The study was supported by the Danish Council for Strategic Research, project number named “Tall Wind”, the Nordic Centre of Excellence CRAICC - TRI-NCoE and the COST ES1002 Action (WIRE); We express our deep sense of gratitude to AEDB and FFC Energy Limited for sharing the wind data with us. Wind characteristic in the wind corridor in Southern Pakistan, the effect of the monsoon N. Memon (1), S.E. Gryning (2), S. Anwar Khan (1), E. Batchvarova (2,3) Zubair A., A. Farhan Khan and M. Umer Khan (2013). Elements of Monsoon Circulation and its Behavior Impact over Pakistan. European Academic Research, Vol. 1, Issue 5. ISSN , Gryning, S.-E., Batchvarova, E., and Floors, R. (2013a). A study on the effect of nudging on long-term boundary-layer profiles of wind and Weibull distribution parameters in a rural coastal area. Journal of Applied Meteorology and Climatology, 2(5):1201– 1207 Gryning, S.-E., Batchvarova, E., Floors, R., Peña, A., Brümmer, B., Hahmann,A. N., and Mikkelsen, T. (2013b). Long-term profiles of wind and Weibull distribution parameters up to 600 m in a rural coastal and an inland suburban area. Boundary-Layer Meteorology. DOI: /s (1)Alternative Energy Development Board, Ministry of Water and Power, Government of Pakistan (2) Technical University of Denmark, Wind Energy Department, Roskilde, Denmark (3) National Institute Meteorology and Hydrology, Bulgarian Academy of Sciences Coastal and inland sites, Measurements and Wind resource References EMS 2013, 9-13 September 2013, Reading, UK - ASI13 Energy Meteorology Wind energy in Pakistan Conclusions i.The wind speed inland is stronger than at the coastline throughout the year for the period of the measurements. ii.The wind direction at both masts is primarily SW. iii.The Weibull distribution provides a good fit to the measured wind speed distribution. iv.The scale parameter (A) in the Weibull distribution is larger than the wind speed and shows the same behaviour. v.The shape parameter (k) in the Weibull distribution shows a clear change with season. The larger values during the summer monsoon period reflect smaller variability in the wind speed. Acknowledgements Any help is welcome in Pakistan’s perpetual electricity shortage. The daily shortfall is an estimated 4,000-5,000MW, with a peak demand of 17,500MW during the summer. Resultant load shedding (blackouts) severely hampers business, industry and agriculture. However the country has vast potential for wind power production, about 50,000 MW of electricity on the 65km-long Gharo-Keti Bandar Wind Corridor in the southern part of Pakistan. The Asian winter and summer monsoon Winter monsoon of the region, northeasterly windSummer monsoon of the region, southwesterly wind FFC mast - inland Hawks Bay mast - coastal The measurements reveal very generous conditions for harvesting of wind energy. Wind power depends not only on the mean wind but also on the variability of the wind which can be described by the Weibull distribution. The measurements reveal a strong seasonality in both the scale (A) and shape (k) parameter. During the monsoon period both the scale and the shape parameter are large as compared to mid-latitude (European) conditions (Gryning et al. 2013a, b), indicating a high mean wind but with a narrow distribution. The effect can likely be attributed to the monsoon as similar seasonal behaviour and values in this range are observed at all 8 meteorological masts in the region of the wind corridor. The meteorological conditions are controlled by the monsoon, and therefore exhibit a considerable annual variability (Zubair et al. 2013). Pakistan has four seasons: a cool, dry winter from December through February; a hot, dry spring from March through May; the summer rainy season, or southwest monsoon period, from June through September; and the retreating monsoon period of October and November. The onset and duration of these seasons vary somewhat according to location. The wind corridor of Pakistan The two-parameter Weibull distribution can be expressed as where f(u) is the frequency of occurrence of the wind speed u, the scale parameter A has units of speed and k is the non-dimensional shape parameter. Weibull distribution