Solar energetic particle flux enhancement as a predictor of geomagnetic activity in a neural network‐based model

Valach, Fridrich; Revallo, Miloš; Bochníček, Josef; Hejda, Pavel

doi:10.1029/2008sw000421

Cited by 27 publications

(23 citation statements)

References 21 publications

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“…They showed that inclusion of information about high energy proton flux data as additional input improves the forecasts. Analysis showed about 51% forecast response (Valach et al 2009). …”

Section: Predictability Of Space Weathermentioning

confidence: 99%

“…The predicted maximum sunspot number for the 24th cycle would be about 101 and the next solar maximum would occur around July 2012; the length of the cycle would be 11 years. Recently, Valach et al (2009), using an artificial neural network model, has tried to predict geomagnetic activity on the basis of solar energetic particle flux enhancements. The model is based on the scheme proposed by Valach et al (2007).…”

Section: Predictability Of Space Weathermentioning

confidence: 99%

“…Solar energetic particles (SEPs), accelerated by the solar flares and the associated CME shocks, are also important aspects of space weather as they may damage electronics onboard spacecraft and may affect human health aboard satellites and aircraft (Valach et al 2009). …”

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confidence: 99%

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Space Weather: Physics, Effects and Predictability

2010

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The time varying conditions in the near-Earth space environment that may affect space-borne or ground-based technological systems and may endanger human health or life are referred to as space weather. Space weather effects arise from the dynamic and highly variable conditions in the geospace environment starting from explosive events on the Sun (solar flares), Coronal Mass Ejections near the Sun in the interplanetary medium, and various energetic effects in the magnetosphere-ionosphere-atmosphere system. As the utilization of space has become part of our everyday lives, and as our lives have become increasingly dependent on technological systems vulnerable to the space weather influences, the understanding and prediction of hazards posed by these active solar events have grown in importance. In this paper, we review the processes of the Sun-Earth interactions, the dynamic conditions within the magnetosphere, and the predictability of space weather effects on radio waves, satellites and ground-based technological systems today.

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“…They showed that inclusion of information about high energy proton flux data as additional input improves the forecasts. Analysis showed about 51% forecast response (Valach et al 2009). …”

Section: Predictability Of Space Weathermentioning

confidence: 99%

Section: Predictability Of Space Weathermentioning

confidence: 99%

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Space Weather: Physics, Effects and Predictability

2010

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“…The areas in which the probability is greater than 50% are shown in red (from ). Valach et al 2009). …”

Section: Response Of the Terrestrial Environment To The Solar Windmentioning

confidence: 99%

Geomagnetic response to solar and interplanetary disturbances

Sáiz

Cerrato

Cid

et al. 2013

J. Space Weather Space Clim.

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The space weather discipline involves different physical scenarios, which are characterised by very different physical conditions, ranging from the Sun to the terrestrial magnetosphere and ionosphere. Thanks to the great modelling effort made during the last years, a few Sun-to-ionosphere/thermosphere physics-based numerical codes have been developed. However, the success of the prediction is still far from achieving the desirable results and much more progress is needed. Some aspects involved in this progress concern both the technical progress (developing and validating tools to forecast, selecting the optimal parameters as inputs for the tools, improving accuracy in prediction with short lead time, etc.) and the scientific development, i.e., deeper understanding of the energy transfer process from the solar wind to the coupled magnetosphere-ionosphere-thermosphere system. The purpose of this paper is to collect the most relevant results related to these topics obtained during the COST Action ES0803. In an end-to-end forecasting scheme that uses an artificial neural network, we show that the forecasting results improve when gathering certain parameters, such as X-ray solar flares, Type II and/or Type IV radio emission and solar energetic particles enhancements as inputs for the algorithm. Regarding the solar wind-magnetosphere-ionosphere interaction topic, the geomagnetic responses at high and low latitudes are considered separately. At low latitudes, we present new insights into temporal evolution of the ring current, as seen by Burton's equation, in both main and recovery phases of the storm. At high latitudes, the PCC index appears as an achievement in modelling the coupling between the upper atmosphere and the solar wind, with a great potential for forecasting purposes. We also address the important role of small-scale field-aligned currents in Joule heating of the ionosphere even under non-disturbed conditions. Our scientific results in the framework of the COST Action ES0803 cover the topics from the short-term solar-activity evolution, i.e., space weather, to the long-term evolution of relevant solar/heliospheric/magnetospheric parameters, i.e., space climate. On the timescales of the Hale and Gleissberg cycles (22-and 88-year cycle respectively) we can highlight that the trend of solar, heliospheric and geomagnetic parameters shows the solar origin of the widely discussed increase in geomagnetic activity in the last century.

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“…Therefore, the algorithms make it possible to construct a forecast model even though the relationship between cause and effect is not clearly understood. Neural networks have been broadly used in space weather applications such as the prediction of solar activities (Wang 2000;Gong et al 2004;Wang et al 2008;Qahwaji et al 2007Qahwaji et al , 2008Henwood et al 2010) and geomagnetic activities (Lundstedt 1992;Hernandez et al 1993;Freeman et al 1993;Valach et al 2009;Ji et al 2013). SPE prediction models have been developed by neu-…”

Section: Introductionmentioning

confidence: 99%

Forecast of Solar Proton Events With Noaa Scales Based on Solar X-Ray Flare Data Using Neural Network

Jeong¹,

Lee²,

Moon³

et al. 2014

Journal of The Korean Astronomical Society

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Abstract:In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.

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Solar energetic particle flux enhancement as a predictor of geomagnetic activity in a neural network‐based model

Cited by 27 publications

References 21 publications

Space Weather: Physics, Effects and Predictability

Space Weather: Physics, Effects and Predictability

Geomagnetic response to solar and interplanetary disturbances

Forecast of Solar Proton Events With Noaa Scales Based on Solar X-Ray Flare Data Using Neural Network

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