Interest of assimilating future Sea Surface Salinity measurements from SMOS and Aquarius missions in an operational ocea

1. Interest of assimilating future Sea Surface Salinity measurements from SMOS and Aquarius missions in an operational ocean forecasting system Benoît Tranchant CERFACS/Mercator Ocean, Toulouse (France) Charles-Emmanuel Testut, Lionel Renault,Nicolas Ferry http://www.mercator.eu.org

2. What can be expected from SSS data products for DA in an operational context? • Improving error statistics • of SSS • of SST/SSS relationships • Improving observation operators with different SSS products •  Simplification of observation operator : Resolution in space and time corresponding to the scales resolved by the model (i.e. model dependent) • Need for stronger links between data segment operations and data assimilation developments • Impact studies: OSE, OSSE

3. The main objectives were to: Understand the most efficient way to assimilate SSS satellite data in order to extract the best reliable information in the context of the Mercator Ocean forecasting system Evaluate the potential impact of two different observing systems Know the level of the observation error from which associated SSS data have a significant influence on the dataassimilation system SAM2. Method: Performing Observing System Simulation Experiments (OSSEs) with simulated AQUARIUS and SMOS SSS data over 1 year (2003). Sensitivity studies to level products L2/L3 Sensitivity studies to observation errors Accuracy of SMOS level-2 products? Observation errors specification ? Sensitivity studies to observing systems Relative skills of SMOS and AQUARIUS products? Incremental benefit of their combination? OSSE Overview

4. Aquarius SMOS Main characteristics of SMOS and Aquarius missions

5. Original SSS from PSY2v1 (1/15°) interpolated on SMOS L2 grid (40kmx40km) Reference data (SSS, SST, W) Direct model TB Auxiliary data (SSS, SST, W) Noise on TB Inverse model Observation error (noise) Retrieved SSS, SST, W Estimated SMOS L2 SSS 1 2 3 5 4 6 7 Simulated SSS L2 products: Instantaneous SSS at pixel scale + Map of the difference (retrieved – reference SSS) for 10th of January = Algorithm to characterize the L2 SSS error, see Boone et al., 2005 or Obligis et al., 2008. Reconstructed SMOS L2 SSS error for January 2003

6. Characteristics of simulated SSS data Aquarius L2 SMOS L2 SMOS L3

7. REFERENCE or CONTROL RUN Hindcast experiment with in-situ, SST and altimeter data assimilation over 2003. OPA model: MNATL(1/3°) covering North Atlantic from 20°S to 70°N. ECMWF daily forcing fluxes DATA ASSIMILATION SCHEME: SAM2 Based on a SEEK filter : Reduced Order Kalman Filter (modal space) 3D multivariate background error covariances: 140 seasonal 3D modes (ψ,T,S) calculated from an hindcast experiment (7 years) Innovation vector: FGAT method (SLA and in situ data), observation operator adapted for largest scales (SST and SSS) TRUTH The native sea surface salinity (SSS) located on the SMOS L2 data points The native SSS comes from the North Atlantic and Mediterranean high resolution (1/15°) MERCATOR OCEAN prototype named PSY2V1 re-sampled at a 1/3° (univariate assimilation of SLA, with a relaxation term to SST and SSS ). OSSE ingredients

8. 1.Sensitivity to level products Mean of difference (PSU) Variance of difference (PSU2) Spatial average of the mean in psu (left) and variance in psu2 (right) of difference between three different estimates: control run or REF (red dashed line), SMOS L3 (black solid line), SMOS L2 (blue solid line) and “truth” every ten days during the year 2003 for the overall domain The assimilation of Level 2 SMOS products seems to be a better approach than the assimilation of Level 3 SMOS products. The SSS constraint from SMOS L3 has a positive impact. Indeed, in comparison to the REF experiment, this simulation has both slightly reduced the bias and the variance of the difference with the “truth”

9. 2.Sensitivity to observation errors Main limitations of results: The threshold found in this study is only valid for these sets of operational data using these sets of observation errors into the MERCATOR ocean forecasting system (1/3°). SMOSL2_1 1xerror SMOS L2_2 2xerror Variance of difference for SMOS L2_x Over 2003 Variance ofdifference (PSU2) SMOS L2_0.5 0.5xerror 10 days unit • The initial observation errors associated with the SMOS L2 products given by CLS (Boone et al., 2005) are satisfactory • This level of observation error specification defines a threshold (minimum requirement) to have a significant impact on the MERCATOR operational forecasting system. It allows to reduce the difference from about 0.5 to 0.3 PSU rms.

10. 3.Sensitivity to observing systems • Three possible explanations • The daily data coverage is very different between these two products • stronger constraint of SSS SMOS L2 compared to the AQUARIUS L2 Products. • The observation error associated to Aquarius L2P is effectively lesser than that of SMOS L2P but not for the same surface. • The spatial resolution: the Aquarius L2P are only able to constraint the scale associated to the Aquarius grid. •  one part of the signal associated to small scales (< 100 km) is not taken into account. Variance ofdifference (PSU2) The combination of the two L2 Products has a weak impact in comparison to the SMOS L2 simulation

11. 3.Sensitivity to observing systems Focus on the data coverage • Each ocean grid point is observed by: • Aquarius measurements every ~7 days • SMOS measurements every ~3 days The decorrelation scales (in days) corresponding to a time correlation of 0.4 from a re-analysis at 1/3° is generally less than4 days (atmospheric exchanges) in the North Atlantic. CONCLUSION A full coverage in the North Atlantic at a sufficient time frequency (4-5 days) is usefull to the SSS assimilation problem in a eddy-permitting model (spatial resolution <1/3°) Temporal decorrelation scales from a re-analysis (MNATL 1/3°) over 11 years (Greiner et al., 2004) day

12. To sum up What is the real gain (%) of assimilating remotely sensed SSS data (comparison to the REF experiment) in term of RMSE RMSE : The root mean square of error/difference (RMSE) between assimilation experiments and “truth” averaged overall the domain.

13. Error balance • This figure shows the time evolution of the mean and the RMS of the SSS increment for all • experiments. • The mean of the increment is quite close to zero for all the experiments. • The RMS of the SSS increment has the same behaviour/amplitude in REF, in SMOS L3 and SMOS L2 .  constraint coming from the assimilated SSS (SMOS L3 and SMOS L2) is relatively relevant to improve the SSS increment pattern. • These results show : • SSS observation error variance and particularly its ratio with regard to the error of the other assimilated data sets seems relatively consistent : there are a compromise between SSS, SLA and SST increments • Our scheme takes into account the new SSSconstraint coming from another data source even if it is far from other operational data,  Contributions of new SSS data to the SSS increment have not disrupted the existing equilibriumbetween all errors.

14. What is the best strategy to optimally use the future SMOS and Aquarius data in the context of ocean prediction systems, from the perspective of monitoring the mesoscale ocean circulation? The use of the synthetic SMOS L2 product gives satisfactory improvement in the model results, since it provides a measurable impact of the quality of ocean analyses from operational systems. The SSS observation error variance as specified by Boone et al., (2005) and particularly its ratio with regard to the error of the other data sets assimilated seems appropriate. The impact of the Aquarius L2 Products is weak compared to the SMOS L2 Products. The combination of the two L2 Products had thus a small effect on final results BUT Simulated SSS data comes from SSS field relatively far from the other assimilated data (operational data). The assimilation system does not correct any fluxes, in particular the E-P fluxes  Underestimate information coming from SSS. Conclusions

15. For more informations… • Tranchant, et al. (2008), Expected impact of the future SMOS and Aquarius Ocean surface salinity missions in the Mercator Ocean operational systems: New perspectives to monitor ocean circulation, Remote Sensing of Environment, 112, pp 1476-1487. • Obligis et al.. (2008) Benefits of the future Sea Surface Salinity measurements from SMOS. generation and characteristics of SMOS geophysical products, IEEE Trans. Geoscience and Remote Sensing, vol. 46, issue 3, 746-753.  • Tranchant et al.(2008), Data assimilation of simulated SSS SMOS products in an ocean forecasting system, Journal of operational Oceanography, Vol. 2008, No 2, August 2008., pp 19-27(9).