Journal of Energy Bioscience 2012, Vol.3, No.1, 1-12
4
and Shimla) recorded for different durations (Table 1).
Wind speed at Mandi was obtained from a literature
on wind climatology in India (Mani and Mooley,
1983).
The measured data included: 1) synoptic hour
values (local time 8:30 and 17:30); 2) daily averages
for durations between synoptic hours and; 3) monthly
averages (not available for Mandi) of wind speeds.
Daily averages of wind speeds were obtained by
averaging the mean for two 12 hour periods starting
from 17:30 hrs, capturing the diurnal variations of the
wind. Wind measurements were standardized to 10 m
using power law equation (1) as per World Meteorological
Organization (WMO) norm (Ramachandra et al., 1997).
V/V
0
= (H/H
0
)
α
(1)
where V
0
is the measured wind speed, V is the
standardized wind speed, H
0
is the measured height, H
is the desired height (10 m) and α is the power law
index. Here α is a measure of roughness due to
frictional and impact forces on the ground surface
which varies according to terrain, time and seasons.
The value of α calculated for most of the regions
representing the Himalayan terrain are well above
0.40
based on long term observations and calculations
(
Mani and Mooley, 1983). In order to minimize
extrapolation errors we considered the least value of
0.40
for Himachal Pradesh. The wind measurement
heights in Himachal Pradesh were standardized using
power law equation with α as 0.4.
Topography of Himachal Pradesh renders enormous
variation to the micro-climate, wind speeds and
direction, adding to complexity of wind resource
assessment in the region. The available IMD surface
wind data were characterized by large gaps and
non-standard measurement heights. In addition, these
stations were not representative of the diverse
agroclimatic zones and particularly unavailable for the
high elevation zone (> 3 500 m) of Himachal Pradesh
(
Figure 2). Capturing the wind regime of its complex
terrain using these data cannot be a desirable option.
Recently, IMD has deployed Automatic Weather
Stations (AWS) at 22 locations in Himachal Pradesh
(
Figure 2) at 2 m heights above the ground (Automatic
Weather Station, 2012,
).
However, according to the communication from IMD,
AWS based wind data were available merely for 3
stations (Bilaspur, Una and Udaipur) for the year 2011.
Hence, we explored long term global wind datasets
synthesised based on prudent models appropriate for
the study area.
Figure 2 Total wind stations in Himachal Pradesh
3.2.
Available synthesised wind data
3.2.1.
NASA SSE
The National Aeronautics and Space Administration
(
NASA) Langley Research Center Surface
Meteorology and Solar Energy (SSE) meteorological
datasets were derived from a variety of
earth-observing satellites. Particularly, NASA-SSE
10-
year (1983~1993) monthly average wind speeds at
1
°X1° spatial resolution for different heights above
the earth’s surface were developed based on a Global
Circulation Model (GCM) applied on the outputs from
Goddard Earth Observing System (GEOS). It is
known that, vegetation and canopy reduces
near-surface wind speeds variably. Hence, based on
parameterizations developed from observations in
Canada, Scandinavia, Africa, and South America,
NASA
synthesised
wind
speeds
for
17
surface/vegetation types at different heights (Takacs et
al., 1994; NASA, 2012,
se/documents/SSE6Methodology.pdf).
According to NASA, synthesised SSE 10 m wind
speed estimates for airport-like flat surfaces were
validated with 30-year average airport wind data over the
