Artificial Intelligence Could Have Predicted All Space Weather Events Associated with the May 2024 Superstorm

Anna Maria Massone; Edoardo Legnaro; Michele Piana; Sabrina Guastavino

REVIEW 4 major objections 6 minor 55 references

Artificial Intelligence Could Have Predicted All Space Weather Events Associated with the May 2024 Superstorm

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that a chain of AI models, trained on data from before the event, could have predicted every major space-weather event of the May 2024 superstorm, from the X-class flares to the CME's 44.21-hour arrival and the G5…

desk verdict An interesting single-event case study with one genuinely impressive CME travel-time result, but the claim to have predicted the entire chain of May 2024 space weather is undercut by the SYM-H nowcast setup and a misquoted uncertainty. read the letter →

arxiv 2501.14684 v1 pith:SI7T7SCU submitted 2025-01-24 astro-ph.SR physics.space-ph

Sabrina Guastavino , Edoardo Legnaro , Anna Maria Massone , Michele Piana This is my paper · ORCID

classification astro-ph.SRphysics.space-ph

keywords spaceweatherforecastingMay2024geomagneticsuperstormcoronalmassejectiontraveltimesolarflarepredictionphysics-drivenneuralnetworkVisionTransformerSYM-Hindexdrag-basedmodel

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that a suite of AI models, applied retrospectively to the May 2024 solar superstorm, could have forecast the whole event chain: the active region's evolution, the M- and X-class flares, the arrival of the merged coronal mass ejection, and the G5 geomagnetic storm. Its headline result is a predicted CME travel time of $44.21 \pm 3.20$ hours against a measured $44.23$ hours, an agreement far tighter than the roughly 12-hour uncertainty of traditional forecasts. The authors present this single extreme event as a case study showing that physics-informed and data-driven machine learning can serve as an operational early-warning tool for critical infrastructure. If the claim holds, the lesson is that the ingredients for warning of extreme space weather already exist and only need to be run in real time.

What carries the argument

The load-bearing object is the drag-based equation (DBM), a kinematic model in which a coronal mass ejection is treated as a single body decelerated by a drag proportional to the square of its speed relative to the solar wind. The paper embeds the analytical solution of that equation in the loss functions of a two-network cascade: the first network estimates the drag parameter, the second predicts travel time, and an ensemble of retrained networks provides the uncertainty. Around this core are three trained deep learners: a Vision Transformer (a deep image-classification network based on self-attention) for active-region classification, a parallel-CNN plus LSTM video network for flare forecasting, and an LSTM (a recurrent network for sequences) fed by in-situ solar wind features such as field magnitude and $B_z$, speed and $V_x$, temperature, helicity, and energy terms for geomagnetic-impact prediction.

What would settle it

Take the same ensemble-training protocol from Section 4.3 and apply it to a set of historical CMEs known to have merged or interacted in transit, where the observed arrival times are recorded; if the median absolute error of predicted travel times is far larger than in the May 2024 case or grows with the number of interacting ejections, the single-body drag assumption is the limiting factor.

Watch

Extended reading notes

Core claim

The central claim is that each link in the May 2024 chain, from flaring in NOAA active region 13644 to the Earth-directed CME that cannibalized several earlier ejections to the resulting G5 storm, could have been predicted by AI tools developed before the event. On magnetogram cutouts, a Vision Transformer tracked AR13664's morphology and assigned it to the $\beta$-gamma class. A video-based CNN plus LSTM model issued alarms for M-class and X-class flares from May 5 to May 13, with one false positive. The physics-driven ensemble predicted the merged CME's travel time as $44.21 \pm 3.20$ hours versus the observed $44.23$ hours, and an LSTM driven by solar wind and SYM-H features forecast the storm's onset and recovery phase one hour ahead. The authors conclude that this accuracy supports AI as both an operational forecasting tool and a reverse-engineering probe for CME interaction physics.

Load-bearing premise

The load-bearing premise is that one rigid cloud, slowed only by a prescribed solar-wind drag, can stand in for the merged ejection that hit Earth on May 10; if the merging of several clouds breaks that single-body picture, the minute-level travel-time prediction would not transfer to other interacting CMEs.

Editorial extensions

If this is right

The same pipeline, run in real time, would have alerted forecasters to a severe Earth-directed storm several days before the May 10 shock.
CME arrival-time uncertainty could shrink from the standard roughly 12 hours to minutes for events where the single-body drag description holds.
The physics-driven network can be used to estimate effective drag parameters from observed travel times, giving a data-driven probe of CME-CME merging and solar wind coupling.
One-hour-ahead SYM-H forecasts of onset and recovery would widen the window for protective action on power grids and satellites.
The chain approach ties flare forecasts to CME and storm predictions, making each stage's output directly actionable for the next.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the travel-time claim rests on one extreme event, the minute-level accuracy should not be read as a calibrated uncertainty until the ensemble is re-run over a catalog of historical interacting CMEs; that is a test the paper does not perform.
The single-body drag loss treats the merged cloud as one body with a prescribed ambient wind, so the method's success here may reflect the specific May 8 merger rather than a general rule; applying it to other cannibalizing CMEs would separate the two.
A natural extension would be to couple the predicted CME arrival and magnetic-field orientation to the storm forecaster, replacing observed in-situ inputs with AI-predicted ones and yielding a fully predictive rather than retrospective pipeline.
The authors' reverse-engineering suggestion implies that per-event drag parameters inferred by the network could be compared with MHD simulations of merging ejections, turning the AI into a hypothesis tester for CME interaction physics.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague, quick take: this is a retrospective application of the authors' own previously published AI models to the May 2024 superstorm. The standout result is the CME travel time: predicted 44.21 ± 3.20 hours against an observed 44.23 hours. That single-point agreement is remarkable and makes the paper worth a look. But the headline claim that AI “could have predicted all space weather events” does not survive close reading.

First, the good. The physics-driven ensemble for CME travel time is a legitimate extension of earlier work, and reporting it for this extreme event is a useful data point. The paper is also transparent in places: it admits the flare model produced a false positive, and it acknowledges that the May 8 CME cannibalized earlier ejections. That honesty is appreciated.

The problems are in the claims. The abstract promises “uncertainty as small as one minute” and the discussion says “error margin of less than one minute.” The actual error is 0.02 hours, which is 72 seconds—not less than a minute—and the ensemble uncertainty is 3.20 hours. That is a misstatement, plain and simple.

The bigger issue is the geomagnetic storm prediction. The LSTM in Section 4.4 takes a 24-hour series that includes SYM-H and predicts the probability that SYM-H drops below −50 nT in the next hour. That is an autoregressive nowcast, not a forecast from solar-wind drivers. The paper gives no persistence baseline, so the claim of accurate onset and recovery prediction is unsubstantiated. This is the load-bearing flaw for the “entire chain” narrative.

The DBM concern is real but secondary. The model assumes a single rigid CME in ambient solar wind, while the event involved CME-CME merging. The paper acknowledges the interaction but does not model it, so the impressive travel-time accuracy may not transfer to other interacting cases. No code or data are provided, and there are no comparisons against operational baselines beyond vague statements.

On balance, this is a modest case study with an inflated title. The CME result deserves attention, and the pipeline integration is new for this specific event. But the geomagnetic “forecast” is essentially persistence, the one-minute claim is wrong, and the generalizability is unproven. A serious referee could push the authors to fix these issues, so I would not desk-reject it.

My recommendation: send it to peer review with a request for major revision. Require toning down the claims, adding a persistence baseline for SYM-H, correcting the one-minute misquote, and discussing the single-body DBM limitation explicitly. Then it could be a useful case study.

Referee Report

4 major / 6 minor

Summary. The paper presents a retrospective case study of three AI-based tools applied to the May 2024 superstorm chain: a Vision Transformer classifies the morphological evolution of NOAA AR 13644 from HMI magnetograms, a video-based CNN-LSTM forecasts M- and X-class flares from 24-hour magnetogram sequences, a physics-driven neural network with a drag-based model predicts the CME travel time from coronal and solar-wind inputs, and an LSTM ingesting 24 hours of in-situ solar-wind and SYM-H data estimates the probability of SYM-H dropping below -50 nT in the next hour. The central claim is that AI could have predicted the entire chain of events, with the CME travel time predicted to 44.21 +/- 3.20 h versus an observed 44.23 h and the geomagnetic storm onset and recovery 'predicted' one hour in advance. The authors conclude that AI offers unprecedented accuracy and outperforms traditional methods, but the supporting evidence is uneven and the headline claims overreach the analysis.

Significance. If the central claim were fully supported, the paper would be a valuable demonstration of end-to-end AI-based space weather forecasting. The physics-informed loss function for the CME travel-time model and the ensemble-uncertainty approach are thoughtful, and the flare-forecasting architecture is clearly described. However, the geomagnetic 'forecast' is an autoregressive nowcast of the target variable, the CME accuracy rests on a single event with a misquoted error, and no quantitative skill scores or baseline comparisons are given for the flare link. The paper is a useful retrospective case study, but it does not substantiate the strong predictive claims in the abstract and title.

major comments (4)

[§2.5 and §4.4] The geomagnetic storm 'prediction' is largely circular because the LSTM input is a 24-hour time series that explicitly includes the SYM-H index, and the output is the probability that SYM-H drops below -50 nT in the next hour. When the storm onset is already present in the input window, the model is an autoregressive nowcast of the target quantity rather than a forecast from solar wind drivers. The claim in §2.5 that AI 'accurately predict[s] not only the onset but also the whole recovery phase 1 hour in advance' is therefore unsupported without a persistence baseline or an input set excluding SYM-H.
[Abstract; §3 Discussion; Table 1] The abstract's 'unprecedented accuracy ... uncertainty as small as one minute' and the Discussion's 'error margin of less than one minute' contradict the reported values. Table 1 gives a predicted travel time of 44.21 +/- 3.20 h against an observed 44.23 h; the difference is 0.02 h (72 s), which is not less than one minute, and the stated uncertainty is +/- 3.20 h, not one minute. The 72 s agreement is a single point with no propagated uncertainties from the cone-model and solar-wind inputs, so it cannot support 'unprecedented accuracy'.
[§2.4, §4.3, Eqs. (1)-(2)] The physics-driven CME model encodes a single-body drag-based equation with ambient solar wind inputs, yet the paper itself describes the May 8 CME as cannibalizing several earlier ejections into a merged cloud (§2.4 and Discussion). The loss functions in Eqs. (1)-(2) contain no term for CME-CME interaction or for the merged body's effective mass and cross-section. The close travel-time agreement for this one interacting event is therefore not evidence that the model can predict interacting CMEs generally, and the paper should either include interacting-event cases in validation or temper the generalization claim.
[§2.3, Fig. 3 top panel] The flare forecasting result contains an acknowledged false positive (the alarm in the window between 2027-05-06 and 2024-05-07, which should read 2024-05-06/07), and no skill scores, confidence intervals, or comparison with a persistent or climatological baseline are provided for this event. The statement that the models 'outperformed traditional methods' (Abstract) is therefore not demonstrated for the flare link of the chain.

minor comments (6)

[§2.3] The date '2027-05-06' is a typo and should read '2024-05-06'.
[§2.5] The phrase 'situ measurements' should be 'in-situ measurements'.
[§2.2] The ViT classification is applied to already recorded magnetograms; the authors should clarify that this is a retrospective classification rather than a forecast, and provide quantitative agreement with the visual classification.
[Table 1] The units row 't h' is unclear; use '[h]' and define 't0' and 'tf' clearly.
[§4.3] The notation 'LC' in Eq. (1) is inconsistent with the narrative; define it explicitly.
[General] No data or code availability statement is provided, which limits reproducibility for this case study.

Circularity Check

1 steps flagged · score 6.0 of 10

Geomagnetic-storm prediction is an autoregressive nowcast because SYM-H is both an input feature and the predicted target; flare and CME links are not circular.

self definitional [Section 2.5 (Forecast of the geomagnetic storm) and Section 4.4 (A neural network for predicting the geomagnetic impact of the storm)]
"the input parameters were represented by the 24-hour time series of features derived from situ measurements and the SYM-H index, which measures the symmetric portion of the horizontal component magnetic field near the equator ... This configuration was designed to predict the probability that the SYM-H index drops below −50 nT within the next hour."

The output is a binary label on SYM-H (drop below −50 nT within the next hour), while a 24-hour history of SYM-H is itself one of the input time series. For an ongoing storm such as the May 2024 event, the onset and recovery phases are already present in the input window, so the model extrapolates the target variable one hour ahead rather than forecasting it from upstream drivers. The paper's statement that 'AI is able to accurately predict not only the onset but also the whole recovery phase 1 hour in advance' is therefore not an independent prediction; by construction it is a persistence-style nowcast of the same quantity being predicted.

full rationale

The derivation chain has three links: flare forecasting, CME travel time, and geomagnetic storm alerting. The flare model uses 24-hour HMI videos to forecast M/X flares in the next 24 hours; the CME model uses cone-model parameters and solar-wind inputs through a DBM-constrained network; neither of these feeds the target quantity back as an input, and the self-citations to Guastavino et al. (2022a, 2023a) and Legnaro et al. (2024) are references to externally published methods, not circular evidence. The geomagnetic-storm link, however, is circular in its construction: Section 2.5 states the input features include the SYM-H index, and Section 4.4 states the network's output is the probability that SYM-H drops below −50 nT in the next hour. Thus the 'prediction of onset and recovery one hour in advance' reduces to a short-horizon autoregressive projection of the target variable. This is a concrete input/target overlap, not an inference about author intent. Because the storm alert is the final and decisive element of the paper's headline claim that AI could have predicted the entire chain, the central claim is partially circular, though the flare and CME components remain genuinely predictive. Score 6 reflects one prediction that reduces by construction while other components retain independent content.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities and claims no new fitted constants; however, the central results rest on several unquantified inputs and on assumptions inherited from prior drag-based and cone-model literature. The main ledger entries are the CME input parameters and the unspecified drag coefficient, together with the domain assumptions listed above.

free parameters (3)

CME drag coefficient C (output of Ndrag) = Not reported in the paper.
The first neural network estimates the drag coefficient used in the DBM analytical solution; the travel-time prediction depends on this value, but neither the estimate nor its uncertainty is reported (Section 4.3).
CME and solar wind input parameters (v0, m, phi, rho, w) = v0 = 2598.65 km/s, m = 1.37e16 g, phi = 166.54 deg, rho = 3 cm^-3, and w = 500 km/s.
These point estimates from the cone model and a semi-empirical solar wind model determine the predicted travel time; no uncertainties are given and no sensitivity analysis is performed (Table 1).
Loss-balance weight lambda in Eq. (2) = Not reported in the paper.
The relative weight of data and physics terms in the CME travel-time loss is a free choice; its value is not stated, so the training procedure is not fully specified (Section 4.3).

assumptions (4)

domain assumption Drag-based model (DBM) governs CME propagation from 20 solar radii to 1 AU.
Invoked in Section 4.3 as the physical core of the loss functions in Eqs. (1)-(2); the event involves interacting and cannibalized CMEs, which the single-body DBM does not represent.
domain assumption Cone model representation of the CME gives reliable v0, width, and direction from LASCO images.
Used in Section 2.4 to build the input vector in Table 1; the cone model assumes a self-similar expanding geometry.
domain assumption Semi-empirical Parker-spiral solar wind model yields the ambient density and speed along the CME path.
Used in Section 2.4 to set rho = 3 cm^-3 and w = 500 km/s, with no validation against in-situ measurements along the path.
domain assumption Models trained on historical intervals transfer to May 2024 conditions.
Flare model trained on 2012-2017, CME model on 1993-2018, storm model on 2005-2023 (Section 4); no distribution-shift analysis is provided.

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Cite this review

Pith. "Pith review of Artificial Intelligence Could Have Predicted All Space Weather Events Associated with the May 2024 Superstorm." pith.science (2026). https://pith.science/paper/SI7T7SCU

@misc{pith2026250114684,
  author       = {Pith},
  title        = {Pith review of: Artificial Intelligence Could Have Predicted All Space Weather Events Associated with the May 2024 Superstorm},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SI7T7SCU}},
  note         = {Machine review of arXiv:2501.14684}
}

abstract

Space weather, driven by solar flares and Coronal Mass Ejections (CMEs), poses significant risks to technological systems. Accurately forecasting these events and their impact on Earth's magnetosphere remains a challenge because of the complexity of solar-terrestrial interactions. This study applied artificial intelligence (AI) to predict the chain of events associated with the May $2024$ superstorm, including solar flares from NOAA active region 13644, Earth-directed CMEs, and a violent geomagnetic storm. Using magnetogram cut-outs, a Vision Transformer was able to classify the evolution of the active region morphologies, and a video-based deep learning method predicted the occurrence of solar flares; a physics-driven model improved the precision of CME travel-time prediction using coronal observations and solar wind measurements; and a data-driven method exploited these in situ measurements to sound alerts of the geomagnetic storm unrolled over time. The results showed unprecedented accuracy in predicting CME arrival with uncertainty as small as one minute. Moreover, these AI models outperformed traditional methods in predicting solar flares occurrences, onset, and recovery phases of the geomagnetic storm. These findings highlight the impressive potential of AI for space weather forecasting and as a tool to mitigate the impact of extreme solar events on critical infrastructure.

Figures

Figures reproduced from arXiv: 2501.14684 by the authors.

**Figure 2.** Classification of AR13664 provided by a Data-E [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** AI-driven forecasting results. The top panel enrols over time the alarms sounded by video-based [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: The figure illustrates the pipeline for classifying active region (AR) magnetograms using a Vi [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: Video-based deep neural network architecture for flare forecasting. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: The physics-driven AI method for the CME characterization. Top panel: the cascade of two [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

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Reference graph

Works this paper leans on

55 extracted references · 45 canonical work pages

[1]

W., Qahwaji, R., Colak, T., et al

Ahmed, O. W., Qahwaji, R., Colak, T., et al. 2013, Solar Physics, 283, 157

2013
[2]

M., & Piana, M

Benvenuto, F., Campi, C., Massone, A. M., & Piana, M. 2020, The Astrophysical Journal Letters, 904, L7

work page 2020
[3]

1995, The SOHO mission, 357

Brueckner, G., Howard, R., Koomen, M., et al. 1995, The SOHO mission, 357

work page 1995
[4]

2024, Statistics and Computing, 34, 1

Camattari, F., Guastavino, S., Marchetti, F., Piana, M., & Perracchione, E. 2024, Statistics and Computing, 34, 1

work page 2024
[5]

M., et al

Campi, C., Benvenuto, F., Massone, A. M., et al. 2019, The Astrophysical Journal, 883, 150

work page 2019
[6]

2019, Space Weather, 17, 1166

Camporeale, E. 2019, Space Weather, 17, 1166

work page 2019
[7]

2018, Machine learning techniques for space weather (Elsevier)

Camporeale, E., Wing, S., & Johnson, J. 2018, Machine learning techniques for space weather (Elsevier)

work page 2018
[8]

2021, The Astrophysical Journal, 915, 38

Cicogna, D., Berrilli, F., Calchetti, D., et al. 2021, The Astrophysical Journal, 915, 38

2021

Show all 55 references

[9]

2024, Scientific Reports, 14, 1

Diaz, J. 2024, Scientific Reports, 14, 1

2024
[10]

2015, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2625–2634

Donahue, J., Anne Hendricks, L., Guadarrama, S., et al. 2015, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2625–2634

2015
[11]

2020, arXiv preprint arXiv:2010.11929

Dosovitskiy, A. 2020, arXiv preprint arXiv:2010.11929

2020 arXiv
[12]

P., Biffis, E., Hapgood, M

Eastwood, J. P., Biffis, E., Hapgood, M. A., et al. 2017, Risk Anal., 37, 206

2017
[13]

2019, Advances in Astronomy, 2019, 9196234

Fang, Y ., Cui, Y ., & Ao, X. 2019, Advances in Astronomy, 2019, 9196234

2019
[14]

2018, Sol

Florios, K., Kontogiannis, I., Park, S.-H., et al. 2018, Sol. Phys., 293, 28

2018
[15]

K., Bloomfield, D

Georgoulis, M. K., Bloomfield, D. S., Piana, M., et al. 2021, Journal of Space Weather and Space Climate, 11, 39

2021
[16]

K., Nindos, A., & Zhang, H

Georgoulis, M. K., Nindos, A., & Zhang, H. 2019, Philosophical Transactions of the Royal Society A, 377, 20180094

2019
[17]

K., & Kumar, A

Goel, A., Goel, A. K., & Kumar, A. 2023, Spatial Information Research, 31, 275

2023
[18]

I., Chen, Y ., Glocer, A., et al

Gombosi, T. I., Chen, Y ., Glocer, A., et al. 2021, Journal of Space Weather and Space Climate, 11, 42

2021
[19]

2016, Geoscience Letters, 3, 1

Gopalswamy, N. 2016, Geoscience Letters, 3, 1

2016
[20]

L., & Howard, R

Gopalswamy, N., Lara, A., Yashiro, S., Kaiser, M. L., & Howard, R. A. 2001, J. Geophys. Res. Space Phys., 106, 29207

2001
[21]

1990, Geophysical Research Letters, 17, 901

Gosling, J., Bame, S., McComas, D., & Phillips, J. 1990, Geophysical Research Letters, 17, 901

1990
[22]

2024, Astrophys

Guastavino, S., Bahamazava, K., Perracchione, E., et al. 2024, Astrophys. J., 971, 94

2024
[23]

2022b, IEEE transactions on neural networks and learning systems, 35, 1993

Guastavino, S., Piana, M., & Benvenuto, F. 2022b, IEEE transactions on neural networks and learning systems, 35, 1993

1993
[24]

2024, arXiv preprint arXiv:2407.07665

Hayakawa, H., Ebihara, Y ., Mishev, A., et al. 2024, arXiv preprint arXiv:2407.07665

2024 arXiv
[25]

2024, Journal of Astronomy and Space Sciences, 41, 171 13

Kwak, Y .-S., Kim, J.-H., Kim, S., et al. 2024, Journal of Astronomy and Space Sciences, 41, 171 13

2024
[26]

2020, Astronomy and Astro- physics, 635, A112

Lara, A., Gopalswamy, N., Niembro, T., P ´erez-Enr´ıquez, R., & Yashiro, S. 2020, Astronomy and Astro- physics, 635, A112

2020
[27]

Legnaro, E., Guastavino, S., Piana, M., & Massone, A. M. 2024, arXiv preprint arXiv:2410.17816

2024 arXiv
[28]

Lugaz, N., Temmer, M., Wang, Y ., & Farrugia, C. J. 2017, Solar Physics, 292, 1

2017
[29]

2022, Pattern Recognition, 132, 108913

Marchetti, F., Guastavino, S., Piana, M., & Campi, C. 2022, Pattern Recognition, 132, 108913

2022
[30]

L., Taktakishvili, A., Pulkkinen, A., et al

Mays, M. L., Taktakishvili, A., Pulkkinen, A., et al. 2015, Sol. Phys., 290, 1775

2015
[31]

E., Carley, E

Morosan, D. E., Carley, E. P., Hayes, L. A., et al. 2019, Nature Astronomy, 3, 452

2019
[32]

Murray, S. A. 2018, Space Weather, 16, 777

2018
[33]

2023, Space Weather, 21, e2023SW003514

Nair, M., Redmon, R., Young, L.-Y ., et al. 2023, Space Weather, 21, e2023SW003514

2023
[34]

2018, Journal of Space Weather and Space Climate, 8, A11

Napoletano, G., Forte, R., Del Moro, D., et al. 2018, Journal of Space Weather and Space Climate, 8, A11

2018
[35]

V ., Mulligan, T., Kilpua, E

Nitta, N. V ., Mulligan, T., Kilpua, E. K., et al. 2021, Space Science Reviews, 217, 82

2021
[36]

2003, Adv

Odstrcil, D. 2003, Adv. Space Res., 32, 497

2003
[37]

Parker, E. N. 1958, Astrophys. J., 128, 664

1958
[38]

D., Thompson, B

Pesnell, W. D., Thompson, B. J., & Chamberlin, P. C. 2012, Sol. Phys., 275, 3

2012
[39]

G., Massone, A

Piana, M., Emslie, A. G., Massone, A. M., & Dennis, B. R. 2022, Hard X-ray Imaging of Solar Flares, V ol. 164 (Springer)

2022
[40]

2007, Living Rev

Pulkkinen, T. 2007, Living Rev. Sol. Phys., 4, 1

2007
[41]

D., et al

Riley, P., Baker, D., Liu, Y . D., et al. 2018, Space Sci. Rev., 214, 21

2018
[42]

H., Schou, J., Bush, R

Scherrer, P. H., Schou, J., Bush, R. I., et al. 2012, Sol. Phys., 275, 207

2012
[43]

Schrijver, C. J. & Siscoe, G. L. 2010, Heliophysics: Space Storms and Radiation: Causes and Effects

2010
[44]

2022, Reviews of Modern Plasma Physics, 6, 8

Shen, F., Shen, C., Xu, M., et al. 2022, Reviews of Modern Plasma Physics, 6, 8

2022
[45]

& Emslie, A

Tandberg-Hanssen, E. & Emslie, A. G. 1988, The physics of solar flares, V ol. 14 (Cambridge University Press)

1988
[46]

2019, Astrophys

Telloni, D., Antonucci, E., Bemporad, A., et al. 2019, Astrophys. J., 885, 120

2019
[47]

2020, Astrophys

Telloni, D., Carbone, F., Antonucci, E., et al. 2020, Astrophys. J., 896, 149

2020
[48]

L., Magli, E., et al

Telloni, D., Schiavo, M. L., Magli, E., et al. 2023, The Astrophysical Journal, 952, 111 V ourlidas, A., Howard, R. A., Esfandiari, E., et al. 2010, Astrophys. J., 722, 1522 V ourlidas, A., Subramanian, P., Dere, K. P., & Howard, R. A. 2000, Astrophys. J., 534, 456 Vrˇsnak, B....

2023
[49]

Wanliss, J. A. & Showalter, K. M. 2006, Journal of Geophysical Research: Space Physics, 111

2006
[50]

Webb, D. F. & Howard, T. A. 2012, Living Rev. Sol. Phys., 9, 3

2012
[51]

Xie, H., Ofman, L., & Lawrence, G. 2004, J. Geophys. Res. Space Phys., 109, A03109

2004
[52]

2019, Neural computation, 31, 1235

Yu, Y ., Si, X., Hu, C., & Zhang, J. 2019, Neural computation, 31, 1235

2019
[53]

G., Webb, D

Zhang, J., Richardson, I. G., Webb, D. F., et al. 2007, J. Geophys. Res. Space Phys., 112, A10102

2007
[54]

P., Plunkett, S

Zhao, X. P., Plunkett, S. P., & Liu, W. 2002, J. Geophys. Res. Space Phys., 107, 1223

2002
[55]

Y ., Stolle, C., & Matzka, J

Zhelavskaya, I., Vasile, R., Shprits, Y . Y ., Stolle, C., & Matzka, J. 2019, Space Weather, 17, 1461 14

2019

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

W., Qahwaji, R., Colak, T., et al

Ahmed, O. W., Qahwaji, R., Colak, T., et al. 2013, Solar Physics, 283, 157

2013

[2] [2]

M., & Piana, M

Benvenuto, F., Campi, C., Massone, A. M., & Piana, M. 2020, The Astrophysical Journal Letters, 904, L7

work page 2020

[3] [3]

1995, The SOHO mission, 357

Brueckner, G., Howard, R., Koomen, M., et al. 1995, The SOHO mission, 357

work page 1995

[4] [4]

2024, Statistics and Computing, 34, 1

Camattari, F., Guastavino, S., Marchetti, F., Piana, M., & Perracchione, E. 2024, Statistics and Computing, 34, 1

work page 2024

[5] [5]

M., et al

Campi, C., Benvenuto, F., Massone, A. M., et al. 2019, The Astrophysical Journal, 883, 150

work page 2019

[6] [6]

2019, Space Weather, 17, 1166

Camporeale, E. 2019, Space Weather, 17, 1166

work page 2019

[7] [7]

2018, Machine learning techniques for space weather (Elsevier)

Camporeale, E., Wing, S., & Johnson, J. 2018, Machine learning techniques for space weather (Elsevier)

work page 2018

[8] [8]

2021, The Astrophysical Journal, 915, 38

Cicogna, D., Berrilli, F., Calchetti, D., et al. 2021, The Astrophysical Journal, 915, 38

2021