Journal of Energy Bioscience 2012, Vol.3, No.1, 1-12
7
Figure 4 Annual average wind speed in Himachal Pradesh
based on 1°×1°spatial resolution NASA-SSE data
Figure 5 Annual average wind speed in Himachal Pradesh based
on 2°×2° spatial resolution NOAA-CIRES reanalysis data
4.1.1
Validation
It is known that the density of vegetation and size of
canopy cover reduces with increasing elevation
(
towards alpine zone) and facilitates higher wind flow.
The surface measurements from IMD (though not
representative of the entire state), validate the
consistent increase in wind speeds with elevation as
illustrated by the three synthesised data, viz.
NASA-SSE, NOAA-CIRES and CRU (Figure 4~6).
Figure 6 Annual average wind speed in Himachal Pradesh
based on 10’×10’ spatial resolution CRU data
Synthesised wind data were cross-compared using
box-plots (Figure 7) to observe the pattern of monthly
wind speed variations over the region. On comparison
with NOAA-CIRES and CRU wind data, NASA-SSE
values were observed to be exaggerated. Although,
NOAA-CIRES data were more distributed due to their
coarseness in spatial resolution, they showed similar
monthly variations as CRU values, with a unimodal
rise in wind speeds during monsoon season (June to
September). Further, these data were validated with
surface measurements of IMD. The RMSE (Root
Mean Square Error) for NASA-SSE and
NOAA-CIRES were 2.57 m/s and 1.92 m/s
respectively. CRU data showed the least RMSE of
1.32
m/s on validation and hence scored over the rest
as the most representative data for the region.
4.1.2
Re-validation
As discussed in section 1.1.1, wind patterns are
retained within the vicinity of surface measurement
sites even upto 30 km, even though magnitudes vary
