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TABLE of CONTENTS

1- INTRODUCTION

2- DATA USED

2.1- Historical Background

2.2- Data Resolution

2.3- Data Acquisition and Selection

2.4- Data Gaps

2.5- Data Processing

3- SEVERE WEATHER MAPS and ALGORITHMS

3.1- VIL (Vertically Integrated Liquid water content)

3.2- GUST (Estimate of maximum surface winds)

3.3- VPI (Vertical Profile Indicator or Overhang)

3.4a- Mesocyclone Detection Algorithm

3.4b- Mesocyclone Tracking Algorithm

4- RESULTS

4.1- Mesocyclone Statistics

4.2- Reflectivity Analysis

5- CONCLUSIONS

6- ACKNOWLEDGEMENTS

7- REFERENCES

1- INTRODUCTION

It has been suggested that the variability of severe weather phenomena could be the most

sensitive manifestation of climate change. For example, an analysis of 29 years of hail data from

central Alberta indicates that the frequency of hail events has changed dramatically over the past

several decades (Smith et al. 1998). Likewise, the climatology of lightning flashes for Alberta

reveals that there has been a significant increase in the number of cloud-to-ground lightning

strikes during the last decade (Kozak 1998). It may be argued that such trends are the inevitable

result of increasing, (and in some cases decreasing), density of observational capabilities, be it

hardware or human as is the case for the visual reporting of tornado and hail events. In contrast, a

climatology by radar, when properly calibrated and when allowance if made for any missing

periods, would have the advantage of being more consistent over the region of interest and over

the entire period of observation. Note that because of its continuous space coverage, radar is

the only instrument that will not miss any severe weather event within its domain of

observation. Radar-derived statistics are thus not dependent on population density.

A number of climatological studies and data collection programs have been conducted

mostly in the US, but they involved observations of severe weather events by instruments other

than radar. (An exception is a recent preliminary attempt undertaken by Mapes, 2001). They have

identified the nature of the various severe weather phenomena, risk factors for different parts of

the country, and detection system technologies. Unfortunately, Canada still lags behind in this

domain, and a comprehensive climatology of severe weather events has yet to be produced. The

work by Lackmann and Gyakum (1996) represents a first step towards this goal. As a component

of the Global Energy and Water Cycle Experiment (GEWEX), they have successfully identified

large-scale precursors to significant precipitation events in the Mackenzie River Basin. More

recently, Fischer (1997) has underlined the unique large-scale precursors to heavy precipitation

events in the Montreal region. We currently have available to us a readily accessible archive of

gridded reanalysis data for the globe that extends back to 1963. It is in the public interest to

identify and to study the precursors to these dangerous events but we must first have a systematic

procedure for identifying them. We believe that radar archives can now provide this objective

source of information that has not yet been tapped.

A climatology of severe weather phenomena from past records is highly desirable since it

provides a base for comparison of future events. An ongoing survey of severe weather events

can be used to monitor climate variability as well as providing information of immediate

practical interest. Observations from a radar network are ideal for gathering the information

necessary to produce such a climatology. The Canadian territory just north of the US border,

where most of the population resides, will be covered by 32 Doppler radars, Lapczak et al.

(1997). Three of these have a relatively long history of operation: the Doppler radar data archives

at King City north of Toronto extend back to 1984, those at Carvel west of Edmonton date back

to 1991, and those at the Marshall Radar Observatory (MRO) of McGill University at Ste-Anne-

de-Bellevue west of Montreal began in 1993. Data from these radars are collected for operational

purposes and a considerable amount of information is already recorded and available for analysis.

Thus, these three Doppler radars are the ideal test-bed for the proposed study.

The overall objectives of our research efforts thus consist of the following:

i) To compile a preliminary severe weather climatology for southern Quebec, southern Ontario,

and Alberta.

ii) To develop a methodology for a future, in-line, automatic climatological compilation to be

implemented in the UNIVERSAL RADAR PROCESSOR (URP) presently developed for

the CANADIAN RADAR NETWORK.

iii) To relate severe weather events to larger scale climatology of weather patterns.

iv) To contribute to the systematic observation of the climate of Canada.

v) To improve our climate models by providing a validation database.

vi) To reduce the uncertainties in our knowledge of the magnitude, rate, and regional distribution

of changes in severe weather climatology.

vii) To determine the planetary-scale precursors that affect the severe weather climatology and

to improve the understanding of key climate processes so that climate models at all scales

might be used to determine future changes in frequency or intensity of severe weather.

Several severe weather detection algorithms are in operational use and their statistics

can be compiled for our purpose. In this report, we will concentrate on presenting the areal,

temporal and geographical distribution of severe weather events as perceived by radar data. We

have completed the derivation of statistics of severe weather events using the archived MRO S-

band radar data from 1993 to 2000. Those for the King City and Carvel C-band radars are

currently being processed. The analysis of one month (July 2000) of Carvel radar data is attached

as a separate document to this report. In a subsequent investigation that will follow our

identification and statistical description of severe weather events, we intend to integrate this

severe weather climatology with a study of planetary-scale precursors of these events. Precedent

for this scale interaction approach is seen in the work of Lackmann and Gyakum (1996), in which

a planetary-scale upstream ridge in the Bering Sea, which induces a downstream trough, is

typically observed two days prior to the onset of significant precipitation in the Mackenzie River

Basin. A preliminary analysis using a similar approach based on a set of 22 days of intense

mesocyclonic activity around Montreal has already been carried out and is included in this report

in Appendix A.

In section 2 of this report we discuss the data archive system of the McGill Radar and

estimate all the data gaps during our 8-year study. In Chapter 3, we describe the type of radar

maps selected to identify severe weather, with particular emphasis on the mesocyclone detection

and tracking algorithms. In the first part of the section on "Results", the characteristics and the

geographical distribution of all detected mesocyclones is presented. The second part deals with

the climatology of severe convection as deduced from the reflectivity maps alone, in particular

from the upper level VIL parameter. Section 5 summarizes the report and offers some

suggestions for future work.

2- DATA USED

2.1 Historical Background

The J. S. Marshall Radar Observatory (MRO) of McGill University has been in real-time

operation since 1970 and was capable of archiving digital data as early as 1974. 'Reflectivity-

only' records were archived on large magnetic tapes until 1993 when Doppler information was

added. However, these records had numerous data gaps due in part to the limited storage capacity

of that medium (~45 to 70 Mbytes) that also suffered from a rapid deterioration with time, that is,

existing records could not be reread. Consequently, they have not been transferred into another

medium like the smaller 2.5 or 5.0 Gbyte exabyte tape that replaced it before the complete

demise of the cumbersome magnetic tape units. Therefore, our investigation of severe weather

events will use simultaneous reflectivity and Doppler data of the 8 summers from 1993 to 2000

inclusive.

During the course of its history, the scanning strategy of the S-band (10 cm) MRO radar

has undergone some modifications but has kept its fundamental objective of achieving 24

elevation angles during its 5-minute cycle. In 1993, a Doppler capability was implemented

without compromising the existing data: radial velocity measurements were collected at all 24

elevation angles simultaneously with the reflectivity information. This was achieved with a pulse-

pair algorithm applied to the first 16 pulse-pairs of each azimuth, followed by a single pulse for

the reflectivity. Thus, a full three-dimensional reflectivity (up to 480 km) and Doppler volume

scan (up to 125 km with a Nyquist interval of

31.2 m/s) was made possible. Shortly afterwards,

in August 1994, the antenna scanning strategy was altered so as to perform two 2.5-minute cycles

of 12 odd and then of 12 even elevation angles in an effort to detect short-lived phenomena like

microbursts. Then, in August 1998, in preparation for the implementation of a dual polarization

capability finalized in July 1999, the scanning and data processing strategy had to be modified in

order to adapt to the requirements of the NCAR VIRAQ board selected to perform the

digitization of the radar signals. Doppler and reflectivity data from the first set of 12 odd

elevation angles were collected at 1200 PRF (maximum range of 125 km) as is the case for the

last 7 angles of the even set where range folding is unlikely. Elevation #2 is sampled at a dual

PRF of 400 and 600 Hz (to achieve long range surveillance up to 375 km) while data from the

remaining 4 even elevation angles (#4 to #10) are obtained at 600 PRF to provide measurements

up to 250 km. These changes were accompanied by significant software modifications that

perform the necessary range and velocity unfolding and thus maintain full Doppler (Nyquist

velocity of 31.2 m/s) and reflectivity volume scans up to a 250 km range for all 24 elevation

angles.

2.2 Data Resolution

The resolution of our data sets followed the changes made throughout the years. One

constant is the geometrical progression in the separation of the 24 elevation angles, from 0.5

degrees above the horizon for elevation #1 to 34.4 degrees for the last elevation. Since 1993, the

azimuth resolution has been fixed at 1 degree, slightly larger than the radar beam width of 0.86

The 8-bit intensity resolution of the Doppler data has been 0.5 m/s, while for reflectivity, it is

1.28 dBZ for data prior August '98 and of 0.40 dBZ afterwards. The range resolution is of 1 km

up to 120 km in range, of 2 km between 120 and 240 km and of 4 km beyond 240 km.

Considering data within the 240 km range, the size of a full volume scan at normal resolution is

about 1 Mbyte. In fact, when recorded, it is substantially less because of the suppression of most

of the 'no-echo' bins. In cases of severe convection, manual intervention allowed archived records

to be at a resolution of 250 meters up to a range of 80 km. This capability is useful in visualizing,

and possibly in improving the detection of mesocyclones.

2.3 Data Acquisition and Selection

Since the advent of digital radar data about 25 years ago, the immense quantity of data

limited most analysis to a few case studies, to a small sample of selected events or to relative

short periods of continuous data. The early international GATE experiment (Hudlow and

Patterson, 1979), is perhaps a notable exception. However, as stated by Kruger et al. (1999),

"recent progress in computer information storage technologies as well as developments of

efficient data formats provided the opportunity to change this situation". The archival procedures

at MRO reflect these improvements. From 1993 until August '98, the volume scans were

immediately written into an exabyte tape at the end of each 5-minute radar cycle. This was

necessary on account of the limited memory capacity of the computer handling the real-time

processing of radar data. Depending on the frequency, extent and location of the radar echoes, the

2.5 Gbyte tape would last from a minimum of about 3 days to a maximum of a week or perhaps

more in winter. Over this 5-year period, about 275 exbyte tapes were recorded. In addition to data

interruptions caused by power failures, radar hardware problems, radar maintenance and

upgrades, some data gaps were the result of "reaching the end of tape" and by "writing errors" by

the tape unit. However, the data lost when severe weather affected our region is relatively small

from 1994 onwards. An assessment is provided in the following section 2.4. From August '98 to

the present, the increased storage capacity of the radar data processing system permits the data to

be first safely stored in its memory. It is subsequently downloaded to a 5 Gbyte tape once every

few weeks as required, eliminating gaps due to tape problems. This procedure also permitted a

more efficient storage of radar precipitation data because 'clear' periods with only ground echoes

and/or anomalous propagation can be easily omitted from the archived data set. As a result, only

forty 5 Gbyte tapes were required from August '98 until September 2000, the last month of our

analysis period. Even if only the 5 months from May to September were considered for our

convective study, about 150 2.5Gbyte and 22 5Gbyte tapes would have to be processed. An

initial pass through this huge data set just to generate and visualize basic radar maps for the

selection of the convective periods would have entailed a prohibitive amount of time.

Fortunately, since the end of June '94, we had initiated at MRO a crude separate archival of only

the low level CAPPI maps at a 2-km resolution on a (240 by 240) grid. These maps are stored

every 30 minutes on the computer hard disk if sufficient echoes are detected in addition to the

normal ground echo coverage on the selected CAPPI height. A simple software program enables

the quick animation of all the stored images of any given day. This "log" feature enormously

facilitates the selection of the convective time intervals for an entire summer by visual inspection

by an experienced radar meteorologist. Since the basis of selection is a low level CAPPI map

rather than an upper level CAPPI that would have been more suitable for unequivocally

identifying convection, we have included all doubtful periods for processing and for the

extraction of climatological statistics.

2.4 Data Gaps

For over 30 years, the MRO has been striving to fulfill the dual role of a real-time

operational radar and of a centre for research and development in association with McGill

University. The adherence to the latter goal has often meant a temporary cessation of its routine

operation in order to implement the hardware modifications for novel approaches (for example,

the recent polarization upgrade completed in July 1999 and the experimentation with a collocated

X-band transmitter during the '90s). As much as possible, these planned 'down times' have been

scheduled to occur in the spring or fall, before or after the 'severe weather' season. However, the

now aging equipment of our radar knows of no such precaution and there have been the

inevitable interruptions due to hardware failures at the most inopportune times. (Those due to

power failures have been drastically reduced after the installation of a UPS (Uninterrupted Power

Supply) system in the late '80s). Data gaps related to archival problems, common prior 1998,

have now been completely eliminated. Nonetheless, some archived data had to be excluded from

our analysis because of some intermittent problems with the PRF circuitry.

It is difficult to estimate the exact number of hours or days with convection lost when the

radar itself is down. In the assessment that follows, we have relied on some brief notes or

comments written by the MRO staff. It has only been since the start of the 2000 summer season

that a detailed account has been kept of the exact number of hours lost and of the nature of the

precipitation during those outages. The latter has been possible only since the RAPID system (see

below) has been able to also process data from 4 other radars. Thus, for the previous years, we

have attempted to provide at least a general overview of the amount of time lost when some level

of convection would have likely occurred over the radar coverage (within 240 km). The 'lost

time' does not mean that severe weather, as defined in terms of the thresholds in the following

section, did continuously occur over the indicated period. 'Lost time' is given in terms of days and

hours. By the former is meant that the radar was down for the entire day when some form of

convection was confirmed or suspected to occur during the preferred period of 11:00 to 21:00

local time. In the case of data interruption during existing convective activity, the number of

'hours lost' have been estimated on the basis of the storm location from the 240 km boundary and

the speed at the onset of the data gap as well as on the diurnal trend noted above. The year 1993

is not included because, after the dopplerization in March of that year, the data archival system

went through some growing pains whereby data were not routinely archived. The absence of the

"log" facility noted in section 2.3 earlier further rendered difficult the inclusion of all convective

events of the existing 1993 data set. Moreover, due to a software error in the range normalization

procedure, the high reflectivities within about 50 km were reduced by a much larger amount.

Data from 1993 has thus been selected from known severe weather days that were the basis of a

study of microbursts made for Transport Canada Aviation, Bellon and Zawadzki (1994). They are

not intended to characterize the level of activity for the entire season and should thus not be used

on a comparative basis. Here is the list of the estimated data loss during convective activity for

the summers of 1994 to 2000 inclusively:

1994: 1 day (6-May) + 2 hours

1995: Radar hardware problems kept the radar out of commission for most of the time between

June 19th and July 17th. Fortunately, this coincided with a very dry period except for

some convection on July 1st, 6th and 7th.

3 days + 2 hours

1996: No convective days were apparently lost

1997: A late season hailstorm on September 29th was lost when the radar was down for repairs.

1 day + 7 hours

1998: 20-May and 8-July could not be processed because the tapes could not be re-read.

2 days + 3 hours

1999: Radar down for polarization upgrade from April 26th until May 31st. Again, this was a

very dry period except for May 8th when a weak tornado may have been responsible for

some wind damage near Ottawa. Then, problems related to the newly installed

polarization equipment persisted for a 2-week period at the end of July, resulting into an

estimated loss of about one 'day'.

2 days + 7 hours

2000: 12 hours

TOTAL: 9 days + 33 hours

The percentage of the entire sample represented by the above 'total losses' cannot be

easily estimated. An attempt is made at the end of the next section.

2.5 Data Processing

Over the years, MRO personnel have developed a number of real-time radar data

processing systems, starting with SHARP (SHort-term Automated Radar Prediction, Bellon and

Austin, 1976). It was followed by PPS (Product Processing System, Austin et al. (1986) and

Duncan et al. 1992). Since 1995, we are constantly upgrading our current RAPID (Radar data

Analysis, Processing and Interactive Display) system (Kilambi et al.1997 and Bellon and

Kilambi, 1999). RAPID can also operate in simulation mode using historical data sets as input. It

ingests reflectivity and Doppler data in spherical coordinates and performs some necessary

'cleaning' prior the generation of a user-selectable number of Cartesian radar maps. As already

mentioned in section 2.1, this 'cleaning' involves the range and velocity unfolding of the raw

volume scans. In addition, since the actual time of data collection is different from elevation to

elevation during the execution of the 5-minute radar cycle, the data at the various elevation

angles are shifted by an appropriate amount that also depends on the average velocity of the

precipitation pattern. This spatial adjustment simulates an instantaneous measurement of the

entire volume scan. Other pre-processing involves the elimination of regular ground echoes and

of anomalous propagation. The latter procedure is essential for estimating surface rainfall but is

less crucial in our work that focuses on the statistics of severe convection obtained mainly with

radar maps generated from higher altitudes.

All Cartesian maps in our study are in the form of a (240 by 240) array at 1- and 2-km

resolution. They are compressed and stored into a cyclical file for re-analysis. The present

configuration allows 25600 maps to be stored per cyclical file but this limit can easily be

increased. The time period included in such data set depends on the number of maps generated

for every radar cycle. We have selected the following 5 types of radar maps as being most

suitable for our study of convective weather:

1- Low level CAPPI (at 2.5 km)

- needed to compute the velocity of the precipitation pattern required by the 'shifting' routine, by

the mesocyclone and cell tracking algorithms and by the rainfall accumulation module.

- the header of this map contains the results of the mesocyclone and 'overhang' algorithms

2- Regular VIL, referred to simply as VIL

- vertical integration of reflectivity from a low altitude (2.5 km) up to echo top

- also needed for the computation of the GUST map

3- Elevated VIL or upper level VIL, referred to simply as UVIL

- vertical integration of reflectivity from a 'middle' altitude (5 km) to echo top

4- GUST

- combines data from the regular VIL and from an echo top map of the 18 dBZ reflectivity

5- Upper level CAPPI (reflectivity at 7 km)

A (3x3) smoother is applied to the latter in order to reduce the noise caused by the limited

sampling of the reflectivity data. Since the VIL and GUST maps are already the results of an

integration (vertical), no smoothing is performed.

(We have also generated 1-hr accumulation maps every 30 minutes, and 2- and 4-hr

accumulation maps every 1 hour, but their analysis is not included in this report.)

Consequently, with 2 resolutions per map, about 130 maps were generated for every hour

of data processed. A typical cyclical product file thus can contain nearly 200 hours of data, which

in many cases, is the entire data set of convective events selected for an entire summer. A total of

10 such files were required to hold all the maps for our 8-year study.

In order to relate the data lost as estimated in section 2.4 to the entire sample processed,

we define a 'convective hour' when one of the following conditions is satisfied on at least one of

the 12 maps of either resolution during that hour:

a) 5 pixels exceeding a VIL threshold of 30 kg/m

b) 5 pixels exceeding a UVIL threshold of 15 kg/m

c) 5 pixels exceeding a GUST threshold of 15 m/s

d) 10 pixels exceeding a reflectivity of 48 dBZ on the 7 km CAPPI maps

e) the presence of an 'overhang' of any size

In table 2.1, we provide the total number of 'convective hours' so defined for each of the

8 years analyzed and within the indicated radar ranges. The first and last day with convection

within 240 km is also given.

Year

12-180 km

12-240 km

First Day

Last Day

1993

119

27 June

10 September

1994

228

254

31 May

29 August

1995

102

113

21 May

5 September

1996

104

113

20 April

26 August

1997

193

210

1 May

2 September

1998

246

273

7 May

28 September

1999

189

233

3 April

13 October

2000

175

224

7 May

21 September

TOTAL

1335

1539

Table 2.1: Number of 'convective hours' in our entire data sample for each of the 8 years

analyzed and within the indicated ranges.

Data from 1993 is not complete.

In the previous section, we estimated a data loss of 9 'days' and of 33 'hours' over the

summers from 1994 to 2000. From the table above, this 7-year period contains over 1400

'convective hours' if data up to a 240-km range is considered. Depending on the number of

'convective hours' that can be arbitrarily attributed to a 'lost day', the total losses can be evaluated

to be of the order of 5%. This is less than the variability observed among the various years as

seen for example in Figs. 4.1, 4.11 and 4.13. Thus, in section 4, the statistical results will be

presented without attempting any yearly adjustment for these estimated losses.

3- SEVERE WEATHER MAPS and ALGORITHMS

In this section we describe in greater detail and exemplify the severe weather maps used

in our study.

3.1 VIL (Vertically Integrated Liquid water content)

This product involves the summation of the radar reflectivities from all the elevation

scans above a sufficiently low altitude and deriving a liquid equivalent as described by Greene

and Clark (1972). Since the liquid water content M is proportional to the (4/7) power of the radar

reflectivity factor Z, the VIL value is heavily dependent on the stronger reflectivities, (> 40

dBZ). In the integration process, the reflectivity of each of the 24 elevation angles is multiplied

by the vertical thickness associated with each elevation angle. This vertical thickness is unrelated

to the actual vertical antenna beam resolution, but depends on the spacing between the 24

elevation angles and, of course, on the range from the radar. The VIL map is an excellent

indicator of severe weather since, as thunderstorms intensify, their updrafts and consequently

their ability to sustain airborne liquid water and other hydrometeors over a deep layer of the

atmosphere drastically increases when compared with stratiform situations or with weak

convective storms. The units of this radar product are expressed in terms of kg/m

. Based on

experience with thunderstorms of the Montreal area, values in excess of 30 or 40 kg/m

represents a severe storm. Unlike a CAPPI map that can show high rainfall rates at only the

selected altitude, or an ET map with high echo tops that may be associated with relatively light

reflectivities, the VIL product best measures the overall '3-D' strength of a storm by 'weighing'

all the reflectivities above a given point. Fig. 3.1a is an example at a resolution if 1 km. Since

"what goes up must come down", a high magnitude for this parameter represents a great potential

for an imminent collapse of the storm reflectivity core often associated with microbursts. As

shown later in eq. (3.1), it is the primary input into the gust algorithm.

There are two input parameters associated with this product:

b) Lowest Height: This parameter specifies the height above which the radar reflectivity

is integrated. This height should be sufficiently high to avoid contamination by ground echoes. If

at distant ranges the height of the lowest elevation angle is already above this 'lowest height', the

observed reflectivity is assumed to extend downward unchanged to the selected height. This

assures that the integration is performed over the same vertical slice of the atmosphere,

regardless of range. For the MRO radar with the Laurentians Hills to the north, the Adirondacks

to the south and the Green Mountains in the southeast, this parameter must be relatively high, at

least 2.5 km. However, spurious values due to the highest ground echoes are present, (around the

120-km range in the southeast in Fig. 3.1a). Therefore, we have also generated an 'elevated', or

'upper level' VIL, or UVIL map, with a lowest height threshold of 5.0 km. This special product

has the advantage of completely eliminating ground echo and anomalous propagation as well

bright band and shadowing effects. It is ideally suited for indicating convection and will thus be

extensively used in our analyses. An example is shown in Fig. 3.1b.

(a)

(b)

Fig. 3.1: Example of a 'regular' VIL in (a), where the vertical integration begins at an altitude of
2.5 km and of a 'upper level' VIL in (b) where the summation begins at a height of 5 km. Both
maps consist of a (240 x 240) array at 1-km resolution. In (a), the tracks of VIL cell greater than
30 kg/m

are overlaid. The time in GMT at the beginning and end of each track is indicated. The

width of the track is related to an equivalent cell diameter as indicated on the bottom right
corner of the map. The color of the track gives the 'liquid content' of the entire cell in units of
kilotons according to the scale on the right of the VIL scale.

b) Surface Extrapolation: When this flag is set, the reflectivity at the lowest height is

extrapolated downward to the surface in order to obtain the actual liquid water content above a

given pixel of the map. The GUST product that uses VIL values as input assumes this to be the

case. However, for reasons just discussed above, no surface extrapolation is performed with the

MRO data.

3.2 GUST (Estimate of maximum surface winds)

The gust algorithm was proposed by Stewart (1991) and is based on the cloud top

penetrative downdraft mechanism developed by Emanuel (1981). This technique involves the

combination of the vertically integrated liquid water content, VIL, and of the thunderstorm echo

top, ET, in order to estimate surface winds below a thunderstorm solely from reflectivity data.

The magnitude of the maximum surface wind gust W can be expressed as

W = (c

VIL - c

)

1/2

(3.1)

where W is in m/s, VIL in kg/m

, ET in km and c

and c

are two constants with a value of 20.6

m/sec

and 3.1 s

respectively.

The echo top is that corresponding to a reflectivity threshold usually taken as 18 dBZ.

Table 3.1 provides the gust estimates in m/s obtained from (3.1) as a function of typical

combinations of VIL and ET magnitudes. Note that a higher echo top for a given VIL reduces

the gust estimate since the amount of liquid water available for evaporation is spread over a

region of the atmosphere that cannot interact with the mid level entrainment. It is the VIL

concentration in the lower altitudes, about < 8 km, that has the potential to enhance the

evaporative process leading to a wet microburst. A VIL value of 30 kg/m

coupled with an echo

top of 8 km yields a gust estimate of 20 m/s.

VIL

Height (km) of 18 dBZ echo

kg/m

9.7

7.3

2.5

0.0

14.0

12.5

10.5

7.5

0.0

17.3

16.1

14.6

12.6

10.0

5.9

0.0

20.1

19.0

17.8

16.2

14.3

11.7

8.1

0.0

22.5

21.6

20.5

19.1

17.5

15.5

13.0

9.5

2.5

0.0

24.7

23.9

22.8

21.7

20.2

18.5

16.5

13.9

10.5

4.4

26.7

25.9

25.0

23.9

22.6

21.1

19.4

17.2

14.6

11.0

28.6

27.8

27.0

26.0

24.8

23.5

21.9

20.0

17.8

15.0

30.3

29.6

28.8

27.9

26.8

25.6

24.1

22.4

20.5

18.1

32.0

31.3

30.6

29.7

28.7

27.5

26.2

24.6

22.9

20.8

33.5

32.9

32.2

31.4

30.4

29.3

28.1

26.6

25.0

23.1

35.0

34.5

33.8

33.0

32.1

31.0

29.8

28.5

27.0

25.3

36.5

35.9

35.3

34.5

33.6

32.6

31.5

30.3

28.8

27.2

Table 3.1: Gust values in m/s as a function of VIL and echo top heights according to Eq. (3.1).

The MRO radar, with 24 elevation angles every 5 minutes is ideally suited, in terms of the

horizontal and vertical resolution of its reflectivity data, for the generation of accurate VIL and ET

maps required by the gust algorithm. The GUST algorithm is being used extensively even with the

current availability of Doppler data since it easily points out storms that are potential candidates for

strong surface winds and that thus require further analysis with Doppler data by the forecasters.

Moreover, its continued use is assured even with the advent of Doppler data because microburst

detection is difficult at ranges beyond about 80 km due to the broadening of the 1 degree radar beam

and to the radar inability to observe low altitude phenomena. An investigation by Amorim et al. (1997)

showed that the high VIL values, and hence the large GUST estimates, are observable on the average

10 minutes prior to a possible microburst detection with Doppler radar. A GUST map can thus be

considered as a short-term forecast of such an event.

Strictly speaking, the gust algorithm ought to be applied only to pulse-type storms

developing in a weakly sheared environment in which an elevated layer of low equivalent

potential temperature is present above a deep and moist low level air. Nevertheless, forecasters at

the Montreal weather office that have been using it since 1991 consider the gust algorithm to be

reliable even for fast moving thunderstorms forming along a frontal boundary. An example of a

GUST map is shown in Fig. 3.2a. To complete the list of severe weather maps, a 7-km CAPPI is

also provided in Fig. 3.2b for the corresponding time. Note how concisely the GUST map

summarizes the location of the potentially severe weather echoes while the 7-km CAPPI map

emphasizes the extent of the leading edge of anvils associated with developed thunderstorms.

(a)

(b)

Fig. 3.2: Examples of a GUST map in (a) and of a 7-km CAPPI map in (b). Note the extensive
anvils downwind from the two major storms portrayed.

3.3 VPI (Vertical Profile Indicator or Overhang)

The VPI product is not a map by itself but rather some information in the form of an

outline around thunderstorms with a potential of severe weather and/or hail that can be added by

the RAPID software onto horizontal cross-section images like CAPPI, Echo Top and VIL maps.

It is also known as the "overhang" (in English) or "surplomb" (in French) indicator. Unlike the

procedure described by Petrocchi (1982) which identifies as many as 9 hail indicators or criteria

that may be retrieved from 3-D radar data, our identification of an accumulation level is based on

the simple comparison of the reflectivities at the mid-level and lower level of the atmosphere.

The user-selectable parameters associated with this module are:

a) Height of the low-level CAPPI that is also used by the forecast and accumulation modules,

selected at 2.5 km in our study

b) Height of mid-level CAPPI (

≥

6 km)

c) Reflectivity threshold at the mid-level CAPPI (

≥

45 dBZ)

d) Difference between mid-level and low level reflectivities (

≥

10 dBZ)

The values of these parameters are undoubtedly climatologically dependent. A mid-level

height of 7 km, an upper level reflectivity threshold of 48 dBZ and a reflectivity difference of 10

dBZ have been selected for our study. They have been used by forecasters of the Montreal area

since 1991. The combination of these thresholds identifies the weak echo region and the

pronounced accumulation of reflectivities aloft that are both associated with the exceptionally

strong updrafts present in severe thunderstorms.

For display purposes, an additional parameter is the radius of the circle (~ 5 km) drawn

around each of the pixels satisfying the above conditions. After eliminating isolated pixels, the

coordinates of the perimeter outlining the combined region of influence of the declared severe

weather pixels are then assigned a special value into any horizontal cross-section maps. Thus,

upon display and/or animation of CAPPIs, Echo Top or VIL maps, only those cells associated

with severe weather are circumscribed by the outline. The irregular outline over the storm in the

west on the CAPPI map of Fig 3.3a indicates the presence of an 'overhang'.

3.4a MESOCYCLONE Detection Algorithm

The mesocyclone identification algorithm developed for the McGill Doppler Radar is

based essentially on a procedure outlined by Zrnic et al. (1985) using NSSL data of Oklahoma

storms. Brown and Wood (1991) and Desrochers and Donaldson (1992) are two other relevant

papers on this topic (plus all the references therein). Results of our mesocyclonic detection

algorithm with thresholds that are applicable to storms of the southern region of Quebec are

summarized by Vaillancourt et al. (1997). It would be sufficient to recall here that mesocyclone

identification involves the following main steps:

1- The detection of the so-called pattern vectors (PVs) that are azimuthal runs of increasing

Doppler velocities along a fixed radar range bin. These PVs must exceed either a high

momentum threshold of 60 (m/s)km or a high shear threshold of 6 (m/s)/km. This

combination corresponds to the moderate filtering technique adopted in the Quebec study

reported by Vaillancourt et al. (1997).

2- The sorting of the more significant PVs into two-dimensional shear features according some

radial (4 km) and azimuthal (5 degrees) proximity criteria.

3- The association of shear features pertaining to the same elevation scan into potential

2-D mesocyclone features. These features must exceed a threshold radial diameter of

2 km and have an elongation such that the ratio of the azimuthal and radial diameter is

within the acceptable values of 0.4 and 1.9.

4- The examination of the vertical structure of the latter in order to classify them as "declared

3-D mesocyclones". Essentially, it is required that the 2-D mesocyclonic features be

detected on a sufficient number of elevation angles so as to give a realistic thickness to

the mesocyclone. A threshold of 2 km has been selected in our study. The volume scan

consisting of 24 elevation angles available every 5 minutes with the McGill Doppler

system is particularly useful in this respect.

5- The association of a "declared 3-D mesocyclone" with a cell exceeding a selectable minimum

reflectivity. For our purpose, we require a reflectivity greater than 40 dBZ on at least 5%

of the pixels within a 6 km by 6 km square centred over the meso on the two elevation

angles that are just above an altitude of 3 km.

6- Finally, a greater confidence in mesocyclone detection is achieved if a "declared 3-D

mesocyclone" is seen to persist for 2 or more radar cycles (

10 min.). The animation

capability of the DISPLAY system can be used to examine this temporal continuity. For

the specific need of our research effort, a mesocyclone-tracking algorithm has been

formulated in order to quantify this temporal continuity.

When a mesocyclone has been detected, its actual location is indicated by a square on

any horizontal cross-section map as in Fig. 3.3a. The size of this square is proportional to the

mean radius of the declared mesocyclone. In Fig. 3.3b and 3.3c, we exemplify how mesocyclonic

features may be better highlighted by smoothing, and especially, by removing the storm

propagation velocity from the measured radial component. This 'Lagrangian' correction does not

affect the appearance of mesocyclones that move in a direction perpendicular to the radar

viewing angle (for example, the mesocyclone in the NNW), but brings into sharper focus the

internal rotation of those moving either directly towards or away from the radar. This occurs

because a significant radial component of the precipitation pattern propagation velocity, which in

this example is in excess of 100 km/h from 250 degrees, is added to the solid body rotation of the

mesocyclones resulting into radial velocities that actually exceeded the Nyquist interval. After a

successful velocity unfolding, the approaching velocities of the vortex in the west reach

magnitudes of the order of 45 to 54 m/s that are not evident from the usual Doppler scale which is

limited by the Nyquist interval at both ends. It is only when the radial component of the overall

motion is subtracted that the mesocyclonic couplet of approaching and receding velocities is

clearly revealed as shown in the zoomed image of Fig. 3.3c.

rotational velocity and of the average maximum shear. The latter two parameters are initially

computed at the level of a pattern vector as follows:

rot

= (V

-V

)/2

and

shear = (V

-V

)/L

where

is the maximum approaching velocity (positive), V

is the maximum receding velocity

(negative) and L is their separation or length of the pattern vector. We have defined the average

maximum shear as the average of the 2-D maxima found at each elevation angle while the peak

rot

is taken to be the maximum among all the PVs making up the mesocyclone. Thus, for the

mesocyclone in the NNW, peak(V

rot

) = 20 m/s while the average of the maximum shear found

over the 10 elevation angles for which a mesocyclonic couplet has been identified is computed to

be 11 m/s/km.

3.4b Mesocyclone Tracking Algorithm

Positional information and other characteristics of the declared mesocylones of a given

radar cycle are stored in the header of the low-level CAPPI map of Fig. 3.3a. Using such

information from all CAPPI maps of a RAPID product file, or for any shorter time period, a

simple tracking algorithm attempts to follow each declared mesocyclone and displays the

resulting trajectory on any suitable map as illustrated in Fig. 3.3d. This algorithm first requires

the velocity of the precipitation pattern which is computed by a cross-correlation technique on 9

sub-areas of the low-level CAPPI map, each being (80 km by 80 km). From the known position

of a mesocyclone of the current radar cycle, the computed velocity is used to forecast its position

on the next cycle. A user-defined 'search radius' outlines a circular neighborhood around this

expected position where the presence of a mesocyclone would be considered as the continuation

of the one being tracked. A mesocyclone is considered tracked if it persists for at least 2 cycles,

that is, 10 minutes. A mesocyclone track or lifetime is maintained even if no subsequent

detections are found for up to a selectable time interval but then reappears within the expected

position on the following cycle. In this case, the 'search radius' is enlarged by a suitable factor. In

our analysis, we have allowed up to 10 minutes, or 2 radar cycles, during which an existing

mesocyclone can go undetected or become untrackable. When a mesocyclone lasts for 3 or more

cycles, the user has the choice of using its actual past positions, rather then the velocity of the

surrounding precipitation, for the purpose of predicting its future location. As each '5-minute

mesocyclone detection' or (m5) is associated with a particular 'tracked mesocyclone' (tm), it is

'tagged' to prevent it from being considered as a possible member of a subsequent tm. The

tracking algorithm terminates when each one of all the m5's has been either associated with a tm

or ignored because of the 10-minute restriction on a tm lifetime. For each tm, the algorithm

outputs its total duration, path length and the average over its lifetime as well as the '5-minute

values' of the following parameters: diameter, depth (vertical thickness), maximum shear and

peak rotational velocity. Note that in the presentation of the results in section 4.1 we will

concentrate mainly on the m5 characteristics since those averaged over the mesocyclone lifetime

are biased by the great variability of the latter.

An extension of this algorithm has also been successfully applied to the tracking of VIL

and GUST cells. The size of these cells is first defined by a user-entered threshold and the peak

values and their locations are tabulated. Secondary peaks are ignored depending on a selectable

proximity threshold and neighboring cells may also be agglomerated into a larger cell. The centre

of gravity of the resulting cells and their displacement, the latter determined by local cross-

correlation, are then used by a tracking algorithm in a manner similar to the one described for

mesocyclones. However, the results for the VIL and GUST maps analysis will not be provided in

terms of cell track statistics because of the inevitable complications arising out of the merging

and splitting of cells that would bias such an approach. Instead, statistics will be presented in

terms of the observed pixel values of these parameters over the specified radar coverage.

4- RESULTS

4.1 Mesocyclone Statistics

When applied over our entire 8-year sample, the first 5 steps of the mesocyclone detection

algorithm described in 3.4a yield a grand total of 1682 detections. Some of these are spurious as

when a shear line is not recognized as such by step #3 or when the peculiar configuration of a

small ground echo and of the surrounding precipitation generates a false alarm as occasionally

seen over the Mercier Bridge, 25 km east of the radar. Some others are true mesocyclones but

short-lived, being captured by only one radar cycle. Both the spurious and the short-lived mesos

are eliminated by the tracking algorithm because the former are usually stationary and fail to be

also present at the expected downwind location. Our persistence requirement of at least 2 radar

cycles implemented in the final step (#6) of our algorithm has maintained 1293 (77%) of the 'm5'

detections distributed among 308 trackable mesocyclones (or 'tm's for short). The yearly

frequency of the latter is portrayed in Fig. 4.1a where it is seen that the years 1998, (with 47),

1996, (with 51), and particularly 1994 with as many as 71, rank as the top 3 years. The year 2000

with only 14 'tm's ranks the lowest. Of course, we cannot ascertain what percentage of these

tracked vortices actually did give rise to a confirmed tornado touchdown along their trajectory or

to other kind of documented damage at the ground. In fact, only about 4 to 6 weak (F0 or F1)

tornadoes per year are confirmed in Southern Quebec, Vaillancourt (1999), although some may

escape visual observations due to the low density of the population in the many forested areas

under our radar coverage.

The distribution of the lifetime of the tracked mesocyclones is provided in Fig. 4.2a while

the distance traveled, or path length, is given in Fig. 4.2b. Here, the 'lifetime' is computed from

the time difference between the initial and final detection and thus includes any intervening gaps.

(a)

(b)

Fig. 4.2: Distribution of the lifetime of the tracked mesocyclones and of their path length.

Five minutes are then added to this difference in order that a mesocyclone observed over say 2

radar cycles yield a lifetime of 10 minutes. The length of the trajectory is simply determined from

the initial and final position, not by summing the 5-minute segments. Unlike the typical

Oklahoma storms lasting for about an hour according to Zrnic et al. (1985), only 22, or 7.1%,

attained this persistence in the Montreal region. However, note that both the duration and the path

length are affected by boundary conditions, that is, by the inability of the algorithm to detect such

relatively small-scale vortices with equal efficiency at the farther ranges. (Refer ahead to Fig. 4.6

for a preview). We have not separated the statistics in terms of mesos whose lifetimes are entirely

within the detectable range and those that appear to emerge or vanish across this undefined

boundary. Since the latter are the stronger storms, it is likely that their path and duration are at the

upper end of the distribution. The typical magnitude for these two parameters for such storms is

thus somewhat larger than indicated by the histograms. On the other hand, the weaker

circulations that may have been present at farther ranges were likely missed by the algorithm and

thus are not part of these statistics. Since we have chosen, for better clarity in display, to limit the

upper scale of these histograms to 100 minutes and to 100 km, there are five mesocyclones which

are 'off limits'. These are identified in Table 4.1 where we have also included the one with a

lifetime of exactly 100 minutes in order to underline the exceptional day that was August 4th,

1994, (as was also June 29-30th, 1998 according to Table 4.2).

Date

Time (UTC)

Duration (hh:mm)

Track Length (km)

4-August-1994

18:21-21:01