scholarly journals Ion Temperatures in Earth's Inner Magnetosphere: Ring Current Dynamics, Transient Effects, and Data-Model Comparisons

2014 ◽  
Author(s):  
Justin G. Elfritz
Keyword(s):  
Data Model ◽  
Ring Current ◽  
Transient Effects ◽  
Inner Magnetosphere ◽  
Model Comparisons

scholarly journals Analyzing electric field morphology through data-model comparisons of the Geospace Environment Modeling Inner Magnetosphere/Storm Assessment Challenge events

2006 ◽  
Vol 111 (A11) ◽  
Author(s):  
Michael W. Liemohn ◽  
Aaron J. Ridley ◽  
Janet U. Kozyra ◽  
Dennis L. Gallagher ◽  
Michelle F. Thomsen ◽  
...  
Keyword(s):  
Electric Field ◽  
Data Model ◽  
Environment Modeling ◽  
Inner Magnetosphere ◽  
Model Comparisons

scholarly journals Understanding storm-time ring current development through data-model comparisons of a moderate storm

2007 ◽  
Vol 112 (A4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jichun Zhang ◽  
Michael W. Liemohn ◽  
Darren L. De Zeeuw ◽  
Joseph E. Borovsky ◽  
Aaron J. Ridley ◽  
...  
Keyword(s):  
Data Model ◽  
Ring Current ◽  
Current Development ◽  
Model Comparisons ◽  
Moderate Storm

scholarly journals Paleovariability: Data Model Comparisons

2014 ◽  
Vol 22 (2) ◽  
pp. 102-102 ◽  
Author(s):  
Chris Brierley ◽  
Kira Rehfeld
Keyword(s):  
Data Model ◽  
Model Comparisons

scholarly journals Another way of deriving the ring current decay time during disturbed periods

1995 ◽  
Vol 38 (2) ◽  
Author(s):  
M. M. Zossi de Artigas ◽  
J. R. Manzano
Keyword(s):  
Ring Current ◽  
Coupling Parameter ◽  
Life Time ◽  
Recovery Phase ◽  
Main Phase ◽  
Physical Analysis ◽  
Inner Magnetosphere ◽  
Current Decay ◽  
The Many ◽  
Energy Dissipated

Coupling parameter, E, and the total energy dissipated by the magnetosphere, UT, are determined for six disturbed periods, following three known criteria for UT computation. It is observed that UT exceeds E for Dst < -90 nT, for alI models. Differences between models reside on the estimated valnes for the particles' life time il1 the equatorial ring current. The values of TR, used in the models, are small during the main phase of the di."turbance, in disagreement with the charge exchange life time of the majority species, H+ and O'-. Based on this conclusion, a different criterion to calculate TR is proposed, differentiating the different stages of the perturbation. TR is calculated, for the main phase of the storm, from the rate of energy deposition estimation, Q, in the ring current. For Dst recovery phase, the vallles are obtained from a ring current decay law computation. The UTvu calculated, physically more coherent with the processes occurring during the event, is now smaller than expected. In this sense, it is understood that the power generated by the solar wind-magnetosphere dy- namo, should also be distributed in the inner magnetosphere, auroral zones and equatorial ring current, as in the outer magnetosphere, plasmoids in the tail shot in antisolar direction. A further adjustment of E, with the Chapman-Ferraro distance, 10' variable, has been made. Although the reslllts, improve the estimation of E, they are sti!l smaller than UT, except UTNU, for some disturbed periods. This result indicates the uncertainty in the computation of the input energy, by using the many expressions proposed in the literature, which are always presented as laws proportional to a given group of parameters, with an unknown factor of proportionality, which deserves more detailed physical analysis.

scholarly journals Storm time dynamics of ring current protons: Implications for the long‐term energy budget in the inner magnetosphere

2016 ◽  
Vol 43 (10) ◽  
pp. 4736-4744 ◽  
Author(s):  
Matina Gkioulidou ◽  
A. Y. Ukhorskiy ◽  
D. G. Mitchell ◽  
L. J. Lanzerotti
Keyword(s):  
Energy Budget ◽  
Ring Current ◽  
Inner Magnetosphere ◽  
Time Dynamics ◽  
Term Energy

Temporal and spatial considerations in data-model comparisons involving transient paleoclimatic simulations

2021 ◽  
Author(s):  
Patrick Bartlein ◽  
Sandy Harrison
Keyword(s):  
Time Series ◽  
Best Practices ◽  
Data Model ◽  
Environmental Data ◽  
Climatic Effects ◽  
Data Archiving ◽  
Model Comparisons ◽  
Climate Reconstructions ◽  
Temporal And Spatial ◽  
Further Development

&lt;p&gt;The increasing availability of time-evolving or transient palaeoclimatic simulations makes it imperative to develop &amp;#8220;best-practices&amp;#8221; for comparing simulations with palaeoclimatic observations including both climate reconstructions and environmental data.&amp;#160; There are two sets of considerations, temporal and spatial, that should guide those comparisons.&amp;#160; The chronology of simulations can in some ways be viewed as exact, as determined by the insolation forcing, but data archiving and reporting conventions, such as reporting summaries that use the modern calendar (that leads to the long-recognized palaeo-calendar effect) can, if ignored, lead to &amp;#8220;built-in&amp;#8221; temporal offsets of thousands of years in such features as temperature or precipitation maxima or minima.&amp;#160; Likewise, there are age uncertainties in time series of palaeoclimatic data that are often ignored, despite the fact that these are large during &amp;#8220;climatically interesting times&amp;#8221; such as the Younger Dryas chronozone.&amp;#160; Similarly, although model resolution is increasing, there is still a mismatch in topography (and its climatic effects) between a model and the &amp;#8220;real world&amp;#8221; sensed by the palaeoclimatic data sources.&amp;#160;&lt;/p&gt;&lt;p&gt;There are existing approaches for dealing with some of these issues, such as calendar-adjustment programs, Monte-Carlo approaches for describing age uncertainties in palaeoclimate time series, or clustering approaches for objectively defining appropriate regions for the calculation of area averages, but there is certainly room for further development.&amp;#160; This abstract is intended to serve as platform for discussion of some of best practices for data-model comparisons in transient mode.&lt;/p&gt;

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