Browsing by Author "Dubey, S."

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    Associative study of Absorbing Aerosol Index (AAI) and precipitation in India during monsoon season (2005 to 2014)
    (2016) Dubey, S.; Mehta, M.; Singh, A.
    Based on their interaction with solar radiations, aerosols may be categorized as absorbing or scattering in nature. The absorbing aerosols are coarser and influence precipitation mainly due to microphysical effect (participating in the formation of Cloud Condensation Nuclei) and radiative forcing (by absorbing electromagnetic radiations). The prominent absorbing aerosols found in India are Black Carbon, soil dust, sand and mineral dust. Their size, distribution, and characteristics vary spatially and temporally. This paper aims at showing the spatio-temporal variation of Absorbing Aerosol Index (AAI) and precipitation over the four most polluted zones of Indian sub-continent (Indo-Gangetic plains 1, Indo-Gangetic plains 2, Central and Southern India) for monsoon season (June, July, August, September) during the last decade (2005 to 2014). Zonal averages AAI have been found to be exhibiting an increasing trend, hence region-wise correlations have been computed between AAI and precipitation during monsoon. Daily Absorption Aerosol Index (AAI) obtained from Aura OMI Aerosol Global Gridded Data Product-OMAEROe (V003) and monthly precipitation from TRMM 3B42-V7 gridded data have been used. � 2016 SPIE.
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    Associative study of Absorbing Aerosol Index (AAI) and precipitation in India during monsoon season (2005 to 2014)
    (SPIE spie@spie.org, 2016) Dubey, S.; Mehta, M.; Singh, A.
    Based on their interaction with solar radiations, aerosols may be categorized as absorbing or scattering in nature. The absorbing aerosols are coarser and influence precipitation mainly due to microphysical effect (participating in the formation of Cloud Condensation Nuclei) and radiative forcing (by absorbing electromagnetic radiations). The prominent absorbing aerosols found in India are Black Carbon, soil dust, sand and mineral dust. Their size, distribution, and characteristics vary spatially and temporally. This paper aims at showing the spatio-temporal variation of Absorbing Aerosol Index (AAI) and precipitation over the four most polluted zones of Indian sub-continent (Indo-Gangetic plains 1, Indo-Gangetic plains 2, Central and Southern India) for monsoon season (June, July, August, September) during the last decade (2005 to 2014). Zonal averages AAI have been found to be exhibiting an increasing trend, hence region-wise correlations have been computed between AAI and precipitation during monsoon. Daily Absorption Aerosol Index (AAI) obtained from Aura OMI Aerosol Global Gridded Data Product-OMAEROe (V003) and monthly precipitation from TRMM 3B42-V7 gridded data have been used. © 2016 SPIE.
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    Associative study of NDVI and precipitation in Indian region during monsoon season using satellite and ground measurements [2000-2013]
    (2016) Mehta, M.; Dubey, S.
    Natural vegetation cover and crop yield vary spatially and temporally in a diverse manner. Understanding this variation requires a robust analysis of important climatic factors such as rainfall, temperature, sunshine hours etc., along with LULC dynamics. In this study, NDVI has been used as an indicator of vegetative greenness and productivity. Based on 0.5��0.5� spatial resolution data of NDVI obtained from MISR, correlation between NDVI and average seasonal precipitation has been analyzed. The precipitation data used is obtained from two sources, TRMM 3B42-V7 and IMD gridded data, both at spatial resolution of 0.25��0.25�. The TRMM and IMD data have also been mutually correlated. Data was acquired for the months of June, July, August and September (JJAS) i.e. monsoon season for 14 years, 2000 to 2013. The correlation coefficients thus obtained are reported significant at a confidence level of 99% (p<0.001). � 2015 IEEE.
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    Associative study of NDVI and precipitation in Indian region during monsoon season using satellite and ground measurements [2000-2013]
    (Institute of Electrical and Electronics Engineers Inc., 2016) Mehta, M.; Dubey, S.
    Natural vegetation cover and crop yield vary spatially and temporally in a diverse manner. Understanding this variation requires a robust analysis of important climatic factors such as rainfall, temperature, sunshine hours etc., along with LULC dynamics. In this study, NDVI has been used as an indicator of vegetative greenness and productivity. Based on 0.5°×0.5° spatial resolution data of NDVI obtained from MISR, correlation between NDVI and average seasonal precipitation has been analyzed. The precipitation data used is obtained from two sources, TRMM 3B42-V7 and IMD gridded data, both at spatial resolution of 0.25°×0.25°. The TRMM and IMD data have also been mutually correlated. Data was acquired for the months of June, July, August and September (JJAS) i.e. monsoon season for 14 years, 2000 to 2013. The correlation coefficients thus obtained are reported significant at a confidence level of 99% (p<0.001). © 2015 IEEE.