Global Energetics of Solar Flares. XI. Flare Magnitude Predictions of the GOES-Class

Abstract

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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References in corpus (24)

• Preprocessing of vector magnetograph data for a non-linear force-free magnetic field reconstruction
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• Dragon-kings: mechanisms, statistical methods and empirical evidence
• Driving major solar flares and eruptions: a review
• A Comparison of Flare Forecasting Methods, I: Results from the "All-Clear" Workshop
• Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram
• Automated Solar Flare Statistics in Soft X-rays over 37 Years of GOES Observations - The Invariance of Self-Organized Criticality during Three Solar Cycles
• Predicting Coronal Mass Ejections Using Machine Learning Methods
• Flare Prediction Using Photospheric and Coronal Image Data
• What is the relationship between photospheric flow fields and solar flares?
• Global Energetics of Solar Flares: II. Thermal Energies
• Global Energetics of Solar Flares: I. Magnetic Energies
• The Thermal Properties of Solar Flares Over Three Solar Cycles Using GOES X-ray Observations
• Are Solar Active Regions with Major Flares More Fractal, Multifractal, or Turbulent than Others?
• Soft X-ray Fluxes of Major Flares Far Behind the Limb as Estimated Using STEREO EUV Images
• The Magnetic Field of Active Region 11158 During the 2011 February 12-17 Flares : Differences between Photospheric Extrapolation and Coronal Forward-Fitting Methods
• Benchmark Test of Differential Emission Measure Codes and Multi-Thermal Energies in Solar Active Regions
• The Vertical Current Approximation Nonlinear Force-Free Field Code - Description, Performance Tests, and Measurements of Magnetic Energies Dissipated in Solar Flares
• On the sensitivity of the GOES flare classification to properties of the electron beam in the thick target model
• The Compatibility of Flare Temperatures Observed with AIA, GOES, and RHESSI
• Self-Organized Criticality in Solar and Stellar Flares: Are Extreme Events Scale-Free ?
• Electron Beams Cannot Directly Produce Coronal Rain
• Magnetic Non-Potentiality of Solar Active Regions and Peak X-Ray Flux of the Associated Flares
• Global Energetics of Solar Flares. IX. Refined Magnetic Modeling

Cited by in corpus (1)

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