Abstract
One of the indicators representing the permissible performance of a marine model is the capability of model in providing an accurate spatiotemporal distribution of the thermal structure of domain. Data assimilation is taken into account as an expedient platform in order to achieve this goal. In this paper, the ensemble Kalman filter (EnKF) was applied as a data assimilation scheme to enhance the sea surface temperature (SST) simulation and whereupon the underwater temperature of the Persian Gulf in the finite volume community ocean model (FVCOM). The daily satellite measured SST data that obtained from advanced very high-resolution radiometer pathfinder were considered as observational assimilation data. The comparisons between the results of both proposed models including FVCOM and SST measurements were carried out to evaluate the efficiency of data assimilation. The comparisons revealed a meaningful improvement in the assimilated simulation of the spatiotemporal SST variability in the whole domain, especially in the shallow parts and near the Hormuz Strait. The root-mean-square error reduced significantly in assimilation run. The statistical comparisons of the results bias denote a positive impact of the data assimilation.









Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Abbaspour M, Rahimi R (2011) Iran Atlas of offshore renewable energies. Renew Energy 36(1):388–398
Ahmadabadi MN, Arab M, Maalek-Ghaini FM (2009) The method of fundamental solutions for the inverse space-dependent heat source problem. Eng Anal Bound Elem 33(10):1231–1235
Al-Rashidi TB, El-Gamily HI, Amos CL, Rakha KA (2009) Sea surface temperature trends in Kuwait Bay. Arab Gulf Nat Hazards 50(1):73–82
Amour I, Mussa Z, Bibov A, Kauranne T (2013) Using ensemble data assimilation to forecast hydrological flumes. Nonlinear Processes Geophys 20(6):955–964
Burchard H (2002) Applied turbulence modeling in marine waters. Springer, Berlin, p 215
Castro Christopher Thomas L, McKee B, Pielke Roger A (2001) The relationship of the North American Monsoon to tropical and North Pacific sea surface temperatures as revealed by observational analyses. J Clim 14(24):4449–4473
Chen C, Liu H, Beardsley RC (2003) An unstructured grid, finite-volume, three-dimensional, primitive equations ocean model: application to coastal ocean and estuaries. J Atmos Ocean Technol 20(1):159–186
Chen C, Beardsley RC, Cowles G (2006) An unstructured grid, finite-volume coastal ocean model. FVCOM user manual. SMAST/UMASSD
Chen C, Malanotte-Rizzoli P, Wei J, Beardsley RC, Lai Z, Xue P, Lyu S, Xu Q, Qi J, Cowles GW (2009) b Application and comparison of Kalman filters for coastal ocean problems: an experiment with FVCOM. J Geophys Res 114:C05011
Clayton AM, Loren AC, Barker DM (2013) Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met office. Q J R Meteorol Soc 139(675):1445–1461
Daescu ND, Navon MI (2003) Adaptive observations in the context of 4D-Var data assimilation. Meteorol Atmos Phys 85(4):205–226
Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99:10143–10162
Fertig EJ, Harlim J, Hunt BR (2007) A comparative study of 4D-VAR and a 4D ensemble Kalman filter: perfect model simulations with Lorenz-96. Tellus A 59(1):96–100
Ghanea M, Moradi M, Kabiri K, Mehdinia A (2016) Investigation and validation of MODIS SST in the northern Persian Gulf. Adv Space Res 57:127–136
Glibert P, Landsberg J, Evans J, Al-Sarawai M, Fraj M, Al-Jarallah M, Haywood A, Ibrahem S, Klesius P, Powell C, Shoemaker C (2002) A fish kill of massive proportion in Kuwait Bay, Arabian Gulf: the roles bacterial disease, harmful algae, and eutrophication. Harmful Algae 1:215–231
Hamill TM, Snyder C (2000) A hybrid ensemble Kalman Filter-3D variational analysis scheme. Mon Weather Rev 128(8):2905–2919
Hamill TM, Whitaker JS (2005) Accounting for the error due to unresolved scales in ensemble data assimilation: a comparison of different approaches. Mon Weather Rev 133(11):3132–3147
Høyer JL, She J (2007) Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J Mar Syst 65(1–4):176–189
Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Phys D 230(1–2):112–126
Iskandar I (2010) Seasonal and interannual patterns of sea surface temperature in Banda Sea as revealed by self-organizing map. Cont Shelf Res 30:1136–1148
Jones D, Price A, Al-Yamani F, Al-Zaidan A (2002) Coastal and marine ecology. In: Khan N, Munawar M, Price A (eds) The Gulf ecosystem: health and sustainability. Backhuys Publishers, Leiden, pp 65–104
Kailasam MK, Rao RS (2010) Impact of global warming on tropical cyclones and monsoons in Global Warming. SA Harris (ed), http://www.intechopen.com/books/globalwarming/impact-of-global-warming-on-indian-monsoons
Kämpf J, Sadrinasab M (2006) The circulation of the Persian Gulf: a numerical study. Ocean Sci 2(1):27–41
Kondrashov D, Sun C, Ghil M (2008) Data assimilation for a coupled ocean-atmosphere model. Part II: parameter estimation. Mon Weather Rev 136(12):5062–5076
Korotaev G, Oguz T, Nikiforov A, Koblinsky C (2003) Seasonal, interannual, and mesoscale variability of the Black sea upper layer circulation derived from altimeter data. J Geophys Res 108(C4):3122:19-15
Kothawale DR, Munot AA, Borgaonkar HP (2008) Temperature variability over the Indian Ocean and its relationship with Indian summer monsoon rainfall. Theor Appl Climatol 92(1-2):31–45
Larsen J, Høyer JL, She J (2007) Validation of a hybrid optimal interpolation and Kalman filter scheme for sea surface temperature assimilation. J Mar Syst 65:122–133
Le Dimet F, Talagrand O (1986) Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus A 38A(2):97–110
Li Z, Chao Y, McWilliams JC, Ide K (2008) A three-dimensional variational data assimilation scheme for the regional ocean modeling system. J Atmos Ocean Technol 25(11):2074–2090
Lorenc AC, Bowler NE, Clayton AM, Pring SR, Fairbairn D (2015) Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP. Mon Weather Rev 143(1):212–229
Manda A, Hirose N, Yanagi T (2005) Feasible method for the assimilation of satellite-derived SST with an ocean. J Atmos Ocean Technol 22(6):746–756
Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys 20:851–875
Moradkhani H, Sorooshian S, Gupta HV, Houser PR (2005) Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv Water Resour 28(2):135–147
Paulson CA, Simpson JJ (1977) Irradiance measurements in the upper ocean. J Phys Oceanogr 7:952–956
Pietrzak JJ, Jakobson B, Burchard H, Vested HJ, Petersen O (2002) A three-dimensional hydrostatic model for coastal and ocean modeling using a generalized topography following coordinate system. Ocean Model 4:173–205
Podesta G, Kilpatrick K, Shenoi S, Brown J, Evans R (1998) AVHRR Pathfinder oceans matchup database 1985–1996 (version 19.0)
Reynolds RM (1993) Physical oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman results from the Mt Mitchell expedition. Mar Pollut Bull 27:35–59
Richard W. Reynolds, Thomas M. Smith, Chunying Liu, Dudley B. Chelton, Kenneth S. Casey, Michael G. Schlax (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20(22):5473–5496
Ruiz J, Pulido M (2015) Parameter estimation using ensemble-based data assimilation in the presence of model error. Mon Weather Rev 143(5):1568–1582
Saleh DK (2010) Stream gage descriptions and streamflow statistics for sites in the Tigris River and Euphrates River Basins. US Department of the Interior, US Geological Survey, Tehran
Seif ZM (2015) Variational ensemble Kalman filtering in hydrology. Ph.D. thesis Lappeenranta University of Technology, Lappeenranta, Finland
Seo GH, Kim S, Choi BJ, Cho YG, Kim YH (2009) Implementation of the ensemble Kalman filter into a Northwest Pacific ocean circulation model, in data assimilation for atmospheric. In: Park SK, Xu L (eds) Oceanic and hydrologic applications. Springer, Berlin, pp 341–352
Sheppard C, Rayner N (2002) Utility of the Hadley Centre sea–ice and sea surface temperature data set (HadISST1) in two widely contrasting coral reef areas. Mar Pollut Bull 44:303–308
Smagorinsky J (1963) General circulation experiments with the primitive equations. I: the basic experiment. Mon Weather Rev 91:99–164
Smith PJ, Thornhill GD, Dance SL, Lawless AS, Mason DC, Nichols NK (2013) Data assimilation for state and parameter estimation: application to morph dynamic modelling. Q J R Meteorol Soc 139(671):314–327
Swift SA, Bower AS (2003) Formation and circulation of dense water in the Persian/Arabian Gulf. J Geophys Res 108(C1):3004
van Leeuwen PJ (2001) An ensemble smoother with error estimates. Mon Weather Rev 129:709–728
Wang X, Barker DM, Snyder C, Hamill TM (2008) A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: observing system simulation experiment. Mon Weather Rev 136(12):5116–5131
Xie J, Zhu J (2010) Ensemble optimal interpolation schemes for assimilating Argo profiles into a hybrid coordinate ocean model. Ocean Model 33:283–298
Yoshikawa Y, Awaji T, Akitomo K (1999) Formation and circulation processes of intermediate water in the Japan Sea. J Phys Oceanogr 29:1701–1722
Zupanski D, Zupanski M (2006) Model error estimation employing an ensemble data assimilation approach. Mon Weather Rev 134(5):1337–1354
Acknowledgements
The authors thank the staff of the Department of Marine Remote Sensing at Iranian National Institute for Oceanography and Atmospheric Science (INIOAS) for providing satellite data.
Author information
Authors and Affiliations
Corresponding author
Additional information
Editorial responsibility: M. Abbaspour.
Rights and permissions
About this article
Cite this article
Abbasi, M.R., Chegini, V., Sadrinasab, M. et al. Improving the Persian Gulf sea surface temperature simulation by assimilating the satellite data via the ensemble Kalman. Int. J. Environ. Sci. Technol. 16, 4113–4122 (2019). https://doi.org/10.1007/s13762-018-1803-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13762-018-1803-y


