ROMSTOMS Tangent Linear and Adjoint Models Andrew Moore

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ROMS/TOMS Tangent Linear and Adjoint Models Andrew Moore, CU Hernan Arango, Rutgers U Arthur

ROMS/TOMS Tangent Linear and Adjoint Models Andrew Moore, CU Hernan Arango, Rutgers U Arthur Miller, Bruce Cornuelle, Emanuele Di Lorenzo, Doug Neilson UCSD

Major Objective • In addition to IOM, provide the ocean modelling community with analysis

Major Objective • In addition to IOM, provide the ocean modelling community with analysis and prediction tools available in meteorology and NWP, using a community OGCM (ROMS).

Overview • NL ROMS: • Perturbation:

Overview • NL ROMS: • Perturbation:

Overview • NL ROMS: • TL ROMS: (TL 1) • AD ROMS: (AD)

Overview • NL ROMS: • TL ROMS: (TL 1) • AD ROMS: (AD)

Overview • Second TLM: (TL 2) • TL 1= Representer Model • TL 2=

Overview • Second TLM: (TL 2) • TL 1= Representer Model • TL 2= Tangent Linear Model

Current Status of TL and AD • All advection schemes • Most mixing and

Current Status of TL and AD • All advection schemes • Most mixing and diffusion schemes • All boundary conditions • Orthogonal curvilinear grids • All equations of state • Coriolis, pressure gradient, etc.

Picard Iteration Test (TL 2):

Picard Iteration Test (TL 2):

1 11 2 2 3 3 4 4 5 • 6 month integration time

1 11 2 2 3 3 4 4 5 • 6 month integration time 5 1 • 50 Picard iterations • 2 3 4 5

Available Drivers (TL 1, AD) • Eigenmodes of • Forcing Singular vectors: • Stochastic

Available Drivers (TL 1, AD) • Eigenmodes of • Forcing Singular vectors: • Stochastic optimals: • Singular vectors: Pseudospectra : and

Southern California Bight (SCB) • Model grid • • 1200 km. X 1000 km

Southern California Bight (SCB) • Model grid • • 1200 km. X 1000 km 10 km resolution, 20 levels Di Lorenzo et al. (2003)

SCB Examples

SCB Examples

Pseudospectrum

Pseudospectrum

Singular Vectors • Energy norm, 5 day growth time

Singular Vectors • Energy norm, 5 day growth time

Confluence and diffluence

Confluence and diffluence

Boundary sensitivity

Boundary sensitivity

Seasonal Dependence

Seasonal Dependence

Stochastic Optimals

Stochastic Optimals

Drivers under development • Ensemble prediction (SVs, FSVs, SOs, following NWP) • 4 D

Drivers under development • Ensemble prediction (SVs, FSVs, SOs, following NWP) • 4 D Variational Assimilation (4 DVar) • Greens function assimilation • IOM interface (IROMS) (NL, TL 1, TL 2, AD)

Publications • Moore, A. M. , H. G Arango, E. Di Lorenzo, B. D.

Publications • Moore, A. M. , H. G Arango, E. Di Lorenzo, B. D. Cornuelle, • A. J. Miller and D. Neilson, 2003: A comprehensive ocean prediction and analysis system based on the tangent linear and adjoint of a regional ocean model. Ocean Modelling, Final revisions. H. G Arango, Moore, A. M. , E. Di Lorenzo, B. D. Cornuelle, A. J. Miller and D. Neilson, 2003: The ROMS tangent linear and adjoint models: A comprehensive ocean prediction and analysis system. Rutgers Tech. Report, In preparation.

What next? • Complete 4 DVar driver • Interface barotropic ROMS to IOM •

What next? • Complete 4 DVar driver • Interface barotropic ROMS to IOM • Complete 3 D Picard iteration test (TL 2) • Interface 3 D ROMS to IOM

SCB Examples

SCB Examples

Confluence and diffluence

Confluence and diffluence

Boundary sensitivity

Boundary sensitivity

Stochastic Optimals

Stochastic Optimals

Tangent linear and adjoint models for variational dataTangent linear and adjoint models for variational data
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ROMSTOMS Status Algorithms Adjoint and Applications Hernan GROMSTOMS Status Algorithms Adjoint and Applications Hernan G
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Chef du service PLP Adjoint Technique Adjoint AdministratifChef du service PLP Adjoint Technique Adjoint Administratif
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2 72 8 TANGENT LINES DERIVATIVES TANGENT LINE2 72 8 TANGENT LINES DERIVATIVES TANGENT LINE
Tangent lines n Recall tangent line is theTangent lines n Recall tangent line is the
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Generalized Linear Models Generalized Linear Models GLM GeneralGeneralized Linear Models Generalized Linear Models GLM General
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Linear Models Tony Dodd Overview Linear models ParameterLinear Models Tony Dodd Overview Linear models Parameter