Potential impact of HF radar and gliders on ocean forecast system
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Transcript of Potential impact of HF radar and gliders on ocean forecast system
www.cmar.csiro.au/bluelink/
Potential impact of HF radar and gliders on ocean forecast system
Peter OkeJune 2009CSIRO Marine and Atmospheric ResearchCentre for Australian Weather and Climate Research
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
Australian Integrated Marine Observing System: NSW node
xx
xx
HFradar
X X - mooring
glider
approved
wish-list
xxxx
xx
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 S1/101/10oo 9/109/10oo 22oo
Method
To assimilate observations we must estimate: the background error covariance (Pb) … 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 (Pa) … errors in the analysis
Pa = [I – Pb HT (HPb HT + R)-1 H] Pb
Bluelink uses an static ensemble, A = [a1 a2 … an], to approximate Pb = A AT / (n-1).
The background ensemble A, with covariance Pb, can be efficiently transformed into the analysis ensemble Aa, with covariance Pa, using a transformation from ensemble square root filter theory.
This is most efficiently done serially – one observation at a time.
Experiments
We consider cases with:
•GOOS + new platform
•just new platform
•just GOOS
Where the GOOS is assumed to be altimeters + SST + Argo/XBT
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.
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
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.
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.
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.
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