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Information Journal Paper

Title

Enhanced predictions of tides in the Persian Gulf through data assimilatio

Pages

  85-95

Abstract

 Hydrodynamic models are widely used for simulating water level and oceanic current; however due to uncertainties involved in this process such as accuracy of input data or realization of the governing equations, there are some errors in the simulation results. Data assimilation is one of the effective solutions to avoid and to limit some of these errors. This manuscript evaluates how Ensemble Kalman Filter, one of the most advance assimilation techniques, can enhance water elevation predictions in the Persian Gulf. The open source Delft3D FM was used as a hydrodynamic model and open source code of Open DA was employed to apply Kalman Filter. The Open DA was coupled inside the code of hydrodynamic model to improve its performance. We have setup several experiments to estimate the best number of groups, error parameter of observations, error parameter of open boundary, and the most effective station for assimilation. The results show that the Data assimilation can effectively improve the hydrodynamic model results and can be used for real applications.

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    APA: Copy

    Shariatmadari, Davood, SIADATMOUSAVI, SEYED MOSTAFA, & Ershadi, Cyrus. (2020). Enhanced predictions of tides in the Persian Gulf through data assimilatio. JOURNAL OF OCEANOGRAPHY, 11(40 ), 85-95. SID. https://sid.ir/paper/399416/en

    Vancouver: Copy

    Shariatmadari Davood, SIADATMOUSAVI SEYED MOSTAFA, Ershadi Cyrus. Enhanced predictions of tides in the Persian Gulf through data assimilatio. JOURNAL OF OCEANOGRAPHY[Internet]. 2020;11(40 ):85-95. Available from: https://sid.ir/paper/399416/en

    IEEE: Copy

    Davood Shariatmadari, SEYED MOSTAFA SIADATMOUSAVI, and Cyrus Ershadi, “Enhanced predictions of tides in the Persian Gulf through data assimilatio,” JOURNAL OF OCEANOGRAPHY, vol. 11, no. 40 , pp. 85–95, 2020, [Online]. Available: https://sid.ir/paper/399416/en

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