Comparison of satellite ion drift velocities with AMIE deduced convection patterns

Bekerat, Hamed; Schunk, R. W.; Scherliess, L.; Ridley, A. J.

doi:10.1016/j.jastp.2005.08.013

Cited by 15 publications

(10 citation statements)

References 26 publications

(28 reference statements)

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“…The differences have been extensively discussed in literature. For example, Bekerat et al [2005] compared the DMSP F-13 ion drift velocities with the convection patterns deduced by AMIE with ground magnetometer input and found that the AMIE pattern adequately represents the DMSP observation about 32% of the time. Kihn et al [2006] performed similar comparison and found that the DMSP F-13 observation matches the AMIE result between 35% and 55% of the time depending on year, season, and activity level.…”

Section: Introductionmentioning

confidence: 99%

Comparing the cross polar cap potentials measured by SuperDARN and AMIE during saturation intervals

Gao¹

2012

J. Geophys. Res.

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Assimilative Mapping of Ionospheric Electrodynamics (AMIE) and Super Dual Auroral Radar Network (SuperDARN) are two commonly used techniques to measure cross polar cap potential (ΦPC). In this study, I compare ΦPC inferred from AMIE with that inferred from SuperDARN during saturation intervals. Measurements from Defense Meteorological Satellite Program (DMSP) and polar cap (PC) index are also used to help estimate true ΦPC. Seven saturation events in 2000 were selected. For each event, I ran the AMIE software driven by magnetometer data only, by magnetometer data and SuperDARN data, and by SuperDARN data only. Then I compared ΦPC obtained from AMIE with that from SuperDARN. I found that: (1) ΦPC inferred from SuperDARN is significantly lower than that inferred from other techniques including AMIE when ΦPC is large (e.g., greater than 150 kV); (2) ΦPC inferred from AMIE with SuperDARN input is much higher than ΦPC inferred from SuperDARN; (3) ΦPC inferred from AMIE with magnetometer input tends to give the same results as that inferred from AMIE with magnetometer and SuperDARN input; (4) ΦPC inferred from AMIE with magnetometer input is, in general, in agreement with ΦPCinferred from AMIE with SuperDARN input. It is suggested that reason for the low values of SuperDARN‐derived ΦPC during saturation intervals is that the SuperDARN algorithm to compute ΦPC from measurements is not applicable to such intervals.

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Section: Introductionmentioning

confidence: 99%

Comparing the cross polar cap potentials measured by SuperDARN and AMIE during saturation intervals

Gao¹

2012

J. Geophys. Res.

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“…The drift in the upper ionosphere is primarily in the E × B direction, and so a measure of the cross-track drift essentially specifies the electric field in the satellite track, which can then be used to estimate the ionospheric potential (e.g., Hairston et al, 2003). Many model validation studies have been conducted comparing DMSP measured cross-track drifts with ionospheric drifts produced by models (e.g., Fedder et al, 1998;Raeder et al, 1998;Ridley et al, 2002;Bekerat et al, 2005;Kihn et al, 2006), and the DMSP measurements are used as the official metric put forth by the National Space Weather Implementation Plan to validate magnetospheric codes.…”

Section: Introductionmentioning

confidence: 99%

Numerical considerations in simulating the global magnetosphere

et al. 2010

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Abstract. Magnetohydrodynamic (MHD) models of the global magnetosphere are very good research tools for investigating the topology and dynamics of the near-Earth space environment. While these models have obvious limitations in regions that are not well described by the MHD equations, they can typically be used (or are used) to investigate the majority of magnetosphere. Often, a secondary consideration is overlooked by researchers when utilizing global models -the effects of solving the MHD equations on a grid, instead of analytically. Any discretization unavoidably introduces numerical artifacts that affect the solution to various degrees. This paper investigates some of the consequences of the numerical schemes and grids that are used to solve the MHD equations in the global magnetosphere. Specifically, the University of Michigan's MHD code is used to investigate the role of grid resolution, numerical schemes, limiters, inner magnetospheric density boundary conditions, and the artificial lowering of the speed of light on the strength of the ionospheric cross polar cap potential and the build up of the ring current in the inner magnetosphere. It is concluded that even with a very good solver and the highest affordable grid resolution, the inner magnetosphere is not grid converged. Artificially reducing the speed of light reduces the numerical diffusion that helps to achieve better agreement with data. It is further concluded that many numerical effects work nonlinearly to complicate the interpretation of the physics within the magnetosphere, and so simulation results should be scrutinized very carefully before a physical interpretation of the results is made. Our conclusions are not limited to the Michigan MHD code, but apply to all MHD models due to the limitations of computational resources.

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“…Hence, there is still significant uncertainty. Utilizing a data assimilation approach to the problem, Kihn and Ridley [2005] and Bekerat et al [2005] showed that this uncertainty could be significantly reduced to approximately 30%.…”

Section: Discussionmentioning

confidence: 99%

Role of variability in determining the vertical wind speeds and structure

Yiğit

Ridley

2011

J. Geophys. Res.

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[1] The University of Michigan's Global Ionosphere Thermosphere Model (GITM) is used to study the temporal and spatial variability of high-latitude vertical winds during the December solstice. Underlying mechanisms controlling the high-latitude distribution and the magnitude of vertical winds in the thermosphere are investigated in a suite of systematic model simulations. First, in a series of six model simulations GITM longitude Â latitude resolution is gradually increased from 5°Â 5°to 2.5°Â 0.3125°, imposing constant moderate solar and low magnetospheric activity in all simulations. Analysis of the high-latitude mean parameters shows that polar distributions of vertical winds and Joule heating demonstrate localized enhancements at high spatial resolution that are not well captured at coarse grid resolution. Second, in simulations with fixed spatial resolution of 2.5°Â 2.5°, the impact of temporally variable magnetospheric conditions on the morphology of the high-latitude vertical winds has been investigated. For this, hemispheric power and the cross polar cap potential values are modified in a series of systematic simulations. The magnitude and temporal variations of ion flows significantly impact the high-latitude Joule heating, which in turn dramatically effects the vertical wind structure. It is demonstrated that variability in the vertical winds are driven by variability in the ion flows and that sudden step-like changes in ion flows can cause the largest enhancements in the vertical winds, in particular, via nonhydrostatic effects. The vertical wind variability is seen to be the largest in the summer Southern Hemisphere with much larger vertical wind magnitudes.Citation: Yiğit, E., and A. J. Ridley (2011), Role of variability in determining the vertical wind speeds and structure, J. Geophys.

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Comparison of satellite ion drift velocities with AMIE deduced convection patterns

Cited by 15 publications

References 26 publications

Comparing the cross polar cap potentials measured by SuperDARN and AMIE during saturation intervals

Comparing the cross polar cap potentials measured by SuperDARN and AMIE during saturation intervals

Numerical considerations in simulating the global magnetosphere

Role of variability in determining the vertical wind speeds and structure

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