International Journal of Latest Research in Engineering and Technology (IJLRET) ISSN: 2454-5031 www.ijlret.com || Volume 03 - Issue 03 || March 2017 || PP. 106-115 www.ijlret.com 106 | Page USE OF MAST AND REMOTE SENSING DATA FOR WIND RESOURCE ASSESSMENT IN KENYA. Victor S Indasi, Lynch M, McGann B, Sutton J, Yu F and Jeanneret F Department of Imaging and Applied physics Curtin University Abstract: The main goal of this study was to characterize the winds at Lake Turkana wind energy site using both mast and Doppler LIDAR measurements. The methods used to achieve this objective include the Advanced LIDAR data volume processing technique (ALVPT), time series, correlation and error analysis. Data was collected at 3 masts: Kalkumpei, Nyiru and Sirima using cup anemometers and wind vanes for the entire year, 2009. The Doppler Lidar collected data for 2 weeks from 11 th to 24 th July 2009. The annual average wind speed at the 3 masts is: 10.44m/s, 10.75m/s & 11.10m/s respectively while the predominant wind direction is south east. Comparison between mast and Lidar measurements was computed during LIDAR data period. The difference between Doppler LIDAR and mast wind speed means is small, the mean standard deviations for both instruments are in close agreement - 0.33, 0.05 & 0.47 and the Correlation is 0.9, 0.6 & 0.73 for Kalkumpei, Nyiru and Sirima respectively. The windspeed derived from Doppler LIDAR datawasoutputintoa20kmby20kmgriddomainandoverlayedona digitalterrainmodel to create a wind atlas map. This map provides a useful productthat can be used to evaluate Wind Atlas Analysis and Application Program (WAsP) generated wind atlases. LIDAR being mobile has shown great potential to assess winds accurately at any particular site for a range of heights over an area of ~ 200 km 2 and with high radial resolution [~150 m]. Use of Doppler LIDAR for wind assessment is still maturing. Equipment configuration and software changes especially the ALVPT may affect measurement quality and accuracy of retrieved wind speed and direction. Keywords: LIDAR measurements, mast measurements, resource assessment, wind energy. Introduction Kenya has experienced a steady increase in energy demand over the past decade which is linked to both the rising population and the expanding economy. According to 2009 national census, the Kenyan population is around 38 million and only 28% has access to electricity (Kirai, 2009). Over the years Kenya has relied on petroleum imports and hydropower to meets it’s ever increasing domestic and commercial energy requirements (Oludhe, 2008), but the frequent droughts thought to be caused by climate change have led to critical power shortages particularly in years when the droughts are more pronounced like 1999 – 2002 (Oludhe, 2008, Kirai, 2009, Muthuri et al., 2009). Renewable energy resources particularly wind energy are progressively being investigated for electricity generation with negligible emissions of greenhouse gases (Theuri, 2008). The wind energy resource is naturally a function of the climate system because electricity is generated by the wind turbines which are moved by the winds. Therefore, the prospect of wind energy power generation will increase the resilience of Kenya’s power generation vis-à-vis potential climate risk variations. The region of study is north-western Kenya where the winds are generated by a low level jet called the Turkana Channel jet. The jet stream is created by the much bigger East African low level jet. The Turkana Channel jet blows lasting through the year from the South East through the valley between the East African and the Ethiopian Highlands extending from the Ocean to the deserts in Sudan (Kinuthia, 1992). The wind is enhanced locally between Mt. Kulal (2300 m ASL) and the Mt Nyiru Range (2750 m ASL). Due to thermal effects, the wind decreases around mid-day and is at full force during the night (Kinuthia, 1992). Both Kinuthia and Asnani (1982) and Kinuthia (1992) observed that, throughout the year, the NE and SE monsoon near the equator branches off from the Indian Ocean, enters the Turkana channel and intensifies, maintaining an average speed of 10ms -1 (Figure 1). Their observations showed quite distinct low-level jet in the channel (Turkana easterly low-level jet) that persists throughout the year. They further hypothesized that the configuration of the Ethiopian highlands and the East African highlands could be playing a critical role in the development and maintenance of the Turkana low-level jet through the orographic channeling effect.