Processor (PUP).
With this device, warnings can be
issued to the public in the event of severe/tornadic conditions. The WSR-88D can also
provide automated detection of severe weather conditions using the Mesocyclone
Algorithm and the Tornado Detection Algorithm (TDA). The new TDA has shown an
improved capability to detect tornadoes in shorter time than required by the
older
Tornado
Vortex Signature (TVS) from previous NEXRAD software builds, although
many in the operational community have already commented that the new TDA
will tend
to
identify regions as tornadic when, in fact, they are not exhibiting tornadic
features.
This
will certainly lead to a higher false alarm rate for operational weather
forecasting
organizations, such as the National Weather Service, in the near future. Care must
be
exercised by operational forecasters to carefully analyze RADAR data by manual
inspection to help prevent false alarms, and to also pick up on tornadic cases
that the
TDA
might miss in the future.
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