The near-Earth magnetic field from magnetospheric and quiet-day ionospheric sources and how it is modeled

Langel, R. A.; Sabaka, Terence J.; Baldwin, R. T.; Conrad, J. A.

doi:10.1016/s0031-9201(96)03190-1

Cited by 55 publications

(33 citation statements)

References 20 publications

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“…However, neither of these models provide the user with estimates of short term variations in the geomagnetic ®eld, such as regular daily variations or geomagnetic disturbances. A number of more comprehensive models, which enable corrections to be made for certain magnetospheric and ionospheric ®eld variations have been described in the literature (Langel et al, 1996;Purucker et al, 1997). The model described is intended as an extension to commonly used ®eld models for users who wish to take the regular geomagnetic daily variation into account.…”

Section: Geomagnetic ®Eld Modelsmentioning

confidence: 99%

The development of a regional geomagnetic daily variation model using neural networks

Sutcliffe¹

2000

Ann. Geophys.

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Abstract. Global and regional geomagnetic ®eld models give the components of the geomagnetic ®eld as functions of position and epoch; most utilise a polynomial or Fourier series to map the input variables to the geomagnetic ®eld values. The only temporal variation generally catered for in these models is the long term secular variation. However, there is an increasing need amongst certain users for models able to provide shorter term temporal variations, such as the geomagnetic daily variation. In this study, for the ®rst time, arti®cial neural networks (ANNs) are utilised to develop a geomagnetic daily variation model. The model developed is for the southern African region; however, the method used could be applied to any other region or even globally. Besides local time and latitude, input variables considered in the daily variation model are season, sunspot number, and degree of geomagnetic activity. The ANN modelling of the geomagnetic daily variation is found to give results very similar to those obtained by the synthesis of harmonic coe cients which have been computed by the more traditional harmonic analysis of the daily variation.

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Section: Geomagnetic ®Eld Modelsmentioning

confidence: 99%

The development of a regional geomagnetic daily variation model using neural networks

Sutcliffe¹

2000

Ann. Geophys.

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“…Due to its advantages (see below) this model is chosen in the present investigation. The initial phase of this kind of comprehensive modeling was first reported in Sabaka and Baldwin (1993) and the second phase in Langel et al (1996). A brief description of the last available model proposed by Sabaka et al (2004) is given in the following.…”

Section: Introductionmentioning

confidence: 99%

Evidence for geomagnetic jerks in comprehensive models

Chambodut

Mandéa

2005

Earth Planet Sp

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The rate of secular variation occasionally undergoes a sudden, sharp change, called a geomagnetic jerk. Such jerks have been detected in geomagnetic time series, centered-over the last four decades-around 1971, 1980, 1991, and 1999; others have been inferred from historical records. The geomagnetic jerks represent a reorganization of the secular variation, implying an internal origin, as established through spherical harmonic and wavelet analysis. However, some characteristics of jerks are not well understood. Here we estimate the occurrence dates for geomagnetic jerks, as they can be detected from a global geomagnetic model. This choice makes the present study novel, for two reasons. First, utilizing the comprehensive modelling approach allows for the use of a secular variation signal free of time-varying external fields and their corresponding induced counterpart, and observatory biases. Second, the model utilizes satellite data when available, in addition to observatory data. Indeed, POGO (1967POGO ( to 1971, MAGSAT (1979MAGSAT ( to 1980, Ørsted (1999 to present time) and CHAMP (2000 to present time) satellite measurements help to separate the different magnetic sources. In this study the CM4 comprehensive model is used for a global search of geomagnetic jerks and their occurrence dates. Our first result indicates that found geomagnetic jerks might not have been worldwide in occurrence. Moreover, the obtained dates suggest that jerks detected in the CM4 model over the last four decades occurred not simultaneously but at slightly different times around 1971, 1980 and 1991.

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“…The modelling technique used is extremely (possibly overly) simple, since the external fields are not parameterised with a Dst dependence (Langel et al, 1996;Olsen et al, 2000), and secular variation over the period of each data set is ignored. The major complication is that the data must be transformed from spacecraft coordinates.…”

Section: Datamentioning

confidence: 99%

Modelling of attitude error in vector magnetic data: application to Ørsted data

Holme

2014

Earth Planet Sp

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Analysis of data from the Ørsted satellite indicates that the largest source of error arises from an anisotropic attitude uncertainty, related to poorly determined rotation about the axis of the star imager. For two data sets from May and December 1999, I quantify this error, and review and apply a formalism designed to allow for this problem. I argue that, when modelled correctly, this attitude uncertainty should not significantly damage the main field models obtained from Ørsted data.

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The near-Earth magnetic field from magnetospheric and quiet-day ionospheric sources and how it is modeled

Cited by 55 publications

References 20 publications

The development of a regional geomagnetic daily variation model using neural networks

The development of a regional geomagnetic daily variation model using neural networks

Evidence for geomagnetic jerks in comprehensive models

Modelling of attitude error in vector magnetic data: application to Ørsted data

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