1. Météo-France status Operational changes since the 22 th North America / Europe Data Exchange meeting and short term plans Jean-François MAHFOUF (and many colleagues)

2. 2 Outline 1.Computing platform 2.Model configurations 3.Use of observations 4.Recent operational changes 5.Ongoing developments and future plans

3. 3 Computing platform Two identical clusters : oKumo : operations oYuki : research (operations if Kumo fails) Each cluster : o10 nodes o16.3 Tflops max oInterconnected using a crossbar network (IXS) o58 Tbytes of scratch (Global File System) On each node : o16 processors o1 Tbyte of memory o2 Tbytes of local disks Scalar front-end TX7 : 16 Intel Itanium2 cores with 32 Gb memory Operational configuration NEC SX9 (Q4 2009)

4. 4 Model configurations

5. 5 Global model ARPEGE (1) ]  Spectral model with variable resolution : T L 798C2.4L70 oResolution from 10 km to 60 km, 70 levels from 17m to 0.05 hPa o16 processors for ARPEGE forecast (7’ for 24h forecast) Forecast ranges [cut-offs] : FC+102 (00 UTC) [2h15], FC+72 (06 UTC) [3h], FC+84 (12 UTC) [1h50], FC+60 (18 UTC) [3h]

6. 6 Global model ARPEGE (2)  4D-Var assimilation (6h window) : o 2 loops of minimization : T107C1L70 (25 iterations) + T323C1L70 (25 iterations) – 2nd inner loop with simplified physics (including large scale condensation) o Variational bias correction scheme since 2008 o Background error variances from an Ensemble Data Assimilation system (4D- Var at lower resolution) since 2008 o 32 processors for assimilation (32’ between cut-off and P0)  Data used : o SYNOP, SHIP, BUOY, AIREP, AMDAR, ACARS, TEMP, PILOT o AMV GOES + Meteosat + MTSAT-1R, MODIS (Terra, Aqua), AVHRR/NOAA o HIRS, AMSU-A, AMSU-B/MHS, NOAA 15, 16, 17, 18, 19 and Aqua o SSM/IS DMSP F16, 17, 18, AIRS/AQUA, IASI/MetOp, GPS-RO, GPS-ZTD o Sea surface winds from ASCAT (and ERS2) o Meteosat CSR o SST from OSTIA + LST from ½° NCEP + SSM/I F15 for sea-ice mask

7. 7 Evolution of the number of observations in ARPEGE SCAT ATOVS IASI SEVIRI SSMI AIRS GPS-RO SEVIRI AIRS 2002 2006 2004 20072008 2010

8. 8 Information content of observations

9. 9 Summary of ATOVS radiance usage SatelliteHIRSAMSU-AAMSU-B/MHSAIRS/IASI NOAA15  NOAA16  NOAA17  NOAA18  NOAA19  AQUA  MetOp   : not relevant  : not available  : received but blacklisted  : used

10. 10 Regional model ALADIN  Spectral limited area model : E199x199L70  70 levels from 17m to 0.05 hPa, horizontal resolution 7.5 km  3D-Var assimilation (6h window) :  Same data as ARPEGE plus SEVIRI radiances (5 channels over 12)  Current operational domains : Antilles-Guyana pre-operational Aladin France French Polynesia pre-operational New-Caledonia pre-operational Aladin La Réunion since 2006 Forecast ranges and cut-offs: FC+54 (00 UTC) [2h15], FC+48 (06 UTC) [3h], FC+42 (12 UTC) [1h50], FC+36 (18 UTC) [3h]

11. 11 Regional model AROME  Spectral limited area non- hydrostatic model : E359x374L60 o60 levels from 17m to 0.05 hPa, horizontal resolution 2.5 km  3D-Var assimilation (3h window) : oSame data as ALADIN plus radar radial winds and reflectivities  Coupling files : hourly ARPEGE forecasts Forecast ranges and cut-offs : FC+30 (00 UTC) [1h30], FC+30 (06 UTC) [3h30], FC+30 (12 UTC) [3h30], FC+30 (18 UTC) [3h30]

12. 12 Additional assimilation and forecasting suites 1.Operational suites for atmospheric models  Global stretched with very short cut off [1h10] with 3DVar FGAT (at 00UTC only)  Short-range ensemble prediction system with ARPEGE (T L 538C2.4L65) : 35 members. Initial perturbations : singular vectors + EnDA – Model perturbations : random use of 10 different physical packages  Limited area model ALADIN : several research, commercial and transportable dynamical adaptation versions 2.Chemical transport model MOCAGE : air quality forecasts up to 96 h  3 domains : Global – Europe – France with horizontal resolutions of 4°, 0.5°, and 0.1°  Satellite observations currently used only for validation 3.Ocean wave model : forecasts up to 102 h  Global, Tropics, Europe, France with horizontal resolutions of 1°, 1°, 0.25°, 0.1°  Assimilation of JASON and ENVISAT altimeter wave height data

13. 13 News on upper-air observations (in 2009) 22 RS

14. 14 News on upper air observations (in 2011) 19 RS

15. 15 Recent operational changes

16. 16 April 2010  Increase in model resolutions : 15 km -> 10 km for ARPEGE; 9.5 km -> 7.5 km for ALADIN; 60 -> 70 levels for ARPEGE/ALADIN; 41 -> 60 levels for AROME  Increased usage of all satellite observations : oThinning from 250 to 125 km => 3.5 more data assimilated oATOVS data from NOAA 19 oAssimilation of low peaking channels from AMSU-B over continents (2) oAssimilation of upper tropospheric wv channels from IASI (9)  Assimilation of radar reflectivities (French network ARAMIS) in AROME  Revised Ensemble Data Assimilation : o4D-Var instead of 3D-Var FGAT oUse of background error variances for unbalanced temperature, surface pressure and divergence on top of vorticity.

17. 17 Pre-selection:  Only data from detector #1  Pattern depending on scanline  Geographical thinning: 1 prof. / 125km IASI pixel and channel selection 9 WV LW - T 68 over sea 50 over land channels

18. 18 Thanks to a new surface emissivity model Example: AMSU-B (atmospheric humidity sounding): only 2 of 5 channels assimilated over land surfaces Microwave sounding channels from AMSU-B

19. 19 Thanks to a new surface emissivity model Example: AMSU-B (atmospheric humidity sounding): only 2 of 5 channels assimilated over land surfaces Microwave sounding channels from AMSU-B

20. 20 November 2010 [1]  Thinning of satellite observations from 125 to 80 km for AROME  Assimilation of window channels from SSM/IS F16 and F17  Assimilation of GPS-RO from GRAS-MetOp + for all GPS-RO use of data between 25 – 36 km  Assimilation of low level channels from AMSU-A and AMSU-B over sea-ice (revised sea-ice emissivity model)  Assimilation of more IASI channels and more AVHRR AMVs over polar regions  Improved algorithm for the choice of 2 ambiguous ASCAT winds among 4

21. 21 CTLEXP Usage of AMSU-B channels (ch 5: 183.31  7.0 GHz) in ARPEGE a)Assimilation of observations over sea-ice Operational since Nov 2010 Microwave sounding channels from AMSU

22. 22 Scores: Positive in particular over NH Scores: Positive in particular over NH Wind scores against radiosoundings (one month, January 2009) Fit to observations: neutral or improvement (temp, profilers, …) Increase about 30% of assimilated AMSU data (sea-ice) a)Assimilation of observations over sea-ice Operational since Nov 2010 Microwave sounding channels from AMSU

23. 23 November 2010 [2]  Increase in AROME domain size (by a factor 1.8) with dedicated surface analysis  OI SST analysis with OSTIA for climatological relaxation  Assimilation of SYNOP relative humidity in ARPEGE (daytime only)  Improved procedure for updating anemometer heights on SHIPs (E-SURFMAR database)  EnDA : background error variances for specific humidity + use for background check  Humidity bias correction for RS data following ECMWF  Use of saturation pressure formula w.r.t. to water for TEMP

24. 24 Forecast scores : rms Z 500 at 72 hours (00 UTC) Europe Northern Hemisphere

25. 25 Current operational changes E-suite 2011  Inclusion of ATOVS/RARS data from Asia/Pacific + Miami and La Réunion HRPT stations from EARS  Inclusion of window channels from SSM/IS F18  Inclusion of model error in EnDA  Use of SURFEX (new vegetation/soil climatology + improved ISBA scheme) in ALADIN/France model  Monitoring of SCAT surface winds from Oceansat2

26. 26 Assimilation of SSM/IS radiances (current e-suite) 00 UTC 12 UTC

27. 27 ARPEGE with very short cut-off (1h10) AMSU-A from Exeter (30 %) EARS AMSU-A (+ 3 %) RARS AMSU-A (+20 %) Long cut-off

28. 28 Future plans Preparation of future satellite missions Increase (and better) usage of observations currently available  Megha-tropiques : MADRAS + SAPHIR (launch ~ end 2011)  NPP : CrIS + ATMS (launch ~ end 2011)  IASI on MetOp-B  Increase usage of IASI radiances at mesoscale – horizontal error/channel correlations – improved observation operator  Increase usage of IR radiances over land : SEVIRI (S. Guedj thesis) + IASI (A. Vincensini thesis)  Use of IASI cloudy radiances (P. Martinet thesis)  ASCAT soil moisture (received operationally since Feb 2011)  Preparation of IRS on MTG (Eumetsat fellow)  Preparation of wind lidar mission (ESA ADM-Aeolus)  GPS data from E-GVAP (Eumetnet)  Radar data from OPERA (Eumetnet)

29. 29 GPS data (ground based and satellite) 0.44 % 14 %

30. 30 Winds from SATOB and scatterometers

31. 31 Observation usage summary DatatypeContactOperationsTests ATOVS elisabeth.gerard @meteo.fr N15,16,17,18,19, Aqua Metop AP-RARS SSM/IS elisabeth.gerard @meteo.fr F16, F17F18 Geosat. winds christophe.payan @meteo.fr GOES, Meteosat, MTSAT Polar winds christophe.payan @meteo.fr AVHRR/NOAA MODIS/Aqua, Terra AVHRR/Metop Scatterometer christophe.payan @meteo.fr ASCATOSCAT Geosat radiances patrick.moll @meteo.fr MeteosatGOES, MTSAT GPS patrick.moll@meteo.fr nathalie.saint-ramond @meteo.fr ZTD RO More stations TerraSar-X/ SAC-C AIRS/IASIvincent.guidard @meteo.fr Aqua, MetopMore channels

32. Thank you for your attention