A Study of the Morphology of Magnetic Storms Great Magnetic Storms

Average characteristics are determined for 74 great magnetic storms with sudden commencements that occurred in 1902-1945. The storm field is resolved for different epochs of storm time into tv;o parts: (i) Dst, which is independent of local time, that is, of longitude A, relative to the sun, and (ii) DS, which depends on A . They are obtained, for each of the three magnetic elements, declination, horizontal force, and vertical force, at eight geomagnetic latitudes ranging from 80°N to 1°S. DS is harmonically analyzed; the first harmonic component is shown to be the main component of DS. The storm-time course of this component is compared with that of Dst; DS attains its maximum earlier and decays more rapidly. The results of the analysis of great storms are compared with those for weak and moderate storms that were reported previously. Some characteristics of Dst change with intensity. Except in magnitude, main characteristics of DS are independent of intensity.The research reported in the document has been sponsored by the Air Force Cambridge Research Center, Air Research and Development Command, under Contract No. AF 19(604)-2163.LIST OF TABLES -- LIST OF FIGURES -- ABSTRACT -- 1. INTRODUCTION -- 2. OBSERVATORIES -- 3. STORM-TIME VARIATIONS : 3.1 Dst in the geomagnetic-north component, Hgm ; 3.2 Dst in the geomagnetic-east component, Egm ; 3.3 Dst in the vertical force Z -- 4. DISTURBANCE DAILY VARIATIONS -- 5. FIRST HARMONIC COMPONENT OF DS -- 6 . HIGHER HARMONIC COMPONENTS OF DS AND SD -- 7. COMPARISON OF Dst AND DS -- 8 . SEASONAL VARIATIONS IN Dst : 8.1 Seasonal variation in Dst(H); season d and season j ; 8.2 Seasonal variation in Dst(H); season e and season s -- 9. SEASONAL VARIATIONS IN DS -- 10. CONCLUSION -- 11. ACKNOWLEDGEMENTS -- REFERENCESYe

Ionospheric response to the corotating interaction region-driven geomagnetic storm of October 2002

Unlike the geomagnetic storms produced by coronal mass ejections (CMEs), the storms generated by corotating interaction regions (CIRs) are not manifested by dramatic enhancements of the ring current. The CIR-driven storms are however capable of producing other phenomena typical for the magnetic storms such as relativistic particle acceleration, enhanced magnetospheric convection and ionospheric heating. This paper examines ionospheric plasma anomalies produced by a CIR-driven storm in the middle- and high-latitude ionosphere with a specific focus on the polar cap region. The moderate magnetic storm which took place on 14–17 October 2002 has been used as an example of the CIR-driven event. Four-dimensional tomographic reconstructions of the ionospheric plasma density using measurements of the total electron content along ray paths of GPS signals allow us to reveal the large-scale structure of storm-induced ionospheric anomalies. The tomographic reconstructions are compared with the data obtained by digital ionosonde located at Eureka station near the geomagnetic north pole. The morphology and dynamics of the observed ionospheric anomalies is compared qualitatively to the ionospheric anomalies produced by major CME-driven storms. It is demonstrated that the CIR-driven storm of October 2002 was able to produce ionospheric anomalies comparable to those produced by CME-driven storms of much greater Dst magnitude. This study represents an important step in linking the tomographic GPS reconstructions with the data from ground-based network of digital ionosondes

Signalling Storms in 3G Mobile Networks

We review the characteristics of signalling storms that have been caused by certain common apps and recently observed in cellular networks, leading to system outages. We then develop a mathematical model of a mobile user's signalling behaviour which focuses on the potential of causing such storms, and represent it by a large Markov chain. The analysis of this model allows us to determine the key parameters of mobile user device behaviour that can lead to signalling storms. We then identify the parameter values that will lead to worst case load for the network itself in the presence of such storms. This leads to explicit results regarding the manner in which individual mobile behaviour can cause overload conditions on the network and its signalling servers, and provides insight into how this may be avoided.Comment: IEEE ICC 2014 - Communications and Information Systems Security Symposiu