Parameterization of monoenergetic electron impact ionization

Fang, Xiaohua; Randall, C. E.; Lummerzheim, D.; Wang, Wenbin; Lu, G.; Solomon, Stanley C.; Frahm, R. A.

doi:10.1029/2010gl045406

Cited by 107 publications

(224 citation statements)

References 17 publications

(20 reference statements)

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“…Since spatiotemporal distribution of ionospheric conductance influences electric field patterns and thus MI coupling processes, realistic input of energy distribution and accurate calculation of ionization rates are necessary to understand the MIT coupling dynamics. Recently, Fang et al (2010) introduced a new ionization model applicable to any electron spectrum with wider energy coverage (100 eV~1 MeV). Inclusion of Fang's ionization model can be a good starting point to improve the current IT models.…”

Section: Summary and Concluding Remarksmentioning

confidence: 99%

Modeling the ionosphere-thermosphere response to a geomagnetic storm using physics-based magnetospheric energy input: OpenGGCM-CTIM results

Connor

Zesta

Fedrizzi

et al. 2016

J. Space Weather Space Clim.

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The magnetosphere is a major source of energy for the Earth's ionosphere and thermosphere (IT) system. Current IT models drive the upper atmosphere using empirically calculated magnetospheric energy input. Thus, they do not sufficiently capture the stormtime dynamics, particularly at high latitudes. To improve the prediction capability of IT models, a physics-based magnetospheric input is necessary. Here, we use the Open Global General Circulation Model (OpenGGCM) coupled with the Coupled Thermosphere Ionosphere Model (CTIM). OpenGGCM calculates a three-dimensional global magnetosphere and a two-dimensional high-latitude ionosphere by solving resistive magnetohydrodynamic (MHD) equations with solar wind input. CTIM calculates a global thermosphere and a high-latitude ionosphere in three dimensions using realistic magnetospheric inputs from the OpenGGCM. We investigate whether the coupled model improves the storm-time IT responses by simulating a geomagnetic storm that is preceded by a strong solar wind pressure front on August 24, 2005. We compare the OpenGGCM-CTIM results with low-earth-orbit satellite observations and with the model results of Coupled Thermosphere-Ionosphere-Plasmasphere electrodynamics (CTIPe). CTIPe is an up-to-date version of CTIM that incorporates more IT dynamics such as a low-latitude ionosphere and a plasmasphere, but uses empirical magnetospheric input. OpenGGCM-CTIM reproduces localized neutral density peaks at~400 km altitude in the high-latitude dayside regions in agreement with in situ observations during the pressure shock and the early phase of the storm. Although CTIPe is in some sense a much superior model than CTIM, it misses these localized enhancements. Unlike the CTIPe empirical input models, OpenGGCM-CTIM more faithfully produces localized increases of both auroral precipitation and ionospheric electric fields near the high-latitude dayside region after the pressure shock and after the storm onset, which in turn effectively heats the thermosphere and causes the neutral density increase at 400 km altitude.

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Section: Summary and Concluding Remarksmentioning

confidence: 99%

Modeling the ionosphere-thermosphere response to a geomagnetic storm using physics-based magnetospheric energy input: OpenGGCM-CTIM results

Connor

Zesta

Fedrizzi

et al. 2016

J. Space Weather Space Clim.

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show abstract

“…There have been several parameterizations for calculating electron impact ionization rates, e.g., Lazarev [1967], Roble and Ridley [1987], Frahm et al [1997], Fang et al [2008], and Fang et al [2010. Large-scale global circulation models (GCMs) have to rely on these empirical models to quickly and self-consistently calculate and incorporate the particle impact ionization.…”

Section: Introductionmentioning

confidence: 99%

“…

Abstract The parameterizations of monoenergetic particle impact ionization in Fang et al (2010) (Fang2010) and Fang et al (2013 are applied to the complex energy spectra measured by DMSP F16 satellite to calculate the ionization rates from electron and ion precipitations for a Northern Hemisphere pass from 0030 UT to 0106 UT on 6 August 2011. Clear enhancement of electron flux is found in the polar cap.

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confidence: 99%

Ionization due to electron and proton precipitation during the August 2011 storm

Huang

et al. 2014

JGR Space Physics

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The parameterizations of monoenergetic particle impact ionization in Fang et al. (2010) (Fang2010) and Fang et al. (2013 are applied to the complex energy spectra measured by DMSP F16 satellite to calculate the ionization rates from electron and ion precipitations for a Northern Hemisphere pass from 0030 UT to 0106 UT on 6 August 2011. Clear enhancement of electron flux is found in the polar cap. The mean electron energy in the polar cap is mostly above 100 eV, while the mean energy in the auroral zone is typically above 1 keV. At the same time, F16 captures a strong Poynting flux enhancement in the polar cap, which is comparable to those in the auroral zone. The particle impact ionization rates using Fang2010 and Fang2013 parameterizations show clear enhancement at F region altitudes mainly due to the low-energy precipitating electrons, peaking probably in the cusp but also showing enhanced levels throughout most of the polar cap region. The general circulation models (GCMs), National Center for Atmospheric Research Thermosphere-Ionosphere-Electrodynamics General Circulation Model, and Global Ionosphere-Thermosphere Model, using their default empirical formulations of particle impact ionization, do not capture the observed features shown in the total particle ionization rate applying the Fang2010 and Fang2013 parameterizations to DMSP measurements. The difference between GCM simulations and Fang2010 and Fang2013 applied to DMSP data is due to the difference of both the inputs to the models and the parameterization of the ionization rates.

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“…Note that the empirical ionospheric conductance model used in the stand‐alone LFM simulation is essentially derived for an incident Maxwellian distribution of precipitating electrons, which is not accurate for precipitating electrons especially for broadband electrons. Given the fact that the distributions of ionospheric condcutance are crucial in regulating the coupling between the magnetosphere and ionosphere [e.g., Lotko et al , ], a more sophisticated calculation of the ionospheric conductance [e.g., Fang et al , ] will be important for further improving the empirical parameters used in the electron precipitation models. However, this type of model improvement is beyond the scope of this study and is more suitable for a follow‐up study using the coupled magnetosphere‐ionosphere‐thermosphere (CMIT) model, which couples the LFM magnetosphere model to the NCAR‐Thermosphere‐Ionosphere Electrodynamics General Circulation Model [ Wiltberger et al , ; Wang et al , ].…”

Section: Discussionmentioning

confidence: 99%

Electron precipitation models in global magnetosphere simulations

et al. 2015

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General methods for improving the specification of electron precipitation in global simulations are described and implemented in the Lyon-Fedder-Mobarry (LFM) global simulation model, and the quality of its predictions for precipitation is assessed. LFM's existing diffuse and monoenergetic electron precipitation models are improved, and new models are developed for lower energy, broadband, and direct-entry cusp precipitation. The LFM simulation results for combined diffuse plus monoenergetic electron precipitation exhibit a quadratic increase in the hemispheric precipitation power as the intensity of solar wind driving increases, in contrast with the prediction from the OVATION Prime (OP) 2010 empirical precipitation model which increases linearly with driving intensity. Broadband precipitation power increases approximately linearly with driving intensity in both models. Comparisons of LFM and OP predictions with estimates of precipitating power derived from inversions of Polar satellite UVI images during a double substorm event (28-29 March 1998) show that the LFM peak precipitating power is > 4× larger when using the improved precipitation model and most closely tracks the larger of three different inversion estimates. The OP prediction most closely tracks the double peaks in the intermediate inversion estimate, but it overestimates the precipitating power between the two substorms by a factor >2 relative to all other estimates. LFMs polar pattern of precipitating energy flux tracks that of OP for broadband precipitation exhibits good correlation with duskside region 1 currents for monoenergetic energy flux that OP misses and fails to produce sufficient diffuse precipitation power in the prenoon quadrant that is present in OP. The prenoon deficiency is most likely due to the absence of drift kinetic physics in the LFM simulation.

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Parameterization of monoenergetic electron impact ionization

Cited by 107 publications

References 17 publications

Modeling the ionosphere-thermosphere response to a geomagnetic storm using physics-based magnetospheric energy input: OpenGGCM-CTIM results

Modeling the ionosphere-thermosphere response to a geomagnetic storm using physics-based magnetospheric energy input: OpenGGCM-CTIM results

Ionization due to electron and proton precipitation during the August 2011 storm

Electron precipitation models in global magnetosphere simulations

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