Skip to main content

Advertisement

Log in

Wind resource assessment over the Persian Gulf and Oman Sea using a numerical model simulation and satellite data

  • Research Article
  • Published:
Journal of Ocean Engineering and Marine Energy Aims and scope Submit manuscript

Abstract

The aim of this study is the evaluation of sources of wind energy in coastal and offshore regions of the Persian Gulf and Oman Sea. A series of simulations by the Weather Research and Forecasting (WRF) model and the Cross-Calibrated Multi-Platform (CCMP) satellite data were used and compared against the observed data during the period 2013–2017. Results indicate overestimation by the WRF model in most of the stations and underestimation of wind speed by the CCMP for relatively strong winds. Maximum and minimum wind speeds in the Persian Gulf occur in its southeastern and northwestern parts, respectively. Maximum wind speed over the Oman Sea occurs in its northeastern, central, and southeastern parts. Maximum extractable wind energy is from the Oman Sea, especially in the eastern parts and also, in some parts of the Sultanate of Oman coastal area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

All data generated or analysed during this study are included in this published article.

References

  • Carvalho D, Rocha A, Gómez-Gesteira M, Santos CS (2014) Comparison of reanalyzed, analyzed, satellite-retrieved and NWP modelled winds with buoy data along the Iberian Peninsula coast. Remote Sens Environ 152:480–492

    Article  Google Scholar 

  • Carvalho D, Rocha A, Gómez-Gesteira M, Santos CS (2017) Offshore winds and wind energy production estimates derived from ASCAT, OSCAT, numerical weather prediction models and buoys—a comparative study for the Iberian Peninsula Atlantic coast. Renew Energy 102:433–444

    Article  Google Scholar 

  • Chang R, Zhu R, Badger M, Hasager CB, Zhou R, Ye D, Zhang X (2014) Applicability of synthetic aperture radar wind retrievals on offshore wind resources assessment in Hangzhou Bay, China. Energies 7(5):3339–3354

    Article  Google Scholar 

  • Charabi Y, Al Hinai A, Al-Yahyai S, Al Awadhi T, Choudri BS (2019) Offshore wind potential and wind atlas over the Oman Maritime Zone. Energ Ecol Environ 4(1):1–14

    Article  Google Scholar 

  • de Linaje NGA, Mattar C, Borvarán D (2019) Quantifying the wind energy potential differences using different WRF initial conditions on Mediterranean coast of Chile. Energy 188:116027

    Article  Google Scholar 

  • Dong C, Huang GG, Cheng G (2021) Offshore wind can power Canada. Energy 236:121422

    Article  Google Scholar 

  • Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two–dimensional model. J Atmos Sci 46:3077–3107

    Article  Google Scholar 

  • Ek MB, Mitchell KE, Lin Y, Rogers E, Grummann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the national centers for environmental prediction operational mesoscale eta model. J Geophys Res 108:8851

    Google Scholar 

  • Elliott DL, Holladay CG, Barchet WR, Foote HP, Sandusky WF (1987) Wind energy resource atlas of the United States. NASA STI/Recon Tech Rep 87:24819

    Google Scholar 

  • Emeis S (2001) Vertical variation of frequency distributions of wind speed in and above the surface layer observed by sodar. Meteorol Z 10(2):141–150

    Article  Google Scholar 

  • Esteban MD, Diez JJ, López JS, Negro V (2011) Why offshore wind energy? Renew Energy 36(2):444–450

    Article  Google Scholar 

  • Ghafarian P, Pegahfar N, Mohammadpour Penchah MR (2019) Simulation of the surface wind field by the WRF model in Oman Sea region with different initial and boundary conditions. J Earth Space Phys 45(1):197–209

    Google Scholar 

  • Gholami S, Ghader S, Khaleghi-Zavareh H, Ghafarian P (2021) Sensitivity of WRF-simulated 10 m wind over the Persian Gulf to different boundary conditions and PBL parameterization schemes. Atmos Res 247:105147

    Article  Google Scholar 

  • Hasager CB, Peña A, Christiansen MB, Astrup P, Nielsen M, Monaldo F, Thompson D, Nielsen P (2008) Remote sensing observation used in offshore wind energy. IEEE J Sel Top Appl Earth Obs Remote Sens 1(1):67–79

    Article  Google Scholar 

  • Hasager CB, Hahmann AN, Ahsbahs T, Karagali I, Sile T, Badger M, Mann J (2020) Europe’s offshore winds assessed with synthetic aperture radar, ASCAT and WRF. Wind Energy Sci 5(1):375–390

    Article  Google Scholar 

  • Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999–2049

    Article  Google Scholar 

  • Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pac J Atmos Sci 42(2):129–151

    Google Scholar 

  • Janjic ZI (1994) The step–mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon Weather Rev 122:927–945

    Article  Google Scholar 

  • Jimenez B, Durante F, Lange B, Kreutzer T, Tambke J (2007) Offshore wind resource assessment with WAsP and MM5: comparative study for the German Bight. Wind Energy 10(2):121–134

    Article  Google Scholar 

  • Karagali I, Peña A, Badger M, Hasager CB (2014) Wind characteristics in the North and Baltic Seas from the QuikSCAT satellite. Wind Energy 17(1):123–140

    Article  Google Scholar 

  • Kibona TE (2020) Application of WRF mesoscale model for prediction of wind energy resources in Tanzania. Sci Afr 7:e00302

    Google Scholar 

  • Kumar R, Stallard T, Stansby PK (2017) Assessment of WRF prediction of velocity profile and turbulence intensity by comparison to field measurement. In: The 27th International Ocean and Polar Engineering Conference. San Francisco, California, USA, June 2017

  • Kumar R, Stallard T, Stansby PK (2021) Large-scale offshore wind energy installation in northwest India: assessment of wind resource using weather research and forecasting and levelized cost of energy. Wind Energy 24(2):174–192

    Article  Google Scholar 

  • Lee JA, Doubrawa P, Xue L, Newman AJ, Draxl C, Scott G (2019) Wind resource assessment for Alaska’s offshore regions: validation of a 14-year high-resolution WRF data set. Energies 12(14):2780

    Article  Google Scholar 

  • Mahmoodi K, Ghassemi H, Razminia A (2020) Wind energy potential assessment in the Persian Gulf: a spatial and temporal analysis. Ocean Eng 216:107674

    Article  Google Scholar 

  • Mahmoodi K, Saybani M, Azad ST (2022) A temporal and spatial resolution wind and wave power resource assessment in the Oman Gulf. Ocean Eng 249:110881

    Article  Google Scholar 

  • Mattar C, Borvarán D (2016) Offshore wind power simulation by using WRF in the central coast of Chile. Renew Energy 94:22–31

    Article  Google Scholar 

  • Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(D14):16663–16682

    Article  Google Scholar 

  • Nezhad MM, Neshat M, Piras G, Garcia DA (2022) Sites exploring prioritisation of offshore wind energy potential and mapping for wind farms installation: Iranian islands case studies. Renew Sustain Energ Rev 168:112791

  • Nie B, Li J (2018) Technical potential assessment of offshore wind energy over shallow continent shelf along China coast. Renew Energy 128:391–399

    Article  Google Scholar 

  • Ohsawa T, Kato M, Uede H, Shimada S, Takeyama Y, Ishihara T (2016) Investigation of WRF configuration for offshore wind resource maps in Japan. Proceedings of the Wind Europe Summit, Hamburg Messe, Hamburg, Germany: 27–29

  • Olauson J (2018) ERA5: The new champion of wind power modelling? Renew Energy 126:322–331

    Article  Google Scholar 

  • Perrone TJ (1979) Winter shamal in the Persian Gulf. Naval Environmental Prediction Research Facility Monterey CA

    Google Scholar 

  • Rehman S, Halawani TO (1994) Statistical characteristics of wind in Saudi Arabia. Renew Energy 4(8):949–956

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ricciardulli L (2017) The climate data guide: CCMP: cross-calibrated multi-platform wind vector analysis. Retrieved from https://climatedataguide.ucar.edu/climatedata/ccmp-cross-calibrated-multi-platformwind-vector-analysis. Accessed 27 Feb 2017

  • Rose S, Apt J (2015) What can reanalysis data tell us about wind power? Renew Energy 83:963–969

    Article  Google Scholar 

  • Saket A, Etemad-Shahidi A (2012) Wave energy potential along the northern coasts of the Gulf of Oman, Iran. Renew Energy 40(1):90–97

    Article  Google Scholar 

  • Salvação N, Soares CG (2018) Wind resource assessment offshore the Atlantic Iberian coast with the WRF model. Energy 145:276–287

    Article  Google Scholar 

  • Sharp E, Dodds P, Barrett M, Spataru C (2015) Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information. Renew Energy 77:527–538

    Article  Google Scholar 

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Liu Z, Berner J, Wang W, Powers JG, Duda MG, Barker DM, Huang XY (2019) A description of the advanced research WRF model version 4. National Center for Atmospheric Research: Boulder, CO, USA, 145

  • Soukissian T (2013) Use of multi-parameter distributions for offshore wind speed modeling: the Johnson SB distribution. Appl Energy 111:982–1000

    Article  Google Scholar 

  • Ulazia A, Sáenz J, Ibarra-Berastegui G, González-Rojí SJ, Carreno-Madinabeitia S (2017) Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean. Appl Energy 208:1232–1245

    Article  Google Scholar 

  • Wang YH, Walter RK, White C, Farr H, Ruttenberg BI (2019) Assessment of surface wind datasets for estimating offshore wind energy along the Central California Coast. Renew Energy 133:343–353

    Article  Google Scholar 

  • Zhou RW, He XF, Zhu R, Cheng X (2010) Numerical simulation of the development potential of wind energy resources over China’s offshore areas. Resour Sci 32(8):1434–1443

    Google Scholar 

Download references

Acknowledgements

This is to certify that the current research project has been supported by a grant (no. 396-033-01-021-01) from the Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Parvin Ghafarian.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghafarian, P., Mohammadpour Penchah, M. Wind resource assessment over the Persian Gulf and Oman Sea using a numerical model simulation and satellite data. J. Ocean Eng. Mar. Energy 9, 377–386 (2023). https://doi.org/10.1007/s40722-022-00273-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40722-022-00273-8

Keywords

Navigation