An Interactive Data Language software package to calculate ionospheric conductivity by using numerical models

Koyama, Yukinobu; Shinbori, Atsuki; Tanaka, Yoshimasa; Hori, Tomoaki; Nosé, M.; Oimatsu, S.

doi:10.1016/j.cpc.2014.08.011

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…This value is 1 or 2 orders of magnitude smaller than in the daytime (10–14 h, LT). The Pedersen and Hall conductivities are calculated with our developed tool (Koyama et al, ). In this tool, the IRI2012, International Geomagnetic Reference Field (IGRF)‐12 (Thébault et al, ), and NRL‐MSIS00 (Picone et al, ) models are used.…”

Section: Observation Data and Analysis Methodsmentioning

confidence: 99%

Characteristics of Seasonal Variation and Solar Activity Dependence of the Geomagnetic Solar Quiet Daily Variation

et al. 2017

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Characteristics of seasonal variation and solar activity dependence of the X and Y components of the geomagnetic solar quiet (Sq) daily variation at Memambetsu in midlatitudes and Guam near the equator have been investigated using long‐term geomagnetic field data with 1 h time resolution from 1957 to 2016. The monthly mean Sq variation in the X and Y components (Sq‐X and Sq‐Y) shows a clear seasonal variation and solar activity dependence. The amplitude of seasonal variation increases significantly during high solar activities and is proportional to the solar F10.7 index. The pattern of the seasonal variation is quite different between Sq‐X and Sq‐Y. The result of the correlation analysis between the solar F10.7 index and the Sq‐X and Sq‐Y shows an almost linear relationship, but the slope of the linear fitted line varies as a function of local time and month. This implies that the sensitivity of Sq‐X and Sq‐Y to the solar activity is different for different local times and seasons. The pattern of the local time and seasonal variations of Sq‐Y at Guam shows good agreement with that of a magnetic field produced by interhemispheric field‐aligned currents (FACs), which flow from the summer to winter hemispheres in the dawn and dusk sectors and from the winter to summer hemispheres in the prenoon to afternoon sectors. The direction of the interhemispheric FAC in the dusk sector is opposite to the concept of Fukushima's model.

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Section: Observation Data and Analysis Methodsmentioning

confidence: 99%

Characteristics of Seasonal Variation and Solar Activity Dependence of the Geomagnetic Solar Quiet Daily Variation

et al. 2017

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“…This is, at least in part, caused by the cancelation of observation noise in the annual means, but there are also some systematic variations that are averaged out. For example, there is a seasonal variation in the ionospheric conductivities influencing any one geomagnetic observatory (Finch, ; Koyama et al, ; Nagatsuma, ; Wallis & Budzinski, ). In the aa index, this effect is reduced by averaging data from two sites, one in each hemisphere, but better cancelation of seasonal effects is achieved by the ap index with its greater number of stations and the use of conversion tables that allow for season.…”

Section: Introductionmentioning

confidence: 99%

The Development of a Space Climatology: 2. The Distribution of Power Input Into the Magnetosphere on a 3‐Hourly Timescale

et al. 2019

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Paper 1 in this series (Lockwood et al., 2018a, https://doi.org/10.1029 showed that the power input into the magnetosphere P α is an ideal coupling function for predicting geomagnetic "range" indices that are strongly dependent on the substorm current wedge and that the optimum coupling exponent α is 0.44 for all averaging timescales, τ, between 1 min and 1 year. The present paper explores the implications of these results. It is shown that the form of the distribution of P α at all averaging timescales τ is set by the interplanetary magnetic field orientation factor via the nature of solar wind-magnetosphere coupling (due to magnetic reconnection in the dayside magnetopause) and that at τ = 3 hr (the timescale of geomagnetic range indices) the normalized P α (divided by its annual mean, that is,

τ=3hr /

τ=1yr ) follows a Weibull distribution with k of 1.0625 and λ of 1.0240. This applies to all years to a useful degree of accuracy. It is shown that exploiting the constancy of this distribution and using annual means to predict the full distribution gives the probability of space weather events in the largest 10% and 5% to within uncertainties of magnitude 10% and 12%, respectively, at the one sigma level.Plain Language Summary This is another step toward constructing a climatology describing the statistics of how space weather has varied over the past 400 years. This climatology will be valuable in the design of systems vulnerable to space weather. Previous work has showed that the probability of an event of a given magnitude occurring in any 1 year can be predicted from average activity levels in that year. This is an approximate but extremely valuable result that helps us understand the occurrence of events before the space age-however, it is a result that would be more valuable and could be used with greater confidence if we understood why it applies. This paper provides us with that understanding.

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“…The conductivity is a highly anisotropic tensor and in the E-layer the electrons begin to gyrate and drift perpendicular to the electric field, while the ions still move in direction of the electric field; the difference in direction is the basis for the Hall conductivity ΣH, which is there larger than the Pedersen conductivity ΣP. The combined conductivity then becomes Σ = ΣP + ΣH 2 /ΣP (Koyama et al, 2014;Ieda et al, 2014;Takeda, 2013;Maeda, 1977).…”

Section: The Ionospheric E-layermentioning

confidence: 99%

Reconstruction of Solar Extreme Ultraviolet Flux 1740 – 2015

Svalgaard

2016

Sol Phys

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Solar Extreme Ultraviolet (EUV) radiation creates the conducting E-layer of the ionosphere, mainly by photo ionization of molecular Oxygen. Solar heating of the ionosphere creates thermal winds which by dynamo action induce an electric field driving an electric current having a magnetic effect observable on the ground, as was discovered by G. Graham in 1722. The current rises and sets with the Sun and thus causes a readily observable diurnal variation of the geomagnetic field, allowing us the deduce the conductivity and thus the EUV flux as far back as reliable magnetic data reach. Highquality data go back to the 'Magnetic Crusade' of the 1830s and less reliable, but still usable, data are available for portions of the hundred years before that. J.R. Wolf and, independently, J.-A. Gautier discovered the dependence of the diurnal variation on solar activity, and today we understand and can invert that relationship to construct a reliable record of the EUV flux from the geomagnetic record. We compare that to the F10.7 flux and the sunspot number, and find that the reconstructed EUV flux reproduces the F10.7 flux with great accuracy. On the other hand, it appears that the Relative Sunspot Number as currently defined is beginning to no longer be a faithful representation of solar magnetic activity, at least as measured by the EUV and related indices. The reconstruction suggests that the EUV flux reaches the same low (but non-zero) value at every sunspot minimum (possibly including Grand Minima), representing an invariant 'solar magnetic ground state'.

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An Interactive Data Language software package to calculate ionospheric conductivity by using numerical models

Cited by 6 publications

References 14 publications

Characteristics of Seasonal Variation and Solar Activity Dependence of the Geomagnetic Solar Quiet Daily Variation

Characteristics of Seasonal Variation and Solar Activity Dependence of the Geomagnetic Solar Quiet Daily Variation

The Development of a Space Climatology: 2. The Distribution of Power Input Into the Magnetosphere on a 3‐Hourly Timescale

Reconstruction of Solar Extreme Ultraviolet Flux 1740 – 2015

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