Far-Field Ocean Conditions and Concentrate Discharges Modeling Along the Saudi Coast of the Red Sea

  • Peng ZhanEmail author
  • Fengchao Yao
  • Aditya R. Kartadikaria
  • Yesubabu Viswanadhapalli
  • Ganesh Gopalakrishnan
  • Ibrahim Hoteit
Conference paper
Part of the Environmental Science and Engineering book series (ESE)


An integrated modeling system is developed to simulate the far-field dispersions of concentrate discharges along the Saudi coast of the Red Sea. It comprises the Weather Research and Forecast (WRF) model for simulating the atmospheric circulations, the MIT general circulation model (MITgcm) for simulating the large-scale ocean conditions, and the Connectivity Modeling System (CMS) for tracking particle pathways. We use the system outputs and remote sensing altimetry data to study and analyze the atmospheric and oceanic conditions along the Saudi coast of the Red Sea and to conduct particle tracking experiments. The model simulations show distinctive patterns of seasonal variations in both the atmospheric conditions and the large-scale ocean circulation in the Red Sea, which are also reflected in the salinity and temperature distributions along the Saudi coast. The impact of this seasonality on the far-field dispersion of concentrate discharges are illustrated in seasonal dispersion scenarios with discharging outfalls located at the northern, central and southern Saudi coasts of the Red Sea.


Empirical Orthogonal Function Mean Dynamic Topography Concentrate Discharge Integrate Modeling System Absolute Dynamic Topography 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST).


  1. Berumen, M. L., et al. (2013). The status of coral reef ecology research in the Red Sea. Coral Reefs, 32(3), 737–748. doi: 10.1007/S00338-013-1055-8.CrossRefGoogle Scholar
  2. Dibarboure, G., Lauret, O., Mertz, F., Rosmorduc, V., & Maheu, C. (2008). SSALTO/DUACS user handbook:(M) SLA and (M) ADT near-real time and delayed time products. Rep. CLS-DOS-NT, 6, 39.Google Scholar
  3. Emery, W. J., & Thomson, R. E. (2001). Data analysis methods in physical oceanography (2nd ed.). Amsterdam: Elsevier.Google Scholar
  4. Haza, A. C., Piterbarg, L. I., Martin, P., Ozgokmen, T. M., & Griffa, A. (2007). A Lagrangian subgridscale model for particle transport improvement and application in the Adriatic Sea using the Navy Coastal Ocean Model. Ocean Modelling, 17(1), 68–91. doi: 10.1016/J.Ocemod.2006.10.004.CrossRefGoogle Scholar
  5. Hegarty, J., Draxler, R. R., Stein, A. F., Brioude, J., Mountain, M., Eluszkiewicz, J., et al. (2013). Evaluation of Lagrangian particle dispersion models with measurements from controlled tracer releases. Journal of Applied Meteorology and Climatology, 52(12), 2623–2637. doi: 10.1175/Jamc-D-13-0125.1.CrossRefGoogle Scholar
  6. Ioc, I. (2003). BODC, 2003. Centenary edition of the GEBCO Digital Atlas, published on CD-ROM on behalf of the Intergovernmental Oceanographic Commission and the International Hydrographic Organization as part of the general bathymetric chart of the oceans. Liverpool, United Kingdom: British Oceanographic Data Centre.Google Scholar
  7. Jiang, H., Farrar, J. T., Beardsley, R. C., Chen, R., & Chen, C. (2009). Zonal surface wind jets across the Red Sea due to mountain gap forcing along both sides of the Red Sea. Geophysical Research Letters, 36(19), L19605. doi: 10.1029/2009GL040008.CrossRefGoogle Scholar
  8. Langodan, S., Cavaleri, L., Viswanadhapalli, Y., & Hoteit, I. (2014). The Red Sea: A natural laboratory for wind and wave modeling. Journal of Physical Oceanography. Accepted with ref no: JPO-D-13-0242.Google Scholar
  9. Lo, J. C. F., Yang, Z. L., & Pielke, R. A. (2008). Assessment of three dynamical climate downscaling methods using the weather research and forecasting (WRF) model. Journal of Geophysical Research: Atmospheres, 113(D9).Google Scholar
  10. Marshall, J., Adcroft, A., Hill, C., Perelman, L., & Heisey, C. (1997a). A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers. Journal of Geophysical Research: Oceans, 102(C3), 5753–5766. doi: 10.1029/96jc02775.CrossRefGoogle Scholar
  11. Marshall, J., Hill, C., Perelman, L., & Adcroft, A. (1997b). Hydrostatic, quasi-hydrostatic, and nonhydrostatic ocean modeling. Journal of Geophysical Research: Oceans, 102(C3), 5733–5752. doi: 10.1029/96jc02776.CrossRefGoogle Scholar
  12. Michalakes, J., Dudhia, J., Gill, D., Henderson, T., Klemp, J., Skamarock, W., et al. (2005). The weather research and forecast model: Software architecture and performance. Use of High Performance Computing in Meteorology. 156–168, doi: 10.1142/9789812701831_0012.
  13. Paris, C. B., Helgers, J., Van Sebille, E., & Srinivasan, A. (2013). Connectivity modeling system: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean. Environmental Modelling and Software, 42, 47–54.CrossRefGoogle Scholar
  14. Piterbarg, L. I. (2001). Short-term prediction of Lagrangian trajectories. Journal of Atmospheric and Oceanic Technology, 18(8), 1398–1410. doi: 10.1175/1520-0426(2001)018<1398:Stpolt>2.0.Co;2.CrossRefGoogle Scholar
  15. Ralston, D. K., Jiang, H. S., & Farrar, J. T. (2013). Waves in the Red Sea: Response to monsoonal and mountain gap winds. Continental Shelf Research, 65, 1–13. doi: 10.1016/J.Csr.2013.05.017.CrossRefGoogle Scholar
  16. Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Dudha, M., et al. (2008). A description of the advanced research WRF ver 30. Technical Note. NCAR/TN-475+STR. 113.Google Scholar
  17. Sofianos, S. S., & Johns, W. E. (2001). Wind induced sea level variability in the Red Sea. Geophysical Reseach Letters, 28(16), 3175–3178. doi: 10.1029/2000gl012442.CrossRefGoogle Scholar
  18. Sofianos, S. S., & Johns, W. E. (2002). An oceanic general circulation model (OGCM) investigation of the Red Sea circulation, 1. Exchange between the Red Sea and the Indian Ocean. Journal of Geophysical Research: Oceans, 107(C11), doi:Artn 3196, doi: 10.1029/2001jc001184.
  19. Sofianos, S. S., Johns, W. E., & Murray, S. P. (2002). Heat and freshwater budgets in the Red Sea from direct observations at Bab el Mandeb. Deep-Sea Research Part II, 49(7–8), 1323–1340. doi: 10.1016/S0967-0645(01)00164-3. (Pii S0967-0645(01)00164-3).CrossRefGoogle Scholar
  20. Stohl, A., Forster, C., Frank, A., Seibert, P., & Wotawa, G. (2005). Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2. Atmospheric Chemistry and Physics, 5, 2461–2474.CrossRefGoogle Scholar
  21. Veneziani, M., Griffa, A., Garraffo, Z. D., & Chassignet, E. P. (2005). Lagrangian spin parameter and coherent structures from trajectories released in a high-resolution ocean model. Journal of Marine Research, 63(4), 753–788. doi: 10.1357/0022240054663187.CrossRefGoogle Scholar
  22. Veneziani, M., Griffa, A., Reynolds, A. M., & Mariano, A. J. (2004). Oceanic turbulence and stochastic models from subsurface Lagrangian data for the northwest Atlantic Ocean. Journal of Physical Oceanography, 34(8), 1884–1906. doi: 10.1175/1520-0485(2004)034<1884:Otasmf>2.0.Co;2.CrossRefGoogle Scholar
  23. Yao, F., Hoteit, I., Pratt, L. J., Bower, A. S., Köhl, A., Gopalakrishnan, G., & Rivas, D. (2014a). Seasonal overturning circulation in the Red Sea: 2. Winter circulation. Journal of Geophysical Research: Oceans, 119(4), 2263–2289. doi: 10.1002/2013JC009331.Google Scholar
  24. Yao, F., Hoteit, I., Pratt, L. J., Bower, A. S., Zhai, P., Köhl, A., & Gopalakrishnan, G. (2014b). Seasonal overturning circulation in the Red Sea: 1. Model validation and summer circulation. Journal of Geophysical Research: Oceans, 119(4), 2238–2262. doi: 10.1002/2013JC009004.Google Scholar
  25. Zhai, P., & Bower, A. (2013). The response of the Red Sea to a strong wind jet near the Tokar Gap in summer. Journal of Geophysical Research: Oceans, 118(1), 422–434. doi: 10.1029/2012jc008444.Google Scholar
  26. Zhan, P., Subramanian, A. C., Yao, F., & Hoteit, I. (2014). Eddies in the Red Sea: A statistical and dynamical study. Journal of Geophysical Research: Oceans, 119(6), 3909–3925, doi: 10.1002/2013JC009563.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Peng Zhan
    • 1
    Email author
  • Fengchao Yao
    • 1
  • Aditya R. Kartadikaria
    • 1
  • Yesubabu Viswanadhapalli
    • 1
  • Ganesh Gopalakrishnan
    • 2
  • Ibrahim Hoteit
    • 1
  1. 1.Division of Physical Sciences and EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
  2. 2.Scripps Institution of OceanographyUniversity of California San DiegoSan DiegoCalifornia

Personalised recommendations