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:: Volume 10, Issue 40 (2019) ::
joc 2019, 10(40): 85-95 Back to browse issues page
Enhanced predictions of tides in the Persian Gulf through data assimilatio
Davood Shariatmadari, Seyed Mostafa Siadatmousavi Dr , Cyrus Ershadi Dr
Iran University of Science and Technology, Tehran, Iran , siadatmousavi@iust.ac.ir
Abstract:   (1044 Views)
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.
Keywords: Data assimilation, Ensemble Kalman Filter, Delft3D-FM, Open DA, tide, Persian Gulf
Full-Text [PDF 1124 kb]   (208 Downloads)    
Type of Study: Research | Subject: Coastal Engineering
Received: 2019/09/22 | Accepted: 2019/12/15 | ePublished: 2020/05/26
1. Abbasi,M.R, Chegini,V Sadrinasab,M, Siadatmousav,S.M,2019, Correcting the Sea Surface Temperature by Data Assimilation Over the Persian Gulf, Iranian Journal of Science and Technology, Transactions A: Science, Springer International Publishing, pp.141-149 [DOI:10.1007/s40995-017-0357-z]
2. Deltares. Delft3D-FM, 2016. URL https://oss.deltares.nl/web/delft3dfm.
3. Geir Evensen, Monte Carlo, and Monte Carlo. with a nonlinear quasi-geostrophic model usingMonte Carlomethods to forecast error statistics. 99, 199 [DOI:10.1029/94JC00572]
4. Kurniawan A, Ooi SK, Hummel S, Gerritsen H (2011) Sensitivity analysis of the tidal representation in Singapore Regional Waters in a data assimilation environment. Ocean Dynam 61 (8):1121-1136 [DOI:10.1007/s10236-011-0415-6]
5. Laurent Bertino, Geir Evensen, and Hans Wackernagel. Sequential Data Assimilation Techniques in Oceanography. International Statistical Review, 71(2):223-241, 2003. ISSN 03067734. [DOI:10.1111/j.1751-5823.2003.tb00194.x]
6. Moeini, M.H., Etemad-Shahidi, A., Chegini, V. and Rahmani, I., 2012, "Wave Data Assimilation Using a Hybrid Approach in the Persian Gulf", Ocean Dynamics,62, pp. 785-797. [DOI:10.1007/s10236-012-0529-5]
7. Moeini, M.H., Etemad-Shahidi, A. and Chegini, V.,2010, "Wave Modeling and Extreme Value Analysis off the Northern Coast of the Persian Gulf", Applied Ocean Research, 32(2), pp. 209-218. [DOI:10.1016/j.apor.2009.10.005]
8. Mohinder S. Grewal and Angus P. Andrews. Applications of Kalman filtering in aerospace 1960 to the present. IEEE Control Systems Magazine, 30(3):69-78, 2010. ISSN 08880611. [DOI:10.1109/MCS.2010.936465]
9. Ooi SK, Zemskyy P, Sisomphon P, Gerritsen H, Twigt D (2009) The effect of grid resolution and weather forcing on hydrodynamic modelling of South East Asian waters In: Proc of 33rd IAHR Congress, Vancouver, Canada, pp 3712-3719
10. P. Courtier, J.-J. Thepaut, and A. Hollingsworth. A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J.Roy. Meteor. Soc., 120, 1994. [DOI:10.1002/qj.49712051912]
11. R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1):35, 1960. ISSN 00219223. [DOI:10.1115/1.3662552]
12. Rama Rao Karri, StefHummel,Ghada El Serafy, and Vladan Babovic. Data Assimilation for Water Levels and Currents in the Singapore Region : An Ensemble Steady State Kalman Filtering Approach. (July), 2012.
13. Rama Rao Karri, Xuan Wang, and Herman Gerritsen. Ensemble based prediction of water levels and residual currents in Singapore regional waters for operational forecasting. Environmental Modelling and Software, 54(April):24-38, 2014. ISSN 13648152. [DOI:10.1016/j.envsoft.2013.12.006]
14. Serpoushan,N, Mostafa Zeinoddini,M, Golestani,M.,2013, An ensemble kalman filter data assimilation scheme for modeling the wave climate in Persian Gulf, American Society of Mechanical Engineers, pp.V005T06A028-V005T06A028 [DOI:10.1115/OMAE2013-10399]
15. S. F. Schmidt. The Kalman filter - Its recognition and development for aerospace applications. Journal of Guidance, Control, and Dynamics, 4(1):4-7, 1981. ISSN 0731-5090.
16. Thomas Hamill and Jeffrey Whitaker. Distance-Dependent Filtering of Background Error Covariance Estimates in an Ensemble Kalman Filter. pages 2776-2790, 2001. https://doi.org/10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2 [DOI:10.1175/1520-0493(2001)1292.0.CO;2]
17. Yanfen Zhang and Dean S. Oliver. Improving the Ensemble Estimate of the Kalman Gain by Bootstrap Sampling. Mathematical Geosciences, 42(3):327-345, 2010. ISSN 18748961. [DOI:10.1007/s11004-010-9267-8]
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Shariatmadari D, Siadatmousavi S M, Ershadi C. Enhanced predictions of tides in the Persian Gulf through data assimilatio. joc. 2019; 10 (40) :85-95
URL: http://joc.inio.ac.ir/article-1-1552-en.html

Volume 10, Issue 40 (2019) Back to browse issues page
نشریه علمی پژوهشی اقیانوس شناسی Journal of Oceanography
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