Assimilation of SWOT simulated observations in a regional ocean model: preliminary experiments
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1 Assimilation of SWOT simulated observations in a regional ocean model: preliminary experiments Benkiran M., Rémy E., Le Traon P.Y., Greiner E., Lellouche J.-M., Testut C.E., and the Mercator Ocean team. Study done in the framework of a CNES/Mercator Ocean convention, in collaboration with CLS.
2 OSSE experiments Observing System Simulation Experiment To quantify the ability of the system to reconstruct the mean and variability of the «true» ocean (model) depending on the assimilated observations. OSSEs allow an exact comparison with the true observed ocean, but results are dependent of the model configuration (resolution/physic) and the assimilation scheme. Question: Benefit of large swath altimetry to constrain the ocean model mesoscale features? IBI 1/36 forced simulation IBI 1/12 assimilation run observations 2
3 Ocean observation simulation Regional ocean model configuration IBI36: Iberic Biscay and Irish seas NEMO ocean model at 1/36 resolution (~3km) and 75 vertical levels Atmospheric forcing at 3h frequency from ECMWF (Bulk Formulae) Atmospheric pressure forcing Tides (including potential) Open boundaries Simulated observations from IBI36 model outputs: Daily mean AMSRE-AVHRR SST at ¼ In-situ T/S profiles SSALTO/DUACS along track SLA SWOT SLA at 7 km x 7 km IBI SST snapshot 3
4 Simulated SWOT observations Simulated SWOT SSH from IBI 1/36 model outputs in 2009 SSH IBI36 (12/03/2009) 5-day SSH observations (09-14/03/2009) J2, J1n, En SWOT 4
5 Assimilation system configuration Ocean model: IBI configuration at 1/12, 75 vertical levels. Data Assimilation system : SAM2v1 (~SEEK) FGAT ( First Guess at Appropriate Time) IAU : Incremental Analysis Update Deep large scale T,S biais correction from 3Dvar 5-day assimilation cycle SLA observation error : 2cm to 3,5 cm, For SWOT : 1,7cm at minimum. 1-year experiments in 2009 In situ T,S SST J2, J1n, En SLA 5-day SWOT tracks and error level in cm. SWOT SLA RUN0 RUN1 x x x RUN2 x x x x 5
6 Jason 2 and SWOT SLA assimilation score Innovation = Observation Model Forecast Residu = Observation Model analysis Cm Cm J A J N Cm 3.61 Cm J A J N Cm 3.80Cm 6
7 SWOT data assimilation: Impact on SSH True ocean SSH estimation in the western mediterranean sea for the 16 th december 2009 No data assimilation Conventional data assimilated Conventional data assimilated + SWOT 7
8 SWOT data assimilation: Impact on SSH (2) SSH estimation on the 6 th of august 2009 Model Forecasts vs IBI36 (truth) No data assimilation Conventional data assimilated Conventional data assimilated + SWOT True ocean 8
9 Power spectrum of SSH and vorticity No data assimilation Conventional data assimilated Conventional data assimilated + SWOT 9
10 Impact on in situ salinity & temperature data 0 Rms Salinity Innovation -100 RUN2 RUN1 RUN0-200 Rms Temperature 0 Innovation RUN2 RUN1 RUN Depth Depth Depth J A J N J A J N J A J N Depth -500 Depth Psu Depth Deg J A J N J A J N J A J N
11 Conclusion & Perspectives The assimilation system is ready to handle the SWOT data set: Deal with a large number of data ( x10 / conventional SLA with the 7km x 7km subsampling). First results are encouraging. Improvement due to SWOT data assimilation: Better reconstruction of the SSH fields, Analysed fields «smoother» than with the conventional tracks: control of the 2D gradients in SSH? Positive impact on the in situ T/S profiles: correct vertical projection. Further investigation of the physical 3D field reconstruction by comparison with the true ocean: SSH reconstruction at different scales, velocity field estimation (and w for biology), improvement of surface tracer trajectories 11
12 Conclusion & Perspectives Toward a more realistic configuration: Subsampling strategy depending on the model configuration (superobs,?), 25h mean SLA simulated. Improvement of the observation error simulation and specification: - how to deal with correlated observation errors, - rolling error, - sensitivity tests. A precise error specification is necessary to successfully assimilate the SWOT SLA. Experiment with the Nadir altimeter. 12
13 13
14 SLA observation operator for IBI NEMO configuration The model spatial resolution, coverage and physical parameterizations largely differ from one system to an other : the model SSH represents different physical processes to take into account the model observation operator and error specification. Observation operator for the AVISO along track SLA SLA model = SSH SSH tide 25h IB 25h LargeScaleFilter( HBar 24h 21days) MDT HBar Data treatment Tide removed IB using daily atm. pressure (ECMWF) MOG2D with 6h frequency forcing MOG2D: Barotropic ocean model simulating the high frequency barotropic response of the ocean to the atmospheric forcings (wind and pressure). 14
15 Error specification in the DA system Error SLA = instrumental error + representativity error + MDT error Instrumental error : 2 cm (Jason, Topex), 3,5 cm (Ers, Envisat) and 5,5 cm for Envisat on its new orbit. Representativity error : error due to missing physic in the model compared to the observation content. It depends on the model configuration: spatial resolution, frequency of the atmospheric forcing, tides MDT error - - = Instrumental error, inflated in shallow and coastal areas Representativity and MDT error The observation covariance matrix R is diagonal and constant in time 15
16 Snapshot of the vorticity field la vorticité relative de surface pour la 15/12/2009 : à gauche pour l expérience RUN0, au milieu l expérience RUN1 et à droite pour l expérience RUN2. 16
17 SLA Increment: RUN1 vs RUN2 Conventional data assimilated Conventional data assimilated + SWOT 17
18 Mercator Ocean: French Operational Oceanography Centre 5 shareholders CNRS, IFREMER, IRD, Météo-France, SHOM and CNES as a key-partner. Missions: Develop and operate systems able to describe, monitor and forecast the global ocean from the deep ocean to the surface. Distribute ocean products and give expertise to users Coordinator of the MyOcean2 project involving ocean operational monitoring & forecasting centers and partners from the maritime community in Europe. Mercator Ocean Centre Ramonville St Agne Toulouse, France 18
19 Outline Ocean observation simulation Ocean model and assimilation system Experimental setup : Observing System Simulation Experiment Preliminary results Perspectives SWOT KaRIN instrument draft ( Study done in the framework of a CNES/Mercator Ocean convention, in collaboration with CLS. 19
20 Mercator Ocean: French Operational Oceanography Centre 5 shareholders CNRS, IFREMER, IRD, Météo-France, SHOM and CNES as a key-partner. Mercator Ocean produces in real time 3D ocean analysis and forecasts at regional to global scale constrained by in situ temperature and salinity observations and remote sensed observations (SST, SLA). Applications range from initialization of seasonal forecast from oceanatmosphere coupled model to regional model boundary forcing, ocean reanalysis... SATELLITE observations SSALTO IN SITU observat ions CORIOLIS SLA, SST MODELING ASSIMILATION (control vector {Hbar,T, S, U, V}) è Hindasts & forecasts MERCATOR T, S profiles 20
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