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Indian Journal of Radio & Space Physics
Vol. 36, April 2007, pp. 128-144
Annual cycle of surface meteorological and solar energy parameters over Orissa
G N Mohapatra, U S Panda & P K Mohanty
Department of Marine Sciences, Berhampur University, Berhampur 760 007, Orissa, India
Email: [email protected]
Received 21 November 2005; revised 17 April 2006; accepted 16 January 2007
Surface meteorological and solar energy parameters over Orissa are studied using observed data from India
Meteorological Department (IMD) and National Aeronautics and Space Administration Surface meteorology and Solar
Energy (NASA SSE) data set. The observed data are mostly on surface meteorological parameters over 17 meteorological
stations over Orissa. SSE data set, Release 3, is a satellite and reanalysis derived 10 year climatology (July 1983-June 1993)
available on global grid mesh of 1° and consisting of both surface meteorological and solar energy parameters. The
usefulness of NASA data set for the Orissa regions are examined by comparing it with IMD data. Comparative views of the
annual cycle of both the data sets reveal the intensities and periods of extremes and their relative agreements /disagreements
in the course of the annual cycle. Closeness of fit and coherence between the two data sets are examined through scatter
plots and cross-spectral analysis, respectively. The results show a better goodness of fit between IMD and NASA data set in
air temperature and lowest standard error of estimate in wind speed. Cross-spectrum analysis shows very good coherence
between IMD and NASA data in the annual and semi-annual bands but lesser coherence in the intra-seasonal band. The
results suggest that NASA data, when used in conjunction with good quality observed data, can make it possible to assess
the renewable energy potential of different districts in Orissa, besides its use for weather and climate study.
Key words: Annual cycle, Insolation, Coherence, Albedo, Solar energy
PACS No: 92.60.-e; 96.60.-j
1 Introduction The State of Orissa lies in the north-eastern part of
the Indian peninsula and is bounded between latitude
(22°36′N -17°49′N) and longitude (81°27′E-87°18′E).
The state has an area of 1, 55,707 km2 and consists of
30 districts (Fig. 1). It is considered as one of the
meteorological subdivisions (No.7) of India and is
covered with 17 meteorological stations. The state is
broadly divided into four geographical regions, viz.
the northern plateau, central river basin, eastern hills
and coastal plains1. The district included in each of
the geographical regions and the elevations of the
areas above sea level have been documented1. The
state experiences mostly three seasons in a year, viz.
hot weather season (March-May), south-west
monsoon season (June-September) and winter season
(December-February). A study on climatic types of
Orissa2
based on climatologies of 17 meteorological
stations using water balance method3
indicates that 12
stations experience dry sub-humid climate (C1), two
stations experience moist sub-humid climate (C2), two
stations semi-arid type (D) and one station humid type
(B1) of climate in the moisture regime. While the
climate variability is very much apparent in the
moisture regime, it is very less and limited within
sub-types only in the thermal regime, as all the 17
stations experience megathermal type of climate.
The annual rainfall of Orissa is 1477 mm, of which
1167 mm, which is nearly 80% of the annual rainfall,
is accounted for by the four months during south-west
monsoon season. The variability of the south-west
monsoon rainfall is about 14% of the normal rain and
hence there have been seasons in the records which
has witnessed excess monsoon rainfall or drought in a
monsoon season. The monsoon sets in over the state
by the first week of June, covers the entire state by the
second week of June and completely withdraws from
the state by about 15 October. Besides the rainfall
during south-west monsoon season, the state
experiences 10% rainfall in the post-monsoon season
(October-November), 3% in the winter season
(December-February) and 8% in the pre-monsoon
season (March-May). The spatial distribution of
coefficient of variation of annual rainfall is very large
and in the range 19-29%. Coefficient of seasonal
rainfall is still larger and varies from a minimum of
15.9 in the coastal station to a maximum of 39.36 in
the western part of the state2. A study
4 on the
incidence of floods and droughts over Orissa using
the weekly and seasonal rainfall data for the period
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1950-1999 revealed that Orissa rainfall behaves
independent of the All-India rainfall for the same type
of extreme events and thus gave emphasis on studying
weather and climate of Orissa by analyzing good
quality long-term data at the district or taluk levels.
Due to increasing frequencies of the extreme weather
events such as tropical cyclones, floods, droughts and
heatwave during the last two decades, the life system
in the state of Orissa has been seriously affected1,5,6
.
Regional climate changes as depicted above have
adversely affected crop production and human life in
the monsoon region besides affecting various sectors
such as agriculture, aviation, energy, industry, etc.
Studies undertaken in India in pre-Indian Ocean
Experiment (INDOEX) and INDOEX phases reveal
that aerosol concentrations are increasing, which
could have marked implications in the regional
climate systems7. Since climatological information is
very vital and is a pre-requisite for planning and
executing various projects, considerable research
efforts are underway to improve our understanding of
climate variability and its influence on the seasonal
weather8. However, the major handicap has been the
unavailability of good quality meteorological data at
finer resolution. Since Orissa has only 17
meteorological centers covered by India
Meteorological Department (IMD), meteorological
information/ data representing the whole state is
scanty and fragmentary in nature. This necessitates an
alternate data source in order to fill the above data
gap. Therefore, a modest attempt has been made in
the present study to assess the potential of Surface
meteorology and Solar Energy (SSE) data from
National Aeronautical and Space Administration
(NASA) available on 1° latitude by 1°
longitude grid
systems. It may be mentioned that the SSE data set
makes it possible to quickly evaluate the potential of
the renewable projects for any region of the world and
is considered to be accurate for preliminary feasibility
studies of renewable energy projects9.
Thus, the
present study aims at examining the annual cycle of
surface meteorological and solar energy parameters
Fig. 1 — Map of Orissa representing 13 grid divisions at grid spacing of 1° latitude and 1° longitude and districts with their boundaries
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over Orissa and to compare the observed surface
meteorological parameters obtained from IMD with
that of NASA SSE data.
2 Data sets used
A brief description of the two types of data sets
used for the present study is as follows:
(a) NASA RELEASE 3 satellite and reanalysis
derived 10 years monthly climatology (July 1983-
June 1993) of (i) Surface meteorology (Air
temperature, °C; Daily temperature range, °C;
Wind speed, m/s; Relative humidity, %; Total
cloud amount, %) and (ii) Solar energy
(Insolation, kWh/m2/day; Clear sky insolation,
kWh/m2/day; Clear sky days; Earth skin
temperature, °C and Surface albedo) data.
(b) IMD (observed) monthly mean Surface
meteorology data (Air temperature, °C; Daily
temperature range, °C; Wind speed, m/s; Relative
humidity, % and Total cloud amount, %) for the
period 1984-1993.
Observed data on air temperature (maximum and
minimum), wind speed, relative humidity and cloud
amount over 17 meteorological centers of IMD in the
state are used, which are mostly monthly mean fields
for the period 1984-1993. This period has been
chosen, as the NASA Release 3 SSE data are also
available for the same period.
The NASA SSE data were downloaded from
NASA’s website (URL: http://eosweb.larc.nasa.gov/
sse/). The RELEASE 3 SSE data set is a satellite and
reanalysis derived 10 year (July 1983-June 1993)
climatology available at 1° latitude by 1° longitude
global grid system. The surface meteorological
parameters, viz. air temperature, daily temperature
range, wind speed, relative humidity and total cloud
amount are satellite and re-analysis-derived
parameters, which are compared with the
corresponding surface metrological parameters
observed by the IMD. Similarly solar energy
parameters of NASA SSE, viz. insolation, clear sky
insolation, clear sky days, earth skin temperature and
surface albedo used in the present study are satellite
and re-analysis derived data sets. Computational
procedures/measurement systems of insolation
includes the Pinker and Laszlo algorithm (Version
2.1)10
, while the other parameters have been estimated
and validated with 30 year average RET Screen
ground monitoring stations weather data base
(http://retscreen.gc.ca). Methodology for NASA
surface meteorology and solar energy parameters are
described in their web site (http://eosweb.larc.nasa.
gov/sse/).
The state of Orissa is divided into 13 grids (1°
latitude by 1° longitude) at which SSE data from
NASA are available (Table 1 and Fig. 1). Out of 13
grids, grid 1 and 2 data could not be used for
comparison, as there are no meteorological centers in
those grids. Table 1 depicts the grid number, the
corresponding meteorological center(s) and district(s).
Grid data from 3 to 13 have been compared with the
corresponding IMD data observed at the different
meteorological centers of the state. However, there
are data gaps (years mentioned within bracket) for
Table 1 — India Meteorological Observatories (Stations) and the grids (climatic types) corresponding to the
stations and districts in Orissa
Grid No. Meteorological stations District (s)
1 Koraput (B1) Absent Koraput, Malkangiri
2 Absent Nabarangpur, Kalahandi
3 Bhawanipatna (D) Rayagada, Kalahandi, Kandhamala
4 Gopalpur (C1) Ganjam, Gajapati
5 Bolangir (D), Titilagarh (C1) Bolangir, Sonepur, Boudh
6 Phulbani & Angul (C1) Kandhamala, Boudh, Angul , Nayagarh
7 Bhubaneswar (C1) Khurda, Nayagarh, Cuttack, Dhenkanal
8 Chandabali (C2),Cuttack (C1), Paradeep (C2) Cuttack, Kendrapara, Jajpur, Jagatsinghpur, Bhadrak
9 Sambalpur (C1) Baragarh, Jharsuguda, Sambalpur
10 Sundergarh (C1) , Jharsuguda (C1) Sambalpur, Deogarh, Sundergarh
11 Keonjhargarh (C1) Keonjhar
12 Balasore (C1), Baripada (C1) Mayurbhanja, Balasore
13 Puri (C1) Puri
C1: Dry sub-humid; C2: Moist sub-humid; D: Semi-arid and B1 : Humid
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some stations such as Chandbali (1986), Paradeep
(1990), Jharsuguda (1986), Sambalpur (1990),
Baripada (1990), Phulbani (1990), Bolangir (1989,
1990), Bhawanipatna (1991, 1992, 1993), Titlagarh
(1987, 1988, 1989, 1990), Angul (1984, 1985, 1987,
1990, 1991) and Sundergarah (1984, 1985, 1986,
1987, 1988, 1989, 1990).
It is worth mentioning that in contrast to ground
measurements, the SSE data set is a continuous and
consistent 10-year global climatology of insolation
and surface meteorology data on a 1° latitude and 1°
longitude grid system. Although the SSE data within a
particular grid cell are not necessarily representative
of a particular microclimate, or point within the cell,
the data are considered to be the average over the
entire area of the cell. For this reason, the SSE data
set is not intended to replace quality ground
measurement data. Its purpose is to fill the gap where
ground measurements are missing, and to augment
areas where ground measurements do exist.
2.1 Data quality
Uncertainty of the Release-3 NASA SSE data were
estimated9 by comparing it with data obtained from
historical ground measurements made by National
Renewable Energy Laboratory (NREL) and Canada’s
Energy Diversification Research Laboratory
(CEDRL). The uncertainty estimated for air
temperature in SSE data is 3.2% for temperature
range9
203-243 K and the uncertainty decreased in
near linear manner to 1.1% as temperature increased
to 263 K and remained constant up to 313 K. Thus,
for the average of temperature in Orissa, the
uncertainty is about 1.1%. An estimated uncertainty
of 9.7% was observed in case of relative humidity9.
Uncertainty estimated for wind speed ranged between
-3 m/s and +2 m/s over Flat Mountain and coastal
stations, whereas uncertainty in continental region is
relatively less (1.4 m/s).
Solar insolation values were obtained using NASA
Langley Parameterized Shortwave Algorithm with
inputs from NASA International Satellite Cloud
Climatology and NASA Goddard Earth Observatory
System (GEOS-I) reanalysis meteorology. The
uncertainty range in the interior region varied between
12.9% and 17%, whereas for coastal zones it varied9
between 12.9% and 15.4%. On an average SSE solar
energy insolation are higher than ground
measurements. However, it was suggested9 that
satellite based NASA SSE insolation estimates are
reasonably consistent for a wide range of global
environments and hence it is worth examining for the
Orissa region.
2.2 Data analysis procedure
Surface meteorological data for 17 meteorological
stations are collected from India Meteorological
Department, Bhubaneswar. Monthly mean fields are
obtained by averaging for a ten year period (1984-
1993). When more than one ground measurement
stations are located in one particular grid (Table 1 and
Fig. 1), data of meteorological stations are averaged
and then compared with SSE data for the particular
grid. Observed surface meteorological parameters are
compared with NASA SSE data by examining the
annual cycles of both the data sources and also
through scatter plots and estimation of statistical
summary such as R2 and standard error estimate. In
order to understand the degree of coherence between
observed (IMD) and NASA data sets, cross-spectrum
analysis is performed using the SIGMA SPEC 3.2
spectral analysis programme11
. The programme uses
standard time series analysis procedure such as
filtering, de-trending, tapering and smoothing, which
are discussed in Mohanty and Dash12
. Time series
consisting of 132 monthly means are subjected to
cross-spectrum analysis following Panofsky and
Brier13
and then the spectral densities are re-
normalized by frequency and the cross-spectra
(coherence squared) are computed. The formula for
the limiting coherence squared of probability level P
is given as:
β = 1 – P 1/[(df/2) –1]
where df/2 is the effective number of Fourier
components in the spectral window. The number of
degrees of freedom (df) is twice the number of
Fourier components. The annual (12 months),
semiannual (6 months) and intra-seasonal frequencies
are determined as 0.083, 0.17 and 0.25 by taking the
inverse of the periodicity in months.
3 Results and discussion Results are presented in four sections. First part
deals with the annual cycle of surface meteorological
parameters based on observed data from IMD and
SSE data from NASA. In second part, annual cycle of
solar energy parameters are discussed based on
NASA data only. Third part examines the relationship
between observed surface meteorological data and
NASA SSE data through scatter plots and subsequent
analysis of their statistical properties. Coherency
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relationship between observed meteorological
parameters and NASA data sets are discussed in the
last section.
3.1 Annual cycle of surface meteorological parameters
Processes in the tropics move from north to south
and back following the annual cycle of the solar
forcing, as the sun crosses the equator twice a year.
However, the distribution of land-sea in the Indian
subcontinent and the resulting heating contrast alter
the circulation pattern in such a way that the annual
cycles do not strictly follow the seasonal reversal of
solar forcing14, 15
. In fact, the geography of this region
dictates the circulation pattern, which is not only
meridional in nature but also zonal in character.
Therefore, the study assumes importance in a coastal
state like Orissa, which has variable physiography.
3.1.1 Air temperature
Figure 2 depicts the monthly mean air temperature
(°C) over 11 grid points in Orissa based on observed
IMD and NASA data sets. Grid 1 (Koraput and
Malkangiri) and Grid 2 (Nabarangpur and Kalahandi)
are not represented, as observed IMD data are not
available for the two grids. Spatial variability in the
annual cycle of air temperature is apparent both in the
IMD and NASA data. However, in grid No. 6
represented by Angul and Phulbani stations, NASA
air temperature is higher than the observed IMD
temperature throughout the annual cycle. In grid Nos.
5, 10, 11 and 12, for part of the annual cycle NASA
temperature is higher than the observed temperature.
But in grids 3, 4, 7, 8 and 13, observed temperature is
higher as compared to NASA temperature throughout
the annual cycle. Table 2 elucidates the intensity and
period of temperature maxima and minima over
different grids in the course of the annual cycle.
Intensity of temperature maxima are same over grid
12. IMD temperature maxima are more than NASA
temperature maxima for grids 3, 4, 5, 7, 8, 10, 11 and
less than NASA temperature maxima for grids 6, 9
and 13. Period of temperature maximum is May in
most of the grids in IMD data, whereas it is April for
NASA data. Intensities of temperature minima are
higher for NASA data as compared to IMD data in
most of the grids except for grids 4, 7 and 8. Period of
temperature minimum is December, both in IMD and
NASA data, for six grids, while in the rest of the five
grids IMD data lags by one month as compared to
NASA data. Thus, the results reveal that there is
agreement in IMD and NASA data over some grids,
while they slightly differ over other grids.
3.1.2 Daily temperature range
Figure 3 depicts the daily temperature range (°C)
over 11 grid points in Orissa. It represents the
difference between daily temperature maximum and
minimum. It is observed that daily temperature ranges
in NASA data are less than those in IMD data from
May to December, while the reverse pattern exists
from January to May. In grid 9, IMD temperature
ranges are higher than those in NASA data throughout
the annual cycle. Period of maximum temperature
range is observed between January and April in IMD
data, while it is mostly in the month of March in
NASA data (Table 2). The intensities of maximum
temperature range are higher in NASA data as
compared to those in IMD data. Period of minimum
temperature range is August for all the grids in NASA
data, while it varies between July and September in
IMD data. The intensities of minimum temperature
range in NASA data are less than those in IMD data
in most of the grids. Thus, agreement between NASA
and IMD data is better in the period of occurrence
than that of intensity.
3.1.3 Wind speed
The annual cycle of wind speed (m/s) is
represented in Fig. 4. Intensity and period of
maximum and minimum wind speed are also shown
in Table 2. It is observed that throughout the annual
cycle wind speed in NASA data are higher than those
in IMD data. Further, the intensities and periods of
occurrence of maximum and minimum wind speed
are also different. NASA wind speed data have been
estimated for 10 m height and is same as the height of
measurement of IMD wind speed. GEOS-1 wind
speed, which were originally provided on a 2° latitude
by 2.5° longitude grid system were interpolated to a
1° grid system for the release of NASA SSE wind
speed data. The agreement between NASA and IMD
data for wind speed is relatively poor, because the
localized topography effects are not accounted for in
the SSE data and the interpolation technique
followed. Further, IMD data is a point measurement
type, while the NASA data based on satellite estimate
are an aerial average and could contribute to the
observed difference between the two data types. The
uncertainty and the higher estimate of NASA wind
speed was also pointed out by Whitlock et, al9.
3.1.4 Relative humidity
Annual cycles of relative humidity (%) based on
IMD and NASA data are shown in Fig. 5. Despite the
difference in magnitude, the patterns of annual cycles
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Fig. 2 — Annual cycle of air temperature (°C) (monthly means for the period 1984-1993) over 11 grid points (1°×1°) in Orissa [Solid
lines represent the observed air temperature from IMD and the dashed lines represent the temperature obtained from NASA data. Grid
point numbers correspond to those shown in Fig. 1.]
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Fig. 3 — Annual cycle of daily temperature range (°C) (monthly means for the period 1984-1993) over 11 grid points (1°×1°) in Orissa
[Solid lines represent the observed daily temperature range from IMD and the dashed lines represent the daily temperature range obtained
from NASA data. Grid point numbers correspond to those shown in Fig. 1.]
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Fig. 4 — Annual cycle of wind speed (m/s) (monthly means for the period 1984-1993) over 11 grid points (1°×1°) in Orissa [Solid lines
represent the observed wind speed from IMD and the dashed lines represent the wind speed obtained from NASA data. Grid point
numbers correspond to those shown in Fig. 1.]
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Fig. 5 — Annual cycle of relative humidity (%) (monthly means for the period 1984-1993) over 11 grid points (1°×1°) in Orissa [Solid
lines represent the observed relative humidity from IMD and the dashed lines represent the relative humidity obtained from NASA data.
Grid point numbers correspond to those shown in Fig.1.]
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are similar both for IMD and NASA data in most of
the grids, which follow traditional seasonal cycle with
highest relative humidity in northern summer and
lowest during northern winter. Except for grids 3, 4
and 13, relative humidities are higher in NASA data
than those in IMD data. Whitlock et al.9 also showed
an average estimated uncertainty of 9.7% for NASA
data. Table 2 depicts the intensity and periods of
maximum and minimum of relative humidity. A close
agreement between the IMD and NASA data is
observed for the period of maximum relative
humidity, which is either August or July in most of
the grids (Table 2). Period of occurrence of relative
humidity minimum is March for almost all grids in
NASA data, while in IMD data the period varies from
November to April/May.
3.1.5 Total daylight cloud amount
The annual cycle of total daylight cloud amount
(%) is shown in Fig. 6. Presence of traditional
seasonal maxima during monsoon period and minima
during winter is very much apparent in the annual
cycles of both IMD and NASA data set for most of
the grids except grid 3. It is observed that throughout
the annual cycle the magnitudes of daylight cloud
amount based on NASA data are higher than those in
IMD data and for all the 11 grids. Considering the
periods of occurrences of maximum and minimum
intensities of total daylight cloud amount, July is
observed as the period of maximum daylight cloud
amount in NASA data for all the grids (Table 2). But
in IMD data, the period of maximum daylight cloud
amount varies between June through September, well
known as the south-west monsoon season. Similarly,
in case of minimum daylight cloud amount, the period
varies between December and January for NASA data
and between November and February for IMD data.
However, there are some grids where close agreement
is observed between IMD and NASA data sets. The
intensities of maximum and minimum daylight cloud
amount in NASA data are significantly higher than
those in IMD data and could be associated with the
uncertainty in the estimation of daylight cloud amount
based on satellite cloud climatology in NASA data.
3.2 Annual cycle of solar energy parameters
Solar energy parameters such as insolation, clear
sky insolation, clear sky days, earth skin temperature
and surface albedo are obtained from NASA satellite
and reanalysis derived insolation and meteorological
data for the 10-year period from July 1983 through to
June 1993. NASA SSE data set has been utilized as a
stand-alone data source by the researchers around the
world involved in Renewable Energy Technologies
(RETs)9. These technologies (RETs) are poised to
change the face of the world’s energy market,
particularly for the rural communities by providing
technologies for solar ovens, simple photovoltaic
panels, construction of commercial buildings and
large thermal and wind generating power plants.
Crucial to the success of the RETs is the availability
of accurate, global solar radiation and meteorology
data. Therefore, the study assumes importance for the
state of Orissa, where no information is available on
solar energy parameters except for one station, i.e.
Bhubaneswar. Thus, this data set could be an
important information base in order to design any
RET Project in the rural belts of Orissa.
Monthly mean climatology of solar energy
parameters for 10-year period over 13 grid points in
Orissa are examined. Looking at the no/less spatial
variability between different grids, monthly mean
values were again averaged for the 13 grids and are
presented in the annual cycle to represent the
conditions for the state of Orissa. Figure 7 depicts the
annual cycle of solar energy parameters for the state
of Orissa.
3.2.1 Insolation
The insolation in the state of Orissa ranges between
3.5 and 6.79 kWh/m2/day. The lowest values are
observed in the southern districts of Koraput and
Malkangiri, whereas highest values are observed in
the district of western Orissa and coastal Orissa. In
the course of the annual cycle, insolation values start
from a lower minimum in January, reach the highest
in April and then a sudden decline is observed to
reach the lowest minimum in July/ August during
monsoon season (Table 3). Later, it gradually
increases again.
3.2.2 Clear sky insolation
Clear sky insolation is the amount of solar radiation
incident on the surface of the earth during clear sky
days (cloud fraction < 10%). The trend of annual
cycle is somewhat different from insolation. May is
observed as the period of maximum insolation and
December as the period of minimum insolation in the
state of Orissa (Table 3), which are respectively the
period of hottest and coldest months in Orissa1. The
values range between 4.73 and 7.8 kWh/m2/day.
3.2.3 Clear sky days
Numbers of clear sky days (cloud fraction < 10%)
are depicted in Fig. 7. December/January is the period
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Fig. 6 — Annual cycle of total daylight cloud amount (%) (monthly means for the period 1984-1993) over 11 grid points (1°×1°) in
Orissa [Solid lines represent the observed daylight cloud amount from IMD and the dashed lines represent the daylight cloud amount
obtained from NASA data. Grid point numbers correspond to those shown in Fig. 1.]
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Fig. 7 — Annual cycle of solar energy parameters over Orissa: (a)
Insolation (kWh/m2/day), (b) Clear sky insolation (kWh/m2/day),
(c) Clear sky days (days), (d) Surface albedo and (e) Earth skin
temperature (°C)
when maximum number of clear sky days is observed
(Table 3) while it becomes zero during the monsoon
season (June to September). Clear sky days are
maximum in the district of western and central Orissa
and minimum in the district of southern and coastal
Orissa.
3.2.4 Surface albedo
Surface albedo is the fraction of insolation reflected
by the surface of the earth and is very much
dependent on the nature of the surface. However, in
the present study a direct proportionality between
surface albedo and clear sky days is observed. Period
representing the maximum number of clear sky days
are also observed as the period of maximum surface
albedo and vice versa (Table 3). Periods of minimum
surface albedo also closely match with the periods of
minimum insolation (Table 3).
3.2.5 Earth skin temperature
Earth skin temperature is the temperature of the
earth’s surface. The annual cycle pattern somewhat
resembles the pattern of clear sky insolation. The
earth skin temperature in Orissa ranges between 16.6
and 30.7 °C. The periods of earth skin temperature
maxima (April) closely match with the periods of
insolation maxima, which is obvious due to the direct
relationship between the two. Periods of earth skin
temperature minima also closely match with that of
clear sky insolation minima. Earth skin temperature
maxima are relatively more in the western districts of
Orissa, whereas the minima show an opposite trend
(Table 3).
3.3 Co-variability between IMD and NASA data sets
In order to establish the goodness of NASA SSE
data sets, they are compared with IMD data sets
through scatter plots and analyzing the regression
equation, R2 and standard error of estimate. Further,
coherence relationship between observed surface
meteorological parameters and NASA insolation and
air temperature are examined at annual (12 months),
semi-annual (6 months) and intra-seasonal (4 months)
frequencies.
3.3.1 Scatter plots
Figure 8 depicts the scatter plots and the slope and
intercept of the linear regression lines, considering
IMD data as independent variable and NASA data as
dependent variable. It is observed that out of the five
scatter plots, goodness of fit is better between IMD
and NASA data in air temperature and is corroborated
by the statistical summary having highest values of R2
and lower standard error of estimate (Table 4).
Excepting total daylight cloud amount, where the
standard error of estimate is quite high and there is a
great deal of scatter, other parameters show relatively
low scatter as well as low standard error of estimates.
The lowest standard error of estimate is observed in
case of wind speed and the goodness of fit is better at
lower wind speed. The results suggest that NASA
temperature compare very well with observed air
temperature. Other NASA meteorological parameters
such as wind speed, relative humidity and temperature
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Fig. 8 — Scatter plots between Observed (IMD) and NASA data
(a) Air temperature (°C), (b) Temperature range (°C), (c) Relative
humidity (%), (d) Wind speed (m/s) and (e) Daylight cloud
amount (%)
Table 4 — Statistical summary of multiple regression analysis
with IMD and NASA data as independent and dependent
variables. (R - Coefficient of multiple correlations, R2 - coefficient
of multiple determinations, df-degree of freedom)
Variable pairs R R2 Df Standard error
of estimate
Air temperature
(IMD/NASA)
0.8274 0.6847 130 1.6766
Temperature range
(IMD/NASA)
0.6076 0.3692 130 3.27206
Relative humidity
(IMD/NASA)
0.637 0.4059 129 9.90622
Wind speed
(IMD/NASA)
0.3751 0.1407 130 0.600106
Total cloud amount
(IMD/NASA)
0.6412 0.4112 130 17.81911
range can also be used for Orissa region but with
caution.
3.3.2 Coherence
The co-variability of two time series can be
measured by cross-spectrum analysis16
. Cross-spectral
technique, especially coherence square, has been used
to characterize the extent of spectral coherence in
many fields of natural sciences. Periodicities in
drought and their association with periodicities in
solar terrestrial phenomena are common in
climatological investigations17,18
. Therefore, attempts
have been made in this study to examine the
coherence relationship between IMD and NASA data.
The parameters considered are observed air
temperature, observed precipitation, NASA air
temperature and NASA insolation. Coherence
relationship has been determined only for grid No. 7,
as a representative grid, because, it is represented by
Bhubaneswar meteorological station, which is also the
regional meteorological center and thus monthly data
for the considered ten-year period are available.
Coherence spectra between different parameters are
depicted in Fig. 9. The parameters are considered in
the following order to facilitate cross-spectrum
analysis: (i) IMD air temperature, (ii) NASA air
temperature, (iii) IMD precipitation and (iv) NASA
insolation.
(a) IMD air temperature: NASA air temperature (i-
ii) — Two dominant spectral peaks are observed at
annual and semi-annual bands. However, the spectral
peaks are of relatively less magnitude in the intra-
seasonal and higher frequencies. Thus, the result
indicates that both IMD and NASA air temperatures
are highly coherent in the annual and semi-annual
bands but less/incoherent in the higher frequencies.
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143
Fig. 9 — Coherence square of (a) IMD air temperature–NASA air
temperature (i-ii), (b) IMD precipitation-IMD air temperature (iii-
i), (c) IMD precipitation-NASA air temperature (iii-ii), (d) IMD
precipitation-NASA insolation (iii-iv), (e) IMD air temperature–
NASA insolation (i-iv) [Dashed horizontal line represents the
99% significance level.]
(b) IMD precipitation-IMD air temperature
(iii-i) — For the combination (iii-i) two spectral
peaks, one in the annual and the other in the semi-
annual band, are distinctly observed. Spectral peak in
the intra-seasonal band and two more in the higher
frequencies are also observed. But the magnitudes of
the spectral peaks at intra-seasonal and other higher
frequencies are relatively less as compared to those in
the annual and semi-annual bands. Thus, the
coherence of IMD precipitation with IMD air
temperature is better in annual and semi-annual
bands.
(c) IMD precipitation-NASA air temperature (iii-
ii) — For the combination (iii-ii) spectral peaks are
almost similar and also of same magnitudes
respectively, as in case (iii-i). Thus, the result
suggests that IMD precipitation is equally coherent
with NASA air temperature, as it is with IMD air
temperature. However, the magnitudes of coherence
in the annual and semi-annual bands are less as
compared to those in IMD air temperature versus
NASA air temperature (i-ii).
(d) IMD precipitation-NASA insolation (iii-iv) —
For the said combination, besides the spectral peaks
at annual, semi-annual and intra-seasonal bands, a
very distinct peak is observed in the lower frequency
(0.01) corresponding to a cycle of 100 months. This
feature is not observed in the other coherence plots.
Thus, coherence of precipitation with insolation at
smaller frequency (100 months) is an interesting
feature, and could be a matter of further study using
observed insolation.
(e) IMD air temperature-NASA insolation (i-iv) —
For this combination, very good coherent relationship
is observed at annual, semi-annual and seasonal
bands. Unlike other combinations, a spectral peak at
higher frequency (0.38) corresponding to a cycle of
2.7 months is also observed. Therefore, it can be
stated that IMD air temperature is coherent with
NASA insolation in annual, semi-annual and intra-
seasonal frequencies and hence NASA insolation
could be of much value in energy related study of
varying time scales.
Thus, the above results on coherence suggest that
NASA air temperature and insolation show very good
coherence relationship with observed air temperature
and precipitation, and can be used for further
applications, where observed data are inadequate or
not available.
4 Summary and conclusions Results of the present study assumes importance as
the NASA SSE data set (satellite and reanalysis
derived 10-year climatology) available at 1° latitude
and 1° longitude grid spacing over the globe,
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INDIAN J RADIO & SPACE PHYS, APRIL 2007
144
formulated for assessing and designing renewable
energy systems for any region of the world, has been
compared with observed IMD data over Orissa.
Annual cycle of surface meteorological parameters
using both the data sets reveal large spatio-temporal
variability in the intensities (maxima and minima) and
periods of surface meteorological parameters and thus
point to the need for closer network of observatories
or data at high resolution to assess the exact nature of
weather and climate variability even in the regional
scale. On the other hand, the annual cycles of solar
energy parameters based on NASA data alone show
only temporal variability and very less spatial
variability and suggest the usefulness of NASA data
for resource assessment and initiating regional
renewable energy programs.
Scatter plots and the corresponding statistical
summary indicate the better goodness of fit between
IMD and NASA data in air temperature. The lowest
standard error of estimate is observed in case of wind
speed. Coherence relationship indicates that IMD and
NASA air temperature are highly coherent in the
annual and semi-annual bands. Besides the spectral
peaks at annual, semi-annual and seasonal bands, a
dominant peak in the lower frequency (100 months)
for IMD precipitation and NASA insolation and a
peak at higher frequency corresponding to a cycle of
2.7 months for IMD air temperature and NASA
insolation are some of the significant features
observed. Therefore, it can be stated that NASA data
when used in conjunction with good quality observed
data, can make it possible to assess the renewable
energy potential of different districts in Orissa besides
its use for weather and climate study. The present
study offers scope to assess the renewable energy
potential of different districts in the state with the use
of NASA data and also provides an alternative
meteorological and solar energy data source for the
data sparse region of Orissa.
Acknowledgements The authors wish to thank NASA and IMD for
making available the necessary data. Authors
acknowledge the financial support extended by the
Department of Science and Technology, New Delhi
under its grant ES/48/002/2001 to carry out part of the
work reported in this paper.
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