Abstract
Space weather monitoring and predictions largely rely on ground magnetic
measurements and geomagnetic indices such as the Disturbance Storm Time
index (Dst or SYM-H), Auroral Electrojet Index (AL) or the Polar Cap
Index (PCI) all constructed using the individual station data. The
global MHD simulations such as the Space Weather Modeling Framework
(SWMF) can give predictions of these indices, driven by solar wind
observations obtained at L1 giving roughly one hour lead time. The
accuracy of these predictions especially during geomagnetic storms is a
key metric for the model performance, and critical to operational space
weather forecasts. In this presentation, we perform the largest
statistical study of global simulation results using a database of 140
storms with minimum Dst below -50 nT during the years from 2010 to 2020.
We compare SWMF results with indices derived from the SuperMAG network,
which with its denser station network provides a more accurate
representation of the true level of activity in the ring current and in
the auroral electrojets. We show that the SWMF generally gives good
results for the SYM-H index, whereas the AL index is typically
underestimated by the model with the model predicting lower than
observed ionospheric activity. We also examine the Cross Polar Cap
Potential (CPCP) and compare it with a model derived using the PCI
(Ridley et al., 2004) as well as with results obtained from the
SuperDARN network. We show that the Ridley et al. CPCP model is much
closer to the SWMF values. The results are used to discuss factors
governing energy dissipation in magnetosphere - ionosphere system as
well as possibilities to improve on the operational space weather
forecasts.