Data assimilation in Aladin

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Data assimilation in Aladin

• Claude Fischer
• Thibaut Montmerle Ludovic Auger Loïk Berre
• Gergely Boloni Zahra Sahlaoui Simona
  Stefanescu
• Arome collaborators

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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

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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, AnalysisModel
  gridmesh9.5km
• 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

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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 gt 96 elements in
  the sample
• For the time being, constant uniform sb

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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
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Impact study Precipitation forecast
2004/07/18 12UTC RR P12 P6
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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
  gt operations started on Monday, July 25th

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About scores 3D-VAR versus Dyn. Adapt.
RMS
bias
3 hour cumulated precipitations
2m relative humidity
Mean sea level pressure
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Scores w/r to radiosondes
Wind bias
Wind RMS
Temperature bias
Temperature RMS
Blue 3D-VAR gt Dyn Adapt Red 3D-VAR lt Dyn Adapt
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A specific case June 21st, 2005
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June 21st Aladin model
P12-P6 RR
3D-VAR
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June 21st Aladin model (.)
P18-P12 RR
3D-VAR
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E-suites V1 and V2

• V1 23 March through 22 May, 2005 gt
• 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
  gt 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

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Lessons from Aladin-France

• Precipitations
• 0,3h gt caution (impact of initialisation, of
  imbalances )
• 3,12h gt the assimilation cycle(s) produce
  their own solution
• 12,24h gt 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

07/07 08/07 22/07
P12-P6 2.32 2.08 1.75
P24-P18 1.37 1.44 1.10
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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

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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!
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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 levels
  geopotential
• degradation in low levels geopotential 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

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(No Transcript)
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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

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4-year mid-term scientific plan Aladin (with
Arome input)

• Variational assimilation
•  B  wavelets, ensemble sampling, non-linear
  and O-equation, gridpoint sb 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

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Thank you for your attention
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End of talk

• Now come some extra slides for whatever

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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 ? -gt Monte-Carlo method
  (Girard, 1987)
• How to compute the statistical expectation ? -gt
  use a time mean over a suitably long range
• Samples of small size -gt aggregate analysis times
  together
• Applied to the NMC statistics

Sadiki and Fischer, Tellus, 2005 Chapnik etal.,
QJRMS, 2004
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Sensitivity experiments
3d-Var ALADIN experiments over 1 month
NMC-lagged backgroundP06H 0.86 lt 1 1.67 gt 1
Standard NMC statistics 0.6 0.53
Ensemble statistics --- 1.44 (by comparison with NMC)
? 3d-Var ARPEGE
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Statistics in the space of observations
radiosondes
ARPEGE 4D-VAR
ALADIN 3D-VAR
ATOVS channels