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Wave data assimilation using a hybrid approach in the Persian Gulf

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Abstract

The main goal of this study is to develop an efficient approach for the assimilation of the hindcasted wave parameters in the Persian Gulf. Hence, the third generation SWAN model was employed for wave modeling forced by the 6-h ECMWF wind data with a resolution of 0.5°. In situ wave measurements at two stations were utilized to evaluate the assimilation approaches. It was found that since the model errors are not the same for wave height and period, adaptation of model parameter does not result in simultaneous and comprehensive improvement of them. Therefore, an approach based on the error prediction and updating of output variables was employed to modify wave height and period. In this approach, artificial neural networks (ANNs) were used to estimate the deviations between the simulated and measured wave parameters. The results showed that updating of output variables leads to significant improvement in a wide range of the predicted wave characteristics. It was revealed that the best input parameters for error prediction networks are mean wind speed, mean wind direction, wind duration, and the wave parameters. In addition, combination of the ANN estimated error with numerically modeled wave parameters leads to further improvement in the predicted wave parameters in contrast to direct estimation of the parameters by ANN.

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Acknowledgments

We acknowledge the Iranian National Institute for Oceanography, the Iranian Meteorological Office and the Iranian National Cartographic Center for providing the used data. We would also thank the SWAN group at the Delft University of Technology (Department of Fluid Mechanics) for providing the wave model. We also express our gratefulness to Dr. K. Rakha and Dr. S. Neelamani from Kuwait Institute for Scientific Research for their help. This study was supported by the Transportation Research Institute, Tehran, I.R. Iran, grant no. 89B8T8P09.

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Authors and Affiliations

  1. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Mohammad Hadi Moeini & Amir Etemad-Shahidi

  2. Griffith School of Engineering, Gold Coast Campus, Griffith University, Gold Coast, QLD, 4222, Australia

    Amir Etemad-Shahidi

  3. Iranian National institute for Oceanography, Tehran, Iran

    Vahid Chegini

  4. Transportation Research Institute, Tehran, Iran

    Iraj Rahmani

Authors
  1. Mohammad Hadi Moeini
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  2. Amir Etemad-Shahidi
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  3. Vahid Chegini
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  4. Iraj Rahmani
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Corresponding author

Correspondence to Amir Etemad-Shahidi.

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Responsible Editor: Chari Pattiaratchi

This article is part of the Topical Collection on Physics of Estuaries and Coastal Seas 2010

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Moeini, M.H., Etemad-Shahidi, A., Chegini, V. et al. Wave data assimilation using a hybrid approach in the Persian Gulf. Ocean Dynamics 62, 785–797 (2012). https://doi.org/10.1007/s10236-012-0529-5

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  • DOI: https://doi.org/10.1007/s10236-012-0529-5

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