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Appearance in Satellite Data

In the satellite images an MCS appears like a mesoscale cloud cluster, which has a circular or oval shape depending on the strength of the upper level wind.

  • In IR, WV and VIS images MCS are characterized by high pixel values (white) in the active part, indicating cloudiness which extends through the whole troposphere.
  • The upstream edges of the cloud cluster are generally very sharp. In situations with strong upper level winds, the high cloud is transported downstream leading to an extended white cloud shield in the IR but a fibrous grey texture in the VIS image. The brightest areas can be found in the active part upstream.
  • In the 0.6+0.8+12.0 RGB combination the active thundercloud is white, while the thinner edges of the anvil are light blue.
  • In the 1.6+0.8+0.6 RGB combination the active thundercloud is cyan, and the anvil can be separated as a little more greyish.
  • In AVHRR imagery the brightest individual cells embedded in a Multi Cell Storm can be detected better than in Meteosat 8 images. Also the overshooting tops of strongest cells embedded in the cirrus anvil can be often be distinguished.
30 May 2005/14.00 UTC - Meteosat 8 IR 10.8 image
30 May 2005/14.00 UTC - Meteosat 8 WV 7.3 image
30 May 2005/14.00 UTC - Meteosat 8 VIS 0.8 image
30 May 2005/14.00 UTC - METEOSAT 8 RGB image (0.6, 0.8 and 12.0)
30 May 2005/14.00 UTC - Meteosat 8 RGB image (1.6, 0.8 and 0.6)
05 July 2002/15.35 UTC - NOAA AVHRR RGB image (0.6, 0.8 and 12.0); overshooting tops indicated

Meteorological Physical Background

Cbs and MCSs form in strong convective processes. These processes and the parameters which describe them are discussed in detail in the Basics chapter "Numerical parameters for small scale convective cloud systems" (see Basics).

The structures described below have been derived from radar observations, as satellite images cannot resolve small-scale single storms or substructures of MCSs.

 

The life cycle of a single cell

The life cycle of a single cell can be separated into three stages:

  • Developing stage
  • Mature stage
  • Dissipating stage

 

Developing stage

The following features characterize the developing of a single cell:

  • The developing stage lasts 5 to 10 minutes
  • A distinct single updraft is prevailing
  • The diameter of the cell is between 2 and 8 km
  • Entrainment at the cloud edges causes the reduction of water vapour content, resulting in the evaporation of water droplets. This causes cooling and consequently reduction of buoyant energy. A supply of humidity from lower levels supports further growth of the developing cell.
  • In the end of this stage lightning is most intense

 

Mature stage

The following features characterize the mature stage of a single cell:

  • The mature stage lasts 25 to 30 minutes
  • Downdrafts develop and are accelerated as a consequence of cooling by the evaporation of cloud droplets
  • Hail stones are no longer kept aloft by the updraft and fall
  • The updraft starts to weaken because the warm humid air below the cell is replaced by cool air from the downbursts
  • The downdraft initiates successive developments of new cells which can be observed as gust fronts
  • Rain and hail are most intense; hail with a diameter over 5 cm develops in the updrafts of the order 30-40 m/s

 

Dissipating stage

  • Downdrafts kill the updraft and the Cb dissipates

 

The role of vertical wind shear

The life time and intensity of a Cb and MCS depend upon the vertical wind shear:

  • The shear perpendicular to the convective line supports updraft
  • Most important is the shear in the lowest layer reaching from the surface up to 2-3 km
  • Cells developing within strong vertical shear have long lifetime and severe weather
  • Most intense thunderstorms develop when there is change both in speed and direction of the wind

 

Multi-cell storms

Multi-cell storms develop from a single cell:

  • The single cell produces a gust front around it; the gust front lifts the air to the level of free convection (LFC), and new cells (daughter cells) form
  • New cell growth is favoured on the downwind side of the moving single cell where the lift is the greatest
  • Daughter cells develop mostly in the right leading side of the mother cell (so called "right movers"), but they can also develop in the left side (so called "left mover"). They can be distinguished from the structure of the vertical wind shear.
  • Left movers move faster than mean low level flow, whereas right movers move slower than it
  • Tornadoes are uncommon in left movers
  • The diameter of the daughter cells is 3-5 km, and the distance to the centre of the thunderstorm is approximately 30 km.
  • The mother cell and daughter cells form together a Multi-Cell Storm. New cells develop ahead of the leading edge of the storm while older cells dissolve in the rear parts
  • Daughter cells develop more rapidly than the mother cell, because entrainment is not slowing the process on the rear side. If the sequence of successive cells is short enough, the Multi-Cell Storm can turn into a Super Cell Storm.

The diagram above (adapted from Browning et al., 1976; Greyshades represent radar reflectivities of 35, 45 and 50 dBz) shows a typical cross section through a Multi-Cell Storm. There are four successive cells in different stages of development, each of which takes about 15 minutes:

  • Cell n-2 is already in dissipating stage
  • Cell n-1 is in mature stage and forms the centre of the storm
  • Cell n (a daughter cell) is in developing stage
  • A shelf cloud n+1 with a crisp, flat base indicating an active updraft forms ahead of cell

During the warm season, MCS triggering peaks at around 2 pm (local solar time) due to the heating in the lower levels of the atmosphere. However, in Europe, where MCS last in average 5.5 hours, 20% of MCS triggering happens during the night (between 10 pm and 9 am), when other lifting mechanisms are expected to play a key role, as low level convergence, mid-level cold pools or out-flows of pre-existing MCSs.

In the European region, details on MCS Climatologies based on satellite or radar data can be found, eg., for Switzerland (Schiesser et al, 1995), Spain (AEMET, 1999) and Finland (Punkka et al, 2009; Saarikalle, 2009). A wider (satellite derived) MCS climatology covering western Europe (including surrounding Atlantic coasts), western Mediterranean Sea and northern Africa is presented in Morel and Senesi (2002b).

A particular mechanism under which convective cells (embedded in a certain flow) form approximately in the same location as pre-existing cells during several hours - train effect - is investigated in the special investigation chapter.

 

Observations and verification by radar

See chapter Key parameters for typical radar products. In CAPPI products the following features typical for MCSs can be seen, although they are not very clear in every case (depending also on the position of the radar compared with the convective system).

Bow echo:

  • Bow or hook shaped strong (>40 dBz) echo area
  • Very strong echoes surrounded by weaker ones within the echo area
  • Begins as a straight line, develops into a bow shape and finally into a Comma shape
  • Lasts for a few hours
  • The region near the centre of the bow is ahead of strong surface winds, thought to be associated with a rear inflow jet entering the MCS

Gust front:

  • Occurs ahead of the leading edge of the reflectivity pattern
  • Clear air echoes coming from insects and debris

Side-lobe echoes:

  • Often seen in radar displays of storms producing hail
  • Running parallel to the distance marker (from the radar position)
  • Are reflections from strong cumulonimbus, created by side-lobes of the antenna beam

Key Parameters

For nearly all conceptual models the "key parameters" are parameters derived from numerical models. In the case of convective development the key parameters are relevant observations and additional artificial satellite images. The following material is used:

  • Satellite imagery with an appropriate cold cloud top enhancement:
    • Typical circular and oval shape of the MCS
    • Cloud top temperature below -32°C: in the case of circular type for an area with diameter of 100-500 km and in the case of oval type for a major axis of 200-900 km
  • Weather reports:
    • Thunder
    • Showers
  • Lightning Reports
  • Radio Soundings
    • Wind Profile
    • Stability

 

Cold cloud top enhancement

30 May 2005/15.00 UTC - Meteosat 8 IR 10,8 enhanced image

 

Weather reports

30 May 2005/18.00 UTC - Meteosat 8 IR 10,8 image; weather events (green: rain and showers, blue: drizzle, red: thunderstorm with precipitation, orange: hail, black: no precipitation )

 

Lightning Reports

30 May 2005/18.00 UTC - Meteosat 8 IR 10,8 image; Lightning interval (yellow: 0-5 min, orange: 5-10 min, red: 10-15 min, magenta: 15-20 min, blue: 20-25 min, green: 25-30 min)

 

Radio Soundings

30 May 2005/12 UTC - Meteosat 8 IR 10,8 image; position of Wroclaw sounding station indicated
30 May 2005/12.00 UTC - Radio sounding Wroclaw; Wind direction and speed
30 May 2005/12.00 UTC - Radio sounding Wroclaw; thick solid: temperature, black dashed: dew point

 

Radar

Radar is a significant tool for observing convective clouds (see Meteorological physical background). Even small scale features within the MCSs at different heights can be observed. Although this manual concentrates on satellite imagery, the following paragraphs provide an introduction to different radar reflectivity products. More details may be found in relevant radar literature.

Constant Altitude Plan Position Indication (CAPPI)

  • Constant altitude data is picked and interpolated from different elevation scans
  • Beyond a certain distance, the lowest available data is selected
  • Avoids somewhat the PPI problems: ground clutter near the radar nd overshooting of the beam far from radar

Tops

  • Tool for following the development of a convective system
  • Used together with the temperature profile
  • When tops reach the value of -15°C, precipitation begins
  • When tops reach the value of -25°C, the chance of thunder is considerable

MAX

  • Tool for identifying the strongest cells in a multicell system
  • High dBz values at high altitudes (e.g. +30 dBz at 5 kilometres) almost certainly relate to hail or wet hail, and indicate strong updrafts
  • The side panels provide more accurate information about the position of cells

Range Height Indicator (RHI) and Cross Section (XSECT):

  • A cross section is a tool for studying the vertical structure and to identify possibly hail-generating cells
  • Weaker intensities in the thinner parts of the anvil (below -10 dBz) can only be detected close to the radar
  • The bright band (maximum reflectivity layer due to melting snow) can be detected even in convective rain in spite of strong vertical movements, which make it weaker than in stratiform rain
  • Even if the radar system is not performing RHI scans, one can usually make cross sections (XSECT) of the same data which is used e.g. for MAX products. XSECT is used like RHI, but it usually has worse vertical resolution.
RHI
XSECT

Accumulated precipitation for N hours (RAINN product)

  • Generated from CAPPI products
  • Points out the areas of maximum precipitation
  • Outstanding resolution in space and time
  • The accuracy of an individual measurement is only about 50%, because far from the radar the measurement volume is quite large and well above the ground
  • Good accuracy is gained when the rain amounts are summarized over a longer period from the whole radar network with some corrections in the overlapping areas

Time - Height cross section of Volume Velocity Processing wind profiles (THVVP - time series of wind profiles)

  • The best mesoscale tool for studying changes in wind shear, given enough time resolution (typically a new sounding every 15 or 30 minutes)
  • Radar winds represent an average of a larger volume, e.g. in this case a cylinder 40 kilometres in radius, 200 metres thick
  • If the wind field is not linear, the average through such a volume can be erroneous

Typical Appearance In Vertical Cross Sections

Cbs and MCSs are small scale or mesoscale CMs and, as such, are embedded within spatial vertical cross sections which tend to characterize the more general convective environment rather than the convective cell itself. The vertical structure within the convective cell is described in detail in the Basic chapter "Numerical parameters for small scale convective cloud systems" (see Basics ).

For more information about the vertical structure of a typical convective environment see chapters Convective Cloud Features In Typical Synoptic Environments:

 

Weather Events

Parameter Description
Precipitation
  • Heavy rain showers.
  • Thunderstorms, frequently with hail showers.
Temperature
  • Remarkable drop possible in the area around the MCS in connection with downdrafts.
Wind (including gusts)
  • The area around the MCS is characterized by a pronounced squall line caused by the outflow of the downdraft.
  • Severe wind gusts and downbursts, even small tornadoes possible.
Other relevant information
  • The most intense weather activities occur during the mature stage of development: in the case of a multi-cell storm, in the centre of the convective complex
30 May 2005/18.00 UTC - Meteosat 8 IR 10,8 image; weather events (green: rain and showers, blue: drizzle, cyan: snow, purple: freezing rain, red: thunderstorm with precipitation, orange: hail, black: no actual precipitation or thunderstorm with precipitation)

References

General Meteorology and Basics

  • BROWNING K. A. (1985): Conceptual models of precipitation systems; Quart. J. R. Meteor. Soc., Vol. 114, p. 293 - 319
  • BROWNING K. A. (1986): Conceptual models of precipitation systems; Weather&Forecasting, Vol. 1, p. 23 - 41
  • CONWAY B. J., GERARD L., LABROUSSE J., LILJAS E., SENESI S., SUNDE J. AND ZWATZ-MEISE V. (1996): COST78 Meteorology - Nowcasting, a survey of current knowledge, techniques and practice - Phase 1 report, Office for official publications of the European Communities
  • HOSKINS B. J., MCINTYRE M. E. and ROBERTSON A. L. W. (1985): On the use and significance of isentropic potential vorticity maps; Quart. J. R. Meteor. Soc., Vol. 111, p. 877 - 946
  • KURZ M. (1990): Synoptische Meteorologie - Leitfäden für die Ausbildung im Deutschen Wetterdienst; 2. Auflage, Selbstverlag des Deutschen Wetterdienstes
  • LILJEQUIST G. H. and CEHAK K. (1984): Allgemeine Meteorologie, 3. Auflage, Braunschweig, Vieweg
  • ZWATZ-MEISE V. and MAHRINGER G. (1990): SATMOD: An interactive system combining satellite images and model output parameters; Weather&Forecasting, Vol. 5, p. 233 - 246

General Satellite Meteorology

  • AEMET (1999): Modelos conceptuales a mesoscala. Sistemas Convectivos de Mesoscala. Biblioteca de Modulos TEMPO. Version 2.0. April (in spanish).
  • BROWNING K. A. and GOLDING B. W. (1994): Mesoscale effects of a dry intrusion within a vigorous cyclone; JCMM - internal report 29
  • DOSWELL, C. A., CIMMS, N. OK: Severe Convection: Hail, Wind and Tornadoes. Lecture in the course Mesoscale Structure of Cyclones, Sardinia, June 2004
  • MARTIN J. E., LOCATELLI J. D. and HOBBS P. V. (1992): The synoptic evolution of a deep tropospheric frontal circulation and attendant cyclogenesis; 5th Conference on Mesoscale Processes, Atlanta, Georgia, 5 - 10 January 1992
  • MOREL C and SENESI S. (2002a): A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology. Quart. J. R. Meteor. Soc., Vol. 128, pp 1953-1971.
  • MOREL C and SENESI S. (2002b): A climatology of mesoscale convective systems over Europe using satellite infrared imagery. II: Characteristics of European mesoscale convective systems. Quart. J. R. Meteor. Soc., Vol. 128, pp 1973-1995.
  • PUNKAA A. - J., FMI: Vaarallinen Konvektio, Lecture in Training Course for Meteorologists, Helsinki, Finland, May 2003
  • PUNKAA A. -J., SCHULTZ D., BISTER M. (2009): High-latitude Mesoscale Convective Systems: An 8-yr climatology of summertime MCSs in Finland. 5th European Conference on Severe Storms, Landshut, Germany, 12-16 October.
  • SAARIKALLE E. (2009): Heavy rain events in Finland 2000-2008. M.S. Thesis - Meteorology. University of Helsinki. Department of Physics.
  • SCHIESSER H. H., HOUZE JR R. A. and HUNTRIESER H. (1993): The Mesoscale Structure of Severe Precipitation Systems in Switzerland. Mon. Wea. Rev., 123, 2070-2097, July.
  • Comet MetEd pages www.meted.ucar.edu

Special Investigation: The Train Effect Or Train Mechanism

A special case of convective development is the so-called train effect or train mechanism, in which many intense convective cells form over the same place in a short period of time. This regime produces large amounts of rainfall, therefore being frequently related to flash flood events.

 

Meteorological Physical Background

The key for understanding why convective systems may produce large precipitation amounts over a certain place is determined by comparing the system motion with the system structure as explained below.

 

Consider a convective line structure (shades indicate radar reflectivity) moving across a specific location (circle) with a motion (C⃗S) nearly normal to the line.

The total rainfall amount at the referred location (shaded area in the graphic) is relatively small.

Now consider the same line structure, passing over the same location, but with its motion with a small normal component to the convective line. In this case the single convective structures embedded in the line will consecutively pass over the same spot.

Precipitation plot will reflect this feature, showing consecutive peaks of precipitation during a longer period, therefore with large amounts of total precipitation.

It is now important to distinguish between system motion and cell motion - system motion represents the movement of the system as whole and cell motion represents the movement of individual cells within the same system (or group of cells as in the case of a MCS or MCC). While cell motion is easily detected following the movement of convective cells in successive satellite or radar images, system motion may be somewhat trickier. For determining system motion from satellite or radar imagery, forecasters need not focus on the movement of single cells (or group of cells) but analyze instead the position of the convective system as a whole for a few hours.

 

Cell Motion

Cell motion () is generally related to the mean wind in a certain tropospheric layer in which cloud systems are embedded. In the example below, cell motion vector is to the right of mean wind vector, which therefore classifies the cell as a right mover. Some studies for MCS and MCC show that cell motion has a higher correlation with wind at 700 hPa.

The estimation of mean wind is obtained from averaging wind vectors in a representative layer, which naturally should vary from case to case. If the cloud layer is well represented by the 850-300 hPa layer, then mean wind can be determined by an average at that layer:

 

Propagation of the system and System Motion

At the end, system motion is what matters to define small or large precipitation amounts in a specific location. System motion ( ) results from combining cell motion ( ) with the propagation of the system as a whole defined by vector .

If the propagation vector differs considerably in azimuth from cell motion (as shown left), then the system motion vector acts in such a way that the system moves to its right during the life-time, affecting different areas.


Adapted Adapted from Corfidi et al (1996) and Doswell et al (1996).

On the other hand, if the propagation vector practically off-sets cell motion (that is, it has almost the same intensity but an opposite direction), then system motion vector is such that the system can be considered quasi-stationary.

Let us now consider 3 steps in the development of an MCS in which cell motion and propagation have opposite directions (expanding the scheme showed in Multi-cell storms - see Meteorological Physical Background ).

Step 1 - Cell I is forming over the position of a low level outflow boundary (plotted for the sake of simplicity as a stationary front) with a strong updraft above and divergence in the upper troposphere.

Step 2 - Cell I is moving eastward and cell II is now forming over the same stationary boundary. The outflow from cell I interacts positively, reinforcing the updraft of cell II


Adapted Adapted from Corfidi et al (1996) and Doswell et al (1996).

Step 3 - A third cell (cell III) is now building over the same location, but this time at the expense of cell II outflow. This confirms a westward propagation with eastward cell motion, resulting in a train effect.

In fact, propagation can be affected by other factors not directly related to the convective storm. Examples of such factors are fronts or sea-breeze fronts. When these processes act as stationary boundaries for the convective system, the development of new cells will therefore fit the train effect. Note that in literature, the train effect has been analyzed together with up-wind development (back-building) and down-wind development.

From stated above, it is easy to conclude that knowing the propagation vector in an operational basis would be the key tool to detect quasi-stationary convective systems, that is, to forecast heavy rainfall and the probability of occurrence of flash flood events. Unfortunately, the determination of the propagation vector is still not completely clear. Corfidi et al (1996) suggested that the speed and direction of the low-level jet (LLJ) could be indicative of the propagation.

AEMET (1999), in a study for MCSs in Iberian Peninsula area, also suggest a simple approximation in which propagation could be retrieved from wind at 850 hPa, that is

In latter studies, Corfidi (1998, 2003) updates the results referring that propagation for MCS does not necessarily occurs most rapidly in the direction of the LLJ, but rather in the direction of the largest system relative low-level convergence - this direction may or may not be the direction of the LLJ.

In the second case, when convergence is maximized in a direction away from the LLJ (for instance due to the existence of gust fronts under conditionally unstable environments), the system motion can be very different from the one predicted by the original technique. In this up-dated technique, the propagation vector may be called Storm Relative Inflow (SRI) vector. The resulting system motion can be very large (downwind propagation), therefore not fitting a train effect scenario.

Note that the author suggests that the propagation for this up-dated technique matches the system motion vector obtained using the original technique.

This may seem surprising, but this suggestion is based on the fact that the system motion obtained using the original technique can be considered as the vector difference between two flows: i) the gust front (considered to be moving at the speed of the mean cloud layer wind) and ii) the low-level flow (considered to be represented by the low-level jet).

Finally, note that the mechanism of training has also been found to happen in systems similar in nature to a MCS but that do not meet the size criteria; these systems, referred to as sub-MCS, are related to moderate precipitation events (Saarikale, 2009).

 

Appearance in Satellite Data

Train effect is clearly seen in satellite images making use of animations of several hours. In the animation shown below (left) for an event on 20 September 1999, the 30 minutes time resolution WV channel of Meteosat 7 can be followed between 02.00 UTC and 18.00 UTC. During the 16 hours you can see the convective area as whole moving southeastwards, with many convective systems forming over the Tyrrhenian sea.

20 September 1999/18.00 UTC - Meteosat 7 WV image
20 September 1999/14.00 UTC - Meteosat 7 WV image
  Loop: 20 September 02.00 - 18.00 UTC half-hourly image loop   Loop: 20 September 07.30 - 14.00 UTC half-hourly image loop

If focusing on a narrower period of time - from 07.30 to 14.00 UTC (right animation above), one can see that the convective systems are forming over the same area, as plotted below left at 10.00 UTC (cell 1 - fully developed) and below right at 11.30 UTC (cell 1 moved northeastward and a new fully developed cell - cell 2 - formed in the same area where cell 1 was 90 minutes earlier).

20 September 1999/10.00 UTC - Meteosat 7 VIS Image
20 September 1999/11.30 UTC Meteosat 7 VIS Image

Two recent situations of train effect observed by MSG are shown below. The first happened in Portugal on 17 to 18 February 2008 producing severe flash floods (with maximum precipitation amounts of 99.7 mm in 6 hours and 153.6 mm in 24 hours in Lisbon). The 24 hour animation of the cloud type product from SAF Nowcasting shows several convective systems forming over Lisbon area during night and early morning on the 18th and also during morning to the southeast of Lisbon, in a similar pattern as the case over the Tyrrhenian sea.

17 February 2008/18.00 UTC - Cloud Type Product
  Loop: 17 February 18.00-18 February 18.00 UTC 15-minutes loop

The second corresponds to an event over the Gulf of Biscay, west of Nantes on 27 July 2006. Besides the train effect easily detected in the animation above over the Gulf Biscay (shown below), there was also another similar situation over southeastern UK on the same day (analyzed later). In the remaining of this special investigation, the focus will be made on the events with MSG data.

27 July 2006/06.00 UTC - Meteosat 8 IR10.8-WV6.2 difference image
(overshooting tops in yellow or red)
  Loop: 27 July 06.00 - 14.00 UTC 15-minutes image loop

 

Key Parameters

Note that in many studies the synthesis of key parameters for large-scale patterns or vertical profiles are simply summarized for flash flood events, with no special focus on the train effect. Although, this special investigation is being focused on the train effect, the following description broadens the scope of analysis for operational application, taking into consideration that according to Doswell et al (1996) virtually all flash floods are produced by MCSs.

Also note that train effect results generally in the production of some convective systems over the same area for up to around 4-5 hours. In a longer range (10-12 hours) the area where the train effect occurs usually tends to move slowly southeastward, starting to affect neighboring areas, as shown above. Therefore, key parameters in model analysis with 6 hours difference can very often depict slightly different affected areas.

 

850-500 hPa Thickness Diffluence

The major convective rainfall events are generally found in areas of diffluence of the thickness parameter, which determine regions of low-level convergence or upper-level diffluence. Naturally this is a large-scale parameter and provides a general indication of the location for convection. If the pattern of thickness diffluence remains relatively stationary with time, the possibility for train effect should be in the forecaster's mind and included in his/her nowcasting activities with satellite or radar imagery.

The case below shows diffluence and convective activity in the air mass RGB in northern Spain, Gulf of Biscay, France and southern UK at 06 and 12 UTC on 27 July 2006.

27 July 2006/06.00 UTC - 500-850 hPa Thickness
27 July 2006/12.00 UTC - 500-850 hPa Thickness
27 July 2006/06.00 UTC - Meteosat 8 air mass RGB (WV6.2-WV7.3;
IR9.7-IR10.8; WV6.2i) image
27 July 2006/12.00 UTC - Meteosat 8 air mass RGB (WV6.2-WV7.3;
IR9.7-IR10.8; WV6.2i) image

 

850hPa Wet Bulb Potential Temperature (Theta W)

Many train effect cases with potential for flash floods happen in "warm" months, within the influence of tropical air masses, both over a ridge or close to a gradient of the wet bulb potential temperature field. This last situation also fits some train effect cases happening during "cold" months. The example presented above shows both cases:

 

Case 1:

27 July 2006/06.00 UTC - Wet Bulb Potential Temperature 850 hPa
27 July 2006/03.00 UTC Meteosat 8 IR10.8-WV6.2 difference image
(overshooting tops in yellow)
27 July 2006/06.00 UTC Meteosat 8 IR10.8-WV6.2 difference
image (overshooting tops in yellow)
27 July 2006/09.00 UTC Meteosat 8 IR10.8-WV6.2 difference
image (overshooting tops in yellow)

In mid-day successive formation of different convective systems was happening over a particular location in the Gulf of Biscay area, having the train effect ended here in the evening with a total duration of around 18 hours.

27 July 2006/10.15 UTC - Meteosat 8 HRV image
27 July 2006/12.15 UTC - Meteosat 8 HRV image
27 July 2006/06.00 UTC - Meteosat 8 HRV image
27 July 2006/06.00 UTC - Meteosat 8 IR10.8-WV6.2 difference image
(overshooting tops in yellow or red)
  Loop: 27 July 06.00 - 14.00 UTC 15-minutes image loop   Loop: 27 July 06.00 - 14.00 UTC 15-minutes image loop

 

Case 2:

From midday onwards during the same day, a formation of different convective systems over south UK, in an area between Leicester, Oxford and Cambridge, also fitted the train effect, but in this case a ridge (check the 18°C isotherm) is stretching from France into the UK.

27 July 2006/12.00 UTC - Wet Bulb Potential Temperature 850 hPa
27 July 2006/13.45 UTC Meteosat 8 HRV image
27 July 2006/14.30 UTC Meteosat 8 HRV image

 

Specific humidity convergence at 1000 hPa

An important key parameter to consider from NWP outputs is the specific humidity convergence at 1000 hPa, which shall have local maxima in the train area. It might be expected that this field depicts other local maxima where the forecaster on duty sees no convective development. Naturally, it is a nowcasting task for the forecaster to find which model proposals for convection are likely to develop in the near future, also taking into consideration expected deviations between the atmospheric models and the atmosphere itself.

Naturally, if only convergence at 1000 hPa is available (and not specific humidity convergence), this can also be an alert, but note that moisture inflow is in fact one of the main ingredients to convection.

27 July 2006/12.00 UTC - Specific Humidity Convergence 1000 hPa (in 10^-5 g/Kg/s). Greenish and bluish colors represent humidity convergence; yellowish and reddish colors represent humidity divergence.
27 July 2006/18.00 UTC - Specific Humidity Convergence 1000 hPa (in 10^-5 g/Kg/s). Greenish and bluish colors represent humidity convergence; yellowish and reddish colors represent humidity divergence.

In the sequence of images below referring to the analyzed time period it is possible to note that:

  • position of cell 2 (fully developed) at 17.00 UTC is over position of cell 1 (first stage) at 16.00 UTC
  • position of cell 4 (fully developed) at 17.00 UTC is over position of cell 3 (first stage) at 16.00 UTC
27 July 2006/16.00 UTC - Meteosat 8 HRV image. Individual cells numbered from 1 to 4
27 July 2006/17.00 UTC Meteosat 8 HRV image. Individual cells numbered from 1 to 4
27 July 2006/18.00 UTC Meteosat 8 HRV image. Only cells 2 and 4 are identifiable.

 

Vorticity Advection at 300hPa

Vorticity advection at 300 hPa has also been found to show areas of local maxima around the spot for successive convection. It should be noted, however, that these maxima may not be in a clear relation to an upper-level jet or strong tropopause anomalies, but rather related to weak tropopause anomalies (see similarities with the conceptual model Enhancement of convection by PV, Convective Cloud Features In Typical Synoptic Environments: Enhancement of convection by PV ). Again, like for specific humidity convergence, other local maxima may be present. Therefore, nowcasting will help finding the first convective bursts and check if there is or not stationarity of the systems.

27 July 2006/18.00 UTC - Vorticity Advection 300 hPa
27 July 2006/18.00 UTC - Meteosat 8 WV6.2 image
27 July 2006/09.00 UTC - Meteosat 8 WV6.2 image.
  Loop: 27 July 06.00 - 14.00 UTC 15-minutes image loop

 

Instability (Boyden Index)

Finally one more obvious condition, which could even be firstly considered, is stability. In this case, the Boyden index is presented, whose threshold for risk of thunderstorm can be set to 95. Note, that one should not need to have the largest values in the area of train effect. In these instants, larger values are reached in Continental Europe, where convection also developed, but not so much fitting to train effect. The convective activity during the afternoon is shown with the deep convection RGB.

27 July 2006/12.00 UTC - Boyden Index
27 July 2006/18.00 UTC - Boyden Index
27 July 2006/15.15 UTC - Meteosat 8 Deep Convection RGB (WV6.2-WV7.3; IR3.9-IR10.8; NIR1.6-VIS0.6) image
27 July 2006/16.00 UTC - Meteosat 8 Deep Convection RGB (WV6.2-WV7.3; IR3.9-IR10.8; NIR1.6-VIS0.6) image
27 July 2006/17.00 UTC - Meteosat 8 Deep Convection RGB (WV6.2-WV7.3; IR3.9-IR10.8; NIR1.6-VIS0.6) image
27 July 2006/18.00 UTC - Meteosat 8 Deep Convection RGB (WV6.2-WV7.3; IR3.9-IR10.8; NIR1.6-VIS0.6) image

Instability together with the last two parameters (specific humidity convergence and vorticity advection) should be evaluated together, in a sense that areas where all the 3 parameters sum up should be followed with more attention in a smaller scale after a larger-scale analysis is provided by thickness (850-500 hPa) and wet bulb potential temperature at 850 hPa.

 

Low-Level Jet and Veering

As seen in the Meteorological Physical Background, the propagation of the convective system may or not be related to a LLJ. In the case where a train effect event is dynamically related to a LLJ, one operational challenge for a forecaster is to identify its existence, whether using model forecasts or observational data from radio soundings or radar. On one side, the model may not reproduce the LLJ and, on the other side, the LLJ position/intensity may not be observed due to a sparse resolution of radio sounding and radar networks for the analysis of this phenomenon.

For the analyzed case on 27 July 2006, the model does not detect any indication of a LLJ in the vicinity of the area of train effect (a LLJ was considered to have an intensity of 20kt or more in some layer above surface and below 850 hPa). Following the previous considerations, either the LLJ was not dynamically important or it was but it was not correctly modeled.

However, for the Lisbon case shown in the satellite image chapter, a LLJ was found both by model and radar in the vicinity of the affected areas. In the radar vertical profile sequence shown below, wind speeds of 35-40kt at a height of approximately 1000m were measured for several hours during the event, with a persistence veering of the wind from SE to SW in the first two km. This was also an indication of warm air advection in the lower levels and, as a consequence, increasing instability.

18 February 2008/01.00 - 07.00 UTC - Radar Coruche; Vertical Wind Profile (in kt) and Average Reflectivity in layers (in dBZ). The radar station is located around 80 km ENE of Lisbon.

This observation somewhat agrees with model data as can been seen in ECMWF analysis at 06.00 UTC. A LLJ is depicted with its axis around 925 hPa and oriented from Cadiz gulf to the northwest of the Iberian Peninsula. The cross section along 41°N parallel finds the LLJ over Porto city. The vertical profile over Lisbon area shows a slight wind maximum around 940hPa and confirms the veering of the wind. Note that the vertical profile was obtained over the area of convective development, which is to the southwest of model LLJ and also not exactly over the radar location.

18 February 2008/06.00 UTC - Wind barbs and isotachs 925 hPa (red line for cross section and red circle for vertical profile location)
18 February 2008/06.00 UTC - Meteosat 9 WV image WV 6.2 image
18 February 2008/06.00 UTC - Vertical cross section; yellow: isotachs
18 February 2008/06.00 UTC - Vertical profile; left: wind speed; right: wind direction

 

Final Note

This Special Investigation started with a preliminary evaluation of around 100 cases of convective systems over Europe between 1992 and 2009 with both Meteosat first and second generation satellites. These cases were previously studied under the scope of EUMETSAT and EUMETRAIN activities. After excluding orographic induced convection, a total of 7 cases of clear visual train effect were explored in detail; 3 of these cases are presented here.

 

References

  • AEMET, 1999: Modelos conceptuales a mesoscala. Sistemas Convectivos de Mesoscala. Biblioteca de Modulos TEMPO. Version 2.0. April (in spanish).
  • COMET Activities at Saint Louis University - http://www.eas.slu.edu/Comet/precip.html
  • CORFIDI S. F., MERRIT J. H. and FRITSCH J. M., 1996: Predicting the movement of Mesoscale Convective Complexes. Weather and Forecasting, 11, 41-46, March.
  • CORFIDI S. F., 1998: Forecasting MCS mode and motion. 19th Conference Severe Local Storms, Minneapolis, MN.
  • CORFIDI S. F., 2003: Cold Pools and MCS Propagation: Forecasting the Motion of Downwind-Developing MCSs. Weather and Forecasting, 18, 997-1017, December.
  • DOSWELL III C. A., BROOKS H. E. and MADDOX R.A., 1996: Flash flood forecasting: An ingredients-based methodology. Weather and Forecasting, 11, 560-581, December.
  • FUNK T. W., 1991: Forecasting Techniques Utilized by the Forecast Branch of the National Meteorological Center during a major convective rainfall event. Weather and Forecasting, 6, 548-564, December.
  • HOUZE Jr R. A., SCHMID W., FOVELL R. G. and SCHIESSER H. H., 1993: Hailstorms in Switzerland: left movers, right movers, and false hooks. Mon. Wea. Rev., 121, 3345-3370, December.
  • SAARIKALLE E., 2009: Heavy rain events in Finland 2000-2008. M.S. Thesis - Meteorology. University of Helsinki. Department of Physics.

Special Investigation: Overshooting Tops

 

Introduction

An overshooting convective cloud top (OT) is a dome-like protrusion above the cumulonimbus anvil. It represents an updraft core of sufficient strength to rise above the storm's equilibrium level (the point where the surrounding air is the same temperature or even warmer) near the tropopause region and penetrate into the lower stratosphere. Because of their relatively short duration and small diameter, recognition of OTs in satellite images is strongly dependent on the spatial and temporal resolution of the satellite instruments.

 

Appearance in Satellite Data

OTs can be most easily identified in the high-resolution visible (HRV) channel imagery as a lumpy-textured area with its characteristic shadowing within the convective cloud in the mature stage. However, this only applies during the daytime. OTs are best seen during early morning or late afternoon hours, when the angle of the sun is low and the shadows cast by OTs are easily seen.

In the color-enhanced IR 10.8 µm images, available during both day and night, a small cluster of very low brightness temperatures can indicate that an OT is present.

OT as it appears in HRVIS image.
OT as it appears in color-enhanced IR 10.8 µm image.
12 September 2012/08.50 UTC - Meteosat 9 HRVIS image. The OT location is marked with a red arrow.
12 September 2012/08.50 UTC - Meteosat 9 color-enhanced IR 10.8 µm image. The OT location is marked with a black arrow.

 

Appearance in RGB composite imagery

RGB combinations of channels make it easier to identify smaller fine-scale cloud structures, such as OTs, which otherwise may be hidden or hard to identify in the satellite images by a human eye.

In the so called Severe Storm RGB (WV6.2-WV7.3, NIR3.9-IR10.8, NIR1.6-VIS0.6) images OTs usually appear as yellow pixels within a reddish convective cloud in the mature stage.

OTs can be most easily identified using an RGB composite of the high resolution visible and infrared 10.8 µm channels (HRVIS, HRVIS, IR10.8i). Because of a resolution that is three times higher than in the other RGB combinations, the 3D structure of the convective clouds is very clear here.

OT as it appears in High resolution visible RGB (HRVIS, HRVIS and IR10.8i) image.
OT as it appears in Severe Storms RGB (WV6.2-WV7.3, NIR3.9-IR10.8, NIR1.6-VIS0.6) image.
12 September 2012/08.50 UTC - Meteosat 9 High resolution visible RGB (HRVIS, HRVIS and IR10.8i) image. The OT location is marked with a red arrow.
12 September 2012/08.50 UTC - Meteosat 9 Severe Storms RGB (WV6.2-WV7.3, NIR3.9-IR10.8, NIR1.6-VIS0.6) image. The OT location is marked with a black arrow.

 

Appearance in 'sandwich' images

'Sandwich' images combine the information from one grey-scale image and one color image. Typically, the underlying image is the HRV image, overlaid with a color enhanced brightness temperature (BT) image or a standard RGB product. For detection and study of the characteristics of the OT, the most appropriate combination is HRV image overlaid with a color enhanced IR 10.8 µm image. Such a combination provides information about the 3D structure of the cloud, but also about the temperature of the cloud top.

12 September 2012/08.50 UTC - Meteosat 9 'sandwich' product (HRV image overlaid with enhanced IR 10.8 µm image ). The OT location is marked with a red arrow.

Another useful combination is produced by an overlaying a HRV image with a Severe Storm RGB. Yellow pixels in a Severe Storm RGB image indicate a largely positive NIR3.9-IR10.8 BTD (brightness temperature difference), due to high reflection of small ice particles, which indicate severe updrafts and possible OTs. It should be mentioned that not all yellow pixels indicate small ice particles, some of them are a consequence of very low BT in IR10.8 µm (app. -70 °C) and relatively low reflectance in IR3.9 µm, indicating only very cold cloud tops with usual updraft and relatively large ice particles.

12 September 2012/08.52 UTC - Meteosat 10 'sandwich' product (HRV image overlaid with Severe Storm RGB image). The OT location is marked with a red arrow.

 

The life cycle of overshooting tops

Rapid-scan satellite data reveals the fact that an OT can exist for less than 15 (even less than 5) minutes and has a maximum diameter of ~15 km.

12 September 2012/06.15 UTC - Meteosat 10 "Sandwich" product
(HRV image overlaid with enhanced IR10.8 image)
12 September 2012/06.00 UTC - Meteosat 10 "Sandwich" product
(HRV image overlaid with enhanced IR10.8 image
  Loop: 12 September 2012/06.15 UTC - 15 minutes image loop   Loop: 12 September 2012/06.00 UTC - 5 minutes image loop

An example of the life cycle of OTs can be seen in the image above. Sandwich products from 06:45 to 12:00 UTC show an intense convective storm developed in northern Italy and spread across western Slovenia. In the mature stage of the storm many OTs could be recognized at the top of the storm in the satellite images. Rapid scan SEVIRI 5 min and 2.5 min satellite data enables recognition of the OTs that last for less than 15, or even less than 5 minutes.

12 September 2012/06.00 UTC - Meteosat 10 "Sandwich" product (HRV image overlaid with enhanced IR10.8 image)
12 September 2012/06.00 UTC - Meteosat 10 "Sandwich" product (HRV image overlaid with Severe Storm RGB image)
  Loop: 12 September 2012/06.00 UTC - 2.5 minutes image loop   Loop: 12 September 2012/06.00 UTC - 2.5 minutes image loop

 

Meteorological Physical Background

Defined as a domelike protrusion above a cumulonimbus anvil, representing the intrusion of an updraft through its equilibrium level (level of neutral buoyancy), an overshooting convective cloud top (OT), sometimes also called "hot tower", is a manifestation of a very strong updraft in the convective cloud. An OT forms when a thunderstorm's updraft, due to momentum from its rapid ascent and the strength of its lift, penetrates its equilibrium level (EL, the point where the surrounding air is about the same temperature or even warmer) near the tropopause region. It also usually penetrates into the lower stratosphere, but it should be noted that overshooting does not necessarily imply penetration into the stratosphere. Sometimes it merely distorts the tropopause region. Above the EL, the air parcels are colder than the surrounding environment. Consequently, the cloud particles become negatively buoyant and sink back toward the equilibrium level, before spreading out into the anvil.

OTs often generate gravity waves which can produce significant turbulence, posing a risk to aviation. Wave-like perturbations in the potential temperature often occur above the OTs and some of these waves may become unstable and broken over the cloud top. The wave breaking process is an irreversible, non-adiabatic mass transfer from the troposphere to the stratosphere. The breaking of the wave produces turbulence but also various storm top plume formations such as anvil-sheet cirrus plume or overshooting cirrus plume, also called "jumping cirrus" due to moisture being injected into the stratosphere from the cloud body (Wang, 2007).

Penetrating convective storms affect the transport of various chemical species, and especially that of water vapor from the troposphere into the stratosphere. Ice particles from OTs can reach a height of up to 18.8 km (Corti et al, 2008). Bearing in mind the importance of water vapor as the absorber of infrared emissions in the atmosphere, it is clear that its distribution in the stratosphere may have an important impact on the global climate (e.g. Liou, 2002).

Typical storm-top satellite signatures, such as cold-ring or cold U/V shapes, are a consequence of very strong convective activity, represented with OT. Exceptionally tall and persistent OTs are the result of a nearly continuous stream of updrafts in a thunderstorm. OTs are more frequently detected over land than over sea. Over sea, OTs often appear close to the coastline. The largest number of OTs generally occur during the afternoon and early evening between app. 15 and 16 UTC. Between 06 and 10 UTC, OTs are rather rarely seen.

 

For Key Parameters see the relevant chapters in MCS

 

Typical Appearance In Vertical Cross Sections

In the case of severe thunderstorm with very strong updraft the overshooting top penetrate through the tropopause into the lower stratosphere. The main features of the penetrating convection are plumes and jumping cirrus which are produced by wave breaking in the environment of high instability. The potential temperature field is characterized by very large gradients in the OT region. During the penetration some moisture is ejected into the lower stratosphere.

Vertical distribution of (equi)potential temperature.
Vertical distribution of humidity.
  Loop: Simulated storm with the overshooting top. Relative humidity with respect to ice (color) overlaid with the potential temperature isotherms (black).

The example below shows a radar cross section of a storm which occured above the Czech Republic on 25 June 2006. The highest OT penetrated through the tropopause and into the lower stratosphere, up to app. 17 km.

25 June 2006 - Radar cross-section of the convective storm with OT from 13:00 to 13:20 UTC. Tropopause is estimated from the Prague 12:00 UTC sounding (marked with red line). Red circle indicates position of the OT. (Adapted from Setvak et al., 2010)

 

Detection and Monitoring

 

Satellite-based OT detection methods

One of the most commonly used methods for detecting the OTs in satellite data is based on the brightness temperature difference (BTD) between the 6.2 µm and 10.8 µm (WV-IR) channels. This technique could be used for day/night OT detection. BTDs greater than 0 K are related to convective clouds with high vertical extension. Positive BTDs appear when deep convective clouds penetrate through the tropopause, moistening the stratosphere. The brightness temperature in the WV channel can be larger than the one in the IR channel by as much as 6 to 8 K. In such cases the brightness temperature in WV channel is greater than the one in the IR channel because of the presence of the water vapor above the cloud top. The contribution of the water vapor is included in the emission from the water vapor band. Because the OT often protrudes into the lower stratosphere, the area where temperature increases with height, the water vapor at that height has a warmer temperature than the cloud top, which makes the BTD positive.

However, in some cases the WV-IR BTD technique shows false alarms (see more information in e.g. Setvák et al., 2007; Setvák et al., 2008; Putsay et al., 2011) which are caused by water vapor anomalies, especially in the anvil region. It should be noted that the positive values of WV-IR BTD can also be caused by the moisture existing in a layer above the cold cloud tops but not being connected to or produced by the storm itself. Moreover, the case studies showed that the enhanced BTD can also occur downwind of the OTs, rather than always above the coldest tops.

06 July 2010/17.05 UTC - Meteosat 8 'sandwich' product (HRV image overlaid with enhanced IR 10.8 µm image)
06 July 2010/17.15 UTC - Meteosat 9 WV-IR BTD

BTD of the ozone channel (9.7 µm) and the IR 10.8 µm channel also shows a positive signature for the cloud tops above 11 km. The signal in this BTD is even more significant than the one in WV-IR BTD, suggesting that it could be a better indicator for deep convective activity.

BTD of carbon dioxide (13.4 µm) and the IR10.8 µm channel can also be used for determining the height of the opaque clouds. The reason is that with higher cloud tops the absorption effect of CO2 gets smaller, bringing the BTD of the CO2 and IR channels close to 0. In the case of very deep convective clouds the BTD becomes positive.

06 July 2010/17.05 UTC - Meteosat 8 'sandwich' product (HRV image overlaid with enhanced IR 10.8 µm image)
06 July 2010/17.15 UTC - Meteosat 9 CO2-IR BTD
06 July 2010/17.15 UTC - Meteosat 9 O3-IR BTD

A combination of WV-IR and O3-IR BTD can be used in order to avoid a significant number of false alarms occurring in WV-IR BTD images, but also to overcome the seasonal variation of the O3-IR BTD, which is caused by the seasonal variation of ozone concentration above the mid latitudes (Mikuš and Strelec Mahović, 2012).

06 July 2010/17.05 UTC - Meteosat 8 'sandwich' product (HRV image overlaid with enhanced IR 10.8 µm image)
06 July 2010/17.15 UTC - Meteosat 9 WV-IR BTD

OTs are usually observed during very strong and severe convective development. For monitoring convective development and the strength of the developed Cbs, in-situ observations and the following remote sensing data are used:

  • Satellite images
  • Radio soundings
  • Radar images
  • Lightning reports
  • Weather reports

For more information about the different detection and observation methods of convective cells, see Manual chapter Convective weather features:

For more information about the key parameters typical for convective systems, see the Basic chapter Numerical parameters for small scale convective cloud systems:

  • Convection and Instability
  • CAPE
  • Stability Indices

CAPE and stability indices provide information about the potential for convection at a certain location, but don't imply the formation of OTs at the top of a developed convective storm.

 

Weather Events

According to investigations, deep convective storms with OTs are significantly correlated with severe weather conditions such as heavy rainfall, damaging winds, large hail, and tornadoes (e.g. Mikuš and Strelec Mahović, 2012; Bedka et al., 2010; Bedka, 2010). Thunderstorms with OTs are also often associated with strong horizontal and vertical wind shear. Cloud to ground lightning and turbulence occur frequently near the OT area, posing a risk to aviation.

The OT severe weather correlation is strong for large hail (diameter > 2cm; 53%) and severe wind (wind speed > 25 m/s; 52%). These events are usually accompanied with relative humidity increase and a temperature drop. The results of the comparison between OTs and weather elements measured on the automatic stations showed that the best correspondence is found for precipitation. Very good correlation (70%) is found between wind gusts and OT occurrence. Although OTs are a transient feature and usually short-lived, the presence of an OT is highly indicative of very intense updrafts and potentially severe weather.

Type of automatic station data Correlation between detected OTs
and measured parameter (%)
wind gust 70
precipitation 80
relative humidity increase 57
temperature drop 64
Table: Correlation between the OTs and each measured parameter on the automatic stations. The analysis has been performed for the periods of May-September 2009 and 2010, i.e. the warm part of the year when convective activity is at its strongest. 1380 cases were recorded during the analyzed period.

 

For a more complete description of the Weather Events see the relevant chapter in MCS.

 

References

  • Bedka, K. M., 2010: Overshooting cloud top detections using MSG SEVIRI Infrared brightness temperatures and their relationship to severe weather over Europe. Atmos. Res., doi:10.1016/j.atmosres.2010.10.001.
  • Bedka, K. M., Brunner, J., Dworak, R., Feltz, W., Otkin, J., Greenwald, T., 2010: Objective Satellite-Based Detection of Overshooting Tops Using Infrared Window Channel Brightness Temperature Gradients. J. Appl. Meteor. Climatol., 49, 181 - 202.
  • Corti, T., B.P. Luo, M. de Reus, D. Brunner, F. Cairo, M.J. Mahoney, G. Martucci, R. Matthey, V. Mitev, F.H. dos Santos, C. Schiller, G. Shur, N.M. Sitnikov, N. Spelten, H.J. Vössing, S. Borrmann and T. Peter (2008), Unprecedented evidence for deep convection hydrating the tropical stratosphere, Geophys. Res. Lett. 35, doi:10.1029/2008GL033641.
  • Liou, K.-N., 2002: An Introduction to Atmospheric Radiation, 2nd edition. Academic Press., 582 pp.
  • Putsay, M., Setvák, M., Simon, A., Kerkmann, J., 2011: Simultaneous BTD (WV6.2-IR10.8) anomaly and above-anvil ice-plume observed above the storm of 06 July 2010, North Italy. 6th European Conference on Severe Storms (ECSS 2011), 3-7 October 2011, Palma de Mallorca, Balearic Islands, Spain.
  • Setvák, M., Rabin, R. M., Wang, P. K., 2007: Contribution of the MODIS instrument to observations of deep convective storms and stratospheric moisture detection in GOES and MSG imagery. Atmos. Res., 83, 505-518.
  • Setvák, M., Lindsey, D.T., Rabin, R.M., Wang, P.K., Demeterová, A., 2008: Indication of water vapor transport into the lower stratosphere above midlatitude convective storms: Meteosat Second Generation satellite observations and radiative transfer model simulations. Atmos. Res., 89, 170-180.
  • Mikuš, P., Strelec Mahović, N., 2012: Satellite-based overshooting top detection methods and the analysis of correlated weather conditions. Atmos. Res., 10.1016/j.atmosres.2012.09.001,http://dx.doi.org/10.1016/j.atmosres.2012.09.001
  • Wang, P. K., 2007: The thermodynamic structure atop a penetrating convective thunderstorm. Atmos. Res., 83, 254-262.

Special Investigation: 'Cold Ring' and 'Cold U/V' Shaped storms

 

Introduction

'Cold ring' and 'Cold U/V' are pairs of cold and warm areas on top of severe convective storms. The original name of these features was 'enhanced-V' (a V-shaped structure on top of convective clouds) because it can be seen only in enhanced infrared satellite images. According to recent studies, there is a significant correspondence between these cloud-top patterns and convective storms' severity (Irsic Zibert et al., 2010; Bedka et al., 2011).

 

Appearance in Satellite Data

Cold rings and cold U/V shapes are features of thermal anomalies on the tops of severe convective storms, visible in enhanced infrared images.

Figure 1: Cold ring as it appears in color-enhanced IR 10.8 µm image
Figure 2: Meteosat 9 color-enhanced infrared (EN IR) 10.8 µm of cold ring-shaped storm from 19 September 2014.
Figure 3: High resolution visible (HRV) satellite image of cold ring-shaped storm from 19 September 2014.

Figure 4: Cold U/V as it appears in color-enhanced IR 10.8 µm image
Figure 5: Meteosat 9 cold-U/V-shaped storm in color-enhanced infrared 10.8 µm satellite image from 19 September 2014.
Figure 6: Meteosat 9 cold-U/V-shaped storm in high resolution visible (HRV) satellite image from 19 September 2014.

 

Video 1: 19 September 2014 - Meteosat 9 color-enhanced infrared 10.8 µm satellite image.

These Meteosat 9 color-enhanced infrared 10.8 µm satellite images from 02:00 to 13:15 UTC show an intense cold U/V storm developing in southeastern France and a cold ring shaped storm over northern Italy.


Video 2: 19 September 2014 UTC - Meteosat 9 'sandwich' product (HRV image overlaid with enhanced IR 10.8 µm image ).

A sandwich product is composed of a HRV satellite image overlaid with a color-enhanced 10.8 µm satellite image. This product shows the 3D structure and temperature distribution of the tops of storm clouds, which are useful in monitoring the development of intense convective storms. Sandwich products from 05:50 to 13:15 UTC show an intense cold U/V storm developing in southeastern France and a cold ring-shaped storm over northern Italy.

 

Meteorological Physical Background

Cold rings and cold U/V features can be differentiated by their shapes. A cold ring is situated around the central warm spot (CWS). In a cold U/V cloud top there is a U-shaped close-in warm area (CWA) and a distant warm area (DWA) farther away from the overshooting tops. The mechanisms of cold-U/Vs with CWA and cold rings with CWSs are still not fully understood. According to the preliminary results of previous investigations (Setvák et al., 2010) suggest that both types of storm result from similar mechanisms, while environmental features determine which of those storm top features will develop. According to model simulations the crucial factors for cold ring or cold U/V shape formation are upper level wind shear and lower stratospheric inversion.

a)
b)
c)
Figure 7: Idealized soundings used in simulations and RAMS (Regional Atmospheric Modeling System) model simulations which provided simulated 10.35 µm satellite images at four different times. A) No upper level wind shear and no tropopause inversion, B) tropopause inversion and no upper level wind shear, C) tropopause inversion and upper level wind shear. Adapted from Setvák et al., 2010.

Figure 7 shows the simulated brightness temperatures of stormcloud tops in three different environments. In the case without upper level wind shear or tropopause inversion, the cold ring and cold U/V shapes have not developed (Fig. A). In the second simulation (Fig. B) a tropopause inversion is present, which results in warmer brightness temperatures: a cold ring shape has developed with a significant temperature difference (~27 K) between the CWS and the cold ring.

In situations where upper level wind shear is present, the cold U/V shape develops (Fig. C). According to previous investigations cold rings typically appear in environments with weak wind shear, while strong wind shear results in the formation of cold U/V shapes.

Satellite observations show that above-anvil plumes develop often in the case of cold U/V-shaped storms. Those plumes are warmer than the anvil, masking the cold anvil top underneath. The warm plumes might resemble CWA areas at first sight; a typical CWA, however, is often larger and of a different shape than an above-anvil plume.

 

Weather Events

Previous studies (Irsic Zibert et al., 2010; Bedka et al., 2011) based on METEOSAT and GOES imagery suggest that the cold ring and cold U/V feature are accompanied by severe weather conditions, such as large hail, severe winds or tornadoes. Severe weather conditions have been observed when a cold ring or cold U/V pattern persists at least 30-40 minutes. Analysis of cold ring and cold U/V events (Bedka et al., 2011) show that in 74% of cases some kind of severe weather conditions were detected (> 2cm hail, > 25m/s wind or tornado).

For a more complete description of the Weather Events see the relevant chapter in MCS.

 

References

  • Bedka, K., Brunner, J., Dworak, R., Feltz, W., 2011: Objective satellite-based overshooting top and enhanced-V/cold ring detection: Validation and relationship with severe. 6th European Conference on Severe Storms (ECSS 2011), 3-7 October 2011, Palma de Mallorca, Balearic Islands, Spain.
  • Irsic Zibert, M., Strajnar, B., Zibert, J., 2010. Cold-ring pattern on satellite images as indication of severe weather. Proc. 2010 EUMETSAT Meteorological Satellite Conf. EUMETSAT, Cordoba, Spain.
  • Setvák, M., Lindsey, D.T., Novák, P., Wang, P.K., Radová, M., Kerkmann, J., Grasso, L., Su, S.-H., Rabin, R.M., Stástka, J., Charvát, Z., 2010. Satellite observed cold-ring-shaped features atop deep convective clouds. Atmos. Res. 97, 80-96.