Adaptive separation of regular and irregular magnetic activity for K indices

Walker, J. K.

doi:10.1016/0021-9169(87)90108-5

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…This was also reported by other observers (e.g. Walker 1987). To solve this problem different sets of parameters are needed for the two cases which again introduce a degree of subjectivity in the choice of what is quiet and what is heavily disturbed data.…”

Section: Resistance To Outliers and Large Bursts Of Variationssupporting

confidence: 89%

“…Another fact, which may even be more important, is the difference in the K-scale range assigned to each observatory. In Walker's (1987) fig. 2 for instance, it can be seen that the reconstructed SR signal does not match the X-component as well as it does the Y-component and he still reported a 97 per cent total agreement.…”

Section: Resultsmentioning

confidence: 99%

“…The subject of computer generated K-indices is presently a very active research area (Alldredge 1960;Hattingh, Nagtegaal & Loubser 1988;Hopgood 1986;Jankowski et al 1988;Rangarajan & Murty 1980;Riddick & Stuart 1984;Van Wijk & Nagtegaal 1977;Walker 1987;Wendt 1988;Wilson 1987).…”

Section: Introductionmentioning

confidence: 99%

“…A good example is given by the work of Wendt (1988) where he pointed out that a combination of different filters for different hours of the day give better results than a single filter for the whole day. Walker (1987) reportea an approach introducing a type of block adaptive harmonic method. He reported very good results, but pointed out that the input parameters of this method must be changed from winter to summer, and in general every few months, to take into account the S, changing with the solar cycle.…”

Section: Introductionmentioning

confidence: 99%

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ComputerK-index estimation by a new linear-phase, robust, non-linear smoothing method

Hattingh

Loubser

Nagtegaal

1989

Geophysical Journal International

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S U M M A R Y This paper describes a new method that can be used to estimate the geomagnetic K-index values automatically with a computer. The underlying philosophy of the method together with a short description of the mathematics involved are discussed. It is shown how existing ideas in the relatively new field of data-adaptive filtering can be modified and extended to develop a powerful Linear-phase Robust Non-linear Smoothing method (LRNS method). The properties of this method are ideally suited for the computer K-index estimation problem.The method was applied to Hermanus digital geomagnetic data which extend to nearly one decade. Even with this large amount of data a 99 per cent agreement with handscaled values was still obtained when differences of fl in the K-values were neglected. A 70-80 per cent total agreement was determined with the majority of the differences occurring during quiet days. This is the result of the very small dynamic range of small K-indices (0 and 1) at Hermanus resulting in the handscaler frequently giving a value of 0 where the computer can detect this small variation and gives a value of 1.What is more important, is that the performance of the method stays virtually the same irrespective of the day or month of the year, or the year or years used in the comparisons. This proves the method's adaptiveness to any changes in the SR pattern irrespective of day-to-day , seasonal or solar activity variations. This is achieved without any changes in the original input parameters. Because of its adaptiveness it is believed that the method will also adapt to data from different geographical locations thus giving us a possible global method.

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Section: Resistance To Outliers and Large Bursts Of Variationssupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ComputerK-index estimation by a new linear-phase, robust, non-linear smoothing method

Hattingh

Loubser

Nagtegaal

1989

Geophysical Journal International

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“…Many algorithms for the derivation of K indices from digital data have been proposed in recent years (Van Wijk & Nagtegaall977; Riddick &Stuart 1984;Hopgood 1986;Walker 1987;Wilson 1987;Jankowski et al 1988;Hattingh, Loubser & Nagtegaal 1989;Golovkov, Papitashvili & Papitashvili 1989;Pirjola, Ryno & Sucksdorff 1990). These algorithms have been tested with different data sets, and using different statistical tests.…”

Section: Introductionmentioning

confidence: 99%

Computer production of Kindices: review and comparison of methods

Menvielle

Papitashvili²,

Hakkinen

et al. 1995

Geophysical Journal International

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The K indices are to be hand-scaled from analogue magnetograms following morphological criteria expressed in the so-called Mayaud rules. All observatories used to follow these rules in the routine hand-scaling of K indices from analogue magnetograms. As a result of digitalization and automation of observatories, however, observers in charge who are experienced in K scaling from analogue magnetograms are becoming increasingly rare. Therefore the organizations running observatories were forced to find compensating machine methods for the production of K indices. The basic features of the K indices and the Mayaud rules are first reviewed, and then the methods developed for computer derivation of K indices are discussed.In order to decide which of the proposed algorithms was suitable, a comparison among them was organized by the IAGA Working Group on geomagnetic indices. The comparison was made with a common data set, using the same statistical tests. The results are summarized and discussed. Further comparisons between the K indices produced by computer with the different methods are presented in this paper. Four methods were acknowledged by the Working Group during the Vienna IUGG general Assembly in 1991. The results confirm that these methods provide acceptable results in comparison to the average quality of hand-scaled indices.A comparison between computer-produced K indices and reference K indices handscaled following the Bartels-Mayaud rules is also presented. It appears that only two acknowledged methods provide computer-produced K in good agreement with handscaled K. The best computer method (the FMI method) is found to be good enough to allow the continuation of the long tradition of producing K indices without any serious jump in the statistics.

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Spherical cap harmonic modelling of high latitude magnetic activity and equivalent sources with sparse observations

Walker

1989

Journal of Atmospheric and Terrestrial Physics

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Adaptive separation of regular and irregular magnetic activity for K indices

Cited by 6 publications

References 13 publications

ComputerK-index estimation by a new linear-phase, robust, non-linear smoothing method

ComputerK-index estimation by a new linear-phase, robust, non-linear smoothing method

Computer production of Kindices: review and comparison of methods

Spherical cap harmonic modelling of high latitude magnetic activity and equivalent sources with sparse observations

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