摘要
Historical evidence indicates that dust storms of considerable ferocity often wreak havoc, posing a genuine threat to the climatic and societal equilibrium of a place. A systematic study, with emphasis on the modeling and forecasting aspects, thus, becomes imperative, so that efficient measures can be promptly undertaken to cushion the effect of such an unforeseen calamity. The present work intends to discover a suitable ARIMA model using dust storm data from northern China from March 1954 to April 2002, provided by Zhou and Zhang (2003), thereby extending the idea of empirical recurrence rate (ERR) developed by Ho (2008), to model the temporal trend of such sand dust storms. In particular we show that the ERR time series is endowed with the following characteristics: 1) it is a potent surrogate for a point process, 2) it is capable of taking advantage of the well developed and powerful time series modeling tools and 3) it can generate reliable forecasts, with which we can retrieve the corresponding mean number of strong sand dust storms. A simulation study is conducted prior to the actual fitting, to justify the applicability of the proposed technique.
Historical evidence indicates that dust storms of considerable ferocity often wreak havoc, posing a genuine threat to the climatic and societal equilibrium of a place. A systematic study, with emphasis on the modeling and forecasting aspects, thus, becomes imperative, so that efficient measures can be promptly undertaken to cushion the effect of such an unforeseen calamity. The present work intends to discover a suitable ARIMA model using dust storm data from northern China from March 1954 to April 2002, provided by Zhou and Zhang (2003), thereby extending the idea of empirical recurrence rate (ERR) developed by Ho (2008), to model the temporal trend of such sand dust storms. In particular we show that the ERR time series is endowed with the following characteristics: 1) it is a potent surrogate for a point process, 2) it is capable of taking advantage of the well developed and powerful time series modeling tools and 3) it can generate reliable forecasts, with which we can retrieve the corresponding mean number of strong sand dust storms. A simulation study is conducted prior to the actual fitting, to justify the applicability of the proposed technique.
