A New Prediction Method for the Arrival Time of Interplanetary Shocks |
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| Authors: | Xueshang Feng Xinhua Zhao |
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| Institution: | (1) SIGMA Weather Group, State Key Laboratory for Space Weather, Center for Space Science and Applied Research, Beijing, 100080, China;(2) Graduate University of the Chinese Academy of Sciences, Beijing, 100049, China |
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| Abstract: | Solar transient activities such as solar flares, disappearing filaments, and coronal mass ejections (CMEs) are solar manifestations
of interplanetary (IP) disturbances. Forecasting the arrival time at the near Earth space of the associated interplanetary
shocks following these solar disturbances is an important aspect in space weather forecasting because the shock arrival usually
marks the geomagnetic storm sudden commencement (SSC) when the IMF Bz component is appropriately southward and/or the solar wind dynamic pressure behind the shock is sufficiently large. Combining
the analytical study for the propagation of the blast wave from a point source in a moving, steady-state, medium with variable
density (wei, 1982; wei and dryer 1991) with the energy estimation method in the ISPM model (smith and dryer 1990, 1995),
we present a new shock propagation model (called SPM below) for predicting the arrival time of interplanetary shocks at Earth.
The duration of the X-ray flare, the initial shock speed and the total energy of the transient event are used for predicting
the arrival of the associated shocks in our model. Especially, the background speed, i.e., the convection effect of the solar
wind is considered in this model. Applying this model to 165 solar events during the periods of January 1979 to October 1989
and February 1997 to August 2002, we found that our model could be practically equivalent to the prevalent models of STOA,
ISPM and HAFv.2 in forecasting the shock arrival time. The absolute error in the transit time in our model is not larger than
those of the other three models for the same sample events. Also, the prediction test shows that the relative error of our
model is ≤10% for 27.88% of all events, ≤30% for 71.52%, and ≤50% for 85.46%, which is comparable to the relative errors of
the other models. These results might demonstrate a potential capability of our model in terms of real-time forecasting. |
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