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Variational assimilation: method & technicalities

Variational assimilation: method & technicalities. Claude Fischer & Patrick Moll, CNRM/GMAP, Météo-France. Overview of some principles of VAR assimilation as applied in our NWP systems. X = vector of model variables. X. X1. X3. X4. X2. t 0. t 1 (analysis). t 2. t.

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Variational assimilation: method & technicalities

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  1. Variational assimilation: method & technicalities Claude Fischer & Patrick Moll, CNRM/GMAP, Météo-France

  2. Overview of some principles of VAR assimilation as applied in our NWP systems

  3. X = vector of model variables X X1 X3 X4 X2 t0 t1 (analysis) t2 t Principle of VAR (in as short as possible …) • 4D variational assimilation : • Search for the model trajectory approaching best the available observations => iterative minimization process • 3D variational assimilation : a « reduced » version of 4D-VAR => no time integration, all obs are considered valid for time t1

  4. Picture view of 4D-VAR assimilation obs Jo previous forecast analysis Jo xb obs background corrected forecast Jb Jo xa obs t0=9h t1=12h t2=15h Assimilation window

  5. General formulation of 4D-VAR in NWP systems • Full (Nonlinear) cost function: • Linearized cost function (k=outer loop):

  6. Incremental formulation (I) Cost function: is the « simplified » (low resolution) increment at outer loop and with Trajectory (analysis) updates:

  7. Incremental formulation (II) To pre-condition the problem, an additional change of variable is performed: CHAVAR New cost function: CHAVARIN

  8. Incremental 4D-Var algorithm (ECMWF documentation)

  9. 3D-VAR – LAM - (I) Cost function => no model integration => There is no time window (1 timeslot) and only one outer loop and Analysis updates => no « simplification » operator ! => the increment is computed on the same grid than the background:

  10. 3D-VAR – LAM - (II) To pre-condition the problem, an additional change of variables is Performed (4D-VAR == 3D-VAR): CHAVAR New cost function: CHAVARIN

  11. Computation of Jo in 4D-VAR where are the « high resolution » departures. These high resolution departures are computed during high the resolution integrations and stored in the ODB files. In practice, one computes: Low Resol. depart. - Obs. + High Resol. depart.

  12. Computation of Jo in LAM 3D-VAR With k=1 and i=0 only and: are the « high resolution » departures. The departures are computed only once, during high the screening step and stored in the ODB files. In the code, one computes like for incremental 4D-VAR: « Low Resol. » depart.- Obs. + « High Resol. » depart.

  13. Chain of operations in a VAR minimisation loop (SIM4D) • compute trajectory (take Xb in 3D-VAR) • read (stored in ODB) • (*) compute Jb and its gradient • compute TL of Jo: And store in the ODB • compute AD of Jo: • compute J: • call minimizer; go to (*) until convergence

  14. Major messages to keep in mind: • 3D-VAR is a reduced (« simpler ») version of 4D-VAR • Aladin 3D-VAR is a code installed inside the global IFS/Arpège code (as we will see in the last slides)

  15. Back to application … with pictures !

  16. SURFACE OBSERVATIONS FOR ALADIN-France

  17. Simulated Reflectivity factor in « beam volum bv» Antenna’s radiation pattern: gaussian function for main lobe (side lobes neglected) Resolution volume, ray path: standard refraction (4/3 Earth’s radius) Assimilation of Reflectivity :Observation operatorimplemented in the 3DVar ALADIN/AROME model level N r j q z h • Bi-linear interpolation of the simulated hydrometeors (T,q, qr, qs, qg) • Compute « radar reflectivity » on each model level Diameter of particules Resolution volum, ray path : standard refraction (4/3 Earth’radius) Backscattering cross section: Rayleigh (attenuation neglected) Microphysic Scheme in AROME

  18. Apparition de bande étroite de front froid: moins intense et légèrement plus sud dans ALADIN. Courbure derrière le front froid: cassure trop nette dans ALADIN (à l’arrière, développement de la convection dans la traîne ou pseudo-limite dans ALADIN)

  19. The VAR assimilation in Ald/Aro:scripting sequence • Observations are taken +/- 3 h around base time in Aladin; +/- 1.5 h in Arome • Analysis time = background time and no « high » / « low » resolution • Full analysis sequence (altitude fields): • Full ODB database enters Screening => computes departures and performs QC and first-guess check • ODB base is compressed (~ keep only active data in the output ODB) • Variational analysis: minimization of cost function • Write out analysis file

  20. Technical aspects about the LAM VAR code inside the IFS

  21. The observation operators in the variational cost function • We have seen that the Jo part of the cost function can be finally written : Jo(x) =  (yik– Hik [xb])TRik-1(yi – Hik [xb]) • Where Hik is the observation operator for obs n° k of type n° i. • The operator Hik is subdividedinto a sequence of operators, each one of which performs part of the transformation from control variable to observed parameters : • Conversion from control variable to model variables • Inverse spectral transforms put the model variables on the gridpoint space • A horizontal interpolation provides vertical profiles of model variables at observation locations • Vertical integration if necessary (hydrostatic equation for geopotential, radiative transfer equation for radiances…) • Vertical interpolation to the level of the observations

  22. The observation operators in the variational cost function • Horizontal interpolations : • A 12-point bi-cubic or 4-point bi-linear horizontal interpolation gives vertical profiles of model variables at observation locations. The surface fields are interpolated bilinearly to avoid spurious maxima and minima • Vertical interpolations : it depends on the variable • Linear in pressure for temperature and specific humidity • Linear in logarithm of pressure for wind • Linear in logarithm of pressure for geopotential (performed in terms of departures from the ICAO standard atmosphere). New operator until a few years, specific to Météo-France, better consistent with hydrostatism. • Vertical interpolations for surface observations (T2m, V10m, Hu2m) are done consistently with the physics of the model

  23. The observations in the variational cost function Adjoints of the observation operators The solution of the minimisation problem is given by J(xa)=0 At each step of the descent algorithm, J has to be computed : J(x) =2B-1(x-xb) – 2HTR-1(y – H[xb]) Where HT is the the adjoint (transpose) of the the observation operator (or the tangent linear of the observation operator when it is non-linear). It means that the adjoints of the obs operators have to be computed, which is easy for the interpolations, but more difficult for the vertical integrations, in particular for the radiative transfer equation or other operators containing non differentiable processes.

  24. DIRECT AND ADJOINT OBSERVATION OPERATORS ymod X (model var.) H2 Hn …………. H1 ….. Jo Chain of direct operators y Hn* Jo H1* H2* …………. ymod X Chain of adjoint operators

  25. Chain of operations in a VAR minimisation loop (SIM4D) • compute trajectory (take Xb in 3D-VAR) • read (stored in ODB) • (*) compute Jb and its gradient • compute TL of Jo: And store in the ODB • compute AD of Jo: • compute J: • call minimizer; go to (*) until convergence

  26. Global // LAM code interfacing for VAR • Simulator and model: Jb=; Jb=2 obshortl dx=B1/2. Minimisation obsvtl cpgtl Var inner loop (SIM4D) J=Jb+Jo; J ecoupl1 einv_trans H.dx H.dx edir_trans GradJo=H*.R-1. (y-H(x)-H.dx) Jo=B1/2. GradJo espcmtl

  27. Global // LAM code interfacing for VAR • Architecture: obshortl Slightly different code: LELAM key coupling Completely different code Fully shared dataflow between IFS and Aladin (especially in gridpoint space), but quite separate dimensioning and addressing in spectral buffers (spherical versus bi-Fourier). Coupling code is of course only LAM.

  28. Global // LAM code interfacing for VAR • Change of variable: cv2spa chavarin cvar2in sqrtbin cvaru3i jgcori ejgvcori ejghcori jgnrsi etransinv_jb cvargptl etransdir_jb ebalstat ebalvert spa1=ylvazx%lamcv*tmeanuver addbgs

  29. Phasing of LAM 3D-VAR • ODB & bator need to work (almost same as Arpège, plus LAMFLAG) • At every new cycle with IFS, a careful inspection of B-matrix setup and change of variable routines, SPECTRAL_FIELDS and CONTROL_VECTOR structures, specific observation-related code is needed (~ 1 week of work). • Additionally, TL and AD LAM code may need to be checked. • LAM 3D-VAR does not use multi-incremental I/O prepared for IFS-Arpège: no WRMLPPADM/RDFPINC, no SAVMINI/GETMINI, NUPTRA>1, « traj » recomputation of innovations

  30. Thank you for your attention

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