Numerical Modelling and Automatic Detection of submesoscale eddies in Persian Gulf Using aVector Geometry Algorithm

Document Type : Research Article

Authors

1 Ph.D. Student, Department of physical oceanography, Faculty of marine science and oceanography, Khorramshahr University of Marine Science and Technology, Khorramshahr, Iran

2 Assistant Professor, Department of physical oceanography, Faculty of marine science and oceanography, Khorramshahr University of Marine Science and Technology, Khorramshahr, Iran

3 Associate Professor, Department of Marine Physic, Faculty of Marine and Oceanic Sciences, University of Mazandaran, Babolsar, Iran

Abstract

Nowadays, marine data containing both observational and measured values as well as the output of numerical models are largely available; But analyzing and processing this data is time consuming and tedious due to the heavy volume of information.Identifying and extracting eddies is one of the most important aspects of physical oceanography, and automatic detection algorithms of eddies are one of the most basic tools for analysing eddies. The general circulation of the Persian Gulf is a cyclonic circulation that is affected by tide, wind stress, and thermohaline forces. In this study, using the Mike model based on the three-dimensional solution of the Navier Stokes equations, assumption of incompressibility, Boussinesq approximation and hydrostatic pressure, the circulation in the Persian Gulf was modeled. Then a vector geometry algorithm has been used for detection of eddies in this region. Four constraints were derived in conformance with the eddy velocity field definition and characteristics in this algorithm. Eddy centers are determined at the points where all of the constraints are satisfied. The four constraints follow: (i) Along an east–west (EW) section, v has to reverse in sign across the eddy center, and its magnitude has to increase away from it; (ii) Along a north–south (NS) section, u has to reverse in sign across the eddy center, and its magnitude has to increase away from it: the sense of rotation has to be the same as for v; (iii) Velocity magnitude has a local minimum at the eddy center; and (iv) Around the eddy center, the directions of the velocity vectors have to change with a constant sense of rotation. The constraints require two parameters to be specified: one for the first, second, and fourth constraints and one for the third one. The first parameter, a, defines how many grids points away the increases in the magnitude of v along the EW axes and u along the NS axes are checked. It also defines the curve around the eddy center along which the change in direction of the velocity vectors is inspected. The second parameter, b, defines the dimension (in grid points) of the area used to define the local minimum of velocity. The main data used to detect eddies are numerical model outputs, including velocity components. These outputs are the result of numerical modeling with thermohaline and wind stress forces. In total, 4308 cyclonic and 2860 anticyclonic eddies are detected at the surface and 617 cyclonic and 329 anticyclonic eddies are found in the deepest layer, depth of 50 meters, for daily data during one year. The number of eddies is highest in winter, and the lowest in summer and the average radius of anticyclonic eddies is maximum in winter and minimum in summer for cyclonic eddies. Most eddies have a radius of 5-10 km and lifespan of 3-6 days. Also, as the lifespan of eddies increases, they penetrate deeper into the water. The percentage of eddy penetration or the ratio of the number of eddies of the deepest layer to the surface layer is 15% for cyclonic eddies and 10% for anticyclonic eddies. This indicates that the energy loss in the cyclonic eddies is less than in the anticyclonic eddies and is probably due to the alignment of the rotating eddy with the overall circulation of the Persian Gulf.

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References

رئیسی، ا، مهرفر، ح.، علی­اکبری­بیدختی، ع.ع.، ترابی­آزاد، م.، لاری، ک.، ناظم­السادات، م.ج.، عظام، م.، 1398، مطالعه پیچک­های میان­مقیاس تحت‌‌تأثیر جریان­های ساحلی در خلیج­فارس و نقش آن در اقلیم منطقه، فصل‌نامه جغرافیای طبیعی، سال دوازدهم، شماره 46، 75-90.
Blain, C.A., 2000, Modelling three-dimensional thermohaline-driven circulation in the Persian Gulf. Proc. Sixth Int. Conf. on Estuarine and Coastal Modeling, Reston, VA, American Society of Civil Engineers, 74–93.
Chaigneau, A., Gizolme, A. and Grados, C., 2008, Mesoscale eddies off Peru in altimeter records: Identification algorithms and eddy spatio-temporal patterns. Prog. Oceanogr., 79 (2–4), 106–119.
ChunHua, Z., HongLin, L., SongTao, L., LianJun, S., Zhao, Z. and HongWei, L., 2015, Automatic detection of oceanic eddies in reanalyzed SST images and its application in the East China Sea, Science China: Earth Sciences, doi: 10.1007/s11430-015-5101-y.
Dong, C., Lin, X., Liu, Y., Nencioli, F., Chao, Y., Guan, Y., Chen, D., Dickey, T. and McWilliams, J.C., 2012, Three-dimensional oceanic eddy analysis in the Southern California Bight from a numerical product, JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, C00H14, doi:10.1029/2011JC007354.
Dong, C. and McWilliams, J.C., 2007, A numerical study of island wakes in the Southern California Bight, Cont. Shelf Res., 27, 1233–1248, doi:10.1016/j.csr.2007.01.016.
Duo, Z., Wang, W. and Wang, H., 2019, Oceanic Mesoscale Eddy Detection Method Based on Deep Learning, Remote Sens. 2019, 11, 1921, 14p.
Kampf, J. and M. Sadrinasab, 2006, The circulation of the Persian Gulf: A numerical study. Ocean Sci., 2, 1–15.
Le Vu, B., Stegner, A. and Arsouze, T., 2018, Angular Momentum Eddy Detection and Tracking Algorithm (AMEDA) and Its Application to Coastal Eddy Formation, Journal of Atmospheric and Oceanic Technology, Volume 35, 739-762.
Li, J., Liang, Y., Zhang, J., Yang, J., Song, P. and Cui, W., 2019, A new automatic oceanic mesoscale eddy detection method using satellite altimeter data based on density clustering, Acta Oceanol. Sin., 2019, 38(5), 134–141.
Matsuoka, D., Araki, F., Inoue, Y. and Sasaki, H., 2016, A New Approach to Ocean Eddy Detection, Tracking, and Event Visualization –Application to the Northwest Pacific Ocean, Procedia Computer Science, Volume 80, 1601-1611.
McGillicuddy, D.J., Robinson, A.R., Siegel, D.A., Jannasch, H.W., Johnson, R., Dickey, T.D., McNeil, J.D., Michaels, A.F. and Knap, A.H., 1998, Influence of mesoscale eddies on new production in the Sargasso Sea. Nature, 394, 263–266.
Nencioli, F., Dong, C., Dickey, T., Washburn, L. and McWilliams, J., 2010, A vector geometry based eddy detection algorithm and its application to high-resolution numerical model products and high-frequency radar surface velocities in the Southern California Bight, J. Atmos. Oceanic Technol., 27, 564–579.
Pous, S., Carton, X. and Lazure, P., 2013, A Process Study of the Wind-Induced Circulation in the Persian Gulf, Open Journal of Marine Science, Scientific Research Publishing, 3(1), 1-11.
Thoppil, P.G. and Hogan, P.J., 2010. A modeling study of circulation and eddies in the Persian Gulf. J. Phys. Oceanogr. 40, 2122–2134.
Tussadiah, A., Syamsuddin, M.L., Pranowo, W.S., Purba, N.P. and Riyantini, I., 2016, Eddy Vertical Structure in Southern Java Indian Ocean: Identification using Automated Eddies Detection, International Journal of Science and Research, Volume 5, Issue 3, 967-971.
Yao, F., 2008. Water Mass Formation and Circulation in the Persian Gulf and Water Exchange with the Indian Ocean (PhD thesis). University of Miami. Available from: (http://scholarlyrepository.miami.edu)
Zhabin, I.A. and Andreev, A.G., 2019, Interaction of Mesoscale and Submesoscale Eddies in the Sea of Okhotsk Based on Satellite Data, Izvestiya, Atmospheric and Oceanic Physics, 2019, Vol. 55, No. 9, pp. 1114–1124.
Journal of the Earth and Space Physics
Volume 47, Issue 1
April 2021
Pages 109-125

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