• Afzal, S., M. Hittawe, S. Ghani, T. Jamil, O. Knio, M. Hadwiger, and I. Hoteit, 2019a: The state of the art in visual analysis approaches for ocean and atmospheric datasets. Comput. Graphics Forum, 38, 881907, https://doi.org/10.1111/cgf.13731.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Afzal, S., and Coauthors, 2019b: RedSeaAtlas: A visual analytics tool for spatio-temporal multivariate data of the Red Sea. Workshop on Visualization in Environmental Sciences (EnvirVis), Porto, Portugal, Eurographics Association, 25–32, https://doi.org/10.2312/envirvis.20191101.

    • Crossref
    • Export Citation
  • Albarakati, S., R. M. Lima, L. Giraldi, I. Hoteit, and O. Knio, 2019: Optimal 3D trajectory planning for AUVs using ocean general circulation models. Ocean Eng ., 188, 106266, https://doi.org/10.1016/j.oceaneng.2019.106266.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 12831296, https://doi.org/10.1175/2009BAMS2618.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Antony, C., S. Langodan, P. R. Shanas, H. P. Dasari, Y. Abualnaja, O. Knio, and I. Hoteit, 2021: Extreme water levels along the central Red Sea coast of Saudi Arabia: processes and frequency analysis. Nat. Hazards, https://doi.org/10.1007/s11069-020-04377-y, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ardhuin, F., and Coauthors, 2010: Semiempirical dissipation source functions for ocean waves. Part I: Definition, calibration and validation. J. Phys. Oceanogr., 40, 19171941, https://doi.org/10.1175/2010JPO4324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Attada, R., H. P. Dasari, J. S. Chowdary, Y. Ramesh Kumar, O. Knio, and I. Hoteit, 2018a: Surface air temperature variability over the Arabian Peninsula and its links to circulation patterns. Int. J. Climatol., 39, 445464, https://doi.org/10.1002/joc.5821.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Attada, R., R. K. Yadav, R. K. Kunchala, H. P. Dasari, O. Knio, and I. Hoteit, 2018b: Prominent modes of summer surface air temperature variability and associated circulation anomalies over the Arabian Peninsula. Atmos. Sci. Lett., 19, e860, https://doi.org/10.1002/asl.860.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Attada, R., H. P. Dasari, A. Parekh, J. S. Chowdary, S. Langodan, O. Knio, and I. Hoteit, 2018c: The role of the Indian summer monsoon variability on Arabian Peninsula summer climate. Climate Dyn ., 52, 33893404, https://doi.org/10.1007/s00382-018-4333-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baretta, J. W., W. Ebenhoh, and P. Ruardij, 1995: The European-Regional-Seas-Ecosystem-Model, a complex marine ecosystem model. Neth. J. Sea Res., 33, 233246, https://doi.org/10.1016/0077-7579(95)90047-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barker, D., and Coauthors, 2012: The Weather Research and Forecasting Model’s Community Variational/Ensemble Data Assimilation System: WRFDA. Bull. Amer. Meteor. Soc., 93, 831843, https://doi.org/10.1175/BAMS-D-11-00167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Belkin, I. M., 2009: Rapid warming of large marine ecosystems. Prog. Oceanogr., 81, 207213, https://doi.org/10.1016/j.pocean.2009.04.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Booij, N., R. C. Ris, and L. H. Holthuijsen, 1999: A third-generation wave model for coastal regions. Part 1: Model description and validation. J. Geophys. Res., 104, 76497666, https://doi.org/10.1029/98JC02622.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brewin, R. J. W., D. E. Raitsos, Y. Pradhan, and I. Hoteit, 2013: Comparison of chlorophyll in the Red Sea derived from MODIS-Aqua and in vivo fluorescence. Remote Sens. Environ., 136, 218224, https://doi.org/10.1016/j.rse.2013.04.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brewin, R. J. W., and Coauthors, 2015: Regional ocean-colour chlorophyll algorithms for the Red Sea. Remote Sens. Environ., 165, 6485, https://doi.org/10.1016/j.rse.2015.04.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brewin, R. J. W., and Coauthors, 2019: Factors regulating the relationship between total and size-fractionated chlorophyll-a in coastal waters of the Red Sea. Front. Microbiol., 10, 1964, https://doi.org/10.3389/fmicb.2019.01964.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cantin, N. E., A. L. Cohen, K. B. Karnauskas, A. M. Tarrant, and D. C. McCorkle, 2010: Ocean warming slows coral growth in the central Red Sea. Science, 329, 322325, https://doi.org/10.1126/science.1190182.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carvalho, S., B. Kürten, G. Krokos, I. Hoteit, and J. Ellis, 2018: The Red Sea. World Seas: An Environmental Evaluation, Vol. II, The Indian Ocean to the Pacific, Academic Press, 49–74.

    • Crossref
    • Export Citation
  • Cember, R. P., 1988: On the sources, formation, and circulation of Red Sea deep water. J. Geophys. Res., 93, 81758191, https://doi.org/10.1029/JC093iC07p08175.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chaidez, V., D. Dreano, S. Agusti, C. M. Duarte, and I. Hoteit, 2017: Decadal trends in Red Sea maximum surface temperature. Sci. Rep., 7, 8144, https://doi.org/10.1038/s41598-017-08146-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Churchill, J. H., Y. Abualnaja, R. Limeburner, and M. Nellayaputhenpeedika, 2018: The dynamics of weather-band sea level variations in the Red Sea. Reg. Stud. Mar. Sci., 24, 336342, https://doi.org/10.1016/j.rsma.2018.09.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collingridge, K., 2012: Modelling risk areas in the North Sea for blooms of the invasive comb jellyfish Mnemiopsis leidyi A. Agassiz, 1865. Aquatic Invasions, 9, 2136, https://doi.org/10.3391/ai.2014.9.1.02.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dasari, H. P., A. Raju, O. Knio, and I. Hoteit, 2017: Analysis of a severe weather event over Mecca, Kingdom of Saudi Arabia using observations and high-resolution modeling. Meteor. Appl., 24, 612627, https://doi.org/10.1002/met.1662.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dasari, H. P., L. Sabique, V. Yesubabu, V. B. Rao, V. P. Papadopoulos, and I. Hoteit, 2018: ENSO influence on the Red Sea convergence zone and associated rainfall. Int. J. Climatol., 38, 761775, https://doi.org/10.1002/joc.5208.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dasari, H. P., S. Desamsetti, S. Langodan, R. Attada, R. K. Kunchala, Y. Viswanadhapalli, O. Knio, and I. Hoteit, 2019: High-resolution assessment of solar energy resources over the Arabian Peninsula. Appl. Energy, 248, 354371, https://doi.org/10.1016/j.apenergy.2019.04.105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dasari, H. P., S. Desamsetti, S. Langodan, S. Singh, L. N. K. Ramakrishna, and I. Hoteit, 2020: Air-quality assessment over NEOM, Kingdom of Saudi Arabia. Atmos. Environ., 230, 117489, https://doi.org/10.1016/j.atmosenv.2020.117489.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • da Silva, J., J. Magalhaes, T. Gerkema, and L. Maas, 2012: Internal solitary waves in the Red Sea: An unfolding mystery. Oceanography, 25, 96107, https://doi.org/10.5670/oceanog.2012.45.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, S. R., L. J. Pratt, and H. Jiang, 2015: The Tokar Gap: Regional circulation, diurnal variability, and moisture transport based on numerical simulations. J. Climate, 28, 58855907, https://doi.org/10.1175/JCLI-D-14-00635.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Dominicis, M., N. Pinardi, G. Zodiatis, and R. Archetti, 2013a: MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting - Part II: Numerical simulations and validations. Geosci. Model Dev., 6, 18711888, https://doi.org/10.5194/gmd-6-1871-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Dominicis, M., N. Pinardi, G. Zodiatis, and R. Lardner, 2013b: MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting - Part I: Theory. Geosci. Model Dev ., 6, 18511869, https://doi.org/10.5194/gmd-6-1851-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deltares, 2016: Delft3D-FLOW: Simulation of multi-dimensional hydrodynamic flow and transport phenomena, including sediments. User manual, version 3.15, Deltares, 684 pp., https://oss.deltares.nl/documents/183920/185723/Delft3D-FLOW_User_Manual.pdf.

  • Desamsetti, S., H. P. Dasari, S. Langodan, E. S. Titi, O. Knio, and I. Hoteit, 2019: Efficient dynamical downscaling of general circulation models using continuous data assimilation. Quart. J. Roy. Meteor. Soc., 145, 31753194, https://doi.org/10.1002/qj.3612.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Vries, A. J., E. Tyrlis, D. Edry, S. O. Krichak, B. Steil, and J. Lelieveld, 2013: Extreme precipitation events in the Middle East: Dynamics of the active Red Sea trough. J. Geophys. Res. Atmos., 118, 70877108, https://doi.org/10.1002/jgrd.50569.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Draxler, R. R., and G. D. Hess, 1997: Description of the HYSPLIT_4 modeling system. NOAA Tech. Memo. ERL ARL-224, NOAA/Air Resources Laboratory, Silver Spring, MD, 24 pp., www.arl.noaa.gov/documents/reports/arl-224.pdf.

  • Dreano, D., D. E. Raitsos, J. Gittings, G. Krokos, and I. Hoteit, 2016: The Gulf of Aden intermediate water intrusion regulates the Southern Red Sea summer phytoplankton blooms. PLOS ONE, 11, e0168440, https://doi.org/10.1371/journal.pone.0168440.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Edwards, A., and S. Head, Eds., 1987: Red Sea. Key Environment Series, Pergamon Press, 451 pp.

  • Edwards, C., A. Moore, I. Hoteit, and B. Cornuelle, 2015: Regional ocean data assimilation. Annu. Rev. Mar. Sci., 7, 2142, https://doi.org/10.1146/annurev-marine-010814-015821.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ellis, J. I., and Coauthors, 2019: Multiple stressor effects on coral reef ecosystems. Global Change Biol ., 25, 41314146, https://doi.org/10.1111/gcb.14819.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • El Mohtar, S., I. Hoteit, O. Knio, L. Issa, and I. Lakkis, 2018: Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea. Ocean Modell ., 131, 114, https://doi.org/10.1016/j.ocemod.2018.08.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eshel, G., M. A. Cane, and M. B. Blumenthal, 1994: Modes of subsurface, intermediate, and deep water renewal in the Red Sea. J. Geophys. Res., 99, 15 94115 952, https://doi.org/10.1029/94JC01131.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Genevier, L. G. C., T. Jamil, D. E. Raitsos, G. Krokos, and I. Hoteit, 2019: Marine heatwaves reveal coral reef zones susceptible to bleaching in the Red Sea. Global Change Biol ., 25, 23382351, https://doi.org/10.1111/gcb.14652.

    • Search Google Scholar
    • Export Citation
  • Gimeno, L., A. Drumond, R. Nieto, R. M. Trigo, and A. Stohl, 2010: On the origin of continental precipitation. Geophys. Res. Lett., 37, L13804, https://doi.org/10.1029/2010GL043712.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gittings, J. A., D. E. Raitsos, G. Krokos, and I. Hoteit, 2018: Impacts of warming on phytoplankton abundance and phenology in a typical tropical marine ecosystem. Sci. Rep., 8, 2240, https://doi.org/10.1038/s41598-018-20560-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gittings, J. A., R. J. W. Brewin, D. E. Raitsos, M. Kheireddine, M. Ouhssain, B. H. Johns, and I. Hoteit, 2019a: Remotely sensing phytoplankton size structure in the Red Sea. Remote Sens. Environ., 234, 111387, https://doi.org/10.1016/j.rse.2019.111387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gittings, J. A., D. E. Raitsos, M. Kheireddine, M. F. Racault, H. Claustre, and I. Hoteit, 2019b: Evaluating tropical phytoplankton phenology metrics using contemporary tools. Sci. Rep., 9, 674, https://doi.org/10.1038/s41598-018-37370-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, D., T. R. Akylas, P. Zhan, A. Kartadikaria, and I. Hoteit, 2016: On the generation and evolution of internal solitary waves in the southern Red Sea. J. Geophys. Res. Oceans, 121, 85668584, https://doi.org/10.1002/2016JC012221.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, D., A. Kartadikaria, P. Zhan, J. S. Xie, M. J. Li, and I. Hoteit, 2018: Baroclinic tides simulation in the Red Sea: Comparison to observations and basic characteristics. J. Geophys. Res. Oceans, 123, 93899404, https://doi.org/10.1029/2018JC013970.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hadri, B., S. Kortas, S. Feki, R. Khurram, and G. Newby, 2015: Overview of the KAUST’s Cray X40 system–Shaheen II. Proc. 2015 Cray User Group, Chicago, IL, Cray User Group, 7 pp., https://cug.org/proceedings/cug2015_proceedings/includes/files/pap129.pdf.

  • Hill, C., C. DeLuca, M. Balaji, M. Suarez, and A. Da Silva, 2004: The architecture of the Earth system modeling framework. Comput. Sci. Eng., 6, 1828, https://doi.org/10.1109/MCISE.2004.1255817.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Höllt, T., A. Magdy, G. Chen, G. Gopalakrishnan, I. Hoteit, C. D. Hansen, and M. Hadwiger, 2013: Visual analysis of uncertainties in ocean forecasts for planning and operation of off-Shore structures. IEEE Pacific Visualization Symp., Sydney, NSW, Australia, IEEE, 59–66, https://doi.org/10.1109/PacificVis.2013.6596144.

    • Crossref
    • Export Citation
  • Höllt, T., A. Magdy, P. Zhan, G. Chen, G. Gopalakrishnan, I. Hoteit, C. D. Hansen, and M. Hadwiger, 2014: Ovis: A framework for visual analysis of ocean forecast ensembles. IEEE Trans. Visualization Comput. Graphics, 20, 11141126, https://doi.org/10.1109/TVCG.2014.2307892.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Höllt, T., M. Hadwiger, O. Knio, and I. Hoteit, 2015: Probability maps for the visualization of assimilation ensemble flow data. Workshop on Visualization in Environmental Sciences (EnvirVis), Cagliari, Italy, Eurographics Association, 43–47, https://doi.org/10.2312/envirvis.20151090.

    • Crossref
    • Export Citation
  • Hoteit, I., B. Cornuelle, A. Köhl, and D. Stammer, 2006: Treating strong adjoint sensitivities in tropical eddy-permitting variational data assimilation. Quart. J. Roy. Meteor. Soc., 131, 36593682, https://doi.org/10.1256/qj.05.97.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoteit, I., T. Hoar, G. Gopalakrishnan, J. Anderson, N. Collins, B. Cornuelle, A. Kohl, and P. Heimbach, 2013: A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico. Dyn. Atmos. Oceans, 63, 123, https://doi.org/10.1016/j.dynatmoce.2013.03.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoteit, I., D. T. Pham, M. E. Gharamti, and X. Luo, 2015: Mitigating observation perturbation sampling errors in the stochastic EnKF. Mon. Wea. Rev., 143, 29182936, https://doi.org/10.1175/MWR-D-14-00088.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoteit, I., X. Luo, M. Bocquet, A. Kohl, and B. Ait-El-Fquih, 2018: Data assimilation in oceanography: Current status and new directions. New Frontiers in Operational Oceanography, E. P. Chassignet et al., Eds., GODAE Ocean View, 465–512, https://doi.org/10.17125/gov2018.

    • Crossref
    • Export Citation
  • IOC, IHO, and BODC, 2003: Centenary edition of the GEBCO Digital Atlas. British Oceanographic Data Centre, CD-ROM.

  • Krokos, G., V. P. Papadopoulos, S. S. Sofianos, H. Ombao, P. Dybczak, and I. Hoteit, 2019: Natural climate oscillations may counteract Red Sea warming over the coming decades. Geophys. Res. Lett., 46, 34543461, https://doi.org/10.1029/2018GL081397.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunchala, R., R. Attada, H. P. Dasari, V. R. Kumar, S. Langodan, Y. Abualnaja, and I. Hoteit, 2018: Aerosol optical depth variability over the Arabian Peninsula as inferred from satellite measurement. Atmos. Environ., 187, 346357, https://doi.org/10.1016/j.atmosenv.2018.06.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunchala, R., R. Attada, H. P. Dasari, K. Ramesh, Y. Abualnaja, K. Ashok, and I. Hoteit, 2019: On the recent amplification of dust over the Arabian Peninsula during 2002-2012. J. Geophys. Res. Atmos., 124, 13 22013 229, https://doi.org/10.1029/2019JD030695.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langodan, S., L. Cavaleri, Y. Viswanadhapalli, and I. Hoteit, 2014: The Red Sea: A natural laboratory for wind and wave modeling. J. Phys. Oceanogr., 44, 31393159, https://doi.org/10.1175/JPO-D-13-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langodan, S., L. Cavaleri, Y. Viswanadhapalli, and I. Hoteit, 2015: Wind-wave source functions in opposing seas. J. Geophys. Res. Oceans, 120, 67516768, https://doi.org/10.1002/2015JC010816.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langodan, S., Y. Viswanadhapalli, H. P. Dasari, O. Knio, and I. Hoteit, 2016a: A high resolution assessment of wind and wave energy potentials in the Red Sea. Appl. Energy, 181, 244255, https://doi.org/10.1016/j.apenergy.2016.08.076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langodan, S., V. Yesubabu, and I. Hoteit, 2016b: The impact of atmospheric data assimilation on wave simulations in the Red Sea. Ocean Eng ., 116, 200215, https://doi.org/10.1016/j.oceaneng.2016.02.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langodan, S., L. Cavaleri, A. Pomaro, V. Yesubabu, L. Bertotti, and I. Hoteit, 2017a: The climatology of the Red Sea – Part 1: The winds. Int. J. Climatol., 37, 45184528, https://doi.org/10.1002/joc.5101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langodan, S., L. Cavaleri, A. Pomaro, V. Yesubabu, L. Bertotti, and I. Hoteit, 2017b: The climatology of the Red Sea – Part 2: The waves. Int. J. Climatol., 37, 45184528, https://doi.org/10.1002/joc.5101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langodan, S., L. Cavaleri, A. Pomaro, J. Portilla, Y. Abualnaja, and I. Hoteit, 2018: Unraveling climatic wind and wave trends in the Red Sea using wave spectra partitioning. J. Climate, 31, 18811895, https://doi.org/10.1175/JCLI-D-17-0295.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langodan, S., L. Cavaleri, A. Pomaro, J. Portilla, and Y. Abualnaja, 2020a: Can we extrapolate climate in an inner basin? The case of the Red Sea. Global Planet. Change, 188, 103151, https://doi.org/10.1016/j.gloplacha.2020.103151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langodan, S., C. Antony, P. R. Shanas, H. P. Dasari, Y. Abualnaja, O. Knio, and I. Hoteit, 2020b: Wave modeling of a reef-sheltered coastal zone in the Red Sea. Ocean Eng ., 207, 107378, https://doi.org/10.1016/j.oceaneng.2020.107378.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luettich, R. A., and J. J. Westerink, 2004: Formulation and numerical implementation of the 2D/3D ADCIRC finite element model Version 44.XX. ADCIRC Tech. Rep., 74 pp., https://adcirc.org/files/2018/11/adcirc_theory_2004_12_08.pdf.

  • Luong, T. M., H. P. Dasari, and I. Hoteit, 2020: Impact of urbanization on the simulation of extreme rainfall in Jeddah, Saudi Arabia. J. Appl. Meteor. Climatol., 59, 953971, https://doi.org/10.1175/JAMC-D-19-0257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marshall, J., A. Adcroft, C. Hill, L. Perelman, and C. Heisey, 1997a: A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers. J. Geophys. Res., 102, 57535766, https://doi.org/10.1029/96JC02775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marshall, J., C. Hill, L. Perelman, and A. Adcroft, 1997b: Hydrostatic, quasi-hydrostatic, and nonhydrostatic ocean modeling. J. Geophys. Res., 102, 57335752, https://doi.org/10.1029/96JC02776.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meredith, S., 2019: ‘Floating bomb’: Decaying oil tanker near Yemen coast could soon explode, experts warn. CNBC, 24 July, https://www.cnbc.com/2019/07/24/oil-tanker-near-yemen-coast-could-soon-explode-experts-warn.html.

  • Nanninga, G. B., P. Saenz-Agudelo, P. Zhan, I. Hoteit, and M. L. Berumen, 2015: Not finding Nemo: Limited reef-scale retention in a coral reef fish. Coral Reefs, 34, 383392, https://doi.org/10.1007/s00338-015-1266-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Osman, E. O., D. J. Smith, M. Ziegler, B. Kürten, C. Conrad, K. M. El-Haddad, C. R. Voolstra, and D. J. Suggett, 2018: Thermal refugia against coral bleaching throughout the northern Red Sea. Global Change Biol ., 24, e474e484, https://doi.org/10.1111/gcb.13895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Papadopoulos, V. P., Y. Abualnaja, S. A. Josey, A. S. Bower, D. E. Raitsos, H. Kontoyiannis, and I. Hoteit, 2013: Atmospheric forcing of the winter air–sea heat fluxes over the northern Red Sea. J. Climate, 26, 16851701, https://doi.org/10.1175/JCLI-D-12-00267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Papadopoulos, V. P., and Coauthors, 2015: Factors governing the deep ventilation of the Red Sea. J. Geophys. Res. Oceans, 120, 74937505, https://doi.org/10.1002/2015JC010996.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paris, C. B., J. Helgers, E. van Sebille, and A. Srinivasan, 2013: Connectivity modeling system: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the Ocean. Environ. Modell. Software, 42, 4754, https://doi.org/10.1016/j.envsoft.2012.12.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Patzert, W. C., 1974: Wind induced reversal in the Red Sea circulation. Deep-Sea Res., 21, 109121, https://doi.org/10.1016/0011-7471(74)90068-0.

    • Search Google Scholar
    • Export Citation
  • Petihakis, G., G. Triantafyllou, I. J. Allen, I. Hoteit, and C. Dounas, 2002: Modelling the spatial and temporal variability of the Cretan Sea ecosystem. J. Mar. Syst., 36, 173196, https://doi.org/10.1016/S0924-7963(02)00186-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pratt, L. J., H. E. Deese, S. P. Murray, and W. Johns, 2000: Continuous dynamical nodes in straits having arbitrary cross sections, with applications to the Bab-al-Mandab. J. Phys. Oceanogr., 30, 25152534, https://doi.org/10.1175/1520-0485(2000)030<2515:CDMISH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raboudi, N. F., B. Ait-El-Fquih, and I. Hoteit, 2018: Ensemble Kalman filtering with one-step-ahead smoothing. Mon. Wea. Rev., 146, 561581, https://doi.org/10.1175/MWR-D-17-0175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Racault, M.-F., D. E. Raitsos, M. L. Berumen, R. J. W. Brewin, T. Platt, S. Sathyendranath, and I. Hoteit, 2015: Phytoplankton phenology indices in coral reef ecosystems: Application to ocean-color observations in the Red Sea. Remote Sens. Environ., 160, 222234, https://doi.org/10.1016/j.rse.2015.01.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raitsos, D. E., I. Hoteit, P. K. Prihartato, T. Chronis, G. Triantafyllou, and Y. Abualnaja, 2011: Abrupt warming of the Red Sea. Geophys. Res. Lett., 38, L14601, https://doi.org/10.1029/2011GL047984.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raitsos, D. E., Y. Pradhan, R. J. W. Brewin, G. Stenchikov, and I. Hoteit, 2013: Remote sensing the phytoplankton seasonal succession of the Red Sea. PLOS ONE, 8, e64909, https://doi.org/10.1371/journal.pone.0064909.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raitsos, D. E., and Coauthors, 2015: Monsoon oscillations regulate fertility of the Red Sea. Geophys. Res. Lett., 42, 855862, https://doi.org/10.1002/2014GL062882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raitsos, D. E., R. J. W. Brewin, P. Zhan, D. Dreano, Y. Pradhan, G. B. Nanninga, and I. Hoteit, 2017: Sensing coral reef connectivity pathways from space. Sci. Rep., 7, 9338, https://doi.org/10.1038/s41598-017-08729-w.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sathyendranath, S., and Coauthors, 2019: An ocean-colour time series for use in climate studies: The experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors, 19, 4285, https://doi.org/10.3390/s19194285.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sivareddy, S., H. Toye, P. Zhan, S. Langodan, G. Krokos, O. Knio, and I. Hoteit, 2020: Impact of atmospheric and model physics perturbations on a high-resolution ensemble data assimilation system of the Red Sea. J. Geophys. Res. Oceans, 125, e2019JC015611, https://doi.org/10.1029/2019JC015611.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2019: A description of the Advanced Research WRF version 4. NCAR Tech. Note NCAR/TN-556+STR, 145 pp., https://doi.org/10.5065/1dfh-6p97.

    • Crossref
    • Export Citation
  • Smeed, D., 1997: Seasonal variation of the flow in the strait of Bab-al-Mandab. Oceanol. Acta, 20, 773781.

  • Sofianos, S., and W. E. Johns, 2007: Observations of the summer Red Sea circulation. J. Geophys. Res., 112, C06025, https://doi.org/10.1029/2006JC003886.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sofianos, S., and W. E. Johns, 2015: Water mass formation, overturning circulation, and the exchange of the Red Sea with the adjacent basins. Red Sea: The Formation, Morphology, Oceanography and Environment of a Young Ocean Basin, N. Rasul and I. C. F. Stewart, Eds., Springer Earth System Sciences, Springer, 343–353.

    • Crossref
    • Export Citation
  • Stammer, D., and Coauthors, 2002: The global ocean circulation during 1992–1997, estimated from ocean observations and a general circulation model. J. Geophys. Res., 107, 3118, https://doi.org/10.1029/2001JC000888.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, R., A. C. Subramanian, A. J. Miller, M. R. Mazloff, I. Hoteit, and B. D. Cornuelle, 2019: SKRIPS v1. 0: A regional coupled ocean-atmosphere modeling framework (MITgcm-WRF) using ESMF/NUOPC, description and preliminary results for the Red Sea. Geosci. Model Dev., 12, 42214244, https://doi.org/10.5194/gmd-12-4221-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolman, H. L., 2008: User manual and system documentation of WAVEWATCH-III version 3.14. NOAA/NWS/NCEP/OMB Tech. Note 268, 220 pp., https://polar.ncep.noaa.gov/mmab/papers/tn276/MMAB_276.pdf.

  • Toye, H., P. Zhan, G. Gopalakrishnan, A. R. Kartadikaria, H. Huang, O. Knio, and I. Hoteit, 2017: Ensemble data assimilation in the Red Sea: Sensitivity to ensemble selection and atmospheric forcing. Ocean Dyn ., 67, 915933, https://doi.org/10.1007/s10236-017-1064-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toye, H., S. Kortas, P. Zhan, and I. Hoteit, 2018: A fault-tolerant HPC scheduler extension for large and operational ensemble data assimilation: Application to the Red Sea. J. Comput. Sci., 27, 4656, https://doi.org/10.1016/j.jocs.2018.04.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toye, H., S. Sivareddy, N. Raboudi, and I. Hoteit, 2020: A hybrid ensemble adjustment Kalman filter based high-resolution data assimilation system for the Red Sea: Implementation and evaluation. Quart. J. Roy. Meteor. Soc., 146, 41084130, https://doi.org/10.1002/qj.3894.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Triantafyllou, G., F. Yao, G. Petihakis, K. Tsiaras, D. E. Raitsos, and I. Hoteit, 2014: Exploring the Red Sea seasonal ecosystem functioning using a three-dimensional biophysical model. J. Geophys. Res. Oceans, 119, 17911811, https://doi.org/10.1002/2013JC009641.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viswanadhapalli, Y., C. V. Srinivas, S. Langodan, and I. Hoteit, 2016: Predicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observations. Quart. J. Roy. Meteor. Soc., 142, 327348, https://doi.org/10.1002/qj.2654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viswanadhapalli, Y., H. P. Dasari, S. Langodan, V. S. Challa, and I. Hoteit, 2017: Climatic features of the Red Sea from a regional assimilative model. Int. J. Climatol., 37, 25632581, https://doi.org/10.1002/joc.4865.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viswanadhapalli, Y., H. P. Dasari, S. Dwivedi, V. R. Madineni, S. Langodan, and I. Hoteit, 2020: Variability of monsoon low-level jet and associated rainfall over India. Int. J. Climatol., 40, 10671089, https://doi.org/10.1002/joc.6256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, T., O. P. Le Maître, I. Hoteit, and O. M. Knio, 2016: Path planning in uncertain flow fields using ensemble method. Ocean Dyn ., 66, 12311251, https://doi.org/10.1007/s10236-016-0979-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., D. E. Raitsos, G. Krokos, J. A. Gittings, P. Zhan, and I. Hoteit, 2019: Physical connectivity simulations reveal dynamic linkages between coral reefs in the southern Red Sea and the Indian Ocean. Sci. Rep., 9, 16598, https://doi.org/10.1038/s41598-019-53126-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, J. S., G. Krokos, S. Sofianos, and I. Hoteit, 2019: Interannual variability of the exchange flow through the strait of Bab-al-Mandeb. J. Geophys. Res. Oceans, 124, 19882009, https://doi.org/10.1029/2018JC014478.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, F., and I. Hoteit, 2018: Rapid red sea deep water renewals caused by volcanic eruptions and the north Atlantic oscillation. Sci. Adv., 4, eaar5637, https://doi.org/10.1126/sciadv.aar5637.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, F., I. Hoteit, L. J. Pratt, A. S. Bower, A. Köhl, G. Gopalakrishnan, and D. Rivas, 2014a: Seasonal overturning circulation in the Red Sea: 2. Winter circulation. J. Geophys. Res. Oceans, 119, 22632289, https://doi.org/10.1002/2013JC009331.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, F., I. Hoteit, L. J. Pratt, A. S. Bower, P. Zhai, A. Köhl, and G. Gopalakrishnan, 2014b: Seasonal overturning circulation in the Red Sea: 1. Model validation and summer circulation. J. Geophys. Res. Oceans, 119, 22382262, https://doi.org/10.1002/2013JC009004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, P., and A. S. Bower, 2013: The response of the Red Sea to a strong wind jet near the Tokar Gap in summer. J. Geophys. Res. Oceans, 118, 421434, https://doi.org/10.1029/2012JC008444.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, P., A. S. Bower, W. M. Smlethie Jr., and L. J. Pratt, 2015: Formation and spreading of Red Sea outflow water in the Red Sea. J. Geophys. Res. Oceans, 120, 65426563, https://doi.org/10.1002/2015JC010751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhan, P., A. C. Subramanian, F. C. Yao, and I. Hoteit, 2014: Eddies in the Red Sea: A statistical and dynamical study. J. Geophys. Res. Oceans, 119, 39093925, https://doi.org/10.1002/2013JC009563.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhan, P., F. Yao, A. R. Kartadikaria, Y. Viswanadhapalli, G. Gopalakrishnan, and I. Hoteit, 2015: Far-field ocean conditions and concentrate discharges modeling along the Saudi coast of the Red Sea. Intakes and Outfalls for Seawater Reverse-Osmosis Desalination Facilities, T. M. Missimer, B. Jones, and R. G. Maliva, Eds., Springer, 501–520, https://doi.org/10.1007/978-3-319-13203-7_21.

    • Crossref
    • Export Citation
  • Zhan, P., A. C. Subramanian, F. C. Yao, A. R. Kartadikaria, D. Q. Guo, and I. Hoteit, 2016: The eddy kinetic energy budget in the Red Sea. J. Geophys. Res. Oceans, 121, 47324747, https://doi.org/10.1002/2015JC011589.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhan, P., G. Gopalakrishnan, A. C. Subramanian, D. Guo, and I. Hoteit, 2018: Sensitivity studies of the Red Sea eddies using adjoint method. J. Geophys. Res. Oceans, 123, 83298345, https://doi.org/10.1029/2018JC014531.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhan, P., G. Krokos, D. Guo, and I. Hoteit, 2019: Three-dimensional signature of the Red Sea eddies and eddy-induced transport. Geophys. Res. Lett., 46, 21672177, https://doi.org/10.1029/2018GL081387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zodiatis, G., R. Lardner, T. M. Alves, Y. Krestenitis, L. Perivoliotis, S. Sofianos, and K. Spanoudaki, 2019: Oil spill forecasting (prediction). J. Mar. Res., 75, 923953, https://doi.org/10.1357/002224017823523982.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Towards an End-to-End Analysis and Prediction System for Weather, Climate, and Marine Applications in the Red Sea

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  • 1 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 2 Massachusetts Institute of Technology, Cambridge, Massachusetts
  • 3 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 4 The University of Texas at Austin, Austin, Texas
  • 5 Saudi Aramco, Damam, Saudi Arabia
  • 6 University of Exeter, Cornwall, United Kingdom
  • 7 Institute of Marine Sciences, Venice, Italy
  • 8 Scripps Institution of Oceanography, La Jolla, California
  • 9 National Center for Medium Range Weather Forecasting, Noida, India
  • 10 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 11 University of Malaga, Malaga, Spain
  • 12 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 13 National Center of Atmospheric Research, Boulder, Colorado
  • 14 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 15 Scripps Institution of Oceanography, La Jolla, California
  • 16 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 17 Scripps Institution of Oceanography, La Jolla, California
  • 18 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 19 University of Hyderabad, Hyderabad, India
  • 20 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 21 University of Hamburg, Hamburg, Germany
  • 22 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 23 Indian Institute of Technology, Delhi, India
  • 24 Lebanese American University, Beirut, Lebanon
  • 25 American University of Beirut, Beirut, Lebanon
  • 26 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 27 Massachusetts Institute of Technology, Cambridge, Massachusetts
  • 28 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 29 Ecole Polytechnique, Palaiseau, France
  • 30 Scripps Institution of Oceanography, La Jolla, California
  • 31 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 32 Hellenic Centre for Marine Research, Anavissos, Greece
  • 33 Plymouth Marine Laboratory, Plymouth, United Kingdom
  • 34 Woods Hole Oceanographic Institution, Woods Hole, Massachusetts
  • 35 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 36 Plymouth Marine Laboratory, Plymouth, United Kingdom
  • 37 National and Kapodistrian University of Athens, Athens, Greece
  • 38 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 39 Plymouth Marine Laboratory, Plymouth, United Kingdom
  • 40 National and Kapodistrian University of Athens, Athens, Greece
  • 41 University of Colorado Boulder, Boulder, Colorado
  • 42 Scripps Institution of Oceanography, La Jolla, California
  • 43 University of Cambridge, Cambridge, United Kingdom, and Texas A&M University, College Station, Texas
  • 44 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 45 Hellenic Centre for Marine Research, Anavissos, Greece
  • 46 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 47 National Atmospheric Research Laboratories, Gadanki, India
  • 48 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • 49 Coastal and Marine Research Laboratory, Crete, Greece
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Abstract

The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.

Deceased.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.

Corresponding author: Ibrahim Hoteit, ibrahim.hoteit@kaust.edu.sa

Abstract

The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.

Deceased.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.

Corresponding author: Ibrahim Hoteit, ibrahim.hoteit@kaust.edu.sa
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