The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs – Space-Weather HMI Active Region Patches

By Monica G. Bobra, Xudong Sun, J. Todd Hoeksema, M. Turmon, Yang Liu, K. Hayashi, G. Barnes, K. Leka. 2014

669 citations recorded by the source corpus.

A new data product from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) called Space-weather HMI Active Region Patches (SHARPs) is now available. SDO/HMI is the first space-based instrument to map the full-disk photospheric vector magnetic field with high cadence and continuity. The SHARP data series provide maps in patches that encompass automatically tracked magnetic concentrations for their entire lifetime; map quantities include the photospheric vector magnetic field and its uncertainty, along with Doppler velocity, continuum intensity, and line-of-sight magnetic field. Furthermore, keywords in the SHARP data series provide several parameters that concisely characterize the magnetic-field distribution and its deviation from a potential-field configuration. These indices may be useful for active-region event forecasting and for identifying regions of interest. The indices are calculated per patch and are available on a twelve-minute cadence. Quick-look data are available within approximately three hours of observation; definitive science products are produced approximately five weeks later. SHARP data are available at jsoc.stanford.edu and maps are available in either of two different coordinate systems. This article describes the SHARP data products and presents examples of SHARP data and parameters.

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/37dac32d5f7501f132690bfeb52573f1209ea6fc

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In Astronomy and Astrophysics

1. Monica G. Bobra
2. S. Couvidat
3. J. Todd Hoeksema
4. G. Barnes
5. Xudong Sun
6. Yang Liu
7. Chang Liu
8. Haimin Wang

Citations in the corpus

• Origins of the Ambient Solar Wind: Implications for Space Weather (2017, neutral): “Analogous databases for regions near the Sun have run the gamut from careful hand-drawings based on daily images (Harvey and Recely 2002; McIntosh 2003) to automated “big data” feature-extraction systems (Martens et al. 2012; Bobra et al. 2014).”
• Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Products and the Random Forest Algorithm (2017, methodology): “(2) We only selected C-, M-, and X-class ARs with records of flares that have identified locations in the NCEI flare catalogs and occur within about ±70◦ of the central meridian (for the optimum vector field data quality; see Bobra et al. 2014).”
• WHY IS THE GREAT SOLAR ACTIVE REGION 12192 FLARE-RICH BUT CME-POOR? (2015, background): “ARs are automatically identified and extracted; deprojected maps are provided in cylindrical equal area coordinate (Bobra et al. 2014).”
• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance (2014, methodology): “sharp_720s, is quite complete, comprising 31 maps of the active patch at each time step with all of the disambiguated field and thermodynamic parameters determined in the inversion, along with uncertainties and error cross-correlation coefficients, as well as the continuum intensity, Doppler velocit”
• SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM (2014, methodology): “As shown in Figure 6 of Bobra et al. (2014), the signal-to-noise in the SHARP parameters, described in Section 2.3, increases significantly beyond ± 70◦ of central meridian.”