1. 25 th EWGLAM/10 th SRNWP Lisbon, Portugal 6-9 October 2003 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 É. Gérard, F. Rabier, N. Fourrié and D. Lacroix (ATOVS) C. Payan and F. Rabier (Météosat)

2. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 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. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 No more 1DVar pre-processing T s 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-guess| channel 4 > 1.5 K 250 km thinning Data coverage more regular, less missing orbits, less scan border pixel removal Use of more channels over land Raw radiances instead of preprocessed radiances

4. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 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 Preprocessed radiances Raw radiances scores computed wrt their own analysis

5. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 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. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 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 With AMSUA (long cut-off) Without AMSUA Southern Hemisphere scores wrt long cut-off analysis with AMSUA data rms difference

7. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 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 @ 00 UTC in oper stretched ARPEGE model) AMSUA data denial experiments

8. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 Research experiments with locally received AMSUA data Nesdis/Bracknell data Data available for the operational production, 1h50 cut-off time Lannion data 45W/40E/70N/30N Even more rapidly available, but smaller area & only NOAA16/NOAA17 EARS data Eumetsat ATOVS Retransmission Service Data rapidly available No blind orbit for NOAA17 (reception centres: Greenland, Norway, Canary Islands) 13 March 2003 12 UTC

9. Impact of EARS and Lannion data in addition to Bracknell data (rms/bias wrt radiosondes) Temperature @ 250 hPa 12 forecast range (hour)  3648 BracknellBracknell+EARS+Lannion 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. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 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 < obs-guess < y, (x,y)=f(latitude) Condition for use  12345678101112131415 Sea Land (orog<1500m) Observation error (K)0.60.60.60.751.02.00.50.6 water vapour channels

11. Forecast scores (rms & bias) over Europe with HIRS without HIRS 24 hour geopotential temperature wind rel. humidity forecast range  48 hour72 hour scores computed wrt own analysis

12. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 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 <  (  o 2 +  b 2 ) … under investigation …

13. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 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. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003  Experiments with the uniform ARPEGE configuration  23 Dec 2002 - 12 Jan 2003  SATOB winds  Only data with QI>0.8 are transmitted  BUFR winds conditional use  Weak constraint: QI>0.6 for upper level winds and over sea for mid-level winds  Strong constraint: QI>0.8-0.9 elsewhere as a function of latitude, level, channel Vis/IR/  w Assimilation of Meteosat data

15. Meteosat 5&7 observation fit to first guess and analysis area=50N/50S/113E/50W Used U component Used V component BUFR versus SATOB more data used rms and bias reduction

16. 25 th EWGLAM/10 th SRNWP, Lisbon, Portugal, 6-9 October 2003 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)