SAFNWC/MSG Cloud type/height.
Application for fog/low cloud situations
22 September 2011
Hervé LE GLEAU, Marcel DERRIEN
Centre de météorologie Spatiale. Lannion
Météo-France
1
Fog or low level clouds ?
It is irrealistic to claim that we can identify fog only using satellite
data: we do not know if the cloud observed from satellite is
reaching the ground.
-> I will therefore not present fog mapping from satellite.
-> Instead:
I will first detail the cloud products extracted from MSG/SEVIRI
satellite imagery using the NWCSAF software, concentrating on
the fog or low level clouds category.
I will then show an example of data fusion with NWCSAF cloud
products to map fog risk.
On-line training. 22 September 2011
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Plan
SAFNWC context
Main features of SAFNWC/MSG cloud algorithms
Cma
cloud mask
CT
cloud type
CTTH cloud top temperature and height
Summary of validation results
Illustration with fog/low cloud situations:
(including example of automatic use for fog risk mapping)
Outlook
On-line training. 22 September 2011
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SAFNWC context
-SAFNWC delivers software to process data from MSG and polar
platforms (METOP/NOAA) .
- 92 registered users, including 29 European NMS and 3 SAFs
(OSISAF, CMSAF, LSASAF)
-SAFNWC/MSG SW includes three cloud products (CMa, CT,
CTTH) developed by Météo-France/Lannion
-Detailed description of cloud algorithms and validation results
available from www.nwcsaf.inm.org
-SAFNC/MSG SW v2011 will be used during this presentation.
On-line training. 22 September 2011
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CMa algorithm: first step
CMa First step:
Clouds and snow are first detected in each pixel of the image using
multispectral theshold techiques :
9Thresholds are computed using :
o Atlas: height map
land/sea mask
o Climatological maps: SST
continental visible reflectance
o NWP short range forecast data (at MF, Arpege 1.5 deg used):
surface temperature,
integrated atmospheric precipitable water
9Thresholds tuned to radiometer’s spectral characteristics with
Radiative Transfer Models in cloud free conditions (6S,RTTOV).
On-line training. 22 September 2011
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Illustration of night-time low cloud identification
T10.8μm – T3.9μm
T8.7μm – T10.8μm
On-line training. 22 September 2011
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Illustration of night-time low cloud identification
Low clouds
T10.8μm – T3.9μm
T8.7μm – T10.8μm
On-line training. 22 September 2011
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Illustration of daytime low cloud identification
VIS 0.6μm
T3.9μm-T10.8μm
VIS 1.6μm
On-line training. 22 September 2011
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Illustration of daytime low cloud identification
Low clouds
VIS 0.6μm
T3.9μm-T10.8μm
VIS 1.6μm
On-line training. 22 September 2011
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Illustration of daytime low cloud identification
Low clouds
VIS 0.6μm
T3.9μm-T10.8μm
Snow
VIS 1.6μm
On-line training. 22 September 2011
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CMa algorithm: second step
CMa Second step:
step
(only available since version v2009 (available to users in march))
Temporal analysis and region-growing technique are applied to detect
low clouds at day-night transition and fast moving clouds:
9For fast moving clouds:
9detect T10.8μm changes within 15 minutes
9For low clouds in day-night transition:
9the areas, cloudy 1hour before, that have unchanged T10.8μm,
T12.0μm and T8.7μm during last hour are said cloudy +
9spatial extension of these cloudy areas to adjacent areas having
similar Vis06μm reflectance and T10.8μm
On-line training. 22 September 2011
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Illustration of improvement with temporal analysis
93°
93° 1h
sooner 80°
Cloud mask + temporal scheme
superimposed on BRF 0.6 μm
On-line training. 22 September 2011
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CMa: decrease of false alarm over snow (night)
The following problem has been reported by users:
« At night during winter cold events,
cloud-free snow-coved grounds may be wrongly classified as clouds ».
These wrong detection are due to any of three tests applied to
T10.8μm, T3.9μm-T10.8μm or T8.7μm-T10.8μm
An empirical approach has been applied to solve the problem (v2011):
relax thresholds when cold snow-coved grounds are expected
On-line training. 22 September 2011
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CMa: decrease of false alarm over snow (night)
Diagnose where strong nocturnal cooling may occur
altitude < 1500m and (Tsnwp < 263K or Tsnwp < 268K and Snow occur. > 5)
Relax three thresholds
-T108thr -5.0K
if T108thr ≥ 255K or
T108thr -5.0K -.4(255.-T108thr) if T108thr < 255K
-T87T108thr +0.4K if T108thr<250K
-T39T18thr=MAX(T39T10.8thr, -0.5x T108thr+129.0) if 250K≤T108thr≤255K or
T39T18thr=-0.15x T108thr +41.5
if T108thr<250K
Clear restoral when detected by T108thr test at any illumination,
or Visible test or T39T108thr test at daytime or twilight:
If t108thr < 250K and T7.3-T10.8> 0.5K
On-line training. 22 September 2011
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CMa: decrease of false alarm over snow (night)
On-line training. 22 September 2011
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CMa: decrease of false alarm over snow (night)
On-line training. 22 September 2011
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CT algorithm
Cloudy pixels are classified according their radiative characteristics:
9Semi-transparent and fractional clouds are distinguished
from low/medium/high clouds using spectral features:
low T10.8μm-T12.0μm, low T8.7μm-T10.8μm
high T10.8μm-T3.9μm (night), high R0.6μm (day)
9Low, mid-level and high clouds are then separated by
comparing their T10.8μm to combination of NWP forecast
temperature at various pressure levels [850, 700, 500 hPa
and at tropopause levels].
On-line training. 22 September 2011
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CT: decrease low/mid-level cloud confusion
Low clouds may be wrongly classified as mid-level clouds in the
presence of a thermal inversion. Two approaches are used to
minimise the confusion:
9 mid-level clouds are reclassified as low clouds if T10.8μmWV73μm is « large »
9 mid-level clouds are reclassified as low clouds if a low level
thermal inversion is detected in the NWP fields input by the
user and if T8.7μm-T10.8μm is lower than a threshold
(decreasing with viewing angles)
The improvement is illustrated over central Europe on
21st December 2007
On-line training. 22 September 2011
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CT: decrease low/mid-level cloud confusion
V2010
V2009
On-line training. 22 September 2011
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CTTH algorithm
9Vertical temperature & humidity profile forecast by NWP needed
9TOA radiances from the top of overcast opaque clouds put
at various pressure levels are simulated with RTTOV
(NWP vertical profiles are temporally interpolated to each slot)
9Cloud top pressure is first extracted using RTTOV simulated
radiances; Method depending on cloud type.
9Cloud top temperature & height are derived from their pressure
(using vertical temperature & humidity profile forecast by NWP).
On-line training. 22 September 2011
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CTTH algorithm
For opaque clouds (known from CT)
The cloud top pressure corresponds to the best fit between
the simulated and measured 10.8μm radiances
For semi-transparent clouds :
Derived from a window channel 10.8μm and a sounding
channel (13.4μm, 7.3μm or 6.2μm)
For broken low clouds
No technique has yet been implemented.
On-line training. 22 September 2011
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Illustration of opaque clouds
cloud top pressure retrieval
On-line training. 22 September 2011
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Measured brightness
temperature
Illustration of opaque clouds
cloud top pressure retrieval
On-line training. 22 September 2011
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Measured brightness
temperature
Retrieved cloud top pressure
Illustration of opaque clouds
cloud top pressure retrieval
On-line training. 22 September 2011
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Illustration of opaque clouds
cloud top pressure retrieval
in case thermal inversion
On-line training. 22 September 2011
25
Illustration of opaque clouds
cloud top pressure retrieval
in case thermal inversion
Measured brightness
temperature
On-line training. 22 September 2011
26
Retrieved cloud top pressure
Illustration of opaque clouds
cloud top pressure retrieval
in case thermal inversion
Measured brightness
temperature
On-line training. 22 September 2011
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Illustration of opaque clouds
cloud top pressure retrieval
in case thermal inversion
On-line training. 22 September 2011
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Illustration of opaque clouds
cloud top pressure retrieval
in case thermal inversion
Measured brightness
temperature
On-line training. 22 September 2011
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(in case of dry air between 850 and 600)
(ie, relative humidity lower than 30%)
Illustration of opaque clouds
cloud top pressure retrieval
in case thermal inversion
Measured brightness
temperature
On-line training. 22 September 2011
30
(in case of dry air between 850 and 600)
(ie, relative humidity lower than 30%)
Retrieved cloud top pressure
Illustration of opaque clouds
cloud top pressure retrieval
in case thermal inversion
Measured brightness
temperature
On-line training. 22 September 2011
31
CTTH pressure example
On-line training. 22 September 2011
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Summary of CMa validation with SYNOP
500 manned continental station over Europe
from 10th Decembre 2010 to 21st March 2011
Following cloudiness are compared:
SEVIRI: average cloudiness in a 5x5 target
SYNOP: total observed cloudiness
POD (%)
FAR (%)
Daytime
98.1
1.7
Night-time
95.7
6.2
Twilight
95.5
1.9
High FAR partly due to error in
night-time human cloud observation.
Lower POD mainly due to low cloud underdetection
On-line training. 22 September 2011
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Summary of CT visual inspection (related to low cloud)
Stability of CT classifier to illumination
Low clouds may be occasionaly undetected at night-time
(especially oceanic rather warm Sc advected above not too cold ground)
Low cloud identication at day-night transition: mainly solved in v2009.
Over land, tendency to classify low clouds as mid-level (in case strong
thermal inversion): mainly solved in v2010
Night-time confusion of snow as clouds: mainly solved in v2011.
On-line training. 22 September 2011
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Validation of low cloud CTTH with ground-based radar
September 2003-October 2004
Following cloud top height are compared:
derived from cloud radar (95Ghz) from SIRTA (LMD, near Paris)
computed from SEVIRI
(CTH_SEVIRI - CTH_radar > 0) = SEVIRI CTH overestimation
Cloud type
Mean (km)
STD (km)
Low opaque
0.28
0.96
Low opaque if thermal
inversion observed in NWP
0.17
0.62
On-line training. 22 September 2011
35
14/02/08: documented by Maria Putsay
(Hungary) on Eumetsat web Image gallery
14/02/2008 01h25
On-line training. 22 September 2011
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14/02/08: documented by Maria Putsay
(Hungary) on Eumetsat web Image gallery
14/02/2008 01h00
On-line training. 22 September 2011
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14/02/08: documented by Maria Putsay
(Hungary) on Eumetsat web Image gallery
14/02/2008 10h40
On-line training. 22 September 2011
38
14/02/08: documented by Maria Putsay
(Hungary) on Eumetsat web Image gallery
14/02/2008 12h00
On-line training. 22 September 2011
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Exemple of automated use for fog risk mapping
A combined use of: SAFNWC/MSG CT , rain accumulation and
NWP analysis (air humidity (2m), wind (10m))
On-line training. 22 September 2011
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Outlook
Future upgrade of NWCSAF SW:
-inclusion of microphysical product:
--cloud phase,
--effective radius size,
--optical thickness,
--water/ice water path
-ready for MTG:
--more channels and better spatial resolution
-long-term development: separation between stratiform and
cumuliform clouds:
--for low clouds : the separation of small cumulus and
stratiform clouds will be useful for fog risk estimation.
On-line training. 22 September 2011
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For further information
For more information on NWCSAF:
www.nwcsaf.org
For further information of NWCSAF software (freely available):
[email protected]
Near-real time NWCSAF products can be visualized on:
www.nwcsaf.org
On-line training. 22 September 2011
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