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Météo-France report for 2010

Météo-France report for 2010. Climate NWP: ARPEGE and AROME. CNRM-CM5 (collab. Cerfacs). Aerosols GHG. Ozone chemistry MOBIDIC. Zonal statistics. 10 years. O 3. Atmosphere ARPEGE-Climate v5.2, 1.4°, T127 L31. Better radiation, aerosol impact, water vapour conservation.

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Météo-France report for 2010

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  1. Météo-France report for 2010 Climate NWP: ARPEGE and AROME

  2. CNRM-CM5 (collab. Cerfacs) Aerosols GHG Ozone chemistry MOBIDIC Zonal statistics 10 years O3 Atmosphere ARPEGE-Climate v5.2, 1.4°, T127L31 Better radiation, aerosol impact, water vapour conservation surface SURFEX Land surfaces ISBA New sub-grid hydrology, tiling 24h 24h 24h OASIS v3 Better interpolations Sea Ice GELATO v5 Ocean NEMO v3.2, 1°, L42 Rivers TRIP Free surface New TKE 24h

  3. + 0.03°C/siècle - 0.09°C/siècle SST evolution Concentration CO2 Trends Observations HadSST CNRM-CM5 Température °C CNRM-CM3 Year

  4. nino3 Variabilité – El Niño CNRM-CM3 CNRM-CM5 HadSST1 °C °C °C années années années Mean 23.2°C STD 1.4°C Mean 25.4°C STD 0.6°C Mean 25.8°C STD 0.5°C

  5. Control DJF AO and NAO variability Nudging (wind and Temperature) DJF 1971-2000 AO (left) and NAO (right) anomalies: For each experiment (in red), ensemble mean anomalies (thick line) are compared to ERA40 (in black) and spread among 5 members is also shown (+/- 1 standard deviation in dashed lines and minimum and maximum anomalies in solid lines). R = ACC with ERA40.

  6. DJF 1976/1977 DJF 2009/2010 Case studies (30-member ensembles) ECMWF Control Nudging DJF SLP anomalies (hPa)

  7. FP6-ENSEMBLES seasonal forecasts over Africa ENSEMBLES stream 2 multi-model RPSS over West Africa for JJA precipitation (1960-2005) (Reference data : GPCC)

  8. Future developments ARPEGE Higher resolution model T798L70C2.4, 4D-Var T107/T323 + Higher density satellite data (125km) + AMSU-B channel 5 over land +IASI WV channels AROME V2 Increase of vertical resolution (60 levels) New version of EDKF Coupling to host model above 200hPa Correction of negative values of hydrometeors Fog sedimentation Improvement of gust wind and mean V10m diagnostics Radar reflectivities, AIRS and IASI in AROME Use of ARPEGE as coupling model Ensemble data assimilation in 4D-Var + T399 Ensemble prediction (Dec 2009) v2, 35 members, uses SVs + EnDA + Physics NWP: main operational changes in 2010

  9. Future resolution: T798C2.4L70

  10. IASI Monthly number of observations used in the global model SEVIRI GPS-RO AIRS SSMI SCAT ATOVS 2007 2010 2004 2008 2006 2002

  11. More humidity in EXP Assimilation of AMSU-B (low peaking humidity channels) over land Evaluation wrt GPS data from AMMA TCWV (EXP-REF) Correlations with GPS

  12. Geopotential scores wrt AC 4months 0.7 0.2 0.9 2.6 10 200 1000 10 200 1000 10 200 1000 0 48 96 0 48 96 48 96 0

  13. Ensemble assimilation (operational with 6 members…) :simulation of the error evolution eb = M ea (+ em ) Flow-dependent B ea Explicit observation perturbations, and implicit (but effective) background perturbations.

  14. Ens Assim. 3D-Var Fgat Errors of the day for 3-hr forecasts provided by the Ensemble Data Assimilation Klaus storm. The error maximum is better forecast by the 4D-Var version of the ensemble assimilation. Ens Assim. 4D-Var 24/01/2009 at 00h/03h

  15. Ensemble prediction system upgrade • PEARP2 based on ARPEGE • Twice a day:06TU up to day 3/ 18TUup to day 4 • 35 membres • Perturbations of initial conditions: • Singular vectors on 4 areas > > > • Use of the ensemble data assimilation AEARP (Assimilation Ensemble ARPege) • amplitudes controlled by the « errors of the day » • Model error :multi-physics (operational + 7 others) • Résolution PEARP2 T358L65 C2.4 (~23km sur la France)

  16. Ensemble assimilation used in PEARP

  17. ComparisonPEARPand EPS Area under the ROC curve for T at 850 hPa> 1.5 σ Results significant at 90 % Ranges 78 h and 78+6h Ranges 30 h and 30+6h 1.0 0.98 0.95 0.95 12-2008 12-2008 12-2009 12-2009

  18. AROME changes: 60 levels alt L41 ARO L60 ARO L70 ARP/ALA (m) oper dbl dbl From L41 to L60 (+ 37% CPU) : • 1st level: 17m to 10m • 27 levels below 3000m (vs 15)

  19. Direct coupling to ARPEGE • Avoid an intermediate coupling step to ALADIN (now at 7.5km) • Spectral relaxation to ARPEGE fields above 100 hPa (for large scales) • A few physics changes: fog sedimentation, new version of EDKF.. 12.5 km

  20. Assimilation of RADAR data 12 After operational wind data, operational assimilation of reflectivities 4 6 4 10 10 24 radars: 16 C-band (yellow) + 8 S-band (green). 2 to 13 vertical levels. 22 Doppler radars (red), 2more soon (dashed)

  21. Caumont, 2006: use model fields in the vicinity of the observation Retrieval method Reflectivity observation operator

  22. AROME Guess AROME Analysis RADAR Zpseudo-an Zobs Données utilisées • Assimilation of rainy data and non-rainy data • Thinning: 1 obs. in 15 kms • Obs Error increases linearly up to 160 kms

  23. Radar 03h Radar 06h Radar 09h Squall line well forecasted on REFL Radar 09h Chronology of the convective event 8 october 2008 Specific humidity Incr REFL (5 cyclings) CTRL Radar 06h Simulated reflectivity field from 3-hour forecast

  24. 00UTC run, 6-30h precip Performance in precipitation forecasts oper test significant *

  25. AROME: Extension of the domain and own surface analysis • ARPEGE: use of flow-dependent errors in observation quality control as well as in the minimisation (variances), tuning of physics (roughness length, microphysics..), use of OSTIA SST • Developments in data usage: • Assimilation of SSMIS • Bias correction of radiosonde humidity (ECMWF coefficients) • Assimilation of AMSU-B data over sea-ice • Assimilation of more GPS-RO data (GRAS, up to 36km for all) • Intercomparison: AROME and COSMO over common domain Outlook

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