Forecasting F<sub>10.7</sub> with solar magnetic flux transport modeling

Henney, C. J.; Toussaint, W. A.; White, S. M.; Arge, C. N.

doi:10.1029/2011sw000748

Cited by 108 publications

(135 citation statements)

References 61 publications

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“…Only by limiting the flux correction to the active region itself could the solution be improved. Similar behavior is found when we apply the sparse reconstruction technique to a time sequence of B r maps generated by the Air Force Data-Assimilative Photospheric Flux Transport (ADAPT) model (Arge et al 2010;Henney et al 2012). This model assimilates observed magnetograms from the visible face of the Sun into a surface flux-transport simulation, so as to approximate the global distribution of B r on the solar surface, as a function of time.…”

Section: Flux Imbalance In ∂ T B R Mapsmentioning

confidence: 80%

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

Yeates

2017

ApJ

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders. Preprint typeset using L A T E X style AASTeX6 v. ABSTRACTAccurate estimates of the horizontal electric field on the Sun's visible surface are important not only for estimating the Poynting flux of magnetic energy into the corona but also for driving time-dependent magnetohydrodynamic models of the corona. In this paper, a method is developed for estimating the horizontal field from a sequence of radial-component magnetic field maps. This problem of inverting Faraday's law has no unique solution. Unfortunately, the simplest solution (a divergence-free electric field) is not realistically localized in regions of non-zero magnetic field, as would be expected from Ohm's law. Our new method generates instead a localized solution, using a basis pursuit algorithm to find a sparse solution for the electric field. The method is shown to perform well on test cases where the input magnetic maps are flux balanced, in both Cartesian and spherical geometries. However, we show that if the input maps have a significant imbalance of flux -usually arising from data assimilation -then it is not possible to find a localized, realistic, electric field solution. This is the main obstacle to driving coronal models from time sequences of solar surface magnetic maps.

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Section: Flux Imbalance In ∂ T B R Mapsmentioning

confidence: 80%

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

Yeates

2017

ApJ

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“…The measured helioseismic farside phase delay values have been parameterized in terms of photospheric magnetic field strength [17], allowing for an estimation of new solar magnetic activity on the solar farside while updating the Earth-side of global synoptic maps. The practical application of the farside data has recently been discussed with regards to space weather parameter forecasting, for example solar wind [2], F 10.7 [22,17] and Lyman-α irradiance [15].…”

Section: Discussionmentioning

confidence: 99%

“…At the same time the localization in the data assimilation eliminates erroneous long distance correlations occurring from the small sample sizes used to estimate the ADAPT model's covariance structure. It is worth noting that all ADAPT maps and results to date have utilized an ensemble least squares (ENLS) data assimilation method [4,22], whereas the ENKF data assimilation methods developed at Los Alamos have only been used so far in a testing and development capacity.…”

Section: Introductionmentioning

confidence: 99%

“…The Air Force Data Assimilative Photospheric Flux Transport (ADAPT) Model [3,4,22,2] incorporates various data assimilation techniques, using a module developed at Los Alamos National Laboratory (LANL) [3], with a photospheric magnetic flux transport model. The ADAPT model is updated with observations by using an ensemble of simulations from a flux transport model based on the method used in the Worden-Harvey (WH) model [44] to represent the distribution of possible solar photospheric states under the influence of differential rotation, meridional flow, supergranular diffusion, and random emergence of weak background flux.…”

Section: Introductionmentioning

confidence: 99%

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Data Assimilation in the ADAPT Photospheric Flux Transport Model

et al. 2015

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Global maps of the solar photospheric magnetic flux are fundamental drivers for simulations of the corona and solar wind and therefore are important predictors of geoeffective events. However, observations of the solar photosphere are only made intermittently over approximately half of the solar surface. The Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model uses localized ensemble Kalman filtering techniques to adjust a set of photospheric simulations to agree with the available observations. At the same time, this information is propagated to areas of the simulation that have not been observed. ADAPT implements a local ensemble transform Kalman filter (LETKF) to accomplish data assimilation, allowing the covariance structure of the flux transport model to influence assimilation of photosphere observations while eliminating spurious correlations between ensemble members arising from a limited ensemble size. We give a detailed account of the implementation of the LETKF into ADAPT. Advantages of the LETKF scheme over previously implemented assimilation methods are highlighted.

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“…An independent approach based on the Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model (Arge et al 2010;Henney et al 2012;Arge et al 2013;Hickmann et al 2015) may be more suitable for such purposes for short-term predictions (on a timescale of days). We emphasize that while the eclipse provides an opportunity to compare the model-predicted structures with the observed corona only at the limb, the coupled SFT and PFSS simulations render the global 3D corona and hence predictions for the large-scale corona across all latitudes and longitudes.…”

Section: Concluding Discussionmentioning

confidence: 99%

The Large-scale Coronal Structure of the 2017 August 21 Great American Eclipse: An Assessment of Solar Surface Flux Transport Model Enabled Predictions and Observations

Nandy

Bhowmik

Yeates

et al. 2018

ApJ

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractOn 2017 August 21, a total solar eclipse swept across the contiguous United States, providing excellent opportunities for diagnostics of the Sun's corona. The Sun's coronal structure is notoriously difficult to observe except during solar eclipses; thus, theoretical models must be relied upon for inferring the underlying magnetic structure of the Sun's outer atmosphere. These models are necessary for understanding the role of magnetic fields in the heating of the corona to a million degrees and the generation of severe space weather. Here we present a methodology for predicting the structure of the coronal field based on model forward runs of a solar surface flux transport model, whose predicted surface field is utilized to extrapolate future coronal magnetic field structures. This prescription was applied to the 2017 August 21 solar eclipse. A post-eclipse analysis shows good agreement between model simulated and observed coronal structures and their locations on the limb. We demonstrate that slow changes in the Sun's surface magnetic field distribution driven by long-term flux emergence and its evolution governs large-scale coronal structures with a (plausibly cycle-phase dependent) dynamical memory timescale on the order of a few solar rotations, opening up the possibility for large-scale, global corona predictions at least a month in advance.

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Forecasting F_10.7 with solar magnetic flux transport modeling

Cited by 108 publications

References 61 publications

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

Data Assimilation in the ADAPT Photospheric Flux Transport Model

The Large-scale Coronal Structure of the 2017 August 21 Great American Eclipse: An Assessment of Solar Surface Flux Transport Model Enabled Predictions and Observations

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Forecasting F10.7 with solar magnetic flux transport modeling

Cited by 108 publications

References 61 publications

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

Data Assimilation in the ADAPT Photospheric Flux Transport Model

The Large-scale Coronal Structure of the 2017 August 21 Great American Eclipse: An Assessment of Solar Surface Flux Transport Model Enabled Predictions and Observations

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Forecasting F_10.7 with solar magnetic flux transport modeling