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Enhancing Ocean Forecasting with Bluelink Data Integration

Learn about the collaboration between the Bureau of Meteorology, CSIRO, and the Royal Australian Navy to improve ocean forecasting models using advanced data assimilation systems. Discover how BRAN1.5 significantly reduces forecast errors and the impact of various observing systems on forecast accuracy.

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Enhancing Ocean Forecasting with Bluelink Data Integration

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  1. Building Bluelink David Griffin, Peter Oke, Andreas Schiller et al. March 2007 CSIRO Marine and Atmospheric Research

  2. Introduction • Bluelink: a partnership between the • Bureau of Meteorology, CSIRO and • the Royal Australian Navy

  3. Introduction • Bluelink: a partnership between the • Bureau of Meteorology, CSIRO and • the Royal Australian Navy • Talk Outline • Ocean Forecasting Australia Model, OFAM • Bluelink Ocean Data Assimilation System, BODAS • Bluelink ReANalysis, BRAN • Bluelink High-Resolution Regional Analysis HRRA

  4. WA example

  5. HRRA - Gridded altimetry and SST,statistically projected to depth:

  6. Free-running model:

  7. BRAN1.0:

  8. BRAN1.5smoother, more realistic, no warm bias

  9. BRAN1.5 cf HRRA – 2005

  10. Where they want it:

  11. Minimum resolution: ~100km ~10km resolution Ocean Forecasting Australia Model, OFAM • Global configuration of MOM4 • Eddy-resolving around Australia • 10 m vertical resolution to 200 m, then coarser • Surface fluxes from ECMWF (for reanalyses) … every 10th grid point shown

  12. Bluelink Ocean Data Assimilation System, BODAS • Multivariate assimilation system: • sea level obs correct h,T,S,U,V Single point assimilation …

  13. -> need both SST and SLA. Plan view of sea-level increments Cross-section of temperature bkgnd (grey) & analysis (black-colour)

  14. BRAN1.0  BRAN1.5  BRAN2.1

  15. BRAN1.0  BRAN1.5  BRAN2.1

  16. Conclusion • BRAN1.0  plenty of lessons learnt • BRAN2.1 realistically reproduces the 3-d time-varying mesoscale ocean circulation around Australia • We are working on ways of assimilating the data tighter without introducing spurious features.

  17. Thank you

  18. An application: dispersal of the larvaeof Southern Rock Lobster

  19. What users want:(a week in advance?)

  20. Bluelink ReANalysis, BRAN • BRAN1.5: • 1/2003 – 6/2006 • Forced with ECMWF forecast fluxes • Assimilates observations once per week • Assimilates SLA from Jason, Envisat and GFO (T/P with-held) • Assimilates AMSRE SST • Assimilates T and S from Argo and ENACT database

  21. BRAN1.5 vs TAO ADCP zonal currents 165E 170W 147E 140W 110W

  22. ANALYSIS 0-DAY FORECAST 7-DAY FORECAST BRAN1.5 vs CLS 1/3o GSLA

  23. Comparisons with with-held T/P altimetry (top) and AMSRE (bottom) Comparisons between BRAN1.5 and with-held T/P altimetry:  RMS error of 8-10 cm  anomaly correlations of 0.6 Comparisons between BRAN1.5 and AMSRE (every 7th day is assimilated):  RMS error of 0.7o  anomaly correlations of 0.7

  24. Observing System Experiments Experiment design • With-hold each component of the observing system • 6-month integration (1st half of 2003) • compare to with-held observations • treat BRAN1.5, with all observations assimilated, as the “truth”

  25. Assimilation of Argo and SST reduces the forecast error of SLA by ~50% compared to the assimilation of altimetry Assimilation of Altimetry and Argo only reduces the forecast error of SST by a small amount Observing System Experiments 2003

  26. Observing System Experiments Metric • Depth average (0-1000 m) of the RMS “error” in potential temperature For the 2003 - GOOS: each component of the GOOS has a unique and important contribution to the forecast skill of upper ocean temperature each component has comparable impact on the forecast skill of the upper ocean temperature

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