A data assimilation system by using DMI ocean model BSHcmod Jiang Zhu, Ye Liu, Shiyu Zhuang, Jun She, Per Institute of Atmospheric Physics Chinese Academy.

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Presentation transcript:

A data assimilation system by using DMI ocean model BSHcmod Jiang Zhu, Ye Liu, Shiyu Zhuang, Jun She, Per Institute of Atmospheric Physics Chinese Academy of Sciences YEOS Annual meeting and Workshop on Yellow Sea Operational Oceanography April 2008, Copenhagen, Denmark

Outlines Motivations Bathymetry-following covariance : A recursive filter approach Some test results Conclusions and next steps

Motivations The North-Baltic Sea and the Yellow Sea are both shallow seas; The North-Baltic Sea is more observed than the Yellow Sea and provides a test bed for a data assimilation system of shallow coastal/shelf seas; BSHcmod for North-Baltic Sea has a SST data assimilation system and does not have a profile data assimilation system yet; We first develop a profile data assimilation system for BSHcmod in North-Baltic Sea. The spatial distribution of T/S profiles observation used in the experiments in 2005 The daily total number of profiles in 2005.

Bathymetry-following covariance : A recursive filter approach Basic formulism: solving a minimization of the following cost function Though theoretically equivalent, the variational approach is used instead of Optimal Interpolation (OI) scheme for easy handling of additional penalty terms added to the cost function; imposing inequality constraints to avoid density reversal.

Considering the narrow channels and complex coastal lines, the inhomogeneous, anisotropic background error covariance is necessary to propagate information. A Bathymetry-following covariance is used in the infinitesimal differential form: Analysis incremental from a single observation Isotropic Anisotropic

An recursive filter using the aspect tensor A defined by can be constructed after determination of the two length scales. RMSE of Temperature is shown as function of the two length scales Lf and Lr using all observed profiles in 26 Apr (9 grid points, 9.5m)

Some test results The assimilation system is setup at the two model grid area : coarse grid area and fine grid area. However, here only implementting the coarse grid area assimilation and only presentatting some coarse assimilation results. We assimilated the T/S profiles into the cmod every day at 12:00 for a 20-day period (Jan to 3 Feb ) The daily total number of profiles in experiment period.

The impact of the assimilation scheme to forcasting effect can be vertified by all the observation data before assimilation and the withheld BSH profiles (the yellow points) Obs Ana For Locations of profiles in the experiment.

The overall RMSEs for T verified daily just before the assimilation.

The overall RMSEs for S verified daily just before the assimilation. Little impact could be due to the large S observation error setup (0.5psu).

The RMSEs for T at 9m verified daily just before the assimilation.

The RMSEs for S at 9m verified daily just before the assimilation.

The temperature analysis increment at 4m depth, on Feb

The salinity analysis increment at 4m depth, on Feb

The temperature analysis increment at 15m depth, on Feb

The salinity analysis increment at 15m depth, on Feb

Obs SimuAssi Verified using independent profiles: Temperature

Obs SimuAssi

S at 6m

S at 30m T at 30m

T at 13m T at 29m Assimilation minus Simulation on Feb. 3, 2005

Conclusions and next steps Assimilation of profiles can improve the temperature and salinity forecasts in the North-Baltic Sea, especially the cold, fresh water mass in the Danish strait is more realistic; More parameter-tuning in the assimilation system; Perform one-year long experiment and verification; Installation in DMI; SST assimilation or water level assimilation.

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