1. SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations
14 January 2009
Hervé LE GLEAU, Marcel DERRIEN
Centre de météorologie Spatiale. Lannion
Météo-France

2. 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

3. SAFNWC context
-SAFNWC delivers software to process data from MSG and polar platforms (METOP/NOAA) .
63 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
-SAFNC/MSG SW v2009 (available to users in march 2009) will be used during this presentation.

4. CMa algorithm: first step
CMa First step:
Clouds and snow are first detected in each pixel of the image using multispectral theshold techiques :
Thresholds are computed using :
Atlas: height map
land/sea mask
Climatological maps: SST
continental visible reflectance
NWP short range forecast data (at MF, Arpege 1.5 deg used):
surface temperature,
integrated atmospheric precipitable water
Thresholds tuned to radiometer’s spectral characteristics with Radiative Transfer Models in cloud free conditions (6S,RTTOV).

5. Illustration of night-time low cloud identification
Low clouds
T10.8mm – T3.9mm
T8.7mm – T10.8mm

6. Illustration of daytime low cloud identification
VIS 0.6mm
Low clouds
Snow
T3.9mm-T10.8mm
VIS 1.6mm

7. CMa algorithm: second step
CMa Second 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:
For fast moving clouds:
detect T10.8mm changes within 15 minutes
For low clouds in day-night transition:
the areas, cloudy 1hour before, that have unchanged T10.8mm, T12.0mm and T8.7mm during last hour are said cloudy +
spatial extension of these cloudy areas to adjacent areas having similar Vis06mm reflectance and T10.8mm

8. Illustration of improvement with temporal analysis

9. Illustration of improvement with temporal analysis
amélioration+
+ Masque nuage+
IR 10.8 mm BRF 0.6 mm

10. CT algorithm low T10.8mm-T12.0mm, low T8.7mm-T10.8mm
Cloudy pixels are classified according their radiative characteristics:
Semi-transparent and fractional clouds are distinguished
from low/medium/high clouds using spectral features:
low T10.8mm-T12.0mm, low T8.7mm-T10.8mm
high T10.8mm-T3.9mm (night), high R0.6mm (day)
Low, mid-level and high clouds are then separated by
comparing their T10.8mm to combination of NWP forecast
temperature at various pressure levels [850, 700, 500 hPa
and at tropopause levels].
Confusion between Low and mid-level in case strong thermal
inversion is reduced by using T10.8mm-WV73mm

11. CT example

12. CTTH algorithm
Vertical temperature & humidity profile forecast by NWP needed
(ARPEGE used to process cloud workshop selected cases)
TOA 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)
Cloud top pressure is first extracted using RTTOV simulated
radiances; Method depending on cloud type.
Cloud top temperature & height are derived from their pressure
(using vertical temperature & humidity profile forecast by NWP).

13. CTTH algorithm For opaque clouds (known from CT)
The cloud top pressure corresponds to the best fit between
the simulated and measured 10.8mm radiances
For semi-transparent clouds :
Derived from a window channel 10.8mm and a sounding
channel (13.4mm, 7.3mm or 6.2mm)
For broken low clouds
No technique has yet been implemented.

14. Illustration of opaque clouds cloud top pressure retrieval
Measured brightness
temperature
Retrieved cloud top pressure
If the thermal inversion is not well simulated by NWP, the cloud may be retrieved too high
Illustration of opaque clouds cloud top pressure retrieval

15. Measured brightness temperature
Retrieved cloud top pressure
Measured brightness temperature
Illustration of opaque clouds
cloud top pressure retrieval
in case thermal inversion

16. CTTH pressure example

17. Summary of CMa validation with SYNOP
500 manned continental station over Europe
from 1st Decembre 2007 to 23rd August 2008
Following cloudiness are compared:
SEVIRI: average cloudiness in a 5x5 target
SYNOP: total observed cloudiness
POD (%)
FAR (%)
Daytime
98.1
2.4
Night-time
95.6
8.7
Twilight
95.5
2.7
High FAR partly due to error in
night-time human cloud observation.
Lower POD mainly due to low cloud underdetection

18. Summary of CT visual inspection (related to low cloud)
Low clouds may be occasionaly undetected at night-time
(especially oceanic rather warm Sc advected above not too cold ground)
Snow is not detected at night-time and may be confused with clouds
Low cloud identication at day-night transition mainly solved in v2009.
Stability of CT classifier to illumination, except for snow (not detected by CMa at nighttime)
Over land, tendency to classify low clouds as mid-level (in case strong thermal inversion)

19. 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

20. 14/02/08: documented by Maria Putsay (Hungary) on Eumetsat web Image gallery
13/02/ h – 14/02/2008 7h45

21. 14/02/08: documented by Maria Putsay (Hungary) on Eumetsat web Image gallery
13/02/ h00 – 14/02/ h00
Utilise avec delay=75

22. 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))

23. Outlook SAFNWC/MSG version (v2009) used in this presentation will be
available to users in march 2009:
improved low cloud detection especially at low solar elevation,
improved cloud top height
-reduced bias,
-configurable grid size for histogram analysis
Following improvements will be included in SAFNWC/MSG v2010:
CMa improvement: use of HRV (Cu, valley fog)
CT improvement:
-implementation of cloud phase identification
-decrease of the confusion between low and mid-level
clouds in case thermal inversion
CTTH improvement: use of RTTOV-9 instead RTTOV-7