Introduction

Wind energy basedelectricity earned prominence in 19^th Century. This suffered a major setback with the highly subsidised fossil-fuel based centralized electricity generation and distribution. However, oil crisis of 1970’s and elevated oil prices revived the global interest in wind based systems (Wise, 2000). India has installed over 14 GW of wind power systems since then and stands fifth in the world (~200 GW) today. In the wake of climatic changes and perishing stock of fossil fuels, wind energy is being widely revered as a clean energy option of 21^st century that has high potential to offset carbon. It has been predicted that wind energy can produce 680 TWh of clean electricity globally in 2012, hence avoiding 408 million tons of CO₂ emissions. This also supports the Clean Development Mechanism (CDM) endorsed by Kyoto Protocol (GWEC, 2012).

Nevertheless, major expanses of the world are still deemed as low wind potential areas, while energy demands are escalating. The overall wind potential in India is estimated to be 65 GW although there is enormous scope for up-scaling. Such low estimates are attributed to the wind resource assessment exercises that are performed with focus onlarge-scale wind turbines based on high winds. It has been argued that this trend towards large-scale wind technology and non-supportive policy intervention has curbed the development of small-scale wind technologies in certain parts of the world (Ross et al., 2012, Barry and Chapman, 2009). Proficient understanding of local wind dynamics with advancement in small-wind wind technologies gives stimulus to decentralised clean energy, particularly in remote areas with appreciable wind regimes (Nouni et al., 2007). This reiterates the need for detailed regional wind resource assessment exercises.

1. Regional wind resource assessment
Wind resource assessment is the primary step towards understanding the local wind dynamics of a region (Ramachandra et al., 1997). Wind flow developed due to the differential heating of earth is modified by its rotation and further influenced by local topography. This results in annual (year to year), seasonal, synoptic (passing weather), diurnal (day and night) and turbulent (second to second) changes in wind pattern  (Hester and  Harrison, 2003). Increased heat energy generated due to industries and escalating population in urban areas result in heat islands which affects the wind flow as well.

1.1. Surface wind measurements
Wind characteristics like speed and direction measured at meteorological stations (surface) aid inassessing the local wind resource. Wind patterns are observed to be tantamount for regions in proximity. However, local winds have high topographical and land cover influence, and assuming the wind data from a measured site applicable for a nearby site of interest calls for error. Monthly wind speed variation for regions within a radius of 30 km shows similar patterns but with difference in magnitude, and the study suggests using 6 years of long term wind data for satisfactory representation of monthly variations (Mani A. and Mooley, 1983). A one year wind speed data maintains an error within ± 10% which reduces to ± 3% for 3 years data but still burden the economics of a wind energy based project (EWEA, 2012). The surface wind datasets sometime fail to capture the diurnal variations especially during the night hours, giving an elevated estimate of the daily average as wind speeds are generally higher in the daylight (Bekele and Palm, 2009). Despite these complexities, wind resource assessments based on the available surface measurements at different sites using statistical tools haveprovided satisfactory results (Ramachandra and Shruthi, 2003; Elamouri and Ben 2008; Ullah et al., 2010, Dahmouni et al., 2011; Tiang and Ishak, 2012).

1.2. Models for prospectingwind
Surface wind measurements being reliable sources of information on the wind regime are available for only few locations. Acquiring surface wind data is expensive and time consuming. These gaps limit the wider spatial and temporal understanding of regional wind characteristics. In this regard, models like Wind Atlas Analysis and Application Program (WAsP) and Computational Fluid Dynamics (CFD) based on local topography and climate help in micro–scale (1–10 km) studies of wind resources. These models are validated with dense surface measurements and are not applicable for regions with thermally forced flows like sea breeze and mountain winds for which meso–scale (10–100 km) models are preferred. A combination of meso–scale and micro–scale models viz. the Karlsruhe Atmospheric Meso–scale Model (KAMM/WAsP), Meso Map and Windscape System along with geoinformatics provide reliable wind prospecting and have been tried for different regions (Coppin et al., 2012). However, these tools are expensive considering the scale of projects in small wind areas.

1.3. Synthesised wind data
Synthesised wind data available from various sources provide preliminary understanding of the wind regime of a region. Depending on the physiographical features and climatic conditions, these data help assess wind potential in the region of interest validated by long term surface wind measurements. Wind resource atlas derived with the help of National Oceanic and Atmospheric Administration (NOAA) and National Aeronautical and Space Agency (NASA) Surface Meteorology and Solar Energy (SSE) wind data, validated with available surface measurements, provided a range of mean wind speeds on a meso–scale wind atlas for New found land (Khanand Iqbal, 2004). Similarly, a wind map for Bangladesh was prepared from synthesised global data of NOAA and NASA-SSE along with terrain specific surface features (Khan et al., 2004). Wind energy potential of the Saharan desert in Algeria was assessed based on NASA-SSE data and prospected to power a wind based desalination plant to support agriculture in the arid region (Mahmoudi, et al., 2009). The application of NASA-SSE data for wind power prospecting in two islands of Fiji was also demonstrated (Kumarand Prasad, 2010). These studies substantiate the advantage and increasing interest in synthesised data for regional wind resource assessment.

The present study explores wind resource potential in Himachal Pradesh, a federal Indian state in Western Himalayas based on synthesised wind data, validated with surface measurements. Seasonal wind profiles showing spatial variation of wind speeds are developed using geospatial techniques. The discussion includes the scope for deploying small-scale wind applications suitable for meeting the local energy requirements.