THE DETECTION OF
MESOSCALE CYCLONES USING
DOPPLER WEATHER RADAR
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.
8 and 9: Finley, C. 1990: Technical
Training: Doppler Weather Radar: Thunderstorm Morphology and Dynamics, 27
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
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
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
To start this discussion, let us first obtain an initial definition of the mesoscale
cyclone, or “mesocyclone”. Rogers and Yau  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  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
Glass and Przybylinski  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
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.
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]
Let’s take another look at the yellow region in figure 8. Here, we have a small,
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]
V =C1 C1=Vmax/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  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
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),
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  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  was that the mesocyclone
should be evident for two volume scans for initial mesocyclone detection. However,
Keighton and Medlin  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]
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
(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
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
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.
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 ]
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
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!
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.
D.W., 1998: WSR-88D Build 10 Training, WSR-88D Operational Support Facility,
C. 1990: Technical Training: Doppler Weather
Radar: Thunderstorm Morphology and Dynamics, 27 August 1990,
F. H., and R. W. Przybylinski, 1994: Operational Considerations for the Detection
of Mesocyclones at
Keighton, S,and J. Medlin, 1995: The WSR-88D Operator’s Guide to Mesocyclone Recognition and Diagnosis, WSR-88D Operational Support Facility, Norman OK
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.
Rogers, R.R, and M.K. Yau, 1976: A Short Course in Cloud Physics, Pergamon Press Canada, Ltd., Suite No. 271, 253 College Street, Toronto, Ontario, Canada, M5T 1R5, 3rd edition, Copyright © 1989.
Rotunno, R., 1993: Supercell Thunderstorm Modelling and Theory. Geophysics Monograph 79, The Tornado: Its Structure, Dynamics, Prediction, and Hazards , 57-73.
Stumpf, G. J., A. Witt, E. D. Mitchell, P. L. Spencer, J. T. Johnson, M. D. Eilts, K. W. Thomas, and D.W Burgess, 1997: National Severe Storms Laboratory Mesocyclone Detection Algorithm for the WSR-88D, National Severe Storms Laboratory, Norman, Oklahoma, Weather and Forecasting, Vol. 13, pp. 304-326.
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
D. W., and L. R. Lemon, 1991: Characteristics of mesocyclones detected during a
NEXRAD test. Preprints, 25th Int'l Conference on Radar Meteorology.,
R. L. Lee, S. S. Parker, S. J. Keighton, and D. L. Floyd, 1995: A Study of mini
supercells observed by WSR-88D radars. Preprints, 27th
Conference on Radar Meteorology, Vail,
R. L., 1996: Improvement of the WSR-88D Mesocyclone Algorithm, WSR-88D
Operational Support Facility,
G. J., and C. Marzban, 1995: NSSL Build 2.0 Mesocyclone Detection Algorithm ( MDA2), National Severe Storms Laboratory,
L. J., and L. Cantrell, 1996: The Role of Vertical Buoyancy Distributions in
Miniature Supercells. Preprints, 18th Conference on Severe Local
Brooks, H. E., C. A. Doswell III, and R. B. Wilhelmson, 1994: The role of midtropospheric winds in the evolution and maintenance of low-level mesocyclones. Mon. Wea. Rev., 122, 126-136.
_____, M. T. Carr, and J. E. Ruthford, 1996: Preliminary analysis of soundings
from VORTEX-95. Preprints, 18th Conf. on Severe
Burgess, D. W., R. L. Lee, S. S. Parker, S. J. Keighton, and D. L. Floyd, 1995: A Study of mini supercells observed by WSR-88D radars. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc., 4-6.
B. N., and R. Prentice, 1996: Mesocyclone characteristics of mini supercell
thunderstorms. Preprints, 15th Conf. On Wea. Anal. and
R. R., 1995: Improvement of the WSR-88D Mesocyclone Algorithm. Documentation available from the WSR-88D Operational Support
C., and H. Paik, 1996: Various Statistical Analyses of MDA Data. Paper currently in press. (Draft available from the NSSL,
Mitchell, E. D., 1995: An enhanced NSSL tornado detection algorithm. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Sci., 406-408.
Rasmussen, E. N., J. M. Straka, R. Davies-Jones, C. A. Doswell III, F. H. Carr, M. D. Eilts, and D. R. MacGorman, 1994: Verification of the origins of rotation in tornadoes experiment: VORTEX. Bull. Amer. Meteor. Soc., 75, 995-1006.
G. J., and C. Marzban, 1995: NSSL Build 2.0 Mesocyclone Detection Algorithm (MDA2), Final Documentation. Available
from the NSSL,
_____, _____, and E. N. Rasmussen, 1995: The new NSSL Mesocyclone Detection Algorithm: A paradigm shift in the understanding of storm-scale circulation detection. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Sci., 208-210.
Trapp, R. J., and E. D. Mitchell, 1995: Characteristics of tornadic vortex signatures detected by WSR-88D radars. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Sci., 211-212.