Geomagnetic &amp;lt;i&amp;gt;D&amp;lt;sub&amp;gt;st&amp;lt;/sub&amp;gt;&amp;lt;/i&amp;gt; index forecast based on IMF data only

Pallocchia, G.; Amata, E.; Consolini, Giuseppe; Marcucci, M. F.; Bertello, Igor

doi:10.5194/angeo-24-989-2006

Cited by 78 publications

(75 citation statements)

References 22 publications

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“…During the three most intense geomagnetic storms of the present solar cycle, solar wind plasma measurements at L1 presented gaps, but there were no gaps in IMF data. Pallocchia et al (2006) remarked that plasma instruments can be affected by enhanced solar X-ray and energetic particle fluxes and can fail more often than magnetometers; moreover, sometimes the solar wind speed exceeds the upper instrumental limits of plasma detectors. On the other hand, during these events, Table 3.…”

Section: New Warning Featuresmentioning

confidence: 99%

Forecasting intense geomagnetic activity using interplanetary magnetic field data

2008

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Abstract. Southward interplanetary magnetic fields are considered traces of geoeffectiveness since they are a main agent of magnetic reconnection of solar wind and magnetosphere. The first part of this work revises the ability to forecast intense geomagnetic activity using different procedures available in the literature. The study shows that current methods do not succeed in making confident predictions. This fact led us to develop a new forecasting procedure, which provides trustworthy results in predicting large variations of D st index over a sample of 10 years of observations and is based on the value B z only. The proposed forecasting method appears as a worthy tool for space weather purposes because it is not affected by the lack of solar wind plasma data, which usually occurs during severe geomagnetic activity. Moreover, the results obtained guide us to provide a new interpretation of the physical mechanisms involved in the interaction between the solar wind and the magnetosphere using Faraday's law.

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Section: New Warning Featuresmentioning

confidence: 99%

Forecasting intense geomagnetic activity using interplanetary magnetic field data

2008

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“…Other techniques for Dst-index predictions include the usage of neural networks, with delayed inputs, with feed-back connections (e.g., Lundstedt and Wintoft 1994;Lundstedt et al 2001;Watanabe et al 2002;Pallocchia et al 2006;Bala and Reiff 2012;Dolenko et al 2014;Lu et al 2016). Bala and Reiff (2012) use the solar wind-magnetosphere coupling function, the Boyle index (Boyle et al 1997), which is an empirical approximation for the polar cap potential dependent on solar wind parameters, as basis functions to train an artificial neural network to predict the Dst-index.…”

Section: Dst-index As a Storm Indicator Measure And Predictormentioning

confidence: 99%

Space Weather Effects Produced by the Ring Current Particles

2017

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One of the definitions of space weather describes it as the time-varying space environment that may be hazardous to technological systems in space and/or on the ground and/or endanger human health or life. The ring current has its contributions to space weather effects, both in terms of particles, ions and electrons, which constitute it, and magnetic and electric fields produced and modified by it at the ground and in space. We address the main aspects of the space weather effects from the ring current starting with brief review of ring current discovery and physical processes and the Dst-index and predictions of the ring current and storm occurrence based on it. Special attention is paid to the effects on satellites produced by the ring current electrons. The ring current is responsible for several processes in the other inner magnetosphere populations, such as the plasmasphere and radiation belts which is also described. Finally, we discuss the ring current influence on the ionosphere and the generation of geomagnetically induced currents (GIC).

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“…In previous studies of Dst prediction by ANNs, different combinations of solar wind and IMF parameters with different time-length (i.e., the correlation time between each input and Dst) have ever been used as the ANNs' inputs, such as group of n, V and IMF B z with 1 h correlation length (Lundstedt et al, 2002) where n is the proton number density of solar wind, and V the solar wind velocity, and group of only IMF of B z , B ( Pallocchia et al, 2006) and others. In the present study we use the parameter combination of n, V , B z , B y and B with 90-min history length for each parameter as input to predict SYM-H index 60 min ahead.…”

Section: Data Sets and External Inputsmentioning

confidence: 99%

“…Various methods have been applied to predict geomagnetic disturbances as indicated by Dst index from solar wind and IMF observations, such as differential equations (Burton et al, 1975;Wang et al, 2003), statistical correlative analysis (Baker, 1986;Temerin and Li, 2002;Yermolaev et al, 2005), and artificial neural networks (ANNs) (Wu and Lundstedt, 1996;Gleisner et al, 1996;Wu and Lundstedt, 1997;Lundstedt et al, 2002;Pallocchia et al, 2006;Amata et al, 2008). Of those, artificial neural networks have shown their great ability of nonlinear mapping in Dst prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

Cai

Zhou

2010

Ann. Geophys.

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Abstract. Similar to the Dst index, the SYM-H index may also serve as an indicator of magnetic storm intensity, but having distinct advantage of higher time-resolution. In this study the NARX neural network has been used for the first time to predict SYM-H index from solar wind (SW) and IMF parameters. In total 73 time intervals of great storm events with IMF/SW data available from ACE satellite during 1998 to 2006 are used to establish the ANN model. Out of them, 67 are used to train the network and the other 6 samples for test. Additionally, the NARX prediction model is also validated using IMF/SW data from WIND satellite for 7 great storms during 1995-1997 and 2005, as well as for the July 2000 Bastille day storm and November 2001 superstorm using Geotail and OMNI data at 1 AU, respectively. Five interplanetary parameters of IMF B z , B y and total B components along with proton density and velocity of solar wind are used as the original external inputs of the neural network to predict the SYM-H index about one hour ahead. For the 6 test storms registered by ACE including two super-storms of min. SYM-H< −200 nT, the correlation coefficient between observed and NARX network predicted SYM-H is 0.95 as a whole, even as high as 0.95 and 0.98 with average relative variance of 13.2% and 7.4%, respectively, for the two super-storms. The prediction for the 7 storms with WIND data is also satisfactory, showing averaged correlation coefficient about 0.91 and RMSE of 14.2 nT. The newly developed NARX model shows much better capability than Elman network for SYM-H prediction, which can partly be attributed to a key feedback to the input layer from the output neuron with a suitable length (about 120 min). This feedback means that nearly real information of the ring current status is effectively directed to take part in the prediction of SYM-H index by ANN. The proper history length of the output-feedback may Correspondence to: S. Y. Ma (syma@whu.edu.cn) mainly reflect on average the characteristic time of ring current decay which involves various decay mechanisms with ion lifetimes from tens of minutes to tens of hours. The Elman network makes feedback from hidden layer to input only one step, which is of 5 min for SYM-H index in this work and thus insufficient to catch the characteristic time length.

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Geomagnetic <i>D<sub>st</sub></i> index forecast based on IMF data only

Cited by 78 publications

References 22 publications

Forecasting intense geomagnetic activity using interplanetary magnetic field data

Forecasting intense geomagnetic activity using interplanetary magnetic field data

Space Weather Effects Produced by the Ring Current Particles

Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

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