A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-day Forecasting Patterns

Park, Sung-Hong; Leka, K. D.; Kusano, K.; Andries, Jesse; Barnes, G.; Bingham, Suzy; Bloomfield, D. Shaun; McCloskey, Aoife E.; Delouille, Véronique; Falconer, D. A.; Gallagher, P. T.; Georgoulis, Manolis K.; Kubo, Yûki; Lee, Kangjin; Lee, Sang‐Woo; Lobzin, V. V.; Mun, JunChul; Murray, Sophie A.; Nageem, Tarek A.M. Hamad; Qahwaji, Rami; Sharpe, Michael; Steenburgh, Rob; Steward, Graham; Terkildsen, Michael

doi:10.3847/1538-4357/ab65f0

Cited by 45 publications

(23 citation statements)

References 29 publications

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“…This analysis is carried out for combinations of day 1/day 2 forecasts and for day 2/day 3 forecasts, separately for Solar Cycle 23 and 24. See Table 3 for the numerical results while Figure 9 represents this information using radar plots, adapted from Park et al (2020), where the authors looked at a similar verification for flare forecasting. For the transition from No Event on day 1 to Event on day 2, it is clear that no perfect forecasts (C-H) were issued for either Solar Cycle.…”

Section: Consecutive Two-day Forecastsmentioning

confidence: 87%

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A Summary of National Oceanic and Atmospheric Administration Space Weather Prediction Center Proton Event Forecast Performance and Skill

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Section: Consecutive Two-day Forecastsmentioning

confidence: 87%

“…See Table 3 for the numerical results while Figure 9 represents this information using radar plots, adapted from Park et al. (2020), where the authors looked at a similar verification for flare forecasting.…”

Section: Probabilistic Proton Event Productsmentioning

confidence: 89%

A Summary of National Oceanic and Atmospheric Administration Space Weather Prediction Center Proton Event Forecast Performance and Skill

et al. 2021

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“…The prediction of these flares is a difficult task, and a wide range of methodologies is applied to proceed. Machine learning approaches seem to have strong potentials (Bobra and Couvidat, 2015;Kusano et al, 2020). In this topical issue the paper titled "Testing and Validating Two Morphological Flare Predictors by Logistic Regression Machine Learning" (Korsós et al) addresses a stringent method that is developed to investigate and assess numerical measures of the mixed states of ARs with opposite magnetic field polarities.…”

Section: Novel Scientific Outcomes Of the Topical Issuementioning

confidence: 99%

Data-driven MHD: Novel Applications to the Solar Atmosphere

2021

Frontiers Research Topics

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Frontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers Journal SeriesThe Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers Journal Series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to QualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

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“…In recent years, these efforts have taken advantage of the rapid innovation in "machine learning" (ML) techniques developed for commercial image classification purposes. Qahwaji & Colak (2007) review some of the early attempts to employ machine learning to the solar flare prediction problem, and a recent series of papers compares the prediction skill of the manual method and several current automated models Leka et al 2019a,b;Park et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Decreasing False Alarm Rates in ML-based Solar Flare Prediction using SDO/HMI Data

Deshmukh,

Flyer,

Van Der Sande

et al. 2021

Preprint

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A hybrid two-stage machine learning architecture that addresses the problem of excessive false positives (false alarms) in solar flare prediction systems is investigated. The first stage is a convolutional neural network (CNN) model based on the VGG-16 architecture that extracts features from a temporal stack of consecutive Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI) magnetogram images to produce a flaring probability. The probability of flaring is added to a feature vector derived from the magnetograms to train an extremely randomized trees (ERT) model in the second stage to produce a binary deterministic prediction (flare/no flare) in a 12-hour forecast window. To tune the hyperparameters of the architecture a new evaluation metric is introduced, the "scaled True Skill Statistic". It specifically addresses the large discrepancy between the true positive rate and the false positive rate in the highly unbalanced solar flare event training datasets. Through hyperparameter tuning to maximize this new metric, our two-stage architecture drastically reduces false positives by ≈ 48% without significantly affecting the true positives (reduction by ≈ 12%), when compared with predictions from the first stage CNN alone. This, in turn, improves various traditional binary classification metrics sensitive to false positives such as the precision, F1 and the Heidke Skill Score. The end result is a more robust 12-hour flare prediction system that could be combined with current operational flare forecasting methods. Additionally, using the ERT-based feature ranking mechanism, we show that the CNN output probability is highly ranked in terms of flare prediction relevance.

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A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-day Forecasting Patterns

Cited by 45 publications

References 29 publications

A Summary of National Oceanic and Atmospheric Administration Space Weather Prediction Center Proton Event Forecast Performance and Skill

A Summary of National Oceanic and Atmospheric Administration Space Weather Prediction Center Proton Event Forecast Performance and Skill

Data-driven MHD: Novel Applications to the Solar Atmosphere

Decreasing False Alarm Rates in ML-based Solar Flare Prediction using SDO/HMI Data

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