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Status and plans for assimilation of satellite data in coupled ocean-ice models

Learn how satellite data, including SST and sea ice concentration, is assimilated into coupled ocean-ice models. Explore the model configuration, assimilation scheme, challenges, and results validation for enhanced forecasts.

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Status and plans for assimilation of satellite data in coupled ocean-ice models

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  1. Status and plans for assimilation of satellite data in coupled ocean-ice models Jon Albretsen and Lars-Anders Breivik

  2. Outline: • Input from satellites • Ice-Ocean model configuration • Assimilation scheme • Results and validation statistics • Challenges in the future

  3. Input from satellites, SST OSI SAF product: 10 km resolution SST fields • composed every 12th hour • At high latitudes these products are based on 1.5 km resolution polar orbiting NOAA satellite passes. Each SST value have corresponding quality flags. The most important properties when it comes to SST data assimilation, are: • Confidence level (from excellent to erroneous or unprocessed value). • Mean coverage of the pixel (i.e. the average of the coverage of the primary 1.5 km resolution SST values used in the 12 hour composite). • No. of primary SST values used in the 12 hour composite.

  4. OSI SAF 12 hour SST composite, valid 09-01-2003 at 00 UTC

  5. The current SST assimilation scheme needs a complete SST data set covering the total model area. Weekly SST field

  6. The current SST assimilation scheme needs a complete SST data set covering the total model area. SST field successively overwritten by OSI SAF SST

  7. Input from satellites, Sea Ice The OSI SAF Sea Ice Concentration product is assimilated. This is a daily product presented on a 10km resolution grid, covering the same area as the SST product. • SSM/I ice concentration product is an analysis derived in 2 steps: • Using the OSI SAF SSM/I hybrid algorithm sea ice concentrationis estimated for each observation node during the analysis interval (1 day). • In the next step, these results are analyzed on the 10 km SAF grid: • Several SSM/I observation nodes with estimated concentrations influence on each analysis grid point. • The radius of influence r, for each SSM/I observation is 18 km. • The weight assigned to each SSM/I observation in the analysis is dependent on: • sn2: the variance of the SSM/I concentration estimate. • dn : the distance between the centre of the SSM/I node and the grid point.

  8. OSI SAF sea ice concentration, valid 08-01-2003 at 12 UTC

  9. Model configuration The coupled sea ice – ocean model consists of: • MIPOM, met.no’s operational ocean model, a version of the sigma coordinate ocean model, POM (Princeton Ocean Model). • MI-IM (Meteorological Institute’s Ice Model), a state-of-art dynamic-thermodynamic sea ice model. A net heatflux is calculated in MI-IM, both in ice-covered and ice-free areas, and used in MIPOM as the surface boundary condition for temperature.

  10. Model area for the coupled sea ice – ocean model • Grid mesh 20 km • Relaxation along open • horizontal boundaries, • at depths greater than • 1000 m and in surface • salinity towards • climatological monthly • means • Atmospheric forcing • from the ECMWF

  11. Assimilation scheme Before each daily 10 days forecast is produced, the models are run 30 hours in hindcast. A heat flux nudging method is used to force the model towards the “observed” SST, which is the complete weekly SST chart successively overlaid by 10 km resolution OSI SAF SST fields valid the same period. Forecast ice conc. ass. -30 -24 0 +240 Analysis time sst ass.

  12. where Mathematically, the assimilation method can be expressed as, wis the weight between the flux correction, Fc, and the heat flux calculated in MI-IM, Fi. Fc is the difference between the analyzed (“observed”) SST, Ta, and the SST from the ocean model, Tm, multiplied by a flux coefficient, k. The value of k and the thickness of the upper vertical grid cell decides how fast the model temperature approaches Ta. The time scale for the nudging method is typically 20 minutes or 3 hours where the total depth is 100 m or 1000 m, respectively. During prognosis time, only the heat flux from MI-IM, Fi, is used as boundary condition for temperature in MIPOM (w=0).

  13. MI-IM receives an updated field with the OSI SAF sea ice concentration valid 24 hours before model analysis time. A simple nudging scheme is then executed the first 6 hours of the hindcast period. The corrected sea ice concentration in the model can be expressed as, cmand cana are the model and analyzed sea ice concentrations, respectively, and the nudging coefficient, K, varies linearly from 0 to 0.5 through the first 6 hours assimilation period.

  14. Results The following results demonstrate some of the impacts the SST and sea ice assimilation has on the forecast. A control run without any kind of assimilation is executed parallel with the main run, and some differences are presented here. The results are retrieved from a model run performed with analysis time 8th of January 2003 at 12 UTC.

  15. Analyzed SST and model SSTfrom the control run at analysis time (+00)

  16. Analyzed SST and model SSTfrom the assimilationrun at analysis time (+00)

  17. SST from the model run with assimilation and the model run without assimilation at analysis time (+00)

  18. SST from the model run with assimilation and the model run without assimilation at +120 hours prognosis time

  19. Sea temperature at 65N,2E from run with assimilation and run without assimilation Depth (m) Prognosis (hours)

  20. Forecast impact, SST: Root Mean Square error Red line: Control run Blue line: Assimilation run

  21. Sea ice concentration from the model run with assimilation (black lines) and corresponding values from OSI SAF product

  22. Forecast impact, ice concentration: Root Mean Square error Red line: Control run Blue line: Assimilation run

  23. Forecast impact, ice concentration: Bias (model-observation) Red line: Control run Blue line: Assimilation run

  24. Conclusions • Assimilation of SST and sea ice concentration has a positive impact on the forecast. • The positive impact on the ocean model’s SST is maintained through the forecast period. • This is not the case for the sea ice concentration where the positive impact is deteriorated after about 5 days forecast. • The impacts of the SST assimilation propagates downwards during prognosis time. • Automatic full cover SST analysis based on microwave (e.g. AMSR) and infra-red • more advanced assimilation techniques EU FP-5 and -6 projects Challenges SST

  25. Challenges Sea Ice: • Achieve a multivariate well balanced analyzed field ( today by nudging techniques) • Optimal choice of ice related parameter to be assimilated.

  26. Challenges Sea Ice:Sea Ice analysis methods • Further development of the OSI SAF multi sensor analysis • Automatic high resolution analysis based on SAR • Multivariate assimilation schemes for high resolution sea ice models ESA GMES, EU FP-6, national projects Cooperation within IICWG !

  27. References Engedahl, H. (1995): Implementation of the Princeton Ocean Model (POM/ECOM-3D) at The Norwegian Meteorological Institute (DNMI). Research Report NO. 5, The Norwegian Meteorological Institute Blumberg, A.F. and G.L. Mellor (1987): A description of a three-dimensional coastal ocean circulation model. Three-dimensional Coastal Ocean Models, Vol. 4, N. Heaps (Ed.), American Geophysical Union, Washington D.C.,1-16. Sætra, Ø., L.P. Røed and J. Albretsen (1999):The DNMI RegClim Ice Model. RegClim general technical report, No. 3. (Available from Norwegian Institute for Air Research, P.O. Box 100, N-2007 Kjeller, Norway). Brieivk, L.-A., S. Eastwood, Ø. Godøy, H. Schyberg, S. Andersen, and R. Tonboe (2001), Sea ice products for EUMETSAT Satellite Application Facility, Canadian Journal of Remote Sensing, Vol. 27, no. 5

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