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MagPy (v2)

Model Description

Uses SDO/HMI vector magnetograms to measure free energy proxies for each AR on the solar disk.  The free energy proxies include the gradient of the magnetic field across the neutral line, the magnetic shear angle across the neutral line, and others.  These free energy proxies are used to relate to historically derived event rates, and with Poisson statistics, probabilities of the occurrence of >M-class flares, >X-class flare, CMEs, Fast CMEs, and SPEs within the next 24 hours. To determine the optimal function relating solar eruption event rate and total non-potentiality of active regions, extensive data mining process was performed during MagPy training sessions to thousands of magnetic field threshold configurations in order to compare skill scores across the validation results to determine the best set of magnetic field thresholds for operational use.

Model Figure(s) :

Model Inputs Description

HMI Line of sight (hmi.m_720s_nrt) 
HMI vector magnetograms (NRT CEA SHARP [hmi.sharp_cea_720s_nrt] and NRT CCD SHARP [hmi.sharp_720s_nrt])
NOAA SRS (solar region summery)

Model Outputs Description

Full disk flare probabilities, full disk SEP probability, CME probability, and fast CME probability 

Model Caveats

MagPy utilizes a 45-heliocenteric degree cone filter to discard magnetograms on the limbs of the solar disk. These magnetograms, distorted by effects like foreshortening, engender high degrees of error in free-energy proxy calculations and spawn false neutral-lines. As these types of magnetograms can severely skew the correlation between the free energy proxies and event-rates, they are left out of the training dataset. MagPy is most accurate within a 45 degree cone from disk center. 

Change Log


	
	 
	

Model Acknowledgement/Publication Policy (if any)


	
	
	

Model Domains:

Solar

Space Weather Impacts:

Ionosphere variability (navigation, communications)
Solar energetic particles - SEPs (human exploration, aviation safety, aerospace assets functionality)

Phenomena :

Coronal_Mass_Ejections
Solar_Energetic_Particles
Solar_Flares

Simulation Type(s):

Empirical

Temporal Dependence Possible? (whether the code results depend on physical time?)

false

Model is available at?

CCMC

Source code of the model is publicly available?

false

CCMC Model Status (e.g. onboarding, use in production, retired, only hosting output, only source is available):

production

Code Language:

Python

Regions (this is automatically mapped based on model domain):

Sun

Contacts :

Tilaye.Tadesse, ModelDeveloper
Ian.Fernandes, ModelDeveloper
Sandro.Taktakishvili, ModelHostContact

Acknowledgement/Institution :

Princeton University

Relevant Links :

Publications :

  • Prior Flaring as a Complement to Free Magnetic Energy for Forecasting Solar Eruptions
  • MAG4 versus alternative techniques for forecasting active region flare productivity
  • A tool for empirical forecasting of major flares, coronal mass ejections, and solar particle events from a proxy of active‐region free magnetic energy
  • Model Access Information :

    Access URL: https://sep.ccmc.gsfc.nasa.gov/probability/
    Access URL Name: Continuous/RT Run (SEP Scoreboard Probability)
    Repository ID: spase://CCMC/Repository/NASA/GSFC/CCMC
    Availability: online
    AccessRights: OPEN
    Format: HTML
    Encoding: None

    Access URL: https://iswa.gsfc.nasa.gov/iswa_data_tree/model/solar/MagPy/2.X/
    Access URL Name: Continuous/RT Run (ISWA data tree)
    Repository ID: spase://CCMC/Repository/NASA/GSFC/CCMC
    Availability: online
    AccessRights: OPEN
    Format: HTML
    Encoding: None

    Linked to Other Spase Resource(s) (example: another SimulationModel) :

    spase://CCMC/SimulationModel/MAG4/v20190904
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    Curator: Chiu Wiegand | NASA Official: Dr. Masha Kuznetsova | Privacy and Security Notices | Accessibility | CCMC Data Collection Consent Agreement