Capabilities of data assimilation in correcting sea surface temperature in the Persian Gulf

Document Type : Original Research Paper


1 Iranian National Institute for Oceanography and Atmospheric Science (INIOAS), Tehran, Iran

2 Graduate Faculty of Environment, University of Tehran, Tehran, Iran

3 School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

10.7508/pj.2017.02. 009


Predicting the quality of water and air is a particular challenge for forecasting systems that support them. In order to represent the small-scale phenomena, a high-resolution model needs accurate capture of air and sea circulations, significant for forecasting environmental pollution. Data assimilation is one of the state of the art methods to be used for this purpose. Due to the importance of thermal structure in monitoring the variations of environmental phenomena, the present study has used Sea Surface Temperature (SST) in data assimilation method to optimize this parameter. SST is one of the most important factors to conduct researches on the ocean, the atmosphere, and their interaction, not to mention monitoring and forecasting air and ocean phenomena as well as commercial and fishing communities and weather forecasts. This study has aimed to present a satellite-derived SST based on pathfinder advanced very high resolution radiometer (AVHRR) data assimilating in FVCOM (finite volume community ocean model) on the Persian Gulf to examine the effect of data assimilation by using the Cressman scheme. The performance of this method has been compared to the optimal interpolation SST (OISST) data, via both visual comparisons and statistical parameters. Applying assimilation method improves correlation coefficient of the model from 0.92 to 0.99. Results demonstrate that the modeled SST has been completely reconstructed by the data assimilated experiment via the Cressman scheme for this region. The spatial and temporal pattern of SST reveals a significant improvement in the entire domain during the investigated period in the gulf.


Abbaspour, M. and Rahimi, R. (2011). Iran atlas of offshore renewable energies. Renew. Energ., 36(1), 388-398.
Ahmadabadi, M.N., Arab, M. and Maalek-Ghaini, F.M. (2009). The method of fundamental solutions for the inverse space-dependent heat source problem. Eng. Anal. Bound. Elem., 33(10), 1231-35.
Barale, V. (2010). Oceanography from Space: Revisited. Springer. 237-238.
Behringer, D.W. (1994). Sea surface height variations in the Atlantic Ocean: A comparison of TOPEX altimeter data with results from an ocean data assimilation system. J. Geophys. Res. Oceans, 99(c12), 24685-90.
Burchard, H. (2002). Applied Turbulence Modelling in Marine Waters. Springer Science & Business Media.
Carton, J.A. and Hackert, E.C. (1990). Data assimilation applied to the temperature and circulation in the Tropical Atlantic, 1983-84. J. Phys. Oceanogr., 20(8), 1150-65.
Castro, C.L., McKee, T.B. and Pielk, R.A. (2001). The relationship of the North American Monsoon to Tropical and North Pacific sea surface temperatures as revealed by observational analyses. J. Climate, 14(24), 4449-73.
Chang, Y.S., Zhang, S.Q., Rosati, A., Delworth, T.L. and Stern, W.F. (2013). An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation. Climate Dyn., 40(3-4), 775–803, doi: 10.1007/s00382-012-1412-2.
Chen, C., Beardsley, R.C. and Cowles, G. (2006). An Unstructured Grid, Finite-Volume Coastal Ocean Model, FVCOM User Manual. SMAST/UMASSD.
Chen, C., Liu, H. and Beardsley, R.C. (2003). An unstructured grid, finite-volume, three-dimensional, primitive equations ocean model: application to coastal ocean and estuaries. J. Aatmos. Ocean Tech., 20(1), 159-86.
Clancy, R.M., Harding, J.M., Pollak, K.D. and May, P. (1992). Quantification of improvements in an operational global-scale ocean thermal analysis system. J. Atmos. Ocean. Tech., 9(1), 55-66.
Clancy, R.M., Phoebus, P.A. and Pollak, K.D. (1990). An operational global-scale ocean thermal analysis system. J. Atmos. Ocean. Tech., 7(2), 233-54.
Clifford, M., Horton, C., Schmitz, J. and Kantha, L.H. (1997). An oceanographic nowcast/forecast system for the Red Sea. J. Geophys. Res.: Oceans, 102(C11), 25101-22.
Derber, J. and Rosati, A. (1989). A global oceanic data assimilation system. J. Phys. Oceanogr., 19, 1333-47.
Dong, X., Lin, R., Zhu, J. and Lu, Z. (2016). Evaluation of ocean data assimilation in CAS-ESM-C: Constraining the SST field. Adv. Atmos. Sci., 33(7), 795-807.
Horton, C., Clifford, M., Schmitz, J. and Kantha, L.H. (1997). A real-time oceanographic nowcast/forecast system for the Mediterranean Sea. J. Geophys. Res.: Oceans, 102(C11), 25123-56.
Kawai, Y., Kawamura, H., Takahashi, S., Hosoda, K., Murakami, H., Kachi, M. and Guan, L. (2006). Satellite-based high-resolution global optimum interpolation sea surface temperature data. J. Geophys. Res., 111(c6), 1-17.
Kilpatrick, K.A., Podesta, G., Walsh, S., Williams, E., Halliwell, V., Szczodrak, M., Brown, O.B., Minnet, P.J. and Evans, R. (2015). A decade of sea surface temperature from MODIS. Remote Sens. Environ., 165, 27-41.
Larsen, J., Høyer, J.L. and She, J. (2007). Validation of a hybrid optimal interpolation and Kalman filter scheme for sea surface temperature assimilation. J. Marine Syst., 65(1), 122-33.
Li, H., Dai, A., Zhou, T. and Lu, J. (2010). Responses of East Asian summer monsoon to historical sst and atmospheric forcing during 1950--2000. Clim. Dynam., 34(4), 501-14.
Manda, A., Hirose, N. and Yanagi, T. (2005). Feasible method for the assimilation of satellite-derived sst with an ocean. J. Atmos. Ocean. Tech., 22(6), 746-756.
Mellor, G.L. and Yamada, T. (1982). Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys., 20(4), 851-75.
Nowicki, A., Dzierzbicka-Głowacka, L., Janecki, M. and Kałas, M. (2015). Assimilation of the satellite SST data in the 3D CEMBS model. Oceanologia, 57(1), 17-27.
Pietrzak, J., Jakobson, J.B., Burchard, H., Vested, H.J. and Petersen, O. (2002). A three-dimensional hydrostatic model for coastal and ocean modelling using a generalised topography following co-ordinate system. Ocean Model., 4(2), 173-205.
Reynolds, R.W., Smith, T.M., Liu, C., Chelton, D.B., Casey, K.S. and Schlax, M.G. (2007). Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20(22), 5473-96.
Saleh, D.K. (2010). Stream Gage Descriptions and Streamflow Statistics for Sites in the Tigris River and Euphrates River Basins, Iraq. US Department of the Interior, US Geological Survey.
She, J., Høyer, J.L. and Larsen, J. (2007). Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea. J. Marine Syst., 65(1), 314-35.
Shu, Y., Zhu, J., Wang, D., Yan, C. and Xiao, X. (2009). Performance of four sea surface temperature assimilation schemes in the South China Sea. Cont. Shelf Res., 29, 1489-1501.
Siegenthaler, J. (2003). Modern hydronic heating for residential and light commercial buildings. Cengage Learning. p. 84.
Smagorinsky, J. (1963). General circulation experiments with the primitive equations. Mon. Weather Rev., 91(3), 99-164.