Rectangular polynomial analysis of the regional geomagnetic field

Düzgit, Z.; Baydemir, Niyazi; Malin, S. R. C.

doi:10.1111/j.1365-246x.1997.tb05333.x

Cited by 10 publications

(13 citation statements)

References 5 publications

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“…These results are quite different from those for RHA and RPA, where a large reduction in the number of coefficients was accompanied by only a small increase in rms residual (Malin et al 1996;Düzgit et al 1997).…”

Section: Spherical Cap Harmonic Analysiscontrasting

confidence: 91%

“…For our own purposes, ZD has heavily modified the Haines programs, but confirmed at each stage that there were no differences in results that could not be accounted for by rounding errors. One of the modifications is to permit ‘honing’, an iterative method of determining only those coefficients that exceed a chosen level of significance (Düzgit et al 1997). This differs from Haines’ method of coefficient selection and results in a different set of coefficients.…”

Section: Spherical Cap Harmonic Analysismentioning

confidence: 99%

“…Although such coefficients help the model to give a closer fit at the data points and thus reduce the rms, their uncertainty means that they reduce the reliability of interpolated values. The reasons for, and a method of doing this are discussed more fully by Düzgit et al (1997). Using their method of honing, only those coefficients that exceed their standard deviations by a factor of t are included, and none of those that are omitted would attain that level of significance if it were included.…”

Section: Spherical Cap Harmonic Analysismentioning

confidence: 99%

“…Alldredge (1981) applied his method of rectangular harmonic analysis (RHA) to a data set based on observatory annual mean values for the European region. The same data were used to assess a modified version of RHA (Malin et al 1996) and rectangular polynomial analysis, RPA (Düzgit et al 1997).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of regional geomagnetic field modelling methods using a standard data set: spherical cap harmonic analysis

Düzgit

Malin

2000

Geophysical Journal International

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Various methods that take account of the potential nature of the field have been proposed for modelling geomagnetic data on a regional scale. Several of these have been applied to a standard data set based on annual mean values from observatories in Europe. Here, we examine some of the properties of spherical cap harmonic analysis when applied to this data set, and compare the quality of fit with that of the other models. It is found that, for this data set, rectangular polynomial analysis provides a compact fit to main field data, but that in most other cases, for both main field and anomaly data, spherical cap harmonic analysis provides the better fit. Although relatively insensitive to chosen cap size, spherical cap harmonic analysis deteriorates more rapidly than the other methods when the number of coefficients is reduced.

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Section: Spherical Cap Harmonic Analysiscontrasting

confidence: 91%

Section: Spherical Cap Harmonic Analysismentioning

confidence: 99%

Section: Spherical Cap Harmonic Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of regional geomagnetic field modelling methods using a standard data set: spherical cap harmonic analysis

Düzgit

Malin

2000

Geophysical Journal International

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“…Magnetic scalar potential (V) . A 2‐D polynomial is adjusted to the magnetic scalar potential V , so that

- {\overset{true\to}{∇}}_{h} V = {\overset{true\to}{B}}_{h}

, where

\overset{B}{true}

is the magnetic field, and the subscript h denotes the horizontal projection of the vector field [e.g., Düzgit et al ., ]. Different degrees have been tested for the polynomials, ranging from 1 to 5.…”

Section: Experimental Designmentioning

confidence: 99%

Improving the modeling of geomagnetically induced currents in Spain

et al. 2017

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Vulnerability assessments of the risk posed by geomagnetically induced currents (GICs) to power transmission grids benefit from accurate knowledge of the geomagnetic field variations at each node of the grid, the Earth's geoelectrical structures beneath them, and the topology and relative resistances of the grid elements in the precise instant of a storm. The results of previous analyses on the threat posed by GICs to the Spanish 400 kV grid are improved in this study by resorting to different strategies to progress in the three aspects identified above. First, although at midlatitude regions the source fields are rather uniform, we have investigated the effect of their spatial changes by interpolating the field from the records of several close observatories with different techniques. Second, we have performed a magnetotelluric (MT) sounding in the vicinity of one of the transformers where GICs are measured to determine the geoelectrical structure of the Earth, and we have identified the importance of estimating the MT impedance tensor when predicting GIC, especially where the effect of lateral heterogeneities is important. Finally, a sensitivity analysis to network changes has allowed us to assess the reliability of both the information about the network topology and resistances, and the assumptions made when all the details or the network status are not available. In our case, the most essential issue to improve the coincidence between model predictions and actual observations came from the use of realistic geoelectric information involving local MT measurements.

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