An Early Diagnostics of the Geoeffectiveness of Solar Eruptions from Photospheric Magnetic Flux Observations: The Transition from SOHO to SDO


Abstract in English

In our previous articles (Chertok et al.: 2013, Solar Phys. 282, 175, and 2015, Solar Phys. 290, 627), we presented a preliminary tool for the early diagnostics of the geoeffectiveness of solar eruptions based on the estimate of the total unsigned line-of-sight photospheric magnetic flux in accompanying extreme-ultraviolet arcades and dimmings. This tool was based on the analysis of eruptions observed in 1996-2005 with SOHO/EIT and MDI. Empirical relationships were obtained to estimate the probable importance of upcoming space weather disturbances caused by an eruption, which just occurred, without data on the associated coronal mass ejections. It was possible to estimate the intensity of a non-recurrent geomagnetic storm (GMS) and Forbush decrease (FD), as well as their onset and peak times. After 2010-2011, data on solar eruptions are obtained with SDO/AIA and HMI. We use relatively short intervals of overlapping EIT-AIA and MDI-HMI detailed observations and a number of large eruptions over the next five years with the 12-hour cadence EIT images to adapt the SOHO diagnostic tool to SDO data. The adopted brightness thresholds select from the EIT 195 AA and AIA 193 AA image practically the same areas of arcades and dimmings with a cross-calibration factor of 3.6-5.8 (5.0-8.2) for the AIA exposure time of 2.0 s (2.9 s). For the same photospheric areas, the MDI magnetic flux systematically exceeds the HMI flux by a factor of 1.4. Based on these results, the empirical diagnostic relationships obtained from SOHO data are adjusted to SDO instruments. Examples of a post-diagnostics based on SDO data are presented. As before, the tool is applicable to non-recurrent GMSs and FDs caused by nearly central eruptions from active regions, provided that the southern component of the interplanetary magnetic field near the Earth is predominantly negative, which is not predicted by this tool.

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