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					  Geostationary Satellite Winds in HIRLAM

                  Carlos Geijo




•Characteristics of AMVs used in this Study

• Assimilation of AMVs in HIRLAM

• Assimilation Experiment Description and
Discussion of Results

• Conclusions and Future Work Outlook.
  SATELLITE VECTORS USED IN THIS STUDY


• Vectors from GOES, M7 and M5 Satellites.

• Two types of Vectors: Vectors derived from VIS (0.5
μ) and from WV(about 6 μ) (only cloud tracking
vectors included) consecutive images.

• Increased Sampling in Space and Time. Reliable
Quality Information included in the BUFR messages.
  ASSIMILATION OF AMVs in HIRLAM


• Screening: Background Cross-Check Settings.

• Calibration of Screening Settings with AMVs
Quality Information.

• Recent developments: observation operators
for AMVs and bias correction schemes.
Settings of First Guess Check for Wind Data:

Compare 0.5 * [ ( uo - ub )2 / σ2 + ( vo - vb )2 / σ2 ] .vs. x(i) * factor

σ2 = σ o 2 + σ b 2
                             σo ( m/s)   x(1), x(2), x(3)   factor            Rejection
                                                                              limit
              Aircraft       4.            8, 18, 20        0.33              ~ 2.4 σ
              (high level)

              Ship           3.            8, 10, 20        1.0               ~ 3.0 σ

              AMVs           2.            8, 18, 20        Sb – So < 4 m/s   < 1.9 σ
                                                            0.2
              (low level)                                   Sb – So > 4 m/s
                                                            0.15
              AMVs           5.            8, 18, 20        Sb – So < 4 m/s   < 1.3 σ
                                                            0.1
              (high level)                                  Sb – So > 4 m/s
                                                            [ 0., 0.075]
Aircraft/AMVs FG Check Results Compared
Ship/AMVs FG Check Results Compared
FG check results compared with QI values
Characterising and Searching out a Bias Correction Method for Satellite AMVs
(N.Bormann,G.Kelly,J.NThépaut )
Assimilation Experiment Brief Description

• Assimilation Algorithm: Hirvda_6.0.0
• HIRLAM Forecast System Version: 6.1.0
• Geographical Area: 15.5N-65N; 66.5W-30E
• Spatial Resolution: 0.5 x 0.5 degrees, 31 levels
• Time period: June 2003, 6 hours assimilation cycle
• Observation usage:
    CNT; default + wind data from SHIPS.
    EXP; as CNTL + AMVs presented in this talk.
    a) Assimilation time window for AMVs shrunk to
    +/- 15 minutes about nominal analysis time.
    b) QI above 0.6
    c) Thinning at 0.5 degrees and 50 hPa by selection of
    vector with best QI.
Spatial Structure of Observation Errors for AMVs
(N.Bormann, S.Saarinen, J.N Thépaut, G.Kelly)
Impact on Fit to Analysis of Aircraft/Ship Data
                        Variational Quality Control

Contribution to the cost function of a given observation split
up in gross-error and gaussian error

JoiQC = - ln poi = - ln ( a + b exp ( - zoi / 2. ) )

ω = grad JoiQC / grad Joi;                 if smaller than a given threshold the
                                           observation is rejected
                                     “a priori” probability of gross     Range of uniform distribution
                                     error
         AIRCRAFF                    0.015                               5 σo
         Rest of observations        0.003                               5 σo



                             FG            VARQC                 FG                  VARQC
         AIRCRAFT           2.3 %          1.4 %                 2.2 %               1.4 %
         SHIPS              0%             0.3 %                0%                   0.3 %
         AMVs               8.8 %          0%                   8.3 %                0%
         (low level)
         AMVs               27.8 %         0%                   22.2 %               0%
         (high level)
Wind Speed at Model Level 28
Wind Speed at Model Level 16
Surface Pressure (Mean Difference EXP-CNT )
Surface Pressure (t-test)
Geopotential at Model Level 25
Geopotential at Model Level 16
         CONCLUSIONS AND PROSPECTS


• New AMVs have proved to have a moderate positive
impact for low European latitudes.

• There is still some tuning work to do.

• Future work should consider 3D-Var assimilation
algorithm enhancements like FGAT and new
background constraints.

• New vectors from recently de-commissioned MSG
satellite should be included as well in future studies.

				
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posted:12/19/2011
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