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Data assimilation in Aladin

Data assimilation in Aladin. Claude Fischer; Thibaut Montmerle; Ludovic Auger; Lo ï k Berre; Gergely Boloni; Zahra Sahlaoui; Simona Stefanescu; Arome collaborators. Data assimilation inside Aladin. 3 « operating Centers »: Budapest, Toulouse (operational), Casablanca (run daily)

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Data assimilation in Aladin

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  1. Data assimilation in Aladin Claude Fischer; Thibaut Montmerle; Ludovic Auger; Loïk Berre; Gergely Boloni; Zahra Sahlaoui; Simona Stefanescu; Arome collaborators

  2. Data assimilation inside Aladin • 3 « operating Centers »: Budapest, Toulouse (operational), Casablanca (run daily) • Dispatched research collaborations: Bratislava (radar), Brussels (wavelet), Bucarest (ensembles, methods), Lisbon (ensembles), Ljubljana (nudging), Prague, Tunis (surface analysis), Sofia (surface analysis and snow), Zagreb (methods) • Existing tools: • Optimal Interpolation (« CANARI ») both for 3D and surface, • 3D-VAR (screening and minimisation), • TL and adjoint Eulerian hydrostatic models

  3. Variational system: what is in it ? • Incremental 3D-VAR, using 80-90 % of the common Arpège/IFS code • Continuous assimilation cycle, 6 hour frequency, long cut-off assimilation cycle and short cut-off production, coupled with Arpège, Analysis=Model gridmesh=9.5km • Observations: • Surface pressure, SHIP winds, synop T2m and RH2m • Aircraft data • SATOB motion winds • Drifting buoys • Soundings (TEMP, PILOT) • Satellite radiances: AMSU-A, AMSU-B, HIRS, Meteosat-8 SEVIRI • No QuikSCAT • Digital filter initialisation

  4. Ensemble B matrix • Bi-periodic spectral structure functions (a torus) • Control variable: vorticity + unbalanced divergence, (T, Ps) and specific humidity • Background error covariances are sampled from an ensemble of Aladin forecasts, with initial conditions from an ensemble of Arpège analyses: « ensemble Jb » • Sample: over 48 days, two pairs of 6 hour forecasts are extracted from the ensemble, and their difference is computed => 96 elements in the sample • For the time being, constant uniform σb

  5. Use of Météosat-8/SEVIRI in the 3DVar of ALADIN • Data processing : • 1 pixel out of 5 is extracted and thinning boxes of 70 km are applied • IR 3.9 m and 13.4 m as well as ozone 9.7 m are blacklisted, as well as IR channels over land • predictor, situation-dependent bias correction applied to the other channels • empirical so are used • first-guess quality control removes too large innovations • CMS/Lannion cloud classification is used to keep IR 8.7 m , 10.8 m ,12 m only in clear sky while the two WV channels are also kept over low clouds Vertical weighting functions Cloud types

  6. Dyn. Adapt. Raingauges 3DVar 3DVar with SEVIRI Impact study : Precipitation forecast 2004/07/18 12UTC RR P12 – P6

  7. Operational implementation in Aladin-France • A one month period over June 2003 • A two week period in July 2004 • First E-suite: March 23rd through May 22nd, 2005 • Second E-suite: June 2nd through July 25th, 2005 => operations started on Monday, July 25th

  8. About scores: 3D-VAR versus Dyn. Adapt. RMS bias 3 hour cumulated precipitations 2m relative humidity Mean sea level pressure

  9. Scores w/r to radiosondes Wind RMS Wind bias Temperature RMS Temperature bias Blue: 3D-VAR > Dyn Adapt Red: 3D-VAR < Dyn Adapt

  10. A specific case: June 21st, 2005

  11. June 21st: Aladin model P12-P6 RR 3D-VAR

  12. June 21st: Aladin model (.) P18-P12 RR 3D-VAR

  13. E-suites: V1 and V2 • V1: 23 March through 22 May, 2005 => • Deteriorated MSLP bias and RMS by about 0.2 hPa • Too strong precipitation amounts at short range (6h, 12h), with significant spin-down • Analyses too wet compared to RS • No increase in the number of « Aladinades » (which is a relief, given the problems on humidity and RR) • V2: started 2nd of June, scheduled for about 1.5 month • Improved SEVIRI bias correction, using a case/location-dependent b.c. (with 4 predictors) • Infrared channels over land blacklisted (problem of poor quality surface temperature) • Additional surface observations ready: T2m, RH2m => quite complementary with SEVIRI data • Digital filter initialization: back to settings from dynamical adaptation • Reduced weighting of Jo with respect to Jb: Sb decreased from 3.24 to 2.25

  14. Lessons from Aladin-France: • Precipitations: • [0,3h] => caution (impact of initialisation, of imbalances …); • [3,12h] => the assimilation cycle(s) produce their own solution; • [12,24h] => a limit of predictability somewhere in this range, mostly due to the growing influence of lateral boundary conditions; • RMS(Dyn Adap – 3DVAR) for 6 hour precipitation (mm/6h), for three days of the test period:

  15. 3DVAR at the Hungarian Met Service Short history: • Implementation with French and Slovak help (June 2000) • November 2002: Quasi-operational parallel suite • Operational application (May 2005) • Basic characteristics: • 6h 3DVAR assim. cycle • 48h production • linear grid • dx ~ 8km • 49 vertical levels

  16. 3DVAR at the Hungarian Met Service • Assimilation details: • 6h cycle (4 long + 2 short cut-off analyses per day) • ARPEGE fields for surface • local 3DVAR for upper air • NMC background error statistics • DFI initialization • 3h coupling in cycle (by the long cut-off ARPEGE analyses and the corresponding 3h forecasts) • Input observations: • SYNOP: surface pressure • TEMP: temperature, wind, pressure, specific humidity • ATOVS/AMSU-A radiances • AMDAR aircraft reports: temperature, wind All the observation types above are used in the ARPEGE assimilation system too, but in a somewhat worse resolution!

  17. 3DVAR at the Hungarian Met Service Assimilation results vs the spin-up version • Objective scores: • generally small improvement for temperature and wind • neutral impact/improvement on high level’s geopotential • degradation in low level’sgeopotential and MSLP BIAS • mixed impact on humidity • Subjective evaluation: • improvement in T2m (0-24h) • improvement in precipitation (0-48h) • degradation (0-24h) / neutral impact (24-48h) in cloudiness • neutral impact on wind

  18. In the near future, for Aladin-France • 3D-VAR FGAT • Jk extra cost function to fit towards the ARPEGE analysis • Test 3-hourly analyses • Better understand the intrication between digital filter initialization, coupling and B-matrix dynamical balances • Innovating observations for the mesoscale: sampling of satellite data, QuikSCAT • Application for AMMA mid-term issues, for the collaboration: • Code convergence with the Hirlam group: mesoscale multi-incremental 4D-VAR and an increasing collaboration on very high resolution (Arome-oriented 2.5 km) • Next 4 year mid-term Aladin scientific plan

  19. 4-year mid-term scientific plan: Aladin (with Arome input) • Variational assimilation: • « B »: wavelets, ensemble sampling, non-linear and Ω-equation, gridpoint σb and cycling, humidity control variable • Algorithms: 3D-VAR FGAT, 4D-VAR in a nutshell • Observations: satellite (cloudy IR, microwave, locally received data, impact of bias correction, wind retrievals), GPS zenital delay, radar reflectivity and Doppler winds, 2m and 10m mesonet • Surface analysis: • Ocean and sea ice SAF SST derived from geostationnary satellites • Snow analysis (with Hirlam), possibly using land SAF snow cover • Off-line soil analysis using « dynamical OI » • Diagnostic fine-grid 3D-VAR hourly analysis for nowcasting

  20. Thank you for your attention

  21. End of talk Now come some extra slides for whatever …

  22. Tuning of background and observation error variances • Desroziers and Ivanov, 2001: (So, Sb) ?  • For a consistent systemSo = Sb = 1 • For a LAM system: • How to compute the Trace? -> Monte-Carlo method (Girard, 1987) • How to compute the statistical expectation ? -> use a time mean over a suitably long range • Samples of small size -> aggregate analysis times together • Applied to the NMC statistics Sadiki and Fischer, Tellus, 2005 Chapnik etal., QJRMS, 2004

  23. Sensitivity experiments  3d-Var ARPEGE

  24. Statistics in the space of observations radiosondes ARPEGE 4D-VAR ALADIN 3D-VAR ATOVS channels

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