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revisiting ECCO observational constraints

revisiting ECCO observational constraints. Gael FORGET ECCO2 meeting Nov. 9 th 2009. Key algorithmic piece. MITgcm/pkg/smooth Weaver and Courtier 2001, QJRMS, Correlation modelling on the sphere using a generalized diffusion equation . tentative applications

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revisiting ECCO observational constraints

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  1. revisiting ECCOobservational constraints Gael FORGET ECCO2 meeting Nov. 9th 2009

  2. Key algorithmic piece • MITgcm/pkg/smooth Weaver and Courtier 2001, QJRMS, Correlation modelling on the sphere using a generalized diffusion equation. • tentative applications Control vector adjustments Data constraints Map model variables @ different resolution

  3. General approach to data constraints • Start with point-wise observations as opposed to mapped products. • Compare with point-wise model values. • Then Smooth/Average point-wise misfits to constrain the large scales specifically. • no inconsistency between model and obs. implied by the smoothing/averaging.  practical approach to error covariances. • GRACE … altimetry … SST … … scatterometer winds … in situ.

  4. Mean Dynamic Topography (MDT) • Rio MDT, 1993-2004 average • Model SSH, between T1 and T2 (e.g. 1992-2009 or 2004-2005) • cross-reference using SLARADS data • MDT misfit= <SSHmodel-SLARADS>T1-T2 -[MDTRio-<SLARADS>93-04]

  5. Mean Dynamic Topography (MDT)

  6. Sea Level Anomalies (SLA) • Absolute Model SSH, over T1-T2 • Observed SSH Anomaly @ T3 in T1-T2 • SLA misfit= SSHmodel (T3 )-SLARADS(T3 ) - <SSHmodel-SLARADS>T1-T2

  7. ECCO-RADS SLA misfits raw

  8. ECCO-RADS SLA misfits @ > 35-days

  9. ECCO-RADS SLA misfits and @ > 300km

  10. Signal/Noise amplitudes raw

  11. Signal/Noise amplitudes @ > 35-days

  12. Signal/Noise amplitudes and @ > 300km

  13. ending notes • MITgcm/pkg/smooth Weaver and Courtier 2001, QJRMS, Correlation modelling on the sphere using a generalized diffusion equation. • cost_bp.F, cost_sshv4.F • … cost_sstv4.F ...

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