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SATELLITE MONITORING OF THE CONVECTIVE STORMS
FORECASTERS’ POINT OF VIEW
Michaela Valachová, Martin SetvákEUMETSAT Workshop at ECMWF User Meeting
Reading, 13 June 2017
Central Forecasting Office, Prague
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• storm forecastingo from long-range forecast
to nowcasting
• storm monitoringo satellites and geometry
o storm appearance
• data visualizationo IR-BT, RGB, sandwich
OBJECTIVES OF PRESENTATION
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• convective storms are challengingo where and when will storm evolve ?
o how dangerous will it be ?
o how long will it last ?
• satellite data are crucialo information every 5 min
o helpful products
o years of experience
FORECASTER’S POINT OF VIEW
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• long-range forecasto up to 10 days
o ensembles, probability
• short-range forecasto today and tomorrow
o synoptic analysis
• nowcastingo now or several hours
o observations, monitoring
STORM FORECASTING
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• operational weather satellites (overview)
• basic appearance on satellite images
• visualization techniqueso cloud-top brightness temperature
» cold-U and cold ring features, overshooting tops
o cloud-top reflectivity in 3.5 – 4 µm» small ice particles, above-anvil (ice) plumes
o RGB and Sandwich products
STORM MONITORING
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Martin Setvák
Satellites with passive radiometers can monitor the cloud tops only, they do not see inside the storms, or even
under them! Thus, these satellites can’t provide us with a direct information about the weather conditions under
the storms, such information has to be inferred (derived) from the satellite data indirectly, utilizing conceptual
models of convective storms.
Storms from above …
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Martin Setvák
Storms from above … which operational weather satellites to use:
geostationary (GEO) or low-Earth orbit (LEO) ones?
2014
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Aqua/MODIS 2013-06-20 12:25 UTC; hail occurrence at 12:26 UTC
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2016-06-20 12:46 UTC Suomi-NPP/VIIRS (375 m) I1 band
2016-06-20 12:45 UTC MSG/SEVIRI (3 km) IR10.8 2016-06-20 12:46 UTC Suomi-NPP/VIIRS (375 m) I5 band
2016-06-20 12:45 UTC MSG/SEVIRI (1 km) HRV
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• size and shape of convective storms affected by:
internal storm dynamics – storm updrafts (strength, duration), storm splitting (supercells)
interaction with other storm cells – merging of anvils
interaction with environment – wind shear,
storm relative winds, moisture
viewing geometry – scan conditions: nadir vs low angle
→ parallax shift effect, re-mapping» problems with accurate geo-referencing of higher clouds
and some of the derived products
BASIC APPEARANCE OF STORMS
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Suomi-NPP/VIIRS band 1 11:05 UTC ~ 375 m MSG/SEVIRI HRV 11:05 UTC ~ 1 km
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Martin Setvák
Projection of various cloud-top features to
surface (georeferencing, navigation, re-
mapping) strongly depends on their actual
height above the ground level and on the
scanning geometry.
Parallax shift in satellite imagery
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SPATIAL RESOLUTION• the position of the satellite:
Meteosat-11 3.4° W
Meteosat-10 0°
Meteosat-9 9.5° E
Meteosat-8 41.5° E
The closer to the sub-satellite point,
the better is the image quality
(lesser distortion, higher image
resolution, smaller parallax shift)
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Martin Setvák
Nebraska, 22/23May 1996, 0045 UTC
GOES 8 (East)
Above-anvil (ice) plumes
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Martin Setvák
GOES 9 (West)
Above-anvil (ice) plumes
Nebraska, 22/23May 1996, 0045 UTC
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Martin Setvák
Impact of the wind shear – weak shear
2006-06-25 13:00, Meteosat-8, north Italy
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Martin Setvák
Impact of the wind shear – strong shear
2013-06-20 15:37, Meteosat-8, south Germany
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• detection of various cloud-top features:o overshooting tops, plumes, gravity waves
o cold-U or cold-ring shapes, small ice particles
VISUALIZATION TECHNIQUES
11 June 2016 13:20 UTC, The Baltics, Meteosat-9 (RSS data)
IR 10.8 BT color-enhancement Storm RGB product Sandwich product
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Martin Setvák
Color enhancement of the IR Brightness Temperature (BT) imagery – replacement of a part
of the grey scale, representing a certain temperature range, by dedicated colors. The color
scale can either be continuous - using a maximum of available colors, or a step-scale, using
only a limited number of colors (each color representing a smaller BT interval).
Color enhancement of the IR brightness temperature imagery
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Martin Setvák
26 May 2007, Germany
COLD-U
CLOSE-IN WARM AREA
(CWA)
DISTANT WARM AREA
(DWA)
Meteosat-9 (MSG2) 15:00 UTC
Cold-U/V (enhanced-V) shaped storms … example and terminology:
HRV IR 10.8 BT ENH
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Martin Setvák
Meteosat-8 (MSG1) 13:45 UTCHRV IR 10.8 BT ENH
COLD RING
CENTRAL WARM SPOT
(CWS)
25 June 2006, Czech Republic and Austria
Cold-ring-shaped storms … example and terminology:
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Martin Setvák
AVHRR
CH 1+2+4
AVHRR
CH 4 ENH
AVHRR
CH 3 ENH
09 July 1987, 1354 UTC, NOAA 9
The 3.7 (3.9) µm cloud-top reflectivity of convective storms
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RGB PRODUCTS
NOAA/AVHRR 20. 4. 2015 8:40 UTC
a channel 1
(0,58-0,68 µm)
b channel 2
(0,73-1,00 µm)
c channel 4
(10,3-11,3 µm)
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Martin Setvák
Example of the “storm RGB” (or “convection RGB”) product:
The 3.7 (3.9) µm cloud-top reflectivity of convective storms
11 June 2014 14:30 UTC Meteosat-10 (MSG-3)
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Martin Setvák
2009-07-09 11:35 UTC NOAA 15 (South Dakota, Minnesota, Nebraska, Iowa, U.S.A.)
The “sandwich” product of IR-window and visible bands
AVHRR band 2 AVHRR band 4 BT (198 – 233 K)
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Martin Setvák
Upper layer: IR-window BT image
Bottom layer (“background”): VIS image
Multi-layer image (in this case 2 layers) … e.g. PSD format (Photoshop)
Visible – color enh. IR-BT sandwich product – principle of the method
Blending options – applied to the upper layer !!!
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Martin Setvák
2009-07-09 11:35 UTC NOAA 15 (South Dakota, Minnesota, Nebraska, Iowa, U.S.A.)
The “sandwich” product of IR-window and visible bands
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Martin Setvák
Visible – color enhanced IR-BT sandwich product
HRV IR10.8-BT
12 July 2011 17:40 UTC MSG-1, Germany
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Martin Setvák
Visible – color enhanced IR-BT sandwich product
HRV sandwich HRV & IR10.8-BT
12 July 2011 17:40 UTC MSG-1, Germany
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Martin Setvák
Visible – color enhanced IR-BT sandwich product
sandwich HRV & IR10.8-BT
12 July 2011 17:40 UTC MSG-1, Germany
sandwich HRV & storm RGB
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• lifecycle:o rapid development, long lasting
o outflow, splitting, right-mover
• cloud-top features:o overshooting tops (OT) ~ storm activity, intensity of updrafts
o cold-U or cold-ring shape in IR-BT ~ related with OT, rapid cooling
o small ice particles, plume
o gravity waves, ship waves ~ related with OT
• shape of the anvil:o weak/strong wind shear or storm-relative winds
SEVERE STORM CHARACTERISTICS
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• storms observed by satellites FIRST
• cloud-top features as an indicator of severity
• satellite data as part of nowcasting tools:o ProbSevere (USA, CIMSS SSEC)
o COALITION (Switzerland, MeteoSwiss)
• Nowcasting SAF (Pilar Ripodas)
APPLICATIONS
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THANK YOU
2013-08-25 00:43 UTC
VIIRS Day-Night Band
(DNB)
Italy, Croatia, Slovenia
City lights
Land and storms
illuminated by Moon
(4 days after full Moon and
3 days before third quarter)
Lightning
© Martin Setvák
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 17:20 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 17:25 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 17:30 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 17:35 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 17:40 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 17:45 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 17:50 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 17:55 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 18:00 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 18:05 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 18:10 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data
12 July 2011 18:15 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT
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Martin Setvák EUM/STG-SWG/32/12/DOC/14
Example of temporal variability of storm-top features in 5-minute MSG data .
12 July 2011 18:20 UTC MSG-1
sandwich HRV & storm RGBsandwich HRV & IR10.8-BT