Comment on “The <i>IDV</i> index: Its derivation and use in inferring long‐term variations of the interplanetary magnetic field strength” by Leif Svalgaard and Edward W. Cliver

Lockwood, Mike; Rouillard, A. P.; Finch, I.; Stamper, R.

doi:10.1029/2006ja011640

Cited by 49 publications

(94 citation statements)

References 18 publications

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“…It can be seen that for no removal of outliers (N r = 0) the fit shows a consistent trend in the fit residuals such that when IDV(1d) NGK is large the fit to it using IDV(1d) HLS is consistently an underestimate, whereas when IDV(1d) NGK is small the fitted value is consistently an overestimate. The right hand plot shows this problem has been solved by the removal of N r = 4 outliers and neither the mean nor the spread of the fit any longer shows a trend in the fit residuals (Lockwood et al, 2006a;Lockwood, 2013). Figure 8 shows a second test of these two fits.…”

Section: Joining the Hls And Ngk Datamentioning

confidence: 99%

“…Figure 6 shows the scatter plot and regression fits of the Bartels rotation means of IDV(1d) NGK and IDV(1d) HLS using ordinary least squares (OLS) regression. The regression slope was found to be somewhat different if least median squares (LMS) or Bayesian least squares (BLS) were used (Lockwood et al, 2006a, and references therein), but the procedures converged on very similar regression lines if outliers were progressively removed. Hence we here use OLS but the largest outliers were removed until the regression converged on a stable line.…”

Section: Joining the Hls And Ngk Datamentioning

confidence: 99%

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Reconstruction of geomagnetic activity and near-Earth interplanetary conditions over the past 167 yr – Part 1: A new geomagnetic data composite

et al. 2013

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Abstract. We present a new composite of geomagnetic activity which is designed to be as homogeneous in its construction as possible. This is done by only combining data that, by virtue of the locations of the source observatories used, have similar responses to solar wind and IMF (interplanetary magnetic field) variations. This will enable us (in Part 2, Lockwood et al., 2013a) to use the new index to reconstruct the interplanetary magnetic field, B, back to 1846 with a full analysis of errors. Allowance is made for the effects of secular change in the geomagnetic field. The composite uses interdiurnal variation data from Helsinki for 1845-1890 (inclusive) and 1893-1896 and from Eskdalemuir from 1911 to the present. The gaps are filled using data from the Potsdam (1891Potsdam ( -1892Potsdam ( and 1897Potsdam ( -1907 and the nearby Seddin observatories (1908)(1909)(1910) and intercalibration achieved using the Potsdam-Seddin sequence. The new index is termed IDV(1d) because it employs many of the principles of the IDV index derived by Svalgaard and Cliver (2010), inspired by the u index of Bartels (1932); however, we revert to using one-day (1d) means, as employed by Bartels, because the use of near-midnight values in IDV introduces contamination by the substorm current wedge auroral electrojet, giving noise and a dependence on solar wind speed that varies with latitude. The composite is compared with independent, early data from European-sector stations, Greenwich, St Petersburg, Parc St Maur, and Ekaterinburg, as well as the composite u index, compiled from 2-6 stations by Bartels, and the IDV index of Svalgaard and Cliver. Agreement is found to be extremely good in all cases, except two. Firstly, the Greenwich data are shown to have gradually degraded in quality until new instrumentation was installed in 1915. Secondly, we infer that the Bartels u index is increasingly unreliable before about 1886 and overestimates the solar cycle amplitude between 1872 and 1883 and this is amplified in the proxy data used before 1872. This is therefore also true of the IDV index which makes direct use of the u index values.

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Section: Joining the Hls And Ngk Datamentioning

confidence: 99%

Section: Joining the Hls And Ngk Datamentioning

confidence: 99%

Reconstruction of geomagnetic activity and near-Earth interplanetary conditions over the past 167 yr – Part 1: A new geomagnetic data composite

et al. 2013

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“…Usually this relationship has been obtained using some form of regression fit. However, as noted by Lockwood et al (2006) and by Article 3 in this series (Lockwood et al, 2016b), there is no definitively correct way of making a regression fit, and tests of fit residuals are essential to ensure that the assumptions made by the regression have not been violated, as this can render the fit inaccurate and misleading for the purposes of scientific deduction of prediction. Article 3 shows that large intercalibration errors from regression techniques (> 30 %) can arise even for correlations exceeding 0.98 and that no one regression method is always reliable in this context: use of regression frequently gives misleading results that amplify the amplitude of solar cycles in data from lower-acuity observers.…”

Section: Definitions Of Sunspot Numbersmentioning

confidence: 99%

Tests of Sunspot Number Sequences: 1. Using Ionosonde Data

et al. 2016

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More than 70 years ago, it was recognised that ionospheric F2-layer critical frequencies [foF2] had a strong relationship to sunspot number. Using historic datasets from the Slough and Washington ionosondes, we evaluate the best statistical fits of foF2 to sunspot numbers (at each Universal Time [UT] separately) in order to search for drifts and abrupt changes in the fit residuals over Solar Cycles 17 -21. This test is carried out for the original composite of the Wolf/Zürich/International sunspot number [R], the new "backbone" group sunspot number [R BB ], and the proposed "corrected sunspot number" [R C ]. Polynomial fits are made both with and without allowance for the white-light facular area, which has been reported as being associated with cycle-to-cycle changes in the sunspot-number-foF2 relationship. Over the interval studied here, R, R BB , and R C largely differ in their allowance for the "Waldmeier discontinuity" around 1945 (the correction factor for which for R, R BB , and R C is, respectively, zero, effectively over 20 %, and explicitly 11.6 %). It is shown that for Solar Cycles 18 -21, all three sunspot data sequences perform well, but that the fit residuals are lowest and most uniform for R BB . We here use foF2 for those UTs for which R, R BB , and R C all give correlations exceeding 0.99 for intervals both before and after the Waldmeier discontinuity. The error introduced by the Waldmeier discontinuity causes R to underestimate the fitted values based on the foF2 data for 1932 -1945, but R BB overestimates them by almost the same factor, implying that the correction for the Waldmeier discontinuity inherent in R BB is too large by a factor of two. Fit residuals are smallest and most uniform for R C , and the ionospheric data support the optimum discontinuity multiplicative correction factor derived from the independent Royal Greenwich Observatory (RGO) sunspot group data for the same interval.Sunspot Number Recalibration

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“…Nau (2016) has neatly summarised the problems: "If any of the assumptions is violated (i.e., if there are nonlinear relationships between dependent and independent variables or the errors exhibit correlation, heteroscedasticity, or non-normality), then the forecasts, confidence intervals, and scientific insights yielded by a regression model may be (at best) inefficient or (at worst) seriously biased or misleading." In the context of sunspot numbers and sunspot-group numbers, Lockwood et al (2016c) found that the most complex problems were associated with non-normal distributions of data errors (especially if linearity or proportionality was inappropriately assumed), which violate the assumptions made by most regression techniques: such errors should always be tested for before a correlation is used for any scientific inference or prediction (Lockwood et al, 2006(Lockwood et al, , 2016c. A normal distribution of fit residuals can be readily tested for using a quantile-quantile (Q -Q) plot (e.g.…”

Section: Summary Of the Tested Data Seriesmentioning

confidence: 99%

“…For example, errors caused by inadequate and/or inappropriate regression techniques were discussed by Lockwood et al (2006) in relation to differences between reconstructions of the magnetic field in near-Earth space from geomagnetic-activity data. Nau (2016) has neatly summarised the problems: "If any of the assumptions is violated (i.e., if there are nonlinear relationships between dependent and independent variables or the errors exhibit correlation, heteroscedasticity, or non-normality), then the forecasts, confidence intervals, and scientific insights yielded by a regression model may be (at best) inefficient or (at worst) seriously biased or misleading."…”

Section: Summary Of the Tested Data Seriesmentioning

confidence: 99%

Tests of Sunspot Number Sequences: 4. Discontinuities Around 1946 in Various Sunspot Number and Sunspot-Group-Number Reconstructions

2016

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We use five test data series to search for, and quantify, putative discontinuities around 1946 in five different annual-mean sunspot-number or sunspot-groupnumber data sequences. . These test data all vary in close association with sunspot numbers, in some cases non-linearly. The tests are carried out using both the before-and-after fitresidual comparison method and the correlation method of Lockwood, Owens, and Barnard, applied to annual mean data for intervals iterated to minimise errors and to eliminate uncertainties associated with the precise date of the putative discontinuity. It is not assumed that the correction required is by a constant factor, nor even linear in sunspot number. It is shown that a non-linear correction is required by R C , R BB , and R ISNv1 , but not by R ISNv2 or R UEA . The five test datasets give very similar results in all cases. By multiplying the probability distribution functions together, we obtain the optimum correction for each sunspot dataset that must be applied to pre-discontinuity data to make them consistent with the postdiscontinuity data. It is shown that, on average, values for 1932 -1943 and 5.2 %, respectively. The correction that was applied to generate R C from R ISNv1 reduces this average factor to 0.5 % but does not remove the non-linear variation with the test data, and other errors remain uncorrected. A valuable test of the procedures used is provided by R UEA , which is identical to the RGO N G values over the interval employed.

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Comment on “The IDV index: Its derivation and use in inferring long‐term variations of the interplanetary magnetic field strength” by Leif Svalgaard and Edward W. Cliver

Cited by 49 publications

References 18 publications

Reconstruction of geomagnetic activity and near-Earth interplanetary conditions over the past 167 yr – Part 1: A new geomagnetic data composite

Reconstruction of geomagnetic activity and near-Earth interplanetary conditions over the past 167 yr – Part 1: A new geomagnetic data composite

Tests of Sunspot Number Sequences: 1. Using Ionosonde Data

Tests of Sunspot Number Sequences: 4. Discontinuities Around 1946 in Various Sunspot Number and Sunspot-Group-Number Reconstructions

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