Lecture 1: Primal 4D-Var

Transcription

1 Lecture 1: Primal 4D-Var

2 Outline ROMS 4D-Var overview 4D-Var concepts Primal formulation of 4D-Var Incremental approach used in ROMS The ROMS I4D-Var algorithm

3 ROMS 4D-Var Priors f b, B f Ensemble 4D-Var b b, B b ROMS η, Q Obs y, R Priors & Hypotheses x b, B Hypothesis Tests 4D-Var Posterior dof impact Analysis error Adjoint 4D-Var Uncertainty Term balance, eigenmodes Forecast Clipped Analyses Ensemble (SV, SO)

4 ROMS 4D-Var Priors f b, B f Ensemble 4D-Var b b, B b ROMS η, Q Obs y, R Priors & Hypotheses x b, B Hypothesis Tests 4D-Var Posterior dof impact Analysis error Adjoint 4D-Var Uncertainty Term balance, eigenmodes Forecast Clipped Analyses Ensemble (SV, SO)

5 ROMS 4D-Var Applications Broquet, G., C.A. Edwards, A.M. Moore, B.S. Powell, M. Veneziani and J.D. Doyle, 2009: Application of 4D-variational data assimilation to the California Current System. Dyn. Atmos. Oceans, 48, Broquet, G., A.M. Moore, H.G. Arango, C.A. Edwards and B.S. Powell, 2009: Ocean state and surface forcing correction using the ROMS-IS4DVAR data assimilation system. Mercator Ocean Quarterly Newsletter, # Broquet, G., A.M. Moore, H.G. Arango, and C.A. Edwards, 2010: Corrections to ocean surface forcing in the California Current System using 4D-variational data assimilation. Ocean Modelling, Under review. Di Lorenzo, E., A.M. Moore, H.G. Arango, B.D. Cornuelle, A.J. Miller, B. Powell, B.S. Chua and A.F. Bennett, 2007: Weak and strong constraint data assimilation in the inverse Regional Ocean Modeling System (ROMS): development and application for a baroclinic coastal upwelling system. Ocean Modeling, 16, Powell, B.S., H.G. Arango, A.M. Moore, E. DiLorenzo, R.F. Milliff and D. Foley, 2008: 4DVAR Data Assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS). Ocean Modelling, 23, Powell, B.S., A.M.Moore, H.G. Arango, E. Di Lorenzo, R.F. Milliff and R.R. Leben, 2009: Near real-time assimilation and prediction in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS). Dyn. Atmos. Oceans, 48, Zhang, W.G., J.L. Wilkin, and J.C. Levin, 2010: Towards an integrated observation and modeling system in the New York Bight using variational methods, Part I. Ocean Modelling, Under review. Zhang, W.G., J.L. Wilkin, and J.C. Levin, 2010: Towards an integrated observation and modeling system in the New York Bight using variational methods, Part II. Ocean Modelling, Under review

6 Data Assimilation f b (t), B f b b (t), B b Prior ROMS x b (0), B x yr, x(t) Obs, y Prior Posterior time Model solutions depends on x b (0), f b (t), b b (t), h(t)

7 x T S = ζ u v State vector Notation & Nomenclature z x(0) () t f = b() t η() t Control vector y y1 = yn Observation vector d= y H ( xb ) ( ) Innovation vector Prior H Observation operator

8 Thomas Bayes ( )

9 Bayes Theorem Conditional probability: p( z y) = p( y z) p( z) p( y) (Wikle and Berliner, 2007) Posterior distribution ( likelihood ) Data distribution Prior Marginal ( ( ) y x R 1 ( y x )) ( ( ) ( )) z zb D z zb = c exp 1 2 H( ) H( ) exp 1 2 Maximum likelihood estimate: identify the minimum of 1 1 J z = z z D z z + y H x R y H x 2 2 T T ( ) ( ) 1 1 NL b ( b) ( ( )) ( ( )) which maximizes p(z y).

10 Variational Data Assimilation Conditional Probability: P( z y) exp J NL ( ) 1 1 J z = z z D z z + y H x R y H x 2 2 T T ( ) ( ) 1 1 NL b ( b) ( ( )) ( ( )) Observation error covariance D= diag( Bx, Bb, Bf, Q) Background error covariance J NL is called the cost or penalty function. Problem: Find z=z a that minimizes J (i.e. maximizes P) using principles of variational calculus. z a is the maximum likelihood or minimum variance estimate.

11 Incremental Formulation f b (t), B f δz ( δx (0), ε ( t), ε ( t), η( t)) T T T T = b f b b (t), B b Prior initial condition increment boundary condition increment forcing increment corrections for model error (Courtier et al., 1994) x b (0), B x 1 T 1 1 ( ) T 1 z D z G z d R ( G z d) J = δ δ + δ δ 2 2 D= diag( Bx, Bb, Bf, Q) Prior (background) error covariance Tangent Linear Model sampled at obs points Obs Error Cov. Innovation d = y H ( xb) (H=linearized obs operator)

12 Incremental Formulation f b (t), B f δz ( δx (0), ε ( t), ε ( t), η( t)) T T T T = b f b b (t), B b Prior initial condition increment boundary condition increment forcing increment corrections for model error x b (0), B x 1 T 1 1 ( ) T 1 z D z G z d R ( G z d) J = δ δ + δ δ 2 2 The minimum of J is identified iteratively by searching for J/ δz=0

13 Incremental Formulation f b (t), B f δz ( δx (0), ε ( t), ε ( t), η( t)) T T T T = b f b b (t), B b Prior initial condition increment boundary condition increment forcing increment corrections for model error x b (0), B x Assumptions: z = z + δ z b (i) δ z zb M = Tangent Linear Model (ii) x(t)= xb (t)+ δ x(t) (iii) δ x(t) Mδ z H = Tangent Linear H (iv) H δ x(t) H Mδz= Gδz

14 x(t) The Tangent Linear Model (TLROMS) Obs, y Prior 0 τ Prior is solution of model: time x (t ) = M ( x (t ), f (t ), b (t )) b i b i-1 b i b i Increment: δ x(t) x (t); δ b f(t) fb(t); etc x(t )= M( x (t ) + δ x(t ), ) i b i-1 i-1 M( x (t ), ) + M δ z b i-1 Nonlinear model x Tangent linear model b

15 z Primal Space Primal vs Dual Formulation Vector of increments Model space y Observation vector Observation space z Dual Space

16 The Solution Analysis: z = z + Kd a b Gain matrix (dual form): T T -1 K = DG ( GDG + R) Gain matrix (primal form): 1 T 1 1 T 1 K = ( D + G R G) G R

17 Gain (dual): Two Spaces T T -1 K = DG ( GDG + R) Gain (primal): N obs N obs 1 T 1 1 T 1 K = ( D + G R G) G R N obs N model N model N model

18 Two Spaces G maps from model space to observation space G T maps from observation space to model space

19 Iterative Solution of Primal Formulation (define IS4DVAR, is4dvar_ocean.h) Recall the incremental cost function: 1 T 1 1 T 1 J = δz D δz+ Gδz d R Gδz d 2 2 Jb ( ) ( ) Jo At the minimum of J we have J δ z = 0 z = D z+ G R G z d 1 T 1 J δ δ δ ( ) Given J and J/ δz, we can identify the δz that minimizes J

20 Matrix-less Operations There are no matrix multiplications! T -1 ( δ ) GR G z d Zonal shear flow

21 Matrix-less Operations There are no matrix multiplications! T -1 ( δ ) GR G z d Tangent Linear Model Zonal shear flow

22 Matrix-less Operations There are no matrix multiplications! T -1 ( δ ) GR G z d Tangent Linear Model Zonal shear flow

23 Matrix-less Operations There are no matrix multiplications! T -1 ( δ ) GR G z d Consider a single Observation Zonal shear flow

24 Matrix-less Operations There are no matrix multiplications! T -1 ( δ ) GR G z d Consider a single Observation Zonal shear flow

25 Matrix-less Operations There are no matrix multiplications! T -1 ( δ ) GR G z d Inverse Obs Error Covariance Zonal shear flow

26 Matrix-less Operations There are no matrix multiplications! T -1 ( δ ) GR G z d Inverse Obs Error Covariance Zonal shear flow

27 Matrix-less Operations There are no matrix multiplications! T -1 ( δ ) GR G z d Adjoint Model Zonal shear flow

28 Matrix-less Operations There are no matrix multiplications! T -1 ( δ ) GR G z d Adjoint Model Green s Function Zonal shear flow J o δ z

29 NLROMS, z b Choose δz x b (t), d Primal 4D-Var Algorithm (I4D-Var) Run TLROMS ( ) Gδz, Gδz d, J δ z R -1 Run ADROMS -1 ( δ ) R G z d T -1 ( δ ) GR G z d Conjugate Gradient Algorithm 1 J δz = D δz+ T 1 GR Gδ z d ( ) NLROMS, z a x a (t)

30 NLROMS, z b Primal 4D-Var Algorithm (I4D-Var) Choose δz Run TLROMS R -1 Outer-loop Inner-loop Run ADROMS Conjugate Gradient Algorithm NLROMS, z a

31 Conjugate Gradient (CG) Methods Contours of J

32 An Example: ROMS CCS COAMPS forcing f b (t), B f ECCO open boundary conditions b b (t), B b x b (0), B x Previous assimilation cycle 30km, 10 km & 3 km grids, levels Veneziani et al (2009) Broquet et al (2009) Moore et al (2010)

33 Observations (y) CalCOFI & GLOBEC SST & SSH TOPP Elephant Seals Ingleby and Huddleston (2007) Argo Data from Dan Costa

34 4D-Var Configuration Case studies for a representative case 3-10 March, outer-loop, 100 inner-loops 7 day assimilation window Prior D: x L h =50 km, L v =30m, σ from clim f L τ =300km, L Q =100km, σ from COAMPS b L h =100 km, L v =30m, σ from clim Super observations formed Obs error R (diagonal): SSH 2 cm SST 0.4 C hydrographic 0.1 C, 0.01psu

35 4D-Var Performance 3-10 March, 2003 (10km, 42 levels) Primal, strong Dual, strong Dual, weak Jmin

36 Summary Strong constraint incremental 4D-Var, primal formulation: define IS4DVAR Drivers/is4dvar_ocean.h Matrix-less iterations to identify cost function minimum using TLROMS and ADROMS

37 References Broquet, G., C.A. Edwards, A.M. Moore, B.S. Powell, M. Veneziani and J.D. Doyle, 2009: Application of 4D-variational data assimilation to the California Current System. Dyn. Atmos. Oceans, 48, Courtier, P., J.-N. Thépaut and A. Hollingsworth, 1994: A strategy for operational implemenation of 4D-Var using an incremental approach. Q. J. R. Meteorol. Soc., 120, Ingleby, B. and M. Huddleston, 2007: Quality control of ocean temperature and salinity profiles - historical and real-time data. J. Mar. Systems, 65, Moore, A.M., H.G. Arango, G. Broquet, B.S. Powell, J. Zavala-Garay and A.T. Weaver, 2010: The Regional Ocean Modeling System (ROMS) 4-dimensional data assimilation systems: Part I. Ocean Modelling, Submitted. Veneziani, M., C.A. Edwards, J.D. Doyle and D. Foley, 2009: A central California coastal ocean modeling study: 1. Forward model and the influence of realistic versus climatological forcing. J. Geophys. Res., 114, C04015, doi: /2008jc Wikle, C.K. and L.M. Berliner, 2007: A Baysian tutorial for data assimilation. Physica D, 230, 1-16.