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Case study of Mesoscale Convective Systems over Hungary on 29 June 2006 with satellite, radar and lightning data
Atmospheric Research 93 (2009) 82–92
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Atmospheric Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a t m o s
Case study of Mesoscale Convective Systems over Hungary on 29 June 2006 with satellite, radar and lightning data Mária Putsay ⁎, Ildikó Szenyán, André Simon Hungarian Meteorological Service, Kitaibel Pál u. 1, H-1024, Budapest, Hungary
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Article history: Received 30 November 2007 Received in revised form 15 October 2008 Accepted 16 October 2008 Keywords: Mesoscale Convective System Satellite Radar Lightning detection Cloud top microphysics
a b s t r a c t On 29 June 2006 two Mesoscale Convective Systems (MCS) crossed Hungary causing severe weather, heavy precipitation, hail and strong wind. The first MCS transformed to a Mesoscale Convective Vortex (MCV) in its dissipating phase. The case was analyzed using different remote sensing devices: satellites, radars and a lightning detection system. Visible images from the METEOSAT-8 satellite were used to discriminate thin and thick parts of the anvil and to identify the overshooting tops. Structures like cold rings and cold-U/V shapes detected from infrared imagery indicate possible penetration of the storm top into the tropopause or lower stratosphere. The near and medium infrared solar channels (and some thermal IR channel differences) provide information on cloud top microphysics. The spatial distribution of the cloud top ice crystal size was investigated with the use of the so called “convective storms” composite imagery obtained from brightness temperature and reflectivity differences of water vapor, infrared and short-wave channels. The MODIS band 1 (0.645 µm) image of the TERRA satellite shows gravity wave generation at the top of the thunderstorm cloud, which could be connected to the strength and pulsations of the updraft. Satellite images were overlaid with radar reflectivities, which are characterized by an asymmetric bow echo. It is concluded that composites of satellite, radar and lightning data help to assess relative locations of main up- and downdrafts and important features of the severe storm. © 2008 Elsevier B.V. All rights reserved.
1. Introduction On 29 June 2006 a Mesoscale Convective System associated with a squall line swept across Hungary between 0600 and 1715 UTC from the West to the East. In the afternoon another large convective system developed over the Julian Alps and Dinaric Mountains, and arrived around 1600 UTC moving in Hungary from the southwest. Both MCSs caused heavy precipitation, hail and strong winds on the same day. Extreme weather conditions occurred: at Lake Balaton a wind gust exceeding 100 km/h was measured. Broken trees fell over roads and railways, and flash flooding occurred, resulting in large amounts of property damage.
⁎ Corresponding author. Tel.: +36 1 3464771; fax: +36 1 3464665. E-mail addresses: [email protected] (M. Putsay), [email protected] (I. Szenyán), [email protected] (A. Simon). 0169-8095/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2008.10.026
A Mesoscale Convective System is a convective cloud and precipitation system that is usually considerably larger than an individual thunderstorm and it is often marked by an extensive middle to upper tropospheric stratiform-anvil cloud of several hundred kilometers in horizontal dimension (Glickman, 2000; Cotton and Anthes, 1989). An MCS should indicate a deep moist convective overturning contiguous with a mesoscale vertical circulation that is at least partially driven by the convective processes (Zipser, 1982). The term MCS fits for several types of thunderstorm systems, e.g. mesoscale convective complexes (Maddox, 1980) or squall lines, which were defined as linearly oriented MCS-s (Maddox, 1980; Bluestein and Jain, 1985). In another definition, MCS is a cloud system where the 85-GHz channel brightness temperature depression bounded by the 250 K isotherm covers an area of at least 2000 km2, with a minimum brightness temperature smaller or equal to 225 K (Mohr and Zipser, 1996). Similar methods can be used for automatic identification of severe
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thunderstorms from satellite remote sensing (Zipser et al., 2006). Some of the MCS storms are characterized by specific features in lightning distribution such as tilted electrical dipoles and high percentage of positive cloud to ground flashes (Dotzek et al., 2005) possibly influenced by vertical circulation and storm relative flow from the convective line to the rear stratiform region of the MCS (MacGorman and Rust, 1998; Hodapp et al., 2008). The paper is divided into seven main sections. Section 2 presents the datasets used and the methodology. Section 3 gives an overview of the synoptic situation. In Section 4 the main features of the storms are investigated using radar and satellite imagery. In Section 5, composites of radar, satellite and lightning data are discussed. The microphysical properties of the cloud top are analyzed in Section 6. A summary is provided in Section 7. 2. Data and methodology The structure and development of the MCSs were studied with different remote sensing methods. Satellite data were obtained from SEVIRI (Schmetz et al., 2002; Kerkmann et al., 2006) instrument of the second-generation METEOSAT-8 satellite and MODIS (King et al., 1992) instrument of TERRA satellite. METEOSAT data were visualized as single channel or RGB images. The RGB compositing technique offers the possibility of compression of the multi-spectral information content for optimum visualization, while at the same time preserving pattern and texture of clouds and surface features. Compositing the RGB image, different channels or channel differences are visualized in the red, green and blue colors. The channels or channel differences are selected according to the physical properties we would like to investigate. Proper enhancement
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of individual color channels ensures the good contrast. EUMETSAT has developed several types of RGB combinations, which are suggested as standard ones (Kerkmann et al., 2006). In our study we use those EUMETSAT-suggested RGBs, which are the most useful in convective situations. For all figures containing an RGB image, the name of the RGB type and the channels or channel differences corresponding to the red, green and blue colors are included. MODIS images were also analyzed. MODIS is a 36-band instrument of the NASA's EOS Terra and Aqua sun-synchronous, near-polar satellites. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m, and the remaining 29 bands at 1 km. We selected the “band 1” channel with the wavelength range of 0.62–0.67 µm, because it is a 250 m spatial resolution visible image. Due to the much higher spatial resolution we can better see the cloud top structure, however only two images per day are taken of the same area. Composite radar reflectivity data of nearly 2 km spatial resolution were measured by three Doppler radars (DWSR2500c and DWSR-2501c types) situated in Pogányvár, Budapest-Lőrinc and Napkor. Positions of intra-cloud (IC), cloud to cloud (CC) and cloud to ground (CG) flashes were identified with the SAFIR lightning detection system (Richard and Auffray, 1985; Richard and Lojou, 1996; Dombai, 2006). Flashes were visualized in 10 min intervals. Images were visualized in the HAWK system (Kertész, 2000). This software was developed in Hungary for duty forecasters as a tool for analyzing different types of meteorological data together. 3. Synoptic situation On the 29th of June, 2006, two MCSs crossed Hungary on the same day. In the following sections we will use the term
Fig. 1. SATREP analysis of the synoptic situation on 29 June 2006 at 12 UTC.
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Fig. 2. Meteosat-8, airmass RGB image, (WV6.2–WV7.3, IR9.7–IR10.8, WV6.2), from 29 June 2006 at 1440 UTC (left) and 1710 UTC (right).
“morning MCS” for the first one, and “afternoon MCS” for the second one. On this day Hungary was situated between an area of low surface pressure over the Mediterranean and an anticyclone centered over Denmark and the North Sea. The easterly–northeasterly flow at the surface was rather weak. A stationary (thermally developed, but dynamically inactive) cold front existed northwest of Hungary for several days. At 12 UTC on June 29, there was an isolated pool of colder air over Germany and an upper air trough at 500 hPa, which extended throughout the Alpine region of Northern Italy and moved slowly eastward. A moderate southwesterly flow was analyzed at this height (10–15 m/s, from ECMWF analysis). The resulting cold advection at upper levels was probably one of the main reasons for the destabilization of the air mass and
generation of convection (Horváth, 2006). The cloud structures at 12 UTC are identified in Fig. 1 on the satellite reports (SATREP) carried out by the Central Institute for Meteorology and Geodynamics in Vienna (ZAMG), taken from the SATREP site (available online at http://www.knmi.nl/satrep/archive.htm). The convective cells of the morning MCS initially developed along the stationary cold front, but then moved deep into the warm air sector over the Carpathian basin. The movement of the convective system was later probably more influenced by mesoscale circulation and local environment (moisture and instability distribution) rather than by largescale flow. This could also be the reason that numerical models (non-hydrostatic MM5 model running operationally at HMS) did not forecast the morning MCS.
Fig. 3. Radar composite image (column maximum of Z) from 29 June 2006 at 1130 UTC.
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Fig. 4. MODIS band 1 (0.645 µm) image from 29 June 2006 at 0925 UTC (left) and the Meteosat-8, HRV (high resolution visible) image from 29 June 2006 at 1010 UTC (right).
The afternoon MCS formed by the joining of a system that developed along the inactive cold front during the previous night, and cells that formed over the Dinaric Mountains in the afternoon (Fig. 2). The numerical models better forecasted the afternoon MCS. Its evolution and movement seemed to be more influenced by synoptic scale processes. 4. The main features of the MCSs as observed by radar and satellite 4.1. The shape of the squall line on the radar imagery The morning MCS formed over the Alps the day before and reached the northwest border of Hungary by early morning as
a strong line of instability. After 1030 UTC, an interesting feature can be seen on the radar images. The shape of the column maximum radar reflectivity shows an asymmetric, comma-shaped bow echo on its northern flank (Fig. 3). The northern part of the line curves more and more with time. The asymmetric bow echo shape of the squall line is due to the mid-level rear inflow jet into the back of the system and the Coriolis effect. The Coriolis effect can have significant impact on MCS evolution, in case of long living and large systems (Wallace and Hobbs, 1977). The mid-level rear inflow jet forms the bowed shape of the reflectivity echo. After the evolution of the so-called bookend vortexes on both ends of the squall line, the Coriolis-effect strengthens the cyclonic vortex on the northern end and weakens the anticyclonic
Fig. 5. METEOSAT-8, IR10.8 infrared image (Tb b 240 K) from 29 June 2006 at 0755 UTC (left) and 0825 UTC (right).
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vortex on the southern end. The conceptual model of the bow echo evolution was originally proposed by Fujita (1978) and later also numerically simulated (e.g. Weisman, 1993). 4.2. Cloud top features from satellite data The second-generation METEOSAT images are acquired every quarter hour at the Hungarian Meteorological Service. We investigated the time series of High Resolution Visible (HRV) and 10.8 µm infrared (IR10.8) images and some useful composite (RGB) images: the so-called ‘HRV cloud’, ‘convective storms’, ‘day microphysical’ and ‘airmass’ RGB images. These RGB images were developed by EUMETSAT for investigating daytime convection (Kerkmann et al., 2006). Cloud top features like overshooting tops, gravity waves, and plumes can be easily seen in the MODIS visible image and in some cases in the lower spatial resolution MSG HRV images as well (Fig. 4). These phenomena are often indicators of strong updrafts and severe storms (e.g. Setvak and Doswell, 1991; Levizzani and Setvak, 1996; Wang, 2003, 2004, 2007; Setvak et al., 2003, 2007). In both images of Fig. 4, the 70– 100% visible reflectivity range was highlighted to emphasize the cloud top features. The MSG IR10.8 channel satellite image (Fig. 5) shows the cloud top temperature structure. In some images (Fig. 5) one can observe “cold rings” and a “cold-U/V” shape, which are
often characteristics of severe systems (e.g. McCann, 1983; Adler and Mack, 1986; Heymsfield et al., 1983a,b; Heymsfield and Blackmer, 1988; Setvak et al., 2007). If the top of a cell has penetrated into the tropopause or the lower stratosphere and it is already in thermal equilibrium with its environment then the cloud top temperature in the middle of the dome will be warmer than the surrounding area. However, the overshooting top is much colder than the environment due to adiabatic cooling. Sometimes a plume (an over anvil cirrus cloud) masks a part of the ring (Setvak et al., 2007) or the wake-effect (Adler and Mack, 1986; Heymsfield et al., 1983a,b) modifies the cloud top temperature structure. Then it is not possible to see the whole ring, only a part of it, showing a cold-U or V shape. Combining the HRV and IR10.8 channels we get the socalled ‘HRV cloud’ composite image, a good tool for investigating convection (Fig. 6). In this composite image, the high-level opaque cold clouds are white, the high-level semitransparent clouds are bluish, the low- and mediumlevel clouds are yellow, the cloudless area is dark grayish, greenish, bluish (depending on the temperature) or yellow if it is covered by snow (Kerkmann et al., 2006). As we put the HRV in two colors and the lower spatial resolution IR image only in one color, the resulting composite image will still have fairly high resolution. The HRV cloud image is a good tool to follow the convective development because the high-level
Fig. 6. Meteosat-8, HRV cloud composite image, (HRV, HRV, IR10.8) from 29 June 2006 at 1340 UTC.
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Fig. 7. METEOSAT-8, HRV cloud image (HRV, HRV, IR10.8) from 29 June 2006 at 1510 UTC (left) and 1610 UTC (right).
clouds can be separated from the lower level clouds, and at high levels we can distinguish the opaque clouds from the semitransparent clouds. 4.3. Dissipating stage of the morning MCS Fig. 7 shows the developing and joining phase of the afternoon MCS and the dissipating phase of the morning MCS. Well-defined overshooting tops are seen in the afternoon MCS. A MCV developed from the morning MCS as evidenced by the cyclonic-curving thin clouds in the 1610 UTC image. Exact reasons for the storm system rotation are not yet known, but the Coriolis effect could have a significant impact due to long life (more than 12 h) and huge size (Wallace and Hobbs, 1977). The problem of the origin of MCV rotation was discussed in several articles (e.g. Trier et al., 2000). Latent heat release and the Coriolis effect were shown to have
significant impacts on large and long-lived convective systems (Zhang and Fritsch, 1988; Weisman and Davis, 1998). A MCV consists of mid-level convergent cyclonic flow and high-level divergent anticyclonic outflow within the anvil. In Fig. 7 it is possible to see the high-level divergent outflow and some remaining active cells. 5. Composite images of radar, satellite and lightning data 5.1. Locations of the main updraft and downdraft based on radar and satellite data In many HRV images the locations of the overshooting tops can be identified, while the radar data can provide the precipitation structure. It can be expected that overshooting tops developed over strong and deep updrafts, while maxima of low level radar reflectivity are usually connected with
Fig. 8. METEOSAT-8, HRV cloud image (HRV, HRV, IR10.8) from 29 June 2006 at 1110 UTC (left) and 1140 UTC (right). Circles indicate the locations of the overshooting tops. Simultaneous radar image is overlaid (column maximum of Z N 35 dBz).
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downdrafts. To investigate their relative locations we marked the locations of the overshooting tops on the HRV images by circles, and overlaid them on a combined radar-satellite image (Fig. 8). To create this combined image, simultaneous radar and satellite data were used. The radar data were interpolated to the ‘European’ satellite time (nominal time + 10 min) using displacement vector fields. Only the high radar reflectivities (column maximum of Z N 35 dBz) were overlaid on the satellite image, to see the convective precipitation. The shapes of a line connecting the overshooting tops and the high radar reflectivities were similar and close to each other. They are shifted a little due to the parallax effect.
Lightning activity was very high in the morning MCS of 29 June 2006. More then 1000 CC flashes were detected per 10 min interval between 6 and 11 UTC. Between 1010 and 1020 UTC almost 8000 CC flashes were detected. The number of the flashes decreased at first about 11 UTC and a further decrease occurred after 12 UTC. Lightning positions were added to the simultaneous radar and satellite images (Fig. 9). The majority of the detected flashes were close to the locations of the overshooting tops and the most intense precipitation. However, we noticed that the band of the flashes was broad and slightly shifted backward relative to the band of the high radar reflectivity. This can be connected with transfer of the charges by MCS circulation as shown in MacGorman and Rust (1998).
5.2. Location of lightning activity 6. Cloud top microphysics as seen on METEOSAT imagery It is expected that the storm electrification originates mainly from collisions between cloud particles in favorable moisture and temperature conditions, e.g. by a graupel-ice mechanism (Takahashi, 1978). Cloud-to-cloud (CC) discharges are usually frequent in the growing phase of the storm, while the maximum intensity of cloud-to-ground (CG) flashes is correlated with precipitation (MacGorman and Rust, 1998).
Cloud top microphysics features, especially the presence of small ice crystals on the storm cloud top, could be an indicator of storm severity (Lindsey et al., 2006). Small ice particles on the cloud top may partly be due to cells with strong updrafts. In strong updrafts the small water particles forming at the cloud base reach the cloud top quickly and have less time for
Fig. 9. METEOSAT-8, radar and lightning data, observed on 29 June 2006, at 0825 UTC: Upper left: HRV (70–100%), upper right: IR10.8 (Tb b 240 K), lower left: HRV (70–100%) and radar (column maximum of Z N 35 dBz), lower right: HRV cloud (HRV, HRV, IR10.8), radar (column maximum of Z N 35 dBz) and 10-minute lightning data (0820–0830 UTC).
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interacting and growing (Kerkmann et al., 2006; Lensky and Rosenfeld, 2006). The 1.6 and 3.9 µm solar channels (and some thermal IR channel differences) contain information on cloud top microphysics: phase and effective size of the particles. In this case study we were interested in the particle size. Several channels (or channel differences) contain information on phase, but only the reflectivity in the 3.9 µm channel is really sensitive to the cloud top effective particle size. There is some partial information on particle size in other channels (channel differences) as well, but only under specific circumstances. Smaller ice crystals scatter 3.9 µm radiation much more efficiently than bigger particles. Nevertheless, even in the presence of small ice crystals, the reflectivity of a storm top is rather low (around 8%) in the 3.9 µm channel (IR3.9) because of the high absorption of ice. To calculate the reflectivity in IR3.9 channel (IR3.9refl), we should have to measure the reflected solar radiation. At night there is no solar radiation, so we cannot retrieve the particle size. Unfortunately, even during the daytime we cannot measure the reflected solar radiation directly, because in this channel the satellite measures a mixed signal: thermal
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radiation and reflected solar radiation, which have similar orders of magnitude. As a consequence, during the daytime we are able to calculate the IR3.9refl, but only after subtracting the thermal radiation from the measured signal. There are several methods to do this, but all of them require approximations, adding some uncertainties to the estimated particle size or IR3.9refl. Rosenfeld and Lensky (1998) developed a method to calculate IR3.9refl. In their method, not only the thermal radiation is estimated and subtracted from the measured value, but also the CO2 and water vapor absorption effects are accounted for. The IR3.9 channel is mainly a window channel, but it has a narrow overlap with a CO2 absorption band, so for accurate IR3.9refl estimates a correction for CO2 absorption is needed. Using the algorithm of Rosenfeld and Lensky (1998), we calculated the IR3.9refl and visualized it by highlighting the 1–6% reflectivity range with a gamma correction (gamma=0.7) to gain a good contrast. In Figs.10 and 11, IR3.9refl is visualized in the lower right frame. Areas with higher than 6% reflectivity are white. Light gray shades correspond to small ice crystals while dark gray shades indicate larger ice crystals. We should mention
Fig. 10. METEOSAT-8 and radar data observed on 29 June 2006 at 1055 UTC. Upper left: HRV (70–100%) and radar (column maximum of Z N 35 dBz), upper right: convective storms RGB (WV6.2–WV7.3, IR3.9–IR10.8, NIR1.6–VIS0.6), lower left: IR10.8 (Tb b 240 K) and lower right: IR3.9refl.
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Fig. 11. Same as Fig. 10, except for 1210 UTC.
that whitish colors can mean not only cloud tops with very small ice particles, but also optically thin clouds. Kerkmann et al. (2006) developed the so-called ‘convective storms RGB’ (upper right parts of Figs. 10 and 11) to visualize the particle size features of high-level cloud tops with good contrast and without the possibility of confusing the ice cloud top composed of small particles and the areas not covered by ice clouds. Their technique uses the brightness temperature difference (DTB) of IR3.9–IR10.8 (in green color) instead of the calculated IR3.9 reflectivity. This difference strongly depends on the IR3.9refl giving an excellent contrast to the RGB. In the convective storms RGB, high-level ice cloud tops are reddish (opaque ice cloud with big particles), yellowish (opaque ice cloud with small particles) or rose (semitransparent ice clouds). Other colors mean water clouds or cloud free areas. Unfortunately, the IR3.9–IR10.8 difference depends not only on the particle size but on the cloud top temperature as well. The same small ice crystals with different temperatures will have different yellowish shades in this RGB image: the colder one would appear more yellowish. In Figs. 10 and 11 we visualize both the IR3.9refl and the convective storms RGB images together with the IR10.8 image and the HRV and radar combined image.
The presence of the small ice particles is indicated on the cloud top of the morning MCS almost everywhere on the anvil. The source of the small ice particles was cells with strong updrafts. The system was already in its mature stage when it arrived over Hungary. A large area of small ice crystals has already spread over the entire anvil following the cloud top airflow. In some cases (Fig. 10) one can see that the IR3.9– IR10.8 brightness temperature difference depends not only on the particle size but also on the IR10.8 brightness temperature data as well (indicated by arrows). Investigating the much younger system over northern Italy in Fig. 11, one can see a concentrated spot of small ice particles appearing and growing near the overshooting top. The afternoon MCS was formed from this system and the cells developing over the Dinaric Mountains after 1310 UTC. 7. Summary On 29 June 2006 two severe MCSs crossed Hungary, causing severe weather, heavy precipitation, hail and strong wind. After a short summary of the synoptic situation, the squall line shape was analyzed in radar images during the mature/dissipating phase of the morning MCS. The reason
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why such an asymmetric bow echo shape evolved was discussed. The paper analyzes the two MCSs using different remote sensing devices: satellites, radars and a lightning detection system. We show how the different remote sensing methods can be used to analyze the development and structure of MCSs. Geostationary satellites have good temporal resolution but observe mostly the cloud tops and cannot directly investigate the inner structure of the storm system and the weather on the surface under the storm. Nevertheless, 12 channel imagery and composite imagery from the second-generation METEOSAT provide a variety of information on the cloud top structure. The visible imagery seems to be important in identifying overshooting tops and gravity waves. The gravity waves clearly visible from MODIS imagery are probably related to pulsations of the updraft as it was numerically simulated by Wang (2003). The infrared channel was used to investigate the cloud top temperature structure, which is usually correlated with the cloud top height. The morning MCS showed the presence of a cold ring, cold-U/V feature in the cloud top temperature distribution. These often indicate the storm severity. The near and medium infrared solar channels (and some thermal IR channel differences) contain information on the cloud top microphysics. The convective storms RGB helps to identify where there are large amounts of small ice particles in the anvil of the MCS, which might be a consequence of a strong updraft. The presence of severe weather in the morning MCS is indicated also by specific features of radar reflectivity, such as a bow echo and comma shape. Superposition of satellite information (e.g. positions of the overshooting tops) with (column maximum) radar reflectivity images could be very useful in forecasting and in assessing the positions of the most intense updrafts and downdrafts. In the near future we expect to have 3D radar information (horizontal and vertical cross sections) and Doppler radar velocities, which will provide much more information about the inner storm structure. The amount and frequency of lightning discharges was also related to the intensity of the MCS. The spatial distribution of lightning flashes shows that the maximum electric activity was close to the maxima of detected radar reflectivity, although a non-negligible amount of discharges appears also in the rear side of the MCS. The transformation of the morning MCS into a Mesoscale Convective Vortex in its dissipating phase could be detected with visible satellite imagery. However, the mechanism of this transformation should be investigated in the future with the use of improved high resolution non-hydrostatic numerical simulations. Acknowledgements Part of this research was supported by the Hungarian Scientific Research Fund (T043010) and by the National Program for Research and Development (NKFP, project number 3/022/2005).The authors would like to thank EUMETSAT for the beneficial training workshops on MSG applications on convection and to the lecturers at these workshops for their valuable instruction, as well as to the authors of the MSG Interpretation Guide. We would like to
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thank our colleague Péter Németh for producing the simultaneous radar data and Ákos Horváth, Kornél Kolláth for helping us with comments and discussions. Thanks to Martin Setvák from the Check Hydrometeorological Institute for discussions and helpful comments. We are grateful to our reviewers for detailed comments, which helped to improve the early version of the manuscript. We thank Kathleen Strabala (UW/CIMSS) for revising the final text. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.atmosres.2008.10.026. References Adler, R.F., Mack, R.A., 1986. Thunderstorm cloud top dynamics as inferred from satellite observations and a cloud top parcel model. J. Atmos. Sci. 43, 1945–1960. Bluestein, H.B., Jain, M.H., 1985. Formation of mesoscale lines of precipitation: severe squall lines in Oklahoma during spring. J. Atmos. Sci. 42, 1711–1732. Cotton, W.R., Anthes, R.A., 1989. Storm and Cloud Dynamics. Academic Press. 880 pp. Dombai, F., 2006. Some experiences on joint analyses of radar and lightning localization data. Proceedings of 1st International Lightning Meteorology Conference, 26–27 April 2006, Tucson, Arizona. 12 pp. Dotzek, N., Rabin, R.M., Carey, L.D., MacGorman, D.R., McCormick, T.L., Demetriades, N.W., Murphy, M.J., Holle, R.L., 2005. Lightning activity related to satellite and radar observations of a mesoscale convective system over Texas on 7–8 April 2002. Atmos. Res. 76, 127–166. Fujita, T.T., 1978. Manual of downburst identification for Project Nimrod. Dept. of the Geophysical Sciences, University of Chicago. SMRP Research Paper, No. 156, p. 104. Glickman, T. (Ed.), 2000. Glossary of meteorology. 2d ed. American Meteorological Society. 855 pp. Heymsfield, G.M., Blackmer, R.H., 1988. Satellite observed characteristics of midwest severe thunderstorm anvils. Mon. Weather Rev. 116, 2200–2224. Heymsfield, G.M., Blackmer, R.H., Schotz, S., 1983a. Upper-level structure of Oklahoma Tornadic storms on 2 May 1979. I: radar and satellite observations. J. Atmos. Sci. 40, 1740–1755. Heymsfield, G.M., Szejwach, G., Schotz, S., Blackmer, R.H., 1983b. Upper-level structure of Oklahoma Tornadic storms on 2 May 1979. II: explanation of “V” pattern and internal warm region in infrared observations. J. Atmos. Sci. 40, 1756–1767. Hodapp, C.L., Carey, L.D., Orville, R.E., 2008. Evolution of radar reflectivity and total lightning characteristics of the 21 April 2006 mesoscale convective system over Texas. Atmos. Res. 89, 113–137. Horváth, Á., 2006. Devastating thunderstorm lines. Légkör 51, 16–19 (in Hungarian). Kerkmann, J., Lutz, H.J., König, M., Prieto, J., Pylkko, P., Roesli, H.P., Rosenfeld, D., Zwatz-Meise, V., Schmetz, J., Schipper, J., Georgiev, C., Santurette, P., 2006. MSG Channels, Interpretation Guide, Weather, Surface Conditions and Atmospheric Constituents. Available online at http://oiswww. eumetsat.org/WEBOPS/msg_interpretation/index.html. Kertész, S., 2000. The HAWK system: recent developments at HMS. Proceedings of the 11th EGOWS meeting held in Helsinki, 5–8 June, 2000, pp. 13–14. King, M.D., Kaufman, Y., Menzel, W.P., Tanré, D., 1992. Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS). IEEE Trans. Geosci. Remote Sens. 30, 1–27. Lensky, I.M., Rosenfeld, D., 2006. The time-space exchangeability of satellite retrieved relations between cloud top temperature and particle effective radius. Atmos. Chem. Phys. 6, 2887–2894. Levizzani, V., Setvak, M., 1996. Multispectral, high-resolution satellite observations of plumes on top of convective storms. J. Atmos. Sci. 53, 361–369. Lindsey, D.T., Hillger, D.W., Grasso, L., Knaff, J.A., Dostalek, J.F., 2006. GOES climatology and analysis of thunderstorms with enhanced 3.9-µm reflectivity. Mon. Weather Rev. 134, 2342–2353. Maddox, R.A., 1980. Mesoscale convective complexes. Bull. Am. Meteorol. Soc. 61, 1374–1387. MacGorman, D.R., Rust, W.D., 1998. The Electrical Nature of Storms. Oxford University Press. 422 pp.
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Atmospheric Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a t m o s
Case study of Mesoscale Convective Systems over Hungary on 29 June 2006 with satellite, radar and lightning data Mária Putsay ⁎, Ildikó Szenyán, André Simon Hungarian Meteorological Service, Kitaibel Pál u. 1, H-1024, Budapest, Hungary
a r t i c l e
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Article history: Received 30 November 2007 Received in revised form 15 October 2008 Accepted 16 October 2008 Keywords: Mesoscale Convective System Satellite Radar Lightning detection Cloud top microphysics
a b s t r a c t On 29 June 2006 two Mesoscale Convective Systems (MCS) crossed Hungary causing severe weather, heavy precipitation, hail and strong wind. The first MCS transformed to a Mesoscale Convective Vortex (MCV) in its dissipating phase. The case was analyzed using different remote sensing devices: satellites, radars and a lightning detection system. Visible images from the METEOSAT-8 satellite were used to discriminate thin and thick parts of the anvil and to identify the overshooting tops. Structures like cold rings and cold-U/V shapes detected from infrared imagery indicate possible penetration of the storm top into the tropopause or lower stratosphere. The near and medium infrared solar channels (and some thermal IR channel differences) provide information on cloud top microphysics. The spatial distribution of the cloud top ice crystal size was investigated with the use of the so called “convective storms” composite imagery obtained from brightness temperature and reflectivity differences of water vapor, infrared and short-wave channels. The MODIS band 1 (0.645 µm) image of the TERRA satellite shows gravity wave generation at the top of the thunderstorm cloud, which could be connected to the strength and pulsations of the updraft. Satellite images were overlaid with radar reflectivities, which are characterized by an asymmetric bow echo. It is concluded that composites of satellite, radar and lightning data help to assess relative locations of main up- and downdrafts and important features of the severe storm. © 2008 Elsevier B.V. All rights reserved.
1. Introduction On 29 June 2006 a Mesoscale Convective System associated with a squall line swept across Hungary between 0600 and 1715 UTC from the West to the East. In the afternoon another large convective system developed over the Julian Alps and Dinaric Mountains, and arrived around 1600 UTC moving in Hungary from the southwest. Both MCSs caused heavy precipitation, hail and strong winds on the same day. Extreme weather conditions occurred: at Lake Balaton a wind gust exceeding 100 km/h was measured. Broken trees fell over roads and railways, and flash flooding occurred, resulting in large amounts of property damage.
⁎ Corresponding author. Tel.: +36 1 3464771; fax: +36 1 3464665. E-mail addresses: [email protected] (M. Putsay), [email protected] (I. Szenyán), [email protected] (A. Simon). 0169-8095/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2008.10.026
A Mesoscale Convective System is a convective cloud and precipitation system that is usually considerably larger than an individual thunderstorm and it is often marked by an extensive middle to upper tropospheric stratiform-anvil cloud of several hundred kilometers in horizontal dimension (Glickman, 2000; Cotton and Anthes, 1989). An MCS should indicate a deep moist convective overturning contiguous with a mesoscale vertical circulation that is at least partially driven by the convective processes (Zipser, 1982). The term MCS fits for several types of thunderstorm systems, e.g. mesoscale convective complexes (Maddox, 1980) or squall lines, which were defined as linearly oriented MCS-s (Maddox, 1980; Bluestein and Jain, 1985). In another definition, MCS is a cloud system where the 85-GHz channel brightness temperature depression bounded by the 250 K isotherm covers an area of at least 2000 km2, with a minimum brightness temperature smaller or equal to 225 K (Mohr and Zipser, 1996). Similar methods can be used for automatic identification of severe
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thunderstorms from satellite remote sensing (Zipser et al., 2006). Some of the MCS storms are characterized by specific features in lightning distribution such as tilted electrical dipoles and high percentage of positive cloud to ground flashes (Dotzek et al., 2005) possibly influenced by vertical circulation and storm relative flow from the convective line to the rear stratiform region of the MCS (MacGorman and Rust, 1998; Hodapp et al., 2008). The paper is divided into seven main sections. Section 2 presents the datasets used and the methodology. Section 3 gives an overview of the synoptic situation. In Section 4 the main features of the storms are investigated using radar and satellite imagery. In Section 5, composites of radar, satellite and lightning data are discussed. The microphysical properties of the cloud top are analyzed in Section 6. A summary is provided in Section 7. 2. Data and methodology The structure and development of the MCSs were studied with different remote sensing methods. Satellite data were obtained from SEVIRI (Schmetz et al., 2002; Kerkmann et al., 2006) instrument of the second-generation METEOSAT-8 satellite and MODIS (King et al., 1992) instrument of TERRA satellite. METEOSAT data were visualized as single channel or RGB images. The RGB compositing technique offers the possibility of compression of the multi-spectral information content for optimum visualization, while at the same time preserving pattern and texture of clouds and surface features. Compositing the RGB image, different channels or channel differences are visualized in the red, green and blue colors. The channels or channel differences are selected according to the physical properties we would like to investigate. Proper enhancement
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of individual color channels ensures the good contrast. EUMETSAT has developed several types of RGB combinations, which are suggested as standard ones (Kerkmann et al., 2006). In our study we use those EUMETSAT-suggested RGBs, which are the most useful in convective situations. For all figures containing an RGB image, the name of the RGB type and the channels or channel differences corresponding to the red, green and blue colors are included. MODIS images were also analyzed. MODIS is a 36-band instrument of the NASA's EOS Terra and Aqua sun-synchronous, near-polar satellites. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m, and the remaining 29 bands at 1 km. We selected the “band 1” channel with the wavelength range of 0.62–0.67 µm, because it is a 250 m spatial resolution visible image. Due to the much higher spatial resolution we can better see the cloud top structure, however only two images per day are taken of the same area. Composite radar reflectivity data of nearly 2 km spatial resolution were measured by three Doppler radars (DWSR2500c and DWSR-2501c types) situated in Pogányvár, Budapest-Lőrinc and Napkor. Positions of intra-cloud (IC), cloud to cloud (CC) and cloud to ground (CG) flashes were identified with the SAFIR lightning detection system (Richard and Auffray, 1985; Richard and Lojou, 1996; Dombai, 2006). Flashes were visualized in 10 min intervals. Images were visualized in the HAWK system (Kertész, 2000). This software was developed in Hungary for duty forecasters as a tool for analyzing different types of meteorological data together. 3. Synoptic situation On the 29th of June, 2006, two MCSs crossed Hungary on the same day. In the following sections we will use the term
Fig. 1. SATREP analysis of the synoptic situation on 29 June 2006 at 12 UTC.
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Fig. 2. Meteosat-8, airmass RGB image, (WV6.2–WV7.3, IR9.7–IR10.8, WV6.2), from 29 June 2006 at 1440 UTC (left) and 1710 UTC (right).
“morning MCS” for the first one, and “afternoon MCS” for the second one. On this day Hungary was situated between an area of low surface pressure over the Mediterranean and an anticyclone centered over Denmark and the North Sea. The easterly–northeasterly flow at the surface was rather weak. A stationary (thermally developed, but dynamically inactive) cold front existed northwest of Hungary for several days. At 12 UTC on June 29, there was an isolated pool of colder air over Germany and an upper air trough at 500 hPa, which extended throughout the Alpine region of Northern Italy and moved slowly eastward. A moderate southwesterly flow was analyzed at this height (10–15 m/s, from ECMWF analysis). The resulting cold advection at upper levels was probably one of the main reasons for the destabilization of the air mass and
generation of convection (Horváth, 2006). The cloud structures at 12 UTC are identified in Fig. 1 on the satellite reports (SATREP) carried out by the Central Institute for Meteorology and Geodynamics in Vienna (ZAMG), taken from the SATREP site (available online at http://www.knmi.nl/satrep/archive.htm). The convective cells of the morning MCS initially developed along the stationary cold front, but then moved deep into the warm air sector over the Carpathian basin. The movement of the convective system was later probably more influenced by mesoscale circulation and local environment (moisture and instability distribution) rather than by largescale flow. This could also be the reason that numerical models (non-hydrostatic MM5 model running operationally at HMS) did not forecast the morning MCS.
Fig. 3. Radar composite image (column maximum of Z) from 29 June 2006 at 1130 UTC.
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Fig. 4. MODIS band 1 (0.645 µm) image from 29 June 2006 at 0925 UTC (left) and the Meteosat-8, HRV (high resolution visible) image from 29 June 2006 at 1010 UTC (right).
The afternoon MCS formed by the joining of a system that developed along the inactive cold front during the previous night, and cells that formed over the Dinaric Mountains in the afternoon (Fig. 2). The numerical models better forecasted the afternoon MCS. Its evolution and movement seemed to be more influenced by synoptic scale processes. 4. The main features of the MCSs as observed by radar and satellite 4.1. The shape of the squall line on the radar imagery The morning MCS formed over the Alps the day before and reached the northwest border of Hungary by early morning as
a strong line of instability. After 1030 UTC, an interesting feature can be seen on the radar images. The shape of the column maximum radar reflectivity shows an asymmetric, comma-shaped bow echo on its northern flank (Fig. 3). The northern part of the line curves more and more with time. The asymmetric bow echo shape of the squall line is due to the mid-level rear inflow jet into the back of the system and the Coriolis effect. The Coriolis effect can have significant impact on MCS evolution, in case of long living and large systems (Wallace and Hobbs, 1977). The mid-level rear inflow jet forms the bowed shape of the reflectivity echo. After the evolution of the so-called bookend vortexes on both ends of the squall line, the Coriolis-effect strengthens the cyclonic vortex on the northern end and weakens the anticyclonic
Fig. 5. METEOSAT-8, IR10.8 infrared image (Tb b 240 K) from 29 June 2006 at 0755 UTC (left) and 0825 UTC (right).
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vortex on the southern end. The conceptual model of the bow echo evolution was originally proposed by Fujita (1978) and later also numerically simulated (e.g. Weisman, 1993). 4.2. Cloud top features from satellite data The second-generation METEOSAT images are acquired every quarter hour at the Hungarian Meteorological Service. We investigated the time series of High Resolution Visible (HRV) and 10.8 µm infrared (IR10.8) images and some useful composite (RGB) images: the so-called ‘HRV cloud’, ‘convective storms’, ‘day microphysical’ and ‘airmass’ RGB images. These RGB images were developed by EUMETSAT for investigating daytime convection (Kerkmann et al., 2006). Cloud top features like overshooting tops, gravity waves, and plumes can be easily seen in the MODIS visible image and in some cases in the lower spatial resolution MSG HRV images as well (Fig. 4). These phenomena are often indicators of strong updrafts and severe storms (e.g. Setvak and Doswell, 1991; Levizzani and Setvak, 1996; Wang, 2003, 2004, 2007; Setvak et al., 2003, 2007). In both images of Fig. 4, the 70– 100% visible reflectivity range was highlighted to emphasize the cloud top features. The MSG IR10.8 channel satellite image (Fig. 5) shows the cloud top temperature structure. In some images (Fig. 5) one can observe “cold rings” and a “cold-U/V” shape, which are
often characteristics of severe systems (e.g. McCann, 1983; Adler and Mack, 1986; Heymsfield et al., 1983a,b; Heymsfield and Blackmer, 1988; Setvak et al., 2007). If the top of a cell has penetrated into the tropopause or the lower stratosphere and it is already in thermal equilibrium with its environment then the cloud top temperature in the middle of the dome will be warmer than the surrounding area. However, the overshooting top is much colder than the environment due to adiabatic cooling. Sometimes a plume (an over anvil cirrus cloud) masks a part of the ring (Setvak et al., 2007) or the wake-effect (Adler and Mack, 1986; Heymsfield et al., 1983a,b) modifies the cloud top temperature structure. Then it is not possible to see the whole ring, only a part of it, showing a cold-U or V shape. Combining the HRV and IR10.8 channels we get the socalled ‘HRV cloud’ composite image, a good tool for investigating convection (Fig. 6). In this composite image, the high-level opaque cold clouds are white, the high-level semitransparent clouds are bluish, the low- and mediumlevel clouds are yellow, the cloudless area is dark grayish, greenish, bluish (depending on the temperature) or yellow if it is covered by snow (Kerkmann et al., 2006). As we put the HRV in two colors and the lower spatial resolution IR image only in one color, the resulting composite image will still have fairly high resolution. The HRV cloud image is a good tool to follow the convective development because the high-level
Fig. 6. Meteosat-8, HRV cloud composite image, (HRV, HRV, IR10.8) from 29 June 2006 at 1340 UTC.
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Fig. 7. METEOSAT-8, HRV cloud image (HRV, HRV, IR10.8) from 29 June 2006 at 1510 UTC (left) and 1610 UTC (right).
clouds can be separated from the lower level clouds, and at high levels we can distinguish the opaque clouds from the semitransparent clouds. 4.3. Dissipating stage of the morning MCS Fig. 7 shows the developing and joining phase of the afternoon MCS and the dissipating phase of the morning MCS. Well-defined overshooting tops are seen in the afternoon MCS. A MCV developed from the morning MCS as evidenced by the cyclonic-curving thin clouds in the 1610 UTC image. Exact reasons for the storm system rotation are not yet known, but the Coriolis effect could have a significant impact due to long life (more than 12 h) and huge size (Wallace and Hobbs, 1977). The problem of the origin of MCV rotation was discussed in several articles (e.g. Trier et al., 2000). Latent heat release and the Coriolis effect were shown to have
significant impacts on large and long-lived convective systems (Zhang and Fritsch, 1988; Weisman and Davis, 1998). A MCV consists of mid-level convergent cyclonic flow and high-level divergent anticyclonic outflow within the anvil. In Fig. 7 it is possible to see the high-level divergent outflow and some remaining active cells. 5. Composite images of radar, satellite and lightning data 5.1. Locations of the main updraft and downdraft based on radar and satellite data In many HRV images the locations of the overshooting tops can be identified, while the radar data can provide the precipitation structure. It can be expected that overshooting tops developed over strong and deep updrafts, while maxima of low level radar reflectivity are usually connected with
Fig. 8. METEOSAT-8, HRV cloud image (HRV, HRV, IR10.8) from 29 June 2006 at 1110 UTC (left) and 1140 UTC (right). Circles indicate the locations of the overshooting tops. Simultaneous radar image is overlaid (column maximum of Z N 35 dBz).
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downdrafts. To investigate their relative locations we marked the locations of the overshooting tops on the HRV images by circles, and overlaid them on a combined radar-satellite image (Fig. 8). To create this combined image, simultaneous radar and satellite data were used. The radar data were interpolated to the ‘European’ satellite time (nominal time + 10 min) using displacement vector fields. Only the high radar reflectivities (column maximum of Z N 35 dBz) were overlaid on the satellite image, to see the convective precipitation. The shapes of a line connecting the overshooting tops and the high radar reflectivities were similar and close to each other. They are shifted a little due to the parallax effect.
Lightning activity was very high in the morning MCS of 29 June 2006. More then 1000 CC flashes were detected per 10 min interval between 6 and 11 UTC. Between 1010 and 1020 UTC almost 8000 CC flashes were detected. The number of the flashes decreased at first about 11 UTC and a further decrease occurred after 12 UTC. Lightning positions were added to the simultaneous radar and satellite images (Fig. 9). The majority of the detected flashes were close to the locations of the overshooting tops and the most intense precipitation. However, we noticed that the band of the flashes was broad and slightly shifted backward relative to the band of the high radar reflectivity. This can be connected with transfer of the charges by MCS circulation as shown in MacGorman and Rust (1998).
5.2. Location of lightning activity 6. Cloud top microphysics as seen on METEOSAT imagery It is expected that the storm electrification originates mainly from collisions between cloud particles in favorable moisture and temperature conditions, e.g. by a graupel-ice mechanism (Takahashi, 1978). Cloud-to-cloud (CC) discharges are usually frequent in the growing phase of the storm, while the maximum intensity of cloud-to-ground (CG) flashes is correlated with precipitation (MacGorman and Rust, 1998).
Cloud top microphysics features, especially the presence of small ice crystals on the storm cloud top, could be an indicator of storm severity (Lindsey et al., 2006). Small ice particles on the cloud top may partly be due to cells with strong updrafts. In strong updrafts the small water particles forming at the cloud base reach the cloud top quickly and have less time for
Fig. 9. METEOSAT-8, radar and lightning data, observed on 29 June 2006, at 0825 UTC: Upper left: HRV (70–100%), upper right: IR10.8 (Tb b 240 K), lower left: HRV (70–100%) and radar (column maximum of Z N 35 dBz), lower right: HRV cloud (HRV, HRV, IR10.8), radar (column maximum of Z N 35 dBz) and 10-minute lightning data (0820–0830 UTC).
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interacting and growing (Kerkmann et al., 2006; Lensky and Rosenfeld, 2006). The 1.6 and 3.9 µm solar channels (and some thermal IR channel differences) contain information on cloud top microphysics: phase and effective size of the particles. In this case study we were interested in the particle size. Several channels (or channel differences) contain information on phase, but only the reflectivity in the 3.9 µm channel is really sensitive to the cloud top effective particle size. There is some partial information on particle size in other channels (channel differences) as well, but only under specific circumstances. Smaller ice crystals scatter 3.9 µm radiation much more efficiently than bigger particles. Nevertheless, even in the presence of small ice crystals, the reflectivity of a storm top is rather low (around 8%) in the 3.9 µm channel (IR3.9) because of the high absorption of ice. To calculate the reflectivity in IR3.9 channel (IR3.9refl), we should have to measure the reflected solar radiation. At night there is no solar radiation, so we cannot retrieve the particle size. Unfortunately, even during the daytime we cannot measure the reflected solar radiation directly, because in this channel the satellite measures a mixed signal: thermal
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radiation and reflected solar radiation, which have similar orders of magnitude. As a consequence, during the daytime we are able to calculate the IR3.9refl, but only after subtracting the thermal radiation from the measured signal. There are several methods to do this, but all of them require approximations, adding some uncertainties to the estimated particle size or IR3.9refl. Rosenfeld and Lensky (1998) developed a method to calculate IR3.9refl. In their method, not only the thermal radiation is estimated and subtracted from the measured value, but also the CO2 and water vapor absorption effects are accounted for. The IR3.9 channel is mainly a window channel, but it has a narrow overlap with a CO2 absorption band, so for accurate IR3.9refl estimates a correction for CO2 absorption is needed. Using the algorithm of Rosenfeld and Lensky (1998), we calculated the IR3.9refl and visualized it by highlighting the 1–6% reflectivity range with a gamma correction (gamma=0.7) to gain a good contrast. In Figs.10 and 11, IR3.9refl is visualized in the lower right frame. Areas with higher than 6% reflectivity are white. Light gray shades correspond to small ice crystals while dark gray shades indicate larger ice crystals. We should mention
Fig. 10. METEOSAT-8 and radar data observed on 29 June 2006 at 1055 UTC. Upper left: HRV (70–100%) and radar (column maximum of Z N 35 dBz), upper right: convective storms RGB (WV6.2–WV7.3, IR3.9–IR10.8, NIR1.6–VIS0.6), lower left: IR10.8 (Tb b 240 K) and lower right: IR3.9refl.
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Fig. 11. Same as Fig. 10, except for 1210 UTC.
that whitish colors can mean not only cloud tops with very small ice particles, but also optically thin clouds. Kerkmann et al. (2006) developed the so-called ‘convective storms RGB’ (upper right parts of Figs. 10 and 11) to visualize the particle size features of high-level cloud tops with good contrast and without the possibility of confusing the ice cloud top composed of small particles and the areas not covered by ice clouds. Their technique uses the brightness temperature difference (DTB) of IR3.9–IR10.8 (in green color) instead of the calculated IR3.9 reflectivity. This difference strongly depends on the IR3.9refl giving an excellent contrast to the RGB. In the convective storms RGB, high-level ice cloud tops are reddish (opaque ice cloud with big particles), yellowish (opaque ice cloud with small particles) or rose (semitransparent ice clouds). Other colors mean water clouds or cloud free areas. Unfortunately, the IR3.9–IR10.8 difference depends not only on the particle size but on the cloud top temperature as well. The same small ice crystals with different temperatures will have different yellowish shades in this RGB image: the colder one would appear more yellowish. In Figs. 10 and 11 we visualize both the IR3.9refl and the convective storms RGB images together with the IR10.8 image and the HRV and radar combined image.
The presence of the small ice particles is indicated on the cloud top of the morning MCS almost everywhere on the anvil. The source of the small ice particles was cells with strong updrafts. The system was already in its mature stage when it arrived over Hungary. A large area of small ice crystals has already spread over the entire anvil following the cloud top airflow. In some cases (Fig. 10) one can see that the IR3.9– IR10.8 brightness temperature difference depends not only on the particle size but also on the IR10.8 brightness temperature data as well (indicated by arrows). Investigating the much younger system over northern Italy in Fig. 11, one can see a concentrated spot of small ice particles appearing and growing near the overshooting top. The afternoon MCS was formed from this system and the cells developing over the Dinaric Mountains after 1310 UTC. 7. Summary On 29 June 2006 two severe MCSs crossed Hungary, causing severe weather, heavy precipitation, hail and strong wind. After a short summary of the synoptic situation, the squall line shape was analyzed in radar images during the mature/dissipating phase of the morning MCS. The reason
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why such an asymmetric bow echo shape evolved was discussed. The paper analyzes the two MCSs using different remote sensing devices: satellites, radars and a lightning detection system. We show how the different remote sensing methods can be used to analyze the development and structure of MCSs. Geostationary satellites have good temporal resolution but observe mostly the cloud tops and cannot directly investigate the inner structure of the storm system and the weather on the surface under the storm. Nevertheless, 12 channel imagery and composite imagery from the second-generation METEOSAT provide a variety of information on the cloud top structure. The visible imagery seems to be important in identifying overshooting tops and gravity waves. The gravity waves clearly visible from MODIS imagery are probably related to pulsations of the updraft as it was numerically simulated by Wang (2003). The infrared channel was used to investigate the cloud top temperature structure, which is usually correlated with the cloud top height. The morning MCS showed the presence of a cold ring, cold-U/V feature in the cloud top temperature distribution. These often indicate the storm severity. The near and medium infrared solar channels (and some thermal IR channel differences) contain information on the cloud top microphysics. The convective storms RGB helps to identify where there are large amounts of small ice particles in the anvil of the MCS, which might be a consequence of a strong updraft. The presence of severe weather in the morning MCS is indicated also by specific features of radar reflectivity, such as a bow echo and comma shape. Superposition of satellite information (e.g. positions of the overshooting tops) with (column maximum) radar reflectivity images could be very useful in forecasting and in assessing the positions of the most intense updrafts and downdrafts. In the near future we expect to have 3D radar information (horizontal and vertical cross sections) and Doppler radar velocities, which will provide much more information about the inner storm structure. The amount and frequency of lightning discharges was also related to the intensity of the MCS. The spatial distribution of lightning flashes shows that the maximum electric activity was close to the maxima of detected radar reflectivity, although a non-negligible amount of discharges appears also in the rear side of the MCS. The transformation of the morning MCS into a Mesoscale Convective Vortex in its dissipating phase could be detected with visible satellite imagery. However, the mechanism of this transformation should be investigated in the future with the use of improved high resolution non-hydrostatic numerical simulations. Acknowledgements Part of this research was supported by the Hungarian Scientific Research Fund (T043010) and by the National Program for Research and Development (NKFP, project number 3/022/2005).The authors would like to thank EUMETSAT for the beneficial training workshops on MSG applications on convection and to the lecturers at these workshops for their valuable instruction, as well as to the authors of the MSG Interpretation Guide. We would like to
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