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The temporal and spatial evolution characteristics of the geomagnetic secular acceleration (SA) are investigated, based on CHAOS-4 core field model during the period of 1997–2013. The SA evolution on a short time scale is associated with the phenomenon of the geomagnetic jerk. More details of the global extent and the occurrence time of the successive jerks (the 1999, 2003, 2007, and 2011 jerks) are obtained. The location, size and reversed polarity pattern for the 1999 jerk are similar to those for the 2003 jerk in the Asian-Indian sector. While the 2007 and 2011 jerks mainly take place in the Atlantic sector. The direction and speed of the shift for the four jerks are different, identified by the occurrence time of the jerks. The zonal motions of the SA patches exhibit an oscillation pattern in the Asian-Indian sector, whereas a purely westward drifting pattern is along the equator in the Atlantic sector. It is believed that the shift of the jerks is related to the motion of SA-B r patches observed at the core-mantle boundary (CMB).

By using Hakamada-Akasofu-Fry (HAF) solar wind model, we simulated 48 solar flare events during Carrington Rotation 1981 to 1985 and compared the simulated shock arrival time with observations by satellites at 1 AU. It is found that the initial solar wind shock speed deduced from the metric Type II radio burst observations plays an important role in the shock arrival time prediction. The match between predicted and observed shock arrival times (SATs) was considerably improved by iteratively adjusting the initial shock speed. We obtained the adjustment factors as a function of the initial shock speed for different hemispheres by statistical analysis and applied them to adjust the initial shock speed driven by the solar flares which occurred in the following five Carrington rotations (1986 similar to 1990), the mean absolute error of the shock arrival time was reduced from 16 (15) hours to 12 (11) hours for the flare occurred on the western (eastern) hemisphere. This indicates that there can be significant improvements for the shock arrival time prediction by adjusting the initial shock speed using the statistical functions.