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Data assimilation in Aladin

Learn about the data assimilation methods within Aladin, including operational centers, dispatched research collaborations, existing tools, variation system, control variables, observations used, and the impact study of precipitation forecasts. Explore lessons learned and insights from Aladin-France operations.

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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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