Statistical Properties and Time Series Modeling of Dst-Index: Long-Term Space Weather Forecast

Details Statistical Properties and Time Series Modeling of Dst-Index: Long-Term Space Weather Forecast Statistical Properties and Time Series Modeling of Dst-Index: Long-Term Space Weather Forecast

Dec 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005agufmsm13a0332t&link_type=abstract

American Geophysical Union, Fall Meeting 2005, abstract #SM13A-0332

Mathematics

Probability

3270 Time Series Analysis (1872, 4277, 4475), 4468 Probability Distributions, Heavy And Fat-Tailed (3265), 7900 Space Weather, 7924 Forecasting (2722), 7954 Magnetic Storms (2788)

Scientific paper

Time series data set of Dst-index from 1957 to 2001 is analyzed for evaluating the occurrence frequency of intense geomagnetic storms (peak Dst < -100 nT). We first investigate the occurrence rate and intensity of storms by means of the peaks-over-threshold (POT) model; which is a method specialized in treating the distribution of very rare (or even never observed until now) events. The 'T-year return level' parameters of the storm strength (max |Dst|), which suggests the event occurring once in T years, are derived from this model. Next, we construct a numerical model of the long-term Dst sequence which is based on its historical statistics. Temporal variation of Dst is designed as a stochastic process, where the variance of one-hour difference Δ Dsti ({Dst}i+1-Dsti) is assumed to be conditional for time i. Conventional time series modeling, such as GARCH, is applied to show the appearance of a storm-associated steep fall of Dst. The distribution of such an event is compared with the results from the POT model in order to evaluate its validity as a tool for a long-term space weather forecast.

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