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These maps cover all India at 10-km (0.1x0.1�) spatial resolution.

The monthly and annual direct normal irradiance (DNI) and global horizontal irradiance (GHI) maps were developed from hourly data spanning January 2002 to December 2008 generated through application of the SUNY satellite to irradiance model (1,2).  A version of this model was previously applied in the region using the European Meteosat 5 and 7 satellites (3,4).  The recent application of the model to India included improvements to the models treatment of high reflectivity surfaces and an update to the Aerosol Optical Depth (AOD) files used as input to the model.  

A more in-depth investigation of monthly AOD data (and their interannual variations) was undertaken because of their strong impact on DNI and reports of elevated aerosol concentrations across India from locally generated dust, long-range transport, smoke from biomass burning and anthropogenic pollution.  

Monthly gridded AOD values were developed for each month of the SUNY model run for India.  This approach was adopted based on evidence of changing AOD over time in India (5-7).  The gridded AOD data set was developed using satellite data from MISR, MERIS, and MODIS.  These data sets were compared with ground-truth data from NASA's Aeronet network and from additional sites with data published in the literature; a total of 39 ground-truth sites were used in this analysis.  Because AOD values tend to be log-normally distributed, the mode of AOD values was chosen to represent each month.  The data set used for a given month was selected based on completeness of satellite data and performance compared with available ground-truth data. The India Meteorological Department (IMD) and the Indian Space Research Organization (ISRO) also collect aerosol data, but these data sets were unavailable for this phase of development.  Improvements to the AOD data used as input to the radiative model, and therefore subsequent improvements in solar resource estimates, may be made in the future with increased availability of ground measured aerosol data.  Due to current modeling limitations, various related atmospheric variables have not been considered here, such as aerosol or gaseous absorption due to elevated pollution.

Comprehensive validation of SUNY model performance through comparisons with ground-measured data was not conducted, as no measured, quality-controled data was available for analysis.  Future such validations will be valuable for better understanding the models performance in characterizing solar radiation across India.

Project Co-ordinator:	Dr. Bibek Bandyopadhyay, Advisor & Head, Solar Energy Centre, Ministry of New & Renewable Energy (GOI)
			Shannon Cowlin, Senior Project Leader, National Renewable Energy Laboratory (NREL), USA

Courtesy (Solar Radiation Map):	Mr. Anthony Lopez, GIS Analyst & Cartographer, National Renewable Energy Laboratory (NREL), USA

Courtesy (GIS Data Layers):	Er. Alekhya Datta, Project Fellow, Solar Energy Centre, Ministry of New & Renewable Energy (GOI)

1. Perez R., et al., A New Operational Satellite-to-Irradiance Model. Solar Energy. 73:307-317, 2002.
2. Perez R., et al., Producing satellite-derived irradiances in complex arid terrain. Solar Energy. 77:363-370, 2004.
3. Perez R., et al., Satellite Derived Resource Assessment in Afghanistan & Pakistan in support of the USAID South Asia Regional Initiative. NREL subcontract # AEJ65517201. 2007 
4. Perez R., et al., Validation of the SUNY Satellite Model in a Meteosat Environment. Proc. ASES Annual Conference, Buffalo, New York, 2009.
5. Datar S.V., et al., Trends in background air pollution parameters over India. Atmos. Envir. 30: 3677-3682, 1996.
6. Porch W., et al., Trends in aerosol optical depth for cities in India. Atmos. Envir. 41: 7524-7532, 2007.
7. Ramanathan V., et al., Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proc. Nat. Acad. Sci. 102: 5326-5333, 2005.

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Note: Investors are however required to set up their own ground measurement unit at the actual project site for more accurate estimation of data of input resources for their investment decisions.