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Hernan G. Arango IMCS, Rutgers

Towards an Operational Satellite-based Analysis/Prediction System using ROMS: An Example for the East Australia Current. Hernan G. Arango IMCS, Rutgers. John L. Wilkin IMCS, Rutgers. Javier Zavala-Garay IMCS, Rutgers. Outline. EAC application Brief summary of IS4DVAR

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Hernan G. Arango IMCS, Rutgers

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  1. Towards an Operational Satellite-based Analysis/Prediction System using ROMS: An Example for the East Australia Current Hernan G. Arango IMCS, Rutgers John L. Wilkin IMCS, Rutgers Javier Zavala-Garay IMCS, Rutgers

  2. Outline • EAC application • Brief summary of IS4DVAR • Updated version on EAC-IS4DVAR analysis/prediction system • Background error covariance modeling • Some preliminary results • Future work

  3. EAC

  4. -24 -28 -32 -36 -40 -44 -48 165 155 150 160 145 East Australia Current Application Configuration

  5. IS4DVAR

  6. IS4DVAR • Given a first guess (a forward trajectory)

  7. IS4DVAR • Given a first guess (a forward trajectory)… • And given the available data…

  8. IS4DVAR • Given a first guess (a forward trajectory)… • And given the available data… • What are the changes (or increment) to the IC so that the forward model fits the observations?

  9. The best fit becomes the reanalysis assimilation window

  10. The final state becomes the IC for the forecast window assimilation window forecast

  11. The final state becomes the IC for the forecast window assimilation window forecast verification

  12. EAC-IS4DVAR

  13. 4DVar Observations XBTs We have moved from SSH 7-Day Averaged AVISO To SSH 4-day avg. from CSIRO (compatible with SXBT=SCTD) SST Daily CSIRO Days since January 1st 2001, 00:00:00

  14. EAC Incremental 4DVar: Surface Versus Sub-surface Observations

  15. EAC Incremental 4DVar: Surface Versus Sub-surface Observations First Guess SSH/SST

  16. EAC Incremental 4DVar: Surface Versus Sub-surface Observations Observations SSH/SST First Guess SSH/SST

  17. EAC Incremental 4DVar: Surface Versus Sub-surface Observations Observations ROMS IS4DVAR: SSH/SST SSH/SST SSH/SST First Guess ROMS IS4DVAR: XBT Only SSH/SST SSH/SST

  18. Comparison between ROMS fit and observed temperature from all XBTs. Note: actual XBT-data was not assimilated, it is used here to evaluate the quality of the reanalysis. SSH+SST

  19. Comparison between ROMS fit and observed temperature from all XBTs. Note: actual XBT-data was not assimilated, it is used here to evaluate the quality of the reanalysis. SSH+SST Assimilation of SST

  20. Comparison between ROMS fit and observed temperature from all XBTs. Note: actual XBT-data was not assimilated, it is used here to evaluate the quality of the reanalysis. SSH+SST Assimilation of SST Erroneousprojection of SSH information

  21. Example of synthetic XBT

  22. Comparison between ROMS fit and observed temperature from all XBTs. Note: actual XBT-data was not assimilated, it is used here to evaluate the quality of the reanalysis. SSH+SST SSH+SST+SynXBT

  23. Comparison between ROMS prediction and observed temperature from all XBTs.

  24. Comparison between ROMS prediction and observed temperature from all XBTs.

  25. Comparison between ROMS prediction and observed temperature from all XBTs.

  26. Comparison between ROMS prediction and observed temperature from all XBTs.

  27. Comparison between ROMS prediction and observed temperature from all XBTs.

  28. some remarks • Good ocean state predictions for up to 2 weeks in advance • Need to correctly project the altimeter data • Proxies for subsurface information can be obtained based on surface information, but need lots of subsurface data to construct a robust empirical relationship • The fact that an empirical (linear) relationship exist suggest that there could be a simple dynamical relationships linking the surface with the subsurface variability • The idea is actually not new (Weaver et al 2006: “multivariate balance operator”)

  29. What we have learned • Need a good unbiased background or first guess • From a “good” model forced by unbiased boundary and surface forcing • From assimilation of climatological seasonal cycle of T and S (e.g. Levitus, CARS). • Adequate modeling the background error covariance B is a crucial. • So far, to model B we need to specify • Spatial decorrelation scales • Standard deviations of the increments.

  30. Assimilation of T-S climatology (CARS)

  31. Background error covariance modeling

  32. How can the information content of one variable be transferred to other variables? • Two possibilities: 1) Using the adjoint model 2) Modeling of the background covariance matrix

  33. Balance operator for mesoscale variability • Forward problem (scalar SSH given a vector dT) • Given dT (e.g., XBT) • Find dS • Find density anomalies • Find SHH. • Inverse problem (given scalar SSH guess the vectors dT and dS).

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