Abstract
This study presents an assessment of the potential for harvesting wind and wave energy from the Red Sea based on an 18-year high-resolution regional atmospheric reanalysis recently generated using the Advanced Weather Research Forecasting model. This model was initialized with ERA-Interim global data and the Red Sea reanalysis was generated using a cyclic three-dimensional variational approach assimilating available data in the region. The wave hindcast was generated using WAVEWATCH III on a 5 km resolution grid, forced by the Red Sea reanalysis surface winds. The wind and wave products were validated against data from buoys, scatterometers and altimeters. Our analysis suggests that the distribution of wind and wave energy in the Red Sea is inhomogeneous and is concentrated in specific areas, characterized by various meteorological conditions including weather fronts, mesoscale vortices, land and sea breezes and mountain jets. A detailed analysis of wind and wave energy variation was performed at three hotspots representing the northern, central and southern parts of the Red Sea. Although there are potential sites for harvesting wind energy from the Red Sea, there are no potential sites for harvesting wave energy because wave energy in the Red Sea is not strong enough for currently available wave energy converters. Wave energy should not be completely ignored, however, at least from the perspective of hybrid wind-wave projects. (C) 2016 Elsevier Ltd. All rights reserved.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 244-255 |
| Number of pages | 12 |
| Journal | Applied Energy |
| Volume | 181 |
| DOIs | |
| State | Published - Aug 24 2016 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: This research work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia and the Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). The simulations carried out in this research work made use of the resources of the Supercomputing Laboratory and/or computer clusters at KAUST. The NCEP FNL, PREPBUFR global observational datasets were obtained from http://rda.ucar.edu. We thank Dr. J. Tom Farrar from Woods Hole Oceanographic Institution (WHOI, Woods Hole, Massachusetts, USA) and Dr. Yasser Abualnaja and Mr. Mohammedali Nellayaputhenpeedika from KAUST for assistance with the buoy data.
