25thEWGLAM/10thSRNWP - PowerPoint PPT Presentation

Title: 25thEWGLAM/10thSRNWP


1
Use of satellite data at Météo-France

• Élisabeth Gérard
• Météo-France/CNRM/GMAP/OBS, Toulouse, France
• on behalf of
• É. Gérard, F. Rabier, N. Fourrié and D. Lacroix
  (ATOVS)
• C. Payan and F. Rabier (Météosat)

2
ATOVS data

• Operational implementation of
• Raw radiances instead of preprocessed radiances
  22 October 2002 ( European American
  profilers)
• NOAA17 on top of NOAA15 NOAA16
  17 December 2002
• AMSUA data only in operations
• AMSUA data denial experiments (OSEs)
• Research experiments with locally received data
• Lannion/EARS in addition to Nesdis/Bracknell
• Research experiments with HIRS

3
Raw radiances instead of preprocessed radiances

• No more 1DVar pre-processing
• Ts in the control variable
• Extrapolation above the top of the model (1 hPa)
  up to 0.1 hPa by regression
•  Rain contamination  test
• observation rejected if obs-guesschannel 4 gt
  1.5 K
• 250 km thinning
• Data coverage more regular, less missing orbits,
  less scan border pixel removal
• Use of more channels over land

4
Raw radiances instead of preprocessed radiances
Time series of rms errors and biases 24 hour
forecast 200 hPa geopotential scores over 1
month 22 Aug - 22 Sep 2002
Northern Hemisphere
Southern Hemisphere
scores computed wrt their own analysis
Preprocessed radiances Raw radiances
5
AMSUA data denial experiments

• Uniform (unstretched) ARPEGE
• 4DVar analysis T107C1L41 (up to 1hPa)
• Forecast T359C1L41 with 1800 s time step
• Experiments with and without AMSUA data 23 Dec
  2002 ? 12 Jan 2003
• One forecast a day from 00 UTC analysis
• Short cut-off time 03h50 (production)
• Long cut-off time 09h55 (assimilation)
• Impact of AMSUA data wrt cut-off time

6
Benefit of AMSUA data
Time series of rms errors and biases 24 hour
forecast 500 hPa geopotential scores over 3
weeks 23 Dec 2002 - 12 Jan 2003
Northern Hemisphere
rms difference
scores wrt long cut-off analysis with AMSUA data
Southern Hemisphere
With AMSUA (long cut-off) Without AMSUA
rms difference
7
AMSUA data denial experiments

• Positive impact of using AMSUA data
• More pronounced benefit
• over Southern Hemisphere than Northern
  Hemisphere,
• over North America than Europe,
• when cut-off time is long
• Increase of gain with forecast range more regular
  with long cut-off time over North America (more
  robust signal)
• When AMSUA data are used, more gain wrt cut-off
  time is expected if short cut-off time is shorter
  (i.e. 1h50 _at_ 00 UTC in oper stretched ARPEGE
  model)

8
Research experiments with locally received AMSUA
data
13 March 2003 12 UTC
Data available for the operational production,
1h50 cut-off time
Nesdis/Bracknell data

• Lannion data 45W/40E/70N/30N
• Even more rapidly available, but smaller area
  only NOAA16/NOAA17

9
Impact of EARS and Lannion data in addition to
Bracknell data (rms/bias wrt radiosondes)
Temperature _at_ 250 hPa
forecast range (hour)
?
12
36
48
Bracknell BracknellEARSLannion
First step assimilation in operational model
ARPEGE Next step assimilation in regional model
ALADIN ( AROME) in
research mode AMSUA, HIRS, AMSUB (observation
density, bias correction, )
10
Research experiments with HIRS

• On top of AMSUA data over 23 Dec 2002 12 Jan
  2003
• 250 km thinning (as for AMSUA)
• Cloud contamination test with channel 8
• rejection if x lt obs-guess lt y, (x,y)f(latitude)

Condition for use ? 1 2 3 4 5 6 7 8 10 11 12 13 14 15
Sea ? ? ? ? ? ? ? ?
Land (oroglt1500m) ?
Observation error (K) 0.6 0.6 0.6 0.75 1.0 2.0 0.5 0.6
water vapour channels
11
Forecast scores (rms bias) over Europewith
HIRS without HIRS
scores computed wrt own analysis
24 hour
48 hour
72 hour
forecast range ?
geopotential
temperature
wind
rel. humidity
12
Results of last experiments with HIRS

• Occurrence of  ringing  problem in stratosphere
  this summer
• channels 5, 6, 7, 14, 15 blacklisted over seaice
• channels 4 15 (tail up to 1hPa) to be
  blacklisted ?
• only channels 11 12 to be assimilated ?
• Cloud contamination test to be revised ?
• First guess check to be revised ?
• (obs-guess)2 lt ?(?o2?b2)
• under investigation

13
Assimilation of Meteosat data

• Use of BUFR winds produced by EUMETSAT with a
  quality index and disseminated every 90 minutes
• compared to
• Use of currently operational SATOB winds produced
  every 6 hours

as a preparation towards the use of other
geostationary satellite data (GOES, etc.)
14
Assimilation of Meteosat data

• Experiments with the uniform ARPEGE configuration
• 23 Dec 2002 - 12 Jan 2003
• SATOB winds
• Only data with QIgt0.8 are transmitted
• BUFR winds conditional use
• Weak constraint QIgt0.6 for upper level winds and
  over sea for mid-level winds
• Strong constraint QIgt0.8-0.9 elsewhere as a
  function of latitude, level, channel Vis/IR/mw

15
Meteosat 57 observation fit to first guess and
analysisarea50N/50S/113E/50W
Used U component

• BUFR versus SATOB
• more data used
• rms and bias reduction

Used V component
16
Present time, near/next future

• for satellite observations
• Tuning of AMSUA data density, rain detection
• HIRS use, cloud detection, obs error tuning,
  blacklist
• ATOVS bias correction wrt analysis (?)
• Assimilation of BUFR winds on going
• AIRS screening, bias correction already
  performed, first assimilation experiments on
  going
• Next future AMSUB, SeaWinds, MSG, MODIS, SSM/I(S)