Annual cycle of surface meteorological and solar energy parameters over Orissa

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1 Indian Journal of Radio & Space Physics Vol. 36, April 2007, pp 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 , Orissa, India pratap_mohanty@yahoo.com 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: e; 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 N) and longitude (81 27 E E). The state has an area of 1, 55,707 km 2 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 plains 1. The district included in each of the geographical regions and the elevations of the areas above sea level have been documented 1. 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 Orissa 2 based on climatologies of 17 meteorological stations using water balance method 3 indicates that 12 stations experience dry sub-humid climate (C 1 ), two stations experience moist sub-humid climate (C 2 ), two stations semi-arid type (D) and one station humid type (B 1 ) 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 in the western part of the state 2. A study 4 on the incidence of floods and droughts over Orissa using the weekly and seasonal rainfall data for the period

2 MOHAPATRA et al.: METEOROLOGY OF ORISSA 129 Fig. 1 Map of Orissa representing 13 grid divisions at grid spacing of 1 latitude and 1 longitude and districts with their boundaries 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 affected 1,5,6. 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 systems 7. 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 weather 8. 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 projects 9. Thus, the present study aims at examining the annual cycle of surface meteorological and solar energy parameters

3 130 INDIAN J RADIO & SPACE PHYS, APRIL 2007 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 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/m 2 /day; Clear sky insolation, kwh/m 2 /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 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 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: 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 ( Methodology for NASA surface meteorology and solar energy parameters are described in their web site ( 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 (B 1 ) Absent Koraput, Malkangiri 2 Absent Nabarangpur, Kalahandi 3 Bhawanipatna (D) Rayagada, Kalahandi, Kandhamala 4 Gopalpur (C 1 ) Ganjam, Gajapati 5 Bolangir (D), Titilagarh (C 1 ) Bolangir, Sonepur, Boudh 6 Phulbani & Angul (C 1 ) Kandhamala, Boudh, Angul, Nayagarh 7 Bhubaneswar (C 1 ) Khurda, Nayagarh, Cuttack, Dhenkanal 8 Chandabali (C 2 ),Cuttack (C 1 ), Paradeep (C 2 ) Cuttack, Kendrapara, Jajpur, Jagatsinghpur, Bhadrak 9 Sambalpur (C 1 ) Baragarh, Jharsuguda, Sambalpur 10 Sundergarh (C 1 ), Jharsuguda (C 1 ) Sambalpur, Deogarh, Sundergarh 11 Keonjhargarh (C 1 ) Keonjhar 12 Balasore (C 1 ), Baripada (C 1 ) Mayurbhanja, Balasore 13 Puri (C 1 ) Puri C 1 : Dry sub-humid; C 2 : Moist sub-humid; D: Semi-arid and B 1 : Humid

4 MOHAPATRA et al.: METEOROLOGY OF ORISSA 131 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 estimated 9 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 range 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 humidity 9. 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 varied 9 between 12.9% and 15.4%. On an average SSE solar energy insolation are higher than ground measurements. However, it was suggested 9 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 ( ). 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 R 2 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 programme 11. The programme uses standard time series analysis procedure such as filtering, de-trending, tapering and smoothing, which are discussed in Mohanty and Dash 12. Time series consisting of 132 monthly means are subjected to cross-spectrum analysis following Panofsky and Brier 13 and then the spectral densities are renormalized by frequency and the cross-spectra (coherence squared) are computed. The formula for the limiting coherence squared of probability level P is given as: 1/[(df/2) 1] β = 1 P 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

5 132 INDIAN J RADIO & SPACE PHYS, APRIL 2007 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 forcing 14, 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 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 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 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, al 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

6 MOHAPATRA et al.: METEOROLOGY OF ORISSA 133 Fig. 2 Annual cycle of air temperature ( C) (monthly means for the period ) 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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8 MOHAPATRA et al.: METEOROLOGY OF ORISSA 135 Fig. 3 Annual cycle of daily temperature range ( C) (monthly means for the period ) 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.]

9 136 INDIAN J RADIO & SPACE PHYS, APRIL 2007 Fig. 4 Annual cycle of wind speed (m/s) (monthly means for the period ) 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.]

10 MOHAPATRA et al.: METEOROLOGY OF ORISSA 137 Fig. 5 Annual cycle of relative humidity (%) (monthly means for the period ) 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.]

11 138 INDIAN J RADIO & SPACE PHYS, APRIL 2007 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 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 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 Insolation The insolation in the state of Orissa ranges between 3.5 and 6.79 kwh/m 2 /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 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 Orissa 1. The values range between 4.73 and 7.8 kwh/m 2 /day Clear sky days Numbers of clear sky days (cloud fraction < 10%) are depicted in Fig. 7. December/January is the period

12 MOHAPATRA et al.: METEOROLOGY OF ORISSA 139 Fig. 6 Annual cycle of total daylight cloud amount (%) (monthly means for the period ) 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.]

13 140 INDIAN J RADIO & SPACE PHYS, APRIL 2007 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) 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, R 2 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. Fig. 7 Annual cycle of solar energy parameters over Orissa: (a) Insolation (kwh/m 2 /day), (b) Clear sky insolation (kwh/m 2 /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 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 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 R 2 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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15 142 INDIAN J RADIO & SPACE PHYS, APRIL 2007 Table 4 Statistical summary of multiple regression analysis with IMD and NASA data as independent and dependent variables. (R - Coefficient of multiple correlations, R 2 - coefficient of multiple determinations, df-degree of freedom) Variable pairs R R 2 D f Standard error of estimate Air temperature (IMD/NASA) Temperature range (IMD/NASA) Relative humidity (IMD/NASA) Wind speed (IMD/NASA) Total cloud amount (IMD/NASA) range can also be used for Orissa region but with caution. 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 (%) Coherence The co-variability of two time series can be measured by cross-spectrum analysis 16. 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 investigations 17,18. 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 (iii) Two dominant spectral peaks are observed at annual and semi-annual bands. However, the spectral peaks are of relatively less magnitude in the intraseasonal 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.

16 MOHAPATRA et al.: METEOROLOGY OF ORISSA 143 Fig. 9 Coherence square of (a) IMD air temperature NASA air temperature (i-ii), (b) IMD precipitation-imd air temperature (iiii), (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 semiannual 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 (iiiii) 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 intraseasonal 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,

17 144 INDIAN J RADIO & SPACE PHYS, APRIL 2007 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. References 1 India Meteorological Department (IMD), Climate of Orissa (The Meteorological Office Press, Pune), 2002, Mohanty P K, Panda U S, Mohapatra G N & Dash S K, Variability in Weather and Climate of Orissa, J Wea Mod (USA), 2005 (Communicated). 3 Subrahmanyam V P, Water Balance and its Applications (with special reference to India), (Andhra University Press, Waltair), 1982, Sikka D R, Proceedings of the Brain Storming Session on Meteorological extremes and local climate variability (Regional Research Laboratory, Bhubaneswar), 2000, Mohanty P K, Panda U S & Mohapatra G N, Proceedings of the National seminar on GIS Application in Rural development with Focus on Disaster Management (National Institute of Rural Development, Hyderabad), 2005, De U S & Mukhopadhay R K, Severe heatwave over Indian subcontinent in 1998 in a perspective of global climate, Curr Sci (India), 75 (1998) Sikka D R, Developments in tropospheric aerosols studies in India, Indian J Radio & Space Phys, 31 (2002) Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report, World Climate News, 19 (2001) Whitlock C H, Brown D E, Chandler W S, DiPasquale R C, Meloche N, Leng G J, Gupta S K, Wilber A C, Ritchey N A, Carlson A B, Kratz D P & Stackhouse P W, Release 3 NASA Surface meteorology and Solar Energy data set for renewable industry use, Paper presented at the 26th Annual Conference of the Solar Energy Society of Canada Inc. & Solar Nova Scotia, Canada, Oct, Pinker R T & Laszlo I, Modeling surface solar irradiance for satellite applications on a global scale, J Appl Meteorol (USA), 31 (1992) Yamartino B, Report on SIGMA SPEC 3.2 Spectral analysis Programme, MA (1996) Mohanty P K & Dash S K, Spectral characteristics of surface fields in the Indian seas and interannual monsoon variability, Indian J Radio & Space Phys, 30(2001) Panofsky H & Brier G, The Pennsylvania State Univ Press Report, 1958, Meehl G A, The annual cycle and the interannual variability in the tropical pacific and Indian ocean region, Mon Weather Rev (USA), 115 (1987) Webster P J, The annual cycle and the predictibility of the tropical coupled ocean atmosphere system, Meteorol & Atmos Phys (USA), 56 (1995) Wallace J M, Spectral studies of tropospheric wave disturbances in the tropical western pacific, Rev Geophys Space Phys, 9 (1971) Pittock A B, Solar variability, weather and climate: An update, Quart J R Met Soc (UK), 109 (1983) Olukayode Oladipo E, Power spectra and coherence of drought in the interior plains, J Climatol (UK), 7 (1987) 477.