Faculty Publications

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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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    Evaluation of chirps satellite rainfall datasets over kerala, india
    (Springer Science and Business Media Deutschland GmbH, 2021) Divya, P.; Shetty, A.
    Climate hazard group infrared precipitation with station data (CHIRPS) is one of the latest high-resolution quasi-global satellite-based rainfall datasets. It is available in daily, pentadal and monthly time scale from the year 1981 to present. In the present study, the performance of the CHIRPS product is evaluated over the Kerala state on a monthly time scale. For the evaluation of this climate hazard group, product rain gauge data from sixty-seven-gauge stations which are distributed all over Kerala was used. Validation statistics such as mean absolute error (MAE), multiplicative bias (Mbias), Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) were used for the evaluation. The results show that the efficiency of this satellite rainfall estimate is very high with an overall NSE value of 0.72. The accuracy of CHIRPS data was very high mainly in the low-lying areas of Kerala, i.e. at the coastal areas and it was found to be decreasing when in approaches towards the Western Ghats. Overall CHIRPS product is good enough for use in water resource applications in Kerala. © Springer Nature Singapore Pte Ltd 2021.
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    A statistical approach for comparison of secondary precipitation products
    (Springer Science and Business Media Deutschland GmbH, 2021) Kommu, R.; Kundapura, S.; Venkatesh, V.
    Meteorological data retrieval is the fundamental process for any hydrological research. Precipitation data collection from some constrained territories like high slant geography and inaccessible areas is exceptionally troublesome. Setting the rain gauges is a matter of expense and timely maintenance. To overcome these issues, satellite sensors producing high spatial and temporal resolution datasets can be utilized in the studies involving precipitation component. These satellite products are affected by biases, and hence, there is a need for calibration and verification by using ground observation data based on the statistical coefficients. In this study, the most accessible satellite data products, i.e., CHIRPS, PERSIANN-CDR and TRMM, are employed to check the accuracies against IMD gridded data for the years 2000–2012 using a statistical approach. Selecting the data product having a high coefficient of correlation and low PBIAS is utmost necessary. The current study was performed based on catchment-to-catchment (C-C) method by comparing IMD gridded data with satellite datasets obtained from Google Earth Engine. The results can highlight the data product which can conquer the issue of data inaccessibility in the investigation territory and can be utilized as reference precipitation dataset for different hydrological applications. © Springer Nature Singapore Pte Ltd 2021.
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