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A new Approach to the Detection and
Tracking of Mesoscale Convective Systems in
the Tropics using MSG
Thomas Fiolleau
Rémy Roca
LMD
Courtesy of SATMOS
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Outline of the talk
• Introduction
– the Hydrological and Energy Cycle in the tropics
– Background on convective systems
• Data and Methodology of the new tracking algorithm
– Illustration of the new tracking methodology
• Comparison of the new algorithm with the area-overlappingtracking methodology
– Analysis of a Case Study over West Africa.
• Conclusions & Perspectives
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• Deep convective cloud central elements of the tropical
climate with a strong influence on the water and energy
cycle.
� the major provider of rainfall at the ground
� the major provider of atmospheric heating through latent heat release
� the main source of cloudiness that drives the radiation budget in the tropics.
Introduction
•Comprehension of watercycle and Energy budget is ofmajor importance to have a better understanding of theTropical climate
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Background on Convective Systems
(Houze et al., 1980
from Houze & Betts1981)
Deep Convective Systemsin the Tropics:
�Organized cloud clustersspanning a wide range ofspatial scale and degree oforganization.
Life cycle of convective systems:
�Schematic cloud structure in an average tropical
convective system in its formative, mature anddissipating stages
(Machado & Rossow 1993)
���� Monitoring these systems through their lifecycle to survey the variability of the tropical water and energy budget from a physicalperspective.
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Data
Use of Geostationary satellite data
• 10.8µm channel from MSG
• Study Area: [40°W:40°E; 15°S:30°N] • Period from June to September 2006
Use of automatic tracking algorithms to detect and follow convective systems
����Characterization of the morphological aspects of Convective Systems:
Degree of organization of convection, of
occurrence, of this type of system, on the
evolution of the cold cloud shield life cycle…
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work related to automatic tracking
algorithms among other techniques
Area-overlapping techniques:Williams and Houze (1987) and Arnaud etal (1992)� automated method based on a minimum overlapping area between MCSs in
successive images. � 233°K threshold.
(Arnaud et al., 1992)
Adaptative threshold techniques:Morel and Senesi (1999)�RDT (Rapid developing Thunderstorm), an
adaptative temperature threshold of the infrared images�Detection of the cloud systems earlier in their
initiation stages.
Correlation techniques:Carvalho and Jones (2001)�developement of an efficient method based on maximum spatial
correlation tracking technique (MASCOTTE)
Split and Merge artefacts of individual systems.���� Characterization of convective systems life cycle.
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Roca et al. 2005
���� Developpement of an algorithm based on an IR image segmentation with no or little dependence on any given threshold.
• Detect and Spread (DAS) technique (Boer and Ramanathan, 1997)
• DAS method tuned to the tropical deep cloud detectionusing INSAT (Roca and Ramanathan, 2000; Roca et al., 2005) and METEOSAT data (Roca et al., 2002).
� Clustering technique which progress from the convective core to the cloud edges in multiple steps:
1- detection of the convective core in multiple steps.
2- Spread up of the convective core to the cold cloud shieldedges in multiple steps.
���� Introduction of an improved method for tracking the tropical MCS based on a 3D approach segmentation.
Methodology (1/4)
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���� Developpement of an algorithm based on an IR image segmentation with no or little dependence on any given threshold.
• Detect and Spread (DAS) technique (Boer and Ramanathan, 1997)
• DAS method tuned to the tropical deep cloud detectionusing INSAT (Roca and Ramanathan, 2000; Roca et al., 2005) and METEOSAT data (Roca et al., 2002).
� Clustering technique which progress from the convective core to the cloud edges in multiple steps:
1- detection of the convective core in multiple steps.
2- Spread up of the convective core to the cold cloud shieldedges in multiple steps.
���� Introduction of an improved method for tracking the tropical MCS based on a 3D approach segmentation.
Methodology (1/4)
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���� Developpement of an algorithm based on an IR image segmentation with no or little dependence on any given threshold.
• Detect and Spread (DAS) technique (Boer and Ramanathan, 1997)
• DAS method tuned to the tropical deep cloud detectionusing INSAT (Roca and Ramanathan, 2000; Roca et al., 2005) and METEOSAT data (Roca et al., 2002).
� Clustering technique which progress from the convective core to the cloud edges in multiple steps:
1- detection of the convective core in multiple steps.
2- Spread up of the convective core to the cold cloud shieldedges in multiple steps.
���� Introduction of an improved method for tracking the tropical MCS based on a 3D approach segmentation.
Methodology (1/4)
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���� Developpement of an algorithm based on an IR image segmentation with no or little dependence on any given threshold.
• Detect and Spread (DAS) technique (Boer and Ramanathan, 1997)
• DAS method tuned to the tropical deep cloud detectionusing INSAT (Roca and Ramanathan, 2000; Roca et al., 2005) and METEOSAT data (Roca et al., 2002).
� Clustering technique which progress from the convective core to the cloud edges in multiple steps:
1- detection of the convective core in multiple steps.
2- Spread up of the convective core to the cold cloud shieldedges in multiple steps.
���� Introduction of an improved method for tracking the tropical MCS based on a 3D approach segmentation.
Methodology (1/4)
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���� Developpement of an algorithm based on an IR image segmentation with no or little dependence on any given threshold.
• Detect and Spread (DAS) technique (Boer and Ramanathan, 1997)
• DAS method tuned to the tropical deep cloud detectionusing INSAT (Roca and Ramanathan, 2000; Roca et al., 2005) and METEOSAT data (Roca et al., 2002).
� Clustering technique which progress from the convective core to the cloud edges in multiple steps:
1- detection of the convective core in multiple steps.
2- Spread up of the convective core to the cold cloud shieldedges in multiple steps.
���� Introduction of an improved method for tracking the tropical MCS based on a 3D approach segmentation.
Methodology (1/4)
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Latitude
Longitude
Time
� Generation of a 3D image corresponding to an IR images sequence, whose spatial axes are longitude and latitude
�DAS technique restricted to high cold clouds and extended in time to form a 3D segmentation technique (2D+time).
�Segmentation of moving objects in an IR image sequence by the DAS3D algorithm.
Methodology (2/4)
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• Schematic of a convective system in the spatio-temporaldomain.
Initiation Mature Warm Dissipating
time
lon
lat
time
time
Z
AnvilConvective core
Tb IR
Methodology (3/4)
(Futyan and Del Genio 2006)
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time
lon
lat
time
lon
lat
t-1 t t+1
- Region growing is performed by using to a 10-
connected spatiotemporal neighbourhood :
8-connected spatial neighbourhood
2-connected temporal neighbourhood (past
and future)
1 - A 3D segmentation of individual
convective cores in the spatiotemporal
domain
DAS3D: a generalized clustering technique which progress from the
convective core to the cloud edges in multiple steps.
2 – A Spread up of the convective core
in the spatio-temporal domain to the
cold cloud shield edges.
� Associate the anvil cloud with the
convective activity
•Initial threshold at 190°K•Warmest threshold value: 235°K•Volume > 5000 km² + Time•Life time> 1H30
Methodology (4/4)
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time
lon
lat
t-1 t t+1
time
lon
lat
DAS3D: a generalized clustering technique which progress from the
convective core to the cloud edges in multiple steps.
•Initial threshold at 190°K•Warmest threshold value: 235°K•Volume > 5000 km² + Time•Life time> 1H30
- Region growing is performed by using to a 10-
connected spatiotemporal neighbourhood :
8-connected spatial neighbourhood
2-connected temporal neighbourhood (past
and future)
1 - A 3D segmentation of individual
convective cores in the spatiotemporal
domain
2 – A Spread up of the convective core
in the spatio-temporal domain to the
cold cloud shield edges.
� Associate the anvil cloud with the
convective activity
Methodology (4/4)
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time
lon
lat
t-1 t t+1
time
lon
lat
DAS3D: a generalized clustering technique which progress from the
convective core to the cloud edges in multiple steps.
•Initial threshold at 190°K•Warmest threshold value: 235°K•Volume > 5000 km² + Time•Life time> 1H30
- Region growing is performed by using to a 10-
connected spatiotemporal neighbourhood :
8-connected spatial neighbourhood
2-connected temporal neighbourhood (past
and future)
1 - A 3D segmentation of individual
convective cores in the spatiotemporal
domain
2 – A Spread up of the convective core
in the spatio-temporal domain to the
cold cloud shield edges.
� Associate the anvil cloud with the
convective activity
Methodology (4/4)
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time
lon
lat
t-1 t t+1
time
lon
lat
DAS3D: a generalized clustering technique which progress from the
convective core to the cloud edges in multiple steps.
•Initial threshold at 190°K•Warmest threshold value: 235°K•Volume > 5000 km² + Time•Life time> 1H30
- Region growing is performed by using to a 10-
connected spatiotemporal neighbourhood :
8-connected spatial neighbourhood
2-connected temporal neighbourhood (past
and future)
1 - A 3D segmentation of individual
convective cores in the spatiotemporal
domain
2 – A Spread up of the convective core
in the spatio-temporal domain to the
cold cloud shield edges.
� Associate the anvil cloud with the
convective activity
Methodology (4/4)
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time
lon
lat
t-1 t t+1
time
lon
lat
DAS3D: a generalized clustering technique which progress from the
convective core to the cloud edges in multiple steps.
•Initial threshold at 190°K•Warmest threshold value: 235°K•Volume > 5000 km² + Time•Life time> 1H30
- Region growing is performed by using to a 10-
connected spatiotemporal neighbourhood :
8-connected spatial neighbourhood
2-connected temporal neighbourhood (past
and future)
1 - A 3D segmentation of individual
convective cores in the spatiotemporal
domain
2 – A Spread up of the convective core
in the spatio-temporal domain to the
cold cloud shield edges.
� Associate the anvil cloud with the
convective activity
Methodology (4/4)
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time
Lon
Lat
A
Illustration of the DAS3D methodology
Schématic realized withthe help of N.GIF
Detection of the convective core set at 220°K
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time
Lon
Lat
A
Schématic realized withthe help of N.GIF
Detection of the convective core set at 220°K
Spread up of the convective core to a 5°K warmer threshold
Illustration of the DAS3D methodology
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time
Lon
Lat
A
Schématic realized withthe help of N.GIF
Detection of the convective core set at 220°K
Spread up of the convective core to a 5°K warmer threshold
Illustration of the DAS3D methodology
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time
Lon
Lat
A
Schématic realized withthe help of N.GIF
Detection of the convective core set at 220°K
Spread up of the convective core to a 5°K warmer threshold
Illustration of the DAS3D methodology
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time
Lon
Lat
A
Schématic realized withthe help of N.GIF
Detection of the convective core set at 220°K
Spread up of the convective core to a 5°K warmer threshold
Illustration of the DAS3D methodology
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time
Lon
Lat
AB
Schématic realized withthe help of N.GIF
Detection of the convective core set at 225°K
Illustration of the DAS3D methodology
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time
Lon
Lat
AB
Schématic realized withthe help of N.GIF
Detection of the convective core set at 225°K
Spread up of the convective core to a 5°K warmer threshold
Illustration of the DAS3D methodology
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time
Lon
Lat
AB
Schématic realized withthe help of N.GIF
Detection of the convective core set at 225°K
Spread up of the convective core to a 5°K warmer threshold
Illustration of the DAS3D methodology
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time
Lon
Lat
AB
Schématic realized withthe help of N.GIF
Detection of the convective core set at 225°K
Spread up of the convective core to a 5°K warmer threshold
Illustration of the DAS3D methodology
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time
Lon
Lat
AB
Schématic realized withthe help of N.GIF
Detection of the convective core set at 225°K
Spread up of the convective core to a 5°K warmer threshold
Illustration of the DAS3D methodology
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time
Lon
Lat
AB
����The warmer anvil cloud defined at 230°K is shared between the system A and thesystem B.
����Individual systems characterized over their life cycle in a 3 dimensional spatiotemporal image
���� Supression of the split and merge artefacts during the life cycle of individualsystems
Detection of the convective core set at 225°K
Spread up of the convective core to a 5°K warmer threshold
Illustration of the DAS3D methodology
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Original Brightness Temperature Clouds after the detect stage at 205°K
Clouds after the detect stage at 220°K Final set of identified clouds at 235°K
EB
A
D
C
B
A
C
E D
B
A
C
E
Outputs of the DAS3D algorithm
���� Segmentation of the IR image in terms of individualconvective systems, including core and anvil.
Case Study September 11th 2006, Niamey at 1630UTC
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Comparison of the DAS3D methodology with the area-overlapping algorithm: Case Study
A
B
A
B
A
B
A
B
CC
DE E
E
1
11
1
1
3
2
1300 UTC 1400 UTC 1630 UTC 1830 UTC 2100 UTC
C
DAS3D
Area-overlapping
B
A
D C
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.
..
.
Cluster A processed by
the DAS3D algorithm
cluster 1 processed by the
area-overlapping algorithm
�Noisy evolution of the cold cloud shield of MCS 1 (area-overlapping) explained
by successive split and merge artefacts during its life cycle.
����Evolution smoother of the cluster A area (determined by the DAS3D
methodology) due to the lack of split or merge artefacts during its life cycle.
Surface Max of MCS 1 :160000km²Surface Max of MCS A: 79000 km²
- Red points and blue points
indicate respectively merges
and splits.
Evolution of the MCS cold cloud shield area through their life cycle
Comparison of the DAS3D methodology with the area-overlapping algorithm: Life cycle
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�Abrupt variations of the propagation speed of cluster 1 (area-overlapping)
explained by successive merge or split artefacts through its life cycle.
���� Evolution smoother of the propagation speed of the cluster A determined by the
DAS3D algorithm
Vmax MCS 1 = 60m/sVmax MCS A = 26m/s
- Red points and blue points
indicate respectively merges and splits.
.
..
.
Cluster A processed by
the DAS3D algorithm
cluster 1 processed by the
area-overlapping algorithm
Propagation speed: displacement
of the centre of gravity
Evolution of the MCS propagation speed through their life cycle
Comparison of the DAS3D methodology with the area-overlapping algorithm: Life cycle
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- Developpement of a new tracking algorithm: DAS3D
- Segmentation of individual convective systems throughtheir life cycle in the spatio-temporal domain
- Detection of the convective systems earlier in their initiation stages and later in their dissipation stages
- Suppression of Split and merge artefacts during the MCS life cycle.
���� Improvement of the characterization of the main morphological aspects of the convective systems lifecycle
Conclusion
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Perspectives
• Extend the convective events to the full upperlevel cloudiness
– Multi spectral observations of SEVIRI sensor.
– Use of the classification of high clouds from theSAFNWC
• Collocation of the low earth orbitingmeasurement (microwave) in space and time to combine rainfall estimates and the MCS cloudshield along the life cycle of the system. (Megha-Topiques)
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Thank you for your attention
I will be graduated next year
I’m looking for a Post-Doc
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10H3H
50%
DAS3D methodology:�population detected by DAS3D: 8475 MCS�Lifetime max: 47h����50% of the total population < 10H
Area-Overlapping methodology:�population detected by the overlapping method : 5775 MCS�Lifetime max: 80,5H (MCS ayant subi plusieurs fusions durant son cycle de vie)����50% of the total population < 3H
Comparison of distributions at the seasonal scale