Wave data assimilation using a hybrid approach in the Persian Gulf |
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| Authors: | Mohammad Hadi Moeini Amir Etemad-Shahidi Vahid Chegini Iraj Rahmani |
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| Institution: | (1) School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran;(2) Griffith School of Engineering, Gold Coast Campus, Griffith University, Gold Coast, QLD, 4222, Australia;(3) Iranian National institute for Oceanography, Tehran, Iran;(4) Transportation Research Institute, Tehran, Iran |
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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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