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.