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Www.cmar.csiro.au/bluelink/ Potential impact of HF radar and gliders on ocean forecast system Peter Oke June 2009 CSIRO Marine and Atmospheric Research.

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Presentation on theme: "Www.cmar.csiro.au/bluelink/ Potential impact of HF radar and gliders on ocean forecast system Peter Oke June 2009 CSIRO Marine and Atmospheric Research."— Presentation transcript:

1 www.cmar.csiro.au/bluelink/ Potential impact of HF radar and gliders on ocean forecast system Peter Oke June 2009 CSIRO Marine and Atmospheric Research Centre for Australian Weather and Climate Research

2 Talk Outline NSW node of the Australian Integrated Marine Observing System Method Assessment of Bluelink background and analysis error estimates Estimation of likely analysis estimates with new observations Summary & conclusions

3 Australian Integrated Marine Observing System: NSW node x x HF radar X X - mooring glider approved wish-list xx x

4 The Bluelink System Ocean Model OFAM MOM4p0d Global model 10 m vert res over top 200 m Data Assimilation BODAS EnOI 120 member ensemble Localised covariances Assimilates along-track ALTIM, coastal SLA, SST & in situ T and S 1/10 o 9/10 o 2o2o2o2o

5 Method To assimilate observations we must estimate:  the background error covariance (P b )… errors in the model  the observation operator (H)… where & what the obs are  the observation error covariance (R)… errors in the obs Given this information we can estimate:  the analysis error covariance (P a )… errors in the analysis P a = [I – P b H T (HP b H T + R) -1 H] P b  Bluelink uses an static ensemble, A = [a 1 a 2 … a n ], to approximate P b = A A T / (n-1).  The background ensemble A, with covariance P b, can be efficiently transformed into the analysis ensemble A a, with covariance P a, using a transformation from ensemble square root filter theory.  This is most efficiently done serially – one observation at a time.

6 Assumed observation errors, R … includes instrument + representation error.

7 Experiments We consider cases with: GOOS + new platform just new platform just GOOS Where the GOOS is assumed to be altimeters + SST + Argo/XBT

8 Impact of sampling error – varying obs distribution Data distribution impacts the results – so we perform each calculation using the distribution of atSLA, SST and T/S from the GOOS for each week of 2006.

9 Evaluation of estimates: SLA Estimated / Theoretical errors are based on the assumed and estimated BGF and analysis errors Actual (BRAN) errors are computed from differences with observations over a 3- year reanalysis Obs assimilated every 7- days include:  AMSRE SST  atSLA  Argo T/S  XBT

10 Assumed BGF errors and estimates analysis errors for all variables

11 Estimates % improvements for different HF radar arrays HF radar + GOOS % error reduction relative to GOOS with only ALTIM+SST+Argo HF radars are assumed to measure daily-mean surface velocity every day with no data gaps.

12 Estimates % improvements for different glider sections (or CTD or mooring lines) Gliders + GOOS % error reduction relative to GOOS with only ALTIM+SST+Argo Gliders are assumed to “fly” along a fixed latitude within 200 km of the coast with a repeat cycles of 2- weeks. Actual glider path … not exactly the same as the assumed E-W tracks.

13 Comparison of different options: showing analysis errors averaged over local regions

14 Conclusions  HF radar obs may reduce U/V errors by as much as 80%; and T/S and sea-level errors by ~60% near the observed regions.  T and S obs near the shelf are likely to provide a more modest benefit … because they are somewhat redundant.  assumed glider tracks are clearly unrealistic – and are more akin to repeat ship-borne CTD sections.  As a result of this study Bluelink will likely develop the capability to assimilate HF radar data (initially in research mode) once data is available – but will likely retain glider observations for validation. Sakov, P., and P. R. Oke 2008: Objective array design: Application to the tropical Indian Ocean. Journal of Atmospheric and Oceanic Technology, 25, 794-807. Oke, P. R., P. Sakov and E. Schulz, 2009: A comparison of shelf observation platforms for assimilation into an eddy-resolving ocean model. Dynamics of Atmospheres and Oceans, in press.

15 END

16 Evaluation of estimates: SST Estimated / Theoretical errors are based on the assumed and estimated BGF and analysis errors Actual (BRAN) errors are computed from differences with observations over a 3- year reanalysis Obs assimilated every 7- days include:  AMSRE SST  atSLA  Argo T/S  XBT

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