A statistical model for long-term forecasting of strong sand dust storms

Dust elevated into the atmosphere by dust storms has numerous environmental consequences. These include contributing to climate change; modifying local weather conditions; producing chemical and biological changes in the oceans; and affecting soil formation, surface water, groundwater quality, crop growth, and survival (Goudie and Middleton, 1992). Societal impacts include disruptions to air, road and rail traffic; interruption of radio services; the myriad effects of static-electricity generation; property damage; and health effects on humans and animals (Warner, 2004).;In this thesis, we extend the idea of empirical recurrence rate (ERR), developed by Ho (2008), to model the temporal trend of the sand-dust storms in northern China. Specifically, we show that the ERR time series has the following characteristics: (1) it is a potent surrogate for a point process; (2) it is created to take advantage of the well-developed and powerful time series modeling tools; and (3) it can produce reliable forecasts, capable of retrieving the corresponding mean numbers of strong sand-dust storms.