Teleconnections and analysis of long-term wind speed variability in the UAE.
Wind energy accounts for a small share of the global energy consumption in spite of its widespread availability. One of the obstacles hindering exploitation of wind energy is the lack of proper wind speed assessment models. The wind energy field credibility has occasionally suffered from wind power...
| Published in: | International Journal of Climatology |
|---|---|
| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
2017
|
| Subjects: | |
| Online Access: | https://espace.inrs.ca/id/eprint/6436/ https://doi.org/10.1002/joc.4700 |
| Summary: | Wind energy accounts for a small share of the global energy consumption in spite of its widespread availability. One of the obstacles hindering exploitation of wind energy is the lack of proper wind speed assessment models. The wind energy field credibility has occasionally suffered from wind power potential estimation studies that were conducted based on very short wind speed records and which did not give consideration to inter-annual wind variability. The objective of this paper is to examine the long-term variability of wind speed in the United Arab Emirates (UAE) and its teleconnections with various global climate indices by using wind speed collected from six ground stations and a reanalysis dataset. Linear correlation analysis and wavelet analysis were used to characterize the interaction. The modified Mann–Kendall test and linear regression indicated that half of the stations show a significant wind speed trend at the 5% level. The cumulative sum and Bayesian change detection methods indicated that five of the stations present change points. Continuous wavelet transform of wind speed showed biannual periodicity in some stations, in addition to the annual one. Wavelet coherence analysis demonstrated that wind speed in the UAE is mainly associated with the North Atlantic Oscillation, East Atlantic Oscillation, El Niño Southern Oscillation and the Indian Ocean Dipole indices. The first two indices simultaneously modulate wind speed in the summer while the last two influence winter and autumn wind speeds. Step-wise multiple linear regression models were developed to select appropriate predictors among the various climate indices. |
|---|