Global Energetics of Solar Flares. XI. Flare Magnitude Predictions of the GOES-Class. (arXiv:2007.04413v1 [astro-ph.SR])

Global Energetics of Solar Flares. XI. Flare Magnitude Predictions of the GOES-Class. (arXiv:2007.04413v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Aschwanden_M/0/1/0/all/0/1">Markus J. Aschwanden</a>

In this study we determine scaling relationships of observed solar flares
that can be used to predict upper limits of the GOES-class magnitude of solar
flares. The flare prediction scheme is based on the scaling of the
slowly-varying potential energy $E_p(t)$, which is extrapolated in time over an
interval of $Delta t le$ 24 hrs. The observed scaling of the dissipated
energy $E_{diss}$ scales with the potential field energy as $E_{diss} propto
E_p^{1.32}$. In addition, the observed scaling relationship of the flare
volume, $V propto E_{diss}^{1.17}$, the multi-thermal energy, $E_{th} propto
V^{0.76}$, the flare emission measure $EM propto E_{th}^{0.79}$, the
EM-weighted temperature $T_{w}$, and the GOES flux, $F_8(t) propto
E_p(t)^{0.92}$, allows us then to predict an upper limit of the GOES-class
flare magnitude in the extrapolated time window. We find a good correlation
(CCC$approx 0.7$) between the observed and predicted GOES-class flare
magnitudes (in 172 X and M-class events). This is the first algorithm that
employs observed scaling laws of physical flare parameters to predict GOES flux
upper limits, an important capability that complements previous flare
prediction methods based on machine-learning algorithms used in space weather
forecasting.

In this study we determine scaling relationships of observed solar flares
that can be used to predict upper limits of the GOES-class magnitude of solar
flares. The flare prediction scheme is based on the scaling of the
slowly-varying potential energy $E_p(t)$, which is extrapolated in time over an
interval of $Delta t le$ 24 hrs. The observed scaling of the dissipated
energy $E_{diss}$ scales with the potential field energy as $E_{diss} propto
E_p^{1.32}$. In addition, the observed scaling relationship of the flare
volume, $V propto E_{diss}^{1.17}$, the multi-thermal energy, $E_{th} propto
V^{0.76}$, the flare emission measure $EM propto E_{th}^{0.79}$, the
EM-weighted temperature $T_{w}$, and the GOES flux, $F_8(t) propto
E_p(t)^{0.92}$, allows us then to predict an upper limit of the GOES-class
flare magnitude in the extrapolated time window. We find a good correlation
(CCC$approx 0.7$) between the observed and predicted GOES-class flare
magnitudes (in 172 X and M-class events). This is the first algorithm that
employs observed scaling laws of physical flare parameters to predict GOES flux
upper limits, an important capability that complements previous flare
prediction methods based on machine-learning algorithms used in space weather
forecasting.

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