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OSTST 2007 - March, 12-15 - Hobart, Tasmania

Ocean Mean Dynamic Topography from altimetry and GRACE: Toward a realistic estimation of the error field. Marie-Helene Rio(1), Philippe Schaeffer(1), Jean-Michel Lemoine(2), Gilles Larnicol(1)

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OSTST 2007 - March, 12-15 - Hobart, Tasmania

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  1. Ocean Mean Dynamic Topography from altimetry and GRACE: Toward a realistic estimation of the error field Marie-Helene Rio(1), Philippe Schaeffer(1), Jean-Michel Lemoine(2), Gilles Larnicol(1) (1) CLS, 8-10 rue Hermes, 31256 Ramonville saint agne, France (2) GRGS, 14 avenue Edouard Belin, 31400 toulouse, France OSTST 2007 - March, 12-15 - Hobart, Tasmania

  2. Large scale (400 km) - = MSS Geoid MDT ? + = MDT ADT SLA ? A number of key issues High resolution (<50 km) Combination of MSS and geoid for MDT computation - Oceanographic analysis - Assimilation into operationnal ocean forecasting systems Computation of high resolution MDT (Rio et al, 2004, 2005) Computation of a realistic error field Error on the geoid, error on the MSS, error on the MDT computation method Context

  3. Rc=133 km A Gaussian filter Rc=200 km Rc=400 km Rc=300 km Combination of MSS and geoid for MDT computation MSS CLS01 – EIGEN-GL04S

  4. rc=133km rc=200km rc=300km rc=400km cm Limits of the gaussian filtering No error estimate on the resulting MDT Creation of spurious strong gradients in specific areas (around islands, along coasts, in strong subduction areas…)

  5. MSS CLS01 error field cm ε2obs= ε2MSS+ ε2Geoid <H2>=A-priori variance 19 cm RMS EGM96 Rc=133km Rc=400km cm GGM02S The CMDT RIO05 (Rio et al, 2005) field is low-pass filtered to 400 km and its variance is computed in 600 km radius domains. Different correlation functions have been tested GGM02C Eigen-3C Eigen-3S Eigen-4S Optimal combination method MDT Hobs=MSS-Geoid A: Covariance matrix of the observations: <Hobs,Hobs> = <H2>Fc(r) + <ε2obs>

  6. rc=200km rc=400km rc=133km rc=300km Results MDT Estimated error field cm cm cm

  7. h’alti h MDTsynth h1000 • the dynamic topographyat 1000m as estimated by (Willis et al, 2007) is added to the dynamic heights. Synthetic MDT estimates • Altimetric Sea Level Anomalies from AVISO are then subtracted to compute synthetic estimates of the Mean Dynamic Topography. Validation Is the estimated error field realistic? A- Method: Comparison to independent synthetic MDT estimates z=0 • In-situ temperature and salinity from XBT and CTD are used to compute dynamic heights relative to 1000m for the period 1993-2005. hinsitu z=-1000m geoid

  8. 133 km filter 400 km filter Solid line: comparison to filtered synthetic heights: 2- Optimal MDT RMS difference to unfiltered synthetic heights = 8.5 cm = 2 RMS e + e + e 2 2 2 om synth MDT + ~ 8.5² + 6.0² 6.0² 2² B- Results RMS differences between synthetic heights and: 1- Gaussian filtered MDTs Dashed line: comparison to unfiltered synthetic heights:

  9. Method based on the knowledge of the observation errors and a-priori statistics of the error field Further improvements are possible: Impact of using the covariance error information in the optimal MDT computation needs to be investigated (available in the case of future GOCE data) Improvements need to be made for the better estimation of the altimetric data error (on MSS and SLA - error on the different corrections used during altimeter data processing) New MSS estimations and realistic error field EIGEN05S, EGM07,… GOCE! Improved estimates of high resolution « combined » MDT Conclusions We investigated the efficiency of optimal method to compute realistic large scale MDT from altimetry and geoid and associated error field. We showed consistency between the obtained error field and how the large scale MDT compares to independent synthetic estimates of the MDT. Future work MDT=MSS-Geoid

  10. The Combined Mean Dynamic Topography RIO05 First Guess:EIGEN-GRACE03S 400 km In-situ data: drifters and dynamic heights 1993-2002 Global: 1/2° resolution grid cm Rio et al, 2005

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