Dynamic plasmapause model based on THEMIS measurements

Liu, Xu; Liu, W.; Cao, Jinbin; Fu, H. S.; Yu, Jiahong; Li, X.

doi:10.1002/2015ja021801

Cited by 59 publications

(127 citation statements)

References 35 publications

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“…The plasma flow that generates the electric field in the magnetosphere (Vasyliūnas, 2001) is mainly controlled by solar wind and the IMF (Darrouzet et al, 2009;Katus et al, 2015;Sandel et al, 2003;and references therein). Adding the proxies of SYM-H for storms and AE for substorms may improve the accuracy of the model, and these two indices are also widely used in previous empirical models (e.g., Moldwin et al, 2002;Ober et al, 1997;Liu et al, 2015). Adding the proxies of SYM-H for storms and AE for substorms may improve the accuracy of the model, and these two indices are also widely used in previous empirical models (e.g., Moldwin et al, 2002;Ober et al, 1997;Liu et al, 2015).…”

Section: Database and Methodologymentioning

confidence: 99%

“…Only in situ measurements are used (Table 1). A plasmapause crossing is defined as a plasma density decrease by a factor of 5 or more within 0.5 Re (Carpenter & Anderson, 1992;Cho et al, 2015;Liu et al, 2015;Moldwin et al, 2002) and is located at the middle of the density drop. The first type is plasma wave instruments (e.g., Akebono, Cluster, IMAGE, Polar, and VAP).…”

Section: Data Overviewmentioning

confidence: 99%

“…For example, the first mathematical representation of the geocentric distance of the plasmapause was proposed by Carpenter and Anderson (1992) using the Kp index. Multiple-parameter-driven models are constructed considering the direct drive by the solar wind as well as different responses from the plasmasphere during storms/substorms; for example, Liu et al (2015) constructed an MLT-dependent plasmapause location model with five geomagnetic indices (SYM-H, AL, AU, AE, and Kp). Since the direct driver of the variations in geospace is the external solar wind, the solar wind and interplanetary magnetic field (IMF) parameters were then introduced into the models (e.g., Bandić et al, 2016;Cho et al, 2015;Larsen et al, 2007;Verbanac et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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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: Database and Methodologymentioning

confidence: 99%

Section: Data Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Otherwise, the knowledge of the magnetospheric index is not needed since we use a local average of all parameters. (Note that a recent alternative could be to use the Liu et al [] density model with a MLT‐dependent location).…”

Section: Wave and Plasma Modelsmentioning

confidence: 99%

Effects of whistler mode hiss waves in March 2013

et al. 2017

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We present simulations of the loss of radiation belt electrons by resonant pitch angle diffusion caused by whistler mode hiss waves for March 2013. Pitch angle diffusion coefficients are computed from the wave properties and the ambient plasma data obtained by the Van Allen Probes with a resolution of 8 h and 0.1 L shell. Loss rates follow a complex dynamic structure, imposed by the wave and plasma properties. Hiss effects can be strong, with minimum lifetimes (of ~1 day) moving from energies of ~100 keV at L ~ 5 up to ~2 MeV at L ~ 2 and stop abruptly, similarly to the observed energy‐dependent inner belt edge. Periods when the plasmasphere extends beyond L ~ 5 favor long‐lasting hiss losses from the outer belt. Such loss rates are embedded in a reduced Fokker‐Planck code and validated against Magnetic Electron and Ion Spectrometer observations of the belts at all energy. Results are complemented with a sensitivity study involving different radial diffusion and lifetime models. Validation is carried out globally at all L shells and energies. The good agreement between simulations and observations demonstrates that hiss waves drive the slot formation during quiet times. Combined with transport, they sculpt the energy structure of the outer belt into an “S shape.” Low energy electrons (<0.3 MeV) are less subject to hiss scattering below L = 4. In contrast, 0.3–1.5 MeV electrons evolve in an environment that depopulates them as they migrate from L ~ 5 to L ~ 2.5. Ultrarelativistic electrons are not affected by hiss losses until L ~ 2–3.

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“…In the plasmaspheric EUV images, the plasmapause was identified as the outermost sharp edge where the intensity of the 30.4 nm emissions drops abruptly [ Goldstein et al ., ; He et al ., ]. Electrostatic analyzer . The Time History of Events and Macroscale Interactions during Substorms (THEMIS) launched in 2007 provided the total electron density from spacecraft potential and electron thermal velocity [ Angelopoulos , ], from which several plasmapause models were constructed [ Cho et al ., ; Liu et al ., ; Verbanac et al ., ]. This has also been done with the magnetospheric plasma analyzer (MPA) instrument on the geosynchronously orbiting spacecraft operated by the Los Alamos National Laboratory [e.g., Moldwin et al ., ; Lawrence et al ., ].…”

Section: Introductionmentioning

confidence: 99%

A new solar wind‐driven global dynamic plasmapause model: 1. Database and statistics

Zhang

Ruan

et al. 2017

JGR Space Physics

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A large database, possibly the largest plasmapause location database, with 49,119 plasmapause crossing events from the in situ observations and 3957 plasmapause profiles (corresponding to 48,899 plasmapause locations in 1 h magnetic local time (MLT) intervals) from optical remote sensing from 1977 to 2015 by 18 satellites is compiled. The responses of the global plasmapause to solar wind and geomagnetic changes and the diurnal, seasonal, solar cycle variations of the plasmapause are investigated based on this database. It is found that the plasmapause shrinks toward the Earth globally and a clear bulge appears in the afternoon to premidnight MLT sector as the solar wind or geomagnetic conditions change from quiet to disturbed. The bulge is clearer during storm times or southward interplanetary magnetic field. The diurnal variations of the plasmapause are most probably the result of the difference between the magnetic dipole tilt and the Earth's spin axis. The seasonal variations of the plasmapause are characterized by equinox valleys and solstice peaks. It is also found that the plasmapause approaches the Earth during high solar activity and expands outward during low solar activity. This database will help us study and understand the evolution properties of the plasmapause shape and the interaction processes of the plasmasphere, the ring current, and the radiation belts in the magnetosphere.

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Dynamic plasmapause model based on THEMIS measurements

Cited by 59 publications

References 35 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

Effects of whistler mode hiss waves in March 2013

A new solar wind‐driven global dynamic plasmapause model: 1. Database and statistics

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