Prediction of the AU, AL, and AE indices using solar wind parameters

Li, Xinlin; Temerin, M.; Liu, Siqing

doi:10.1002/2013ja019188

Cited by 48 publications

(61 citation statements)

References 46 publications

(70 reference statements)

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“…The 3-D-SWGDP can be used to develop other magnetospheric dynamic models and possess a potential ability to forecast the dynamic evolution of the plasmasphere by using the upstream observations of solar wind and IMF parameters as well as prediction models of SYM-H (Cai et al, 2010) and AE (Takalo & Timonen, 1997;Luo et al, 2013). This model is parameterized by solar wind speed V sw , IMF B z , SYM-H, and AE, with smooth MLT and magnetic latitude dependence, which will give a 3-D structure of the plasmapause location.…”

Section: Discussionmentioning

confidence: 99%

Development of a 3‐D Plasmapause Model With a Back‐Propagation Neural Network

et al. 2019

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Several empirical models have been previously developed to study the characteristics of the global plasmasphere. A three-dimensional solar wind-driven global dynamic plasmapause model was developed in this study using a back-propagation neural network based on multisatellite measurements. Our database contains 37,859 plasmapause crossing events from 4 January 1995 to 31 December 2015 covering all 24 magnetic local times (MLTs) from −66 • to 79 • magnetic latitudes. This model is parameterized by solar wind speed V sw , interplanetary magnetic field B z , SYM-H, and AE indices with smooth MLTs and latitude dependence. As the first 3-D empirical model, this model corresponds to the true plasmapause structure and is useful for observing space weather and other applications especially for predicting the shape of the plasmapause by forecasting the input parameters. Moreover, the proposed model is not only highly sensitive to these parameters, but also to their changes over days, seasons, and years; this means that the diurnal, seasonal, and annual variations of the parameters can be captured by the model. Therefore, this model can provide a more accurate plasmapause position compared with previous models and can also be used as a basic reference to develop other models such as a global plasmaspheric density model. Key Points:• A new solar wind-driven 3-D dynamic plasmapause model based on multisatellites is constructed using a back-propagation neural network • This model can potentially be used to forecast the configuration of the plasmasphere • This model has higher precision than previous models

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Section: Discussionmentioning

confidence: 99%

Development of a 3‐D Plasmapause Model With a Back‐Propagation Neural Network

et al. 2019

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“…The complex model and its recent update [ Luo et al ., ] are far too complex to summarize, and the reader is referred to its full page description in the later paper. The purpose of additional terms is to take account of daily and seasonal variations in AL (and AU ) due to station location and orientation of the dipole.…”

Section: Coupling Functionsmentioning

confidence: 99%

“…As shown by Luo et al . [] and ourselves McPherron et al . [], the strengths of solar wind coupling to both AU and AL depend on factors other than solar wind variables.…”

Section: Coupling Functionsmentioning

confidence: 99%

An optimum solar wind coupling function for the AL index

2015

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We define a coupling function as a product of solar wind factors that partially linearizes the relation between it and a magnetic index. We consider functions that are a product of factors of solar wind speed V, density N, transverse magnetic field B ⊥ , and interplanetary magnetic field (IMF) clock angle θ c each raised to a different power. The index is the auroral lower (AL index) which monitors the strength of the westward electrojet. Solar wind data 1995-2014 provide hour averages of the factors needed to calculate optimum exponents. Nonlinear inversion determines both the exponents and linear prediction filters of short data segments. The averages of all exponents are taken as optimum exponents and for V, N, B ⊥ , and sin(θ c /2) are [1.92, 0.10, 0.79, 3.67] with errors in the second decimal. Hourly values from 1966 to 2014 are used next to calculate the optimum function (opn) and the functions VBs (eys), epsilon (eps), and universal coupling function (ucf). A yearlong window is advanced by 27 days calculating linear prediction filters for the four functions. The functions eps, eys, ucf, and opn, respectively, predict 43.7, 61.2, 65.6, and 68.3% of AL variance. The opn function is 2.74% better than ucf with a confidence interval 2.60-2.86%. Coupling strength defined as the sum of filter weights (nT/mV/m) is virtually identical for all functions and varies systematically with the solar cycle being strongest (188 nT/mV/m) at solar minimum and weakest (104) at solar maximum. Saturation of the polar cap potential approaching solar maximum may explain the variation.

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“…We see that the correlation between a p and AE is significant (up to 0.6377); Vs has positive correlation with ap, AE, and F10.7, while negative correlation with Ni; a p and AE have almost no correlation with X l ; X l has some positive correlation with F10.7, but no correlation with AE, a p , Ni, and Vs. Prediction models of the geomagnetic AL and AE indices also showed significant relationships with Vs rather than Ni [Li et al, 2007;Luo et al, 2013]. However, the multifractal curves of AE, Vs, and Ni indicate that AE may actually have a more significant scaling correlation with Ni than with Vs. Multifractal analyses can detect higher-order non-Gaussian features in the data, while correlation analyses can only trace out second-order properties.…”

Section: Resultsmentioning

confidence: 91%

“…The models proposed by Li et al [2007] showed that the AL index is strongly dependent on the solar wind magnetic field and velocity but is practically independent of the solar wind density. Further, the AE model proposed by Luo et al [2013] showed that solar flux F10.7 also plays a significant role in auroral activity.…”

Section: Introductionmentioning

confidence: 99%

Underlying scaling relationships between solar activity and geomagnetic activity revealed by multifractal analyses

Anh

Eastes

2014

JGR Space Physics

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This paper identifies some scaling relationships between solar activity and geomagnetic activity. We examine the scaling properties of hourly data for two geomagnetic indices (a p and AE), two solar indices (solar X-rays X l and solar flux F10.7), and two inner heliospheric indices (ion density Ni and flow speed Vs) over the period 1995-2001 by the universal multifractal approach and the traditional multifractal analysis. We found that the universal multifractal model (UMM) provides a good fit to the empirical K(q) and (q) curves of these time series. The estimated values of the Lévy index in the UMM indicate that multifractality exists in the time series for a p , AE, X l , and Ni, while those for F10.7 and Vs are monofractal.The estimated values of the nonconservation parameter H of this model confirm that these time series are conservative which indicate that the mean value of the process is constant for varying resolution. Additionally, the multifractal K(q) and (q) curves, and the estimated values of the sparseness parameter C 1 of the UMM indicate that there are three pairs of indices displaying similar scaling properties, namely a p and X l , AE and Ni, and F10.7 and Vs. The similarity in the scaling properties of pairs (a p , X l ) and (AE, Ni) suggests that a p and X l , AE and Ni are better correlated-in terms of scaling-than previous thought, respectively. But our results still cannot be used to advance forecasting of a p and AE by X l and Ni, respectively, due to some reasons.

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Prediction of the AU, AL, and AE indices using solar wind parameters

Cited by 48 publications

References 46 publications

Development of a 3‐D Plasmapause Model With a Back‐Propagation Neural Network

Development of a 3‐D Plasmapause Model With a Back‐Propagation Neural Network

An optimum solar wind coupling function for the AL index

Underlying scaling relationships between solar activity and geomagnetic activity revealed by multifractal analyses

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