Long-term memory of atmospheric aerosols over India

Abstract:

Long-term memory of atmospheric variables is a least understood facet in atmospheric science. The temporal and spatial distribution of atmospheric aerosols depends largely on the atmospheric parameters. Time series analysis using a stochastic model reveals that atmospheric aerosols over India exhibit a long-term memory. Our analysis confirms that by using Autoregressive Integrated Moving Average (ARIMA) model we can parsimoniously model the aerosol optical depth (AOD) over the Indian region with a reasonably good accuracy. This major advantage of this method is that by using past observations we were able to generate forecasts for next 3 years. The forecasts thus generate shows a good fit with the observations. This persistence is due to the presence of temporal dependence between successive observations.