Empirically modeled global distribution of magnetospheric chorus amplitude using an artificial neural network

Kim, Kyung-Chan; Shprits, Y. Y.; Lee, Jaejin; Hwang, Junga

doi:10.1002/jgra.50595

Cited by 16 publications

(20 citation statements)

References 63 publications

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“…Based on the Kullback‐Leibler theory, the most widespread distribution was observed with B s ( D KLlh =0.0766) and solar wind velocity ( D KLsf =0.0635) followed by pressure ( D KLlh =0.0517) and density ( D KLlh =0.0500). This suggests that B s and velocity are the most influential solar wind parameter that affect the evolution of the magnetospheric chorus wave intensities, consistent with the results of Kim et al [] who presented an empirical model of the global distributions of the magnetospheric chorus amplitude using an artificial neural network and utilized the instantaneous measurement of the solar wind parameters as input. However, the present study takes into account the time delay introduced by the magnetospheric system.…”

Section: Discussionsupporting

confidence: 88%

Statistical study of chorus wave distributions in the inner magnetosphere using Ae and solar wind parameters

et al. 2014

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Energetic electrons within the Earth's radiation belts represent a serious hazard to geostationary satellites. The interactions of electrons with chorus waves play an important role in both the acceleration and loss of radiation belt electrons. The common approach is to present model wave distributions in the inner magnetosphere under different values of geomagnetic activity as expressed by the geomagnetic indices. However, it has been shown that only around 50% of geomagnetic storms increase flux of relativistic electrons at geostationary orbit while 20% causes a decrease and the remaining 30% has relatively no effect. This emphasizes the importance of including solar wind parameters such as bulk velocity (V), density (n), flow pressure (P), and the vertical interplanetary magnetic field component (Bz) that are known to be predominately effective in the control of high energy fluxes at the geostationary orbit. Therefore, in the present study the set of parameters of the wave distributions is expanded to include the solar wind parameters in addition to the geomagnetic activity. The present study examines almost 4 years (1 January 2004 to 29 September 2007) of Spatio-Temporal Analysis of Field Fluctuation data from Double Star TC1 combined with geomagnetic indices and solar wind parameters from OMNI database in order to present a comprehensive model of wave magnetic field intensities for the chorus waves as a function of magnetic local time, L shell (L), magnetic latitude ( m ), geomagnetic activity, and solar wind parameters. Generally, the results indicate that the intensity of chorus emission is not only dependent upon geomagnetic activity but also dependent on solar wind parameters with velocity and southward interplanetary magnetic field Bs (Bz < 0), evidently the most influential solar wind parameters. The largest peak chorus intensities in the order of 50 pT are observed during active conditions, high solar wind velocities, low solar wind densities, high pressures, and high Bs. The average chorus intensities are more extensive and stronger for lower band chorus than the corresponding upper band chorus.

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Section: Discussionsupporting

confidence: 88%

Statistical study of chorus wave distributions in the inner magnetosphere using Ae and solar wind parameters

et al. 2014

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“…[] and Kim et al . [], we regard that our models perform better than those previous models. Furthermore, we point out a few differences to note.…”

Section: Discussion and Limitations Of The Developed Modelsmentioning

confidence: 75%

A prediction model for the global distribution of whistler chorus wave amplitude developed separately for two latitudinal zones

et al. 2015

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Whistler mode chorus waves are considered to play a central role in accelerating and scattering electrons in the outer radiation belt. While in situ measurements are usually limited to the trajectories of a small number of satellites, rigorous theoretical modeling requires a global distribution of chorus wave characteristics. In the present work, by using a large database of chorus wave observations made on the Time History of Events and Macroscale Interactions during Substorms satellites for about 5 years, we develop prediction models for a global distribution of chorus amplitudes. The development is based on two main components: (a) the temporal dependence of average chorus amplitudes determined by correlating with the preceding solar wind and geomagnetic conditions as represented by the interplanetary magnetic field (IMF) B z and AE index and (b) the determination of spatial distribution pattern of chorus amplitudes, specifically, the profiles in L in all 2 h magnetic local time zones, which are categorized by activity levels of either the IMF B z or AE index. Two separate models are developed: one based only on the IMF B z and the other based only on AE. Both models predict chorus amplitudes for two different latitudinal zones separately: |magnetic latitude (MLAT)| < 10°, and |MLAT| = 10°-25°. The model performance is measured by the coefficient of determination R 2 and the rank-order correlation coefficient (ROCC) between the observations and model prediction results. When tested for a new data interval of~1.5 years, the AE-based model works slightly better than the IMF B z -based model: for the AE-based model, the mean R 2 and ROCC values are~0.46 and~0.78 for |MLAT| < 10°, respectively, and~0.4 and~0.74 for |MLAT| = 10°-25°, respectively; for the IMF B z -based model, the mean R 2 and ROCC values are~0.39 and~0.74 for |MLAT| < 10°, respectively, and~0.33 and~0.70 for |MLAT| = 10°-25°, respectively. We provide all of the model information in the text and supporting information so that the developed chorus models can be used for the existing outer radiation belt electron models.

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“…A recent example was reported by K.‐C. Kim et al [, ] for the modeling of whistler mode chorus and plasmaspheric hiss, respectively. In these studies the authors perform extensive cross‐correlation analyses to determine the parameters and time lags that produce the highest correlation with the quantity to be predicted and then use only those values to train a single‐layer ANN to perform the function fitting.…”

Section: Introductionmentioning

confidence: 99%

A unified approach to inner magnetospheric state prediction

Bortnik

Thorne

et al. 2016

JGR Space Physics

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This brief technique paper presents a method of reconstructing the global, time‐varying distribution of some physical quantity Q that has been sparsely sampled at various locations within the magnetosphere and at different times. The quantity Q can be essentially any measurement taken on the satellite including a variety of waves (chorus, hiss, magnetosonic, and ion cyclotron), electrons of various energies ranging from cold to relativistic, and ions of various species and energies. As an illustrative example, we chose Q to be the electron number density (inferred from spacecraft potential) measured by three Time History of Events and Macroscale Interactions during Substorms (THEMIS) probes between 2008 and 2014 and use the SYM‐H index, taken at a 5 min cadence for the 5 h preceding each observed data point as the main regressor, although the predictor can also be any suitable geomagnetic index or solar wind parameter. Results show that the equatorial electron number density can be accurately reconstructed throughout the whole of the inner magnetosphere as a function of space and time, even capturing the dynamics of elementary plasmaspheric plume formation and corotation, suggesting that the dynamics of various other physical quantities could be similarly captured. For our main model, we use a simple, fully connected feedforward neural network with two hidden layers having sigmoidal activation functions and an output layer with a linear activation function to perform the reconstruction. The training is performed using the Levenberg‐Marquardt algorithm and gives typical RMS errors of ~1.7 and regression of >0.93, which is considered excellent. We also present a discussion on the different applications and future extensions of the present model, for modeling various physical quantities.

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Empirically modeled global distribution of magnetospheric chorus amplitude using an artificial neural network

Cited by 16 publications

References 63 publications

Statistical study of chorus wave distributions in the inner magnetosphere using Ae and solar wind parameters

Statistical study of chorus wave distributions in the inner magnetosphere using Ae and solar wind parameters

A prediction model for the global distribution of whistler chorus wave amplitude developed separately for two latitudinal zones

A unified approach to inner magnetospheric state prediction

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