Soni, Kirti and Parmar, Kulwinder Singh and Kapoor, Sangeeta and Kumar, Nishant (2016) Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling. Science of the Total Environment, 553. 258-265 . ISSN 0048-9697

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A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), s Litionary R -squared, R -squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 0.133589 and vice -versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend.

Item Type: Article
Additional Information: Copyright for this article belongs to M/s Elsevier.
Uncontrolled Keywords: Atmospheric management Aerosol optical depth MODIS Kanpur AERONET Time series analysis
Subjects: Meteorology & Atmospheric Sciences
Depositing User: Dr. Rajpal Walke
Date Deposited: 20 Apr 2018 06:42
Last Modified: 20 Apr 2018 06:42

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