Automatic selection of Dst storms and their seasonal variations in two versions of Dst in 50 years

Abstract

A computer program is developed to automatically identify the geomagnetic storms in Dst index by applying four selection criteria that minimize non-storm-like fluctuations. The program is used to identify the storms in Kyoto Dst and USGS Dst in 50 years . The identified storms (DstMin ≤ −50 nT) are used to investigate their seasonal variations. It is found that the overall seasonal variations of the storm parameters such as occurrence, average intensity (average DstMin) and average strength (average ⟨Dst MP ⟩) in both versions of Dst exhibit clear semiannual variations with equinoctial maxima and solstice minima; and the maxima and minima in intensity and strength (~±17% each) are less than those in occurrence (~±28%). Wavelet spectra of the storms reveal the existence of distinct semiannual component in four solar cycles (SCs 20-23) and weak longer and shorter-period components in some SCs. The semiannual variation observed also in the mean energy input during the main phase (MP) of the storms estimated from Dst is interpreted in terms of the (1) equinoctial mechanism based on the varying angle between the Earth-Sun line and Earth's dipole axis and (2) Russell-Mcpherron effect based on the varying angle between the GSM Z-axis and GSE Y-axis; and the yearly range of the dipole tilt angle µ (23.2°) involved in the equinoctial mechanism is found larger than the title angle θ (16.3°) involved in the RM effect.

Latitudinal, Diurnal, and Seasonal Variations in the Accuracy of an RTK Positioning System and Its Relationship With Ionospheric Irregularities

“…Additionally, it has been shown that the Dst, which is a measurement of ring current activity, is closely related to the dipole tilt angle, that is, the angle between the Earth‐sun line and the earth's dipole axis. Statistical analysis over 50 years (Balan et al., 2017) show a strong dependence of geomagnetic storms and the dipole tilt angle. Additionally (O'Brien & McPherron, 2002), showed that a combination of the dipole tilt and the Russel‐McPherron effect is accountable for diurnal and seasonal variations in ring current injections, more than either of those alone.…”

IpsDst of Dst Storms Applied to Ionosphere‐Thermosphere Storms and Low‐Latitude Aurora

“…MPO here stands for MP onset and T MP represents the MP time duration. The selection criteria minimize nonstorm like fluctuations and identified 793 storms in 1957–2007 (Balan, TulasiRam, et al, ). The storms include 39 super storms (DstMin ≤ −250 nT), 308 intense storms (−250 < DstMin ≤ −100 nT), and 446 moderate storms (−100 < DstMin ≤ −50 nT).…”

“…The present paper applies the parameter IpsDst derived for the storms (DstMin ≤ −50 nT) automatically identified in Kyoto Dst (Balan, TulasiRam, et al, ) to investigate ionospheric storms, thermospheric storms, and low‐latitude aurora. In addition to IpsDst, the paper uses DstMin, Kp max , AE max , <Kp MP > (or IpsKp), and <AE MP > (or IpsAE) for comparisons.…”

Capability of Geomagnetic Storm Parameters to Identify Severe Space Weather

“…The average offset is -8.50 nT in all data together and -5.0 nT in quiet-time (Dst >-25 nT) data alone (Balan et al 2017b). For the Carrington storm, the H-component data measured at Bombay (Tsurutani et al 2003) and calculated by Cliver & Dietrich (2013) (Balan et al 2017b). Although the storms in the two indices exhibit similar variations, they have differences in DstMin and in its time of occurrence, which can cause differences in IpsDst.…”

“…The indices have a correlation of 0.96 and the offset is mainly negative. The average offset is -8.50 nT in all data together and -5.0 nT in quiet-time (Dst >-25 nT) data alone (Balan et al 2017b). For the Carrington storm, the H-component data measured at Bombay (Tsurutani et al 2003) and calculated by Cliver & Dietrich (2013) (Balan et al 2017b).…”