Journal of Energy Bioscience 2012, Vol.3, No.1, 1-12
6
checks to minimize data consolidation errors. The
Gobal Land One-km Base Elevation Project (GLOBE)
data of the National Geophysical Data Center (NGDC)
were re-sampled to 10’×10’ (ten minute spatial
resolution) elevation grids where every cell with more
than 25% land surface (those below 25% being
considered water bodies) represents the average
elevation of 100~400 GLOBE elevation points. The
climatic average of wind speeds measured at 2 to 20 m
anemometer heights (assumed to be standardized
during collection) collated from 3 950 global
meteorological stations together with the information
on latitude, longitude and elevation were interpolated
based on a geo-statistical technique called thin plate
smoothing splines. Elevation as a co-predictor
considers topographic influence on the wind speed
and proximity of a region to the measuring station
improves the reliability of the interpolated data.
During interpolation inconsistent data were removed
appropriately. This technique was identified to be
steadfast in situations of data sparseness or irregularity
(
New et al., 2002). The 10’ ×10’ spatial resolution wind
speed data as climatic averages were available for all
global regions (excluding Antarctica). These were
accessed at
/hr g/tmc/.
3.3.
Wind profiling
Synthesised wind speed data collected from
NASA-SSE, NOAA-CIRES and CRU covering the
study area of Himachal Pradesh grid-wise is
represented in Figure 3 along with IMD surface
measurement sites for which data were provided.
Based on the spatial as well as ground (field visits)
understanding of agroclimatic zones in Himachal
Pradesh, 10 m surface/vegetation influenced wind
speeds were collected from NASA-SSE for 14 grids
(
locations) at 1°×1° spatial resolution. These
surface/vegetation types included: 1) rough glacial
snow/ice (six locations above 3 500 m); 2)
needleleaf-evergreen trees (three locations within 1
000-3 500
m) and; 3) broadleaf-needleleaf trees (five
locations below 1 000 m). Similarly, 2°×2° coarse
spatial resolution wind speeds for 9 grids and 10’ ×10’
high spatial resolution wind speeds for 266 grids were
collected from NOAA-CIRES and CRU respectively
(
Figure 3). The collected wind speed data from
NASA-SSE, NOAA-CIRES and CRU were
interpolated using geospatial techniques to observe the
annual wind speed regime over Himachal Pradesh.
These data were validated and cross-compared with
available surface wind speed measurements using
statistical methods to identify the most representative
data for the study area. Regional variations of this
wind data were re-validated with surface measurement
in proximity by generating buffers of 10 km around
the IMD sites. Monthly average wind speed values
from the representative synthesised data were used to
produce seasonal wind profiles for Himachal Pradesh.
These seasonal wind variations were compared with
surface measurements from IMD.
Figure 3 Synthesized as well as surface (IMD) wind speed
locations for Himachal Pradesh
4
Results and Discussions
4.1
Representative synthesised wind data
Himachal Pradesh is a complex terrain with features
like vegetation and local relief influencing wind
speeds in the region. Wind regime represented by
NASA-SSE data (Figure 4) varied from (2.14±0.42) to
(4.45
±0.48) m/s over the region. Long term but coarse
NOAA-CIRES data (Figure 5) showed annual average
wind speed variation similar to NASA-SSE, ranging
from (1.96±0.73) to (4.29±0.70) m/s. High spatial
resolution CRU wind speeds varied from (1.02±0.30)
to (2.88±0.41) m/s (Figure 6). The three synthesised
data, although different in magnitudes, showed a
consistent increase in annual average wind speeds
with increasing elevation that is also coherent with the
agro-climate zones of Himachal Pradesh.
