Predicting well-connected SEP events from observations of solar soft X-rays and near-relativistic electrons

Núñez, Marlon

doi:10.1051/swsc/2018023

Cited by 14 publications

(10 citation statements)

References 20 publications

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“…Further, Michalek (2009) showed a good correlation between the energy of the CME and the peak of the X-ray flare. Finally, recent studies showed that the X-ray flux ratio is a good predictor for extreme solar events (Núñez, 2018;Kahler and Ling, 2018).…”

Section: Model Features and Data Sourcesmentioning

confidence: 98%

“…To assess the viability of moving beyond solar wind-driven models using operationally-available data, we also investigate the inclusion of solar X-ray flux data as a model parameter. Solar Xray flux was chosen as recent studies have shown that these data can be used to forecast solar flare activity (Winter and Balasubramaniam, 2015) as well as Solar Energetic Particle (SEP) events (Núñez, 2018). While the majority of large geomagnetic storms are caused by CMEs (e.g., Gonzalez et al, 1999), it has been shown that CMEs are correlated with solar flare activity (Zhou et al, 2003;Kay et al, 2003;Wu et al, 2008;Lippiello et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic prediction of geomagnetic storms and theK_pindex

Chakraborty

Morley

2020

J. Space Weather Space Clim.

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Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index Kp in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic Kp predictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3–hour-ahead Kp prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (Kp>5-) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.

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Section: Model Features and Data Sourcesmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Probabilistic prediction of geomagnetic storms and theK_pindex

Chakraborty

Morley

2020

J. Space Weather Space Clim.

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“…The US National Weather Service uses this measure to assess predictors because, according to them [37], it is an unbiased verification statistic appropriate for predicting rare events, and, for this reason, it is used to assess severe weather predictors. CSI has also been used in SEP event prediction modeling for optimizing POD and FAR [38][39][40]. The CSI combines POD and FAR as follows: CSI = (POD −1 + (1 − FAR) −1 − 1) −1 .…”

Section: Optimization Of the Decision Treementioning

confidence: 99%

Predicting >10 MeV SEP Events from Solar Flare and Radio Burst Data

Núñez

Paul-Pena

2020

Universe

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The prediction of solar energetic particle (SEP) events or solar radiation storms is one of the most important problems in the space weather field. These events may have adverse effects on technology infrastructures and humans in space; they may also irradiate passengers and flight crews in commercial aircraft flying at polar latitudes. This paper explores the use of ≥ M2 solar flares and radio burst observations as proxies for predicting >10 MeV SEP events on Earth. These observations are manifestations of the parent event at the sun associated with the SEP event. As a consequence of processing data at the beginning of the physical process that leads to the radiation storm, the model may provide its predictions with large anticipation. The main advantage of the present approach is that the model analyzes solar data that are updated every 30 min and, as such, it may be operational; however, a disadvantage is that those SEP events associated with strong well-connected flares cannot be predicted. For the period from November 1997 to February 2014, we obtained a probability of detection of 70.2%, a false alarm ratio of 40.2%, and an average anticipation time of 9 h 52 min. In this study, the prediction model was built using decision trees, an interpretable machine learning technique. This approach leads to outputs and results comparable to those derived by the Empirical model for Solar Proton Event Real Time Alert (ESPERTA) model. The obtained decision tree shows that the best criteria to differentiate pre-SEP scenarios and non-pre-SEP scenarios are the peak and integrated flux for soft X-ray flares and the radio type III bursts.

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“…Regarding solar EM data, this scheme has been used with SXR flux (Núñez, 2011(Núñez, , 2015Núñez et al, 2017), and microwave (MW) flux density at 5 and 9 GHz (Zucca et al, 2017). Regarding in-situ particle data, this scheme has been used with differential proton fluxes; recently, it has also been used with relativistic electron data (Núñez, 2018).…”

Section: The Modelmentioning

confidence: 99%

“…In this paper we study the use of EUV observations in the UMASEP scheme and analyze the possibility of using them in real-time operations. The UMASEP scheme has been used to develop several real-time prediction tools: UMASEP-10 (Núñez, 2011), UMASEP-100 (Núñez, 2015) and HESPERIA UMASEP-500 (Núñez et al, 2017;Núñez, 2018) which predict >10 MeV, >100 MeV and >500 MeV SEP events, respectively. Since 2010, the UMASEP-10's forecasts are disseminated by NASA's integrated Space Weather Analysis system (iSWA), and the model has shown promise on an operational level (Tsagouri et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Predicting well-connected SEP events from observations of solar EUVs and energetic protons

Núñez

Nieves‐Chinchilla

Pulkkinen

2019

J. Space Weather Space Clim.

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This study shows a quantitative assessment of the use of Extreme Ultraviolet (EUV) observations in the prediction of Solar Energetic Proton (SEP) events. The UMASEP scheme (Space Weather, 9, S07003, 2011; 13, 2015, 807–819) forecasts the occurrence and the intensity of the first hours of SEP events. In order to predict well-connected events, this scheme correlates Solar Soft X-rays (SXR) with differential proton fluxes of the GOES satellites. In this study, we explore the use of the EUV time history from GOES-EUVS and SDO-AIA instruments in the UMASEP scheme. This study presents the results of the prediction of the occurrence of well-connected >10 MeV SEP events, for the period from May 2010 to December 2017, in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and the average and median of the warning times. The UMASEP/EUV-based models were calibrated using GOES and SDO data from May 2010 to October 2014, and validated using out-of-sample SDO data from November 2014 to December 2017. The best results were obtained by those models that used EUV data in the range 50–340 Å. We conclude that the UMASEP/EUV-based models yield similar or better POD results, and similar or worse FAR results, than those of the current real-time UMASEP/SXR-based model. The reason for the higher POD of the UMASEP/EUV-based models in the range 50–340 Å, was due to the high percentage of successful predictions of well-connected SEP events associated with <C4 flares and behind-the-limb flares, which amounted to 25% of all the well-connected events during the period May 2010 to December 2017. By using all the available data (2010–2017), this study also concluded that the simultaneous use of SXRs and EUVs in 94 Å in the UMASEP-10 tool for predicting all >10 MeV SEP events, improves the overall performance, obtaining a POD of 92.9% (39/42) compared with 81% (34/42) of the current tool, and a slightly worse FAR of 31.6% (18/57) compared with 29.2% (14/58) of the current tool.

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Predicting well-connected SEP events from observations of solar soft X-rays and near-relativistic electrons

Cited by 14 publications

References 20 publications

Probabilistic prediction of geomagnetic storms and theK_pindex

Probabilistic prediction of geomagnetic storms and theK_pindex

Predicting >10 MeV SEP Events from Solar Flare and Radio Burst Data

Predicting well-connected SEP events from observations of solar EUVs and energetic protons

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Predicting well-connected SEP events from observations of solar soft X-rays and near-relativistic electrons

Cited by 14 publications

References 20 publications

Probabilistic prediction of geomagnetic storms and theKpindex

Probabilistic prediction of geomagnetic storms and theKpindex

Predicting >10 MeV SEP Events from Solar Flare and Radio Burst Data

Predicting well-connected SEP events from observations of solar EUVs and energetic protons

Contact Info

Product

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Probabilistic prediction of geomagnetic storms and theK_pindex

Probabilistic prediction of geomagnetic storms and theK_pindex