BT Tsurutani

DN Baker

EW Cliver

GW Prölss

JE Borovsky

L Svalgaard

M Sugiura

M Wik

MA Hapgood

Medford

N Balan

P Cannon

RA Marshall

RC Carrington

WD Gonzalez

Geoscience Letters

English

N. Balan

I. S. Batista

S. Tulasi Ram

P. K. Rajesh

Crossref

Balan et al. Geosci. Lett.  (2016) 3:3 DOI 10.1186/s40562-016-0036-5RESEARCH LETTERA new parameter of geomagnetic storms for the severity of space weatherN. Balan1* , I. S. Batista1, S. Tulasi Ram2 and P. K. Rajesh3Abstract Using the continuous Dst data available since 1957 and H component data for the Carrington space weather event of 1859, the paper shows that the mean value of Dst during the main phase of geomagnetic storms, called mean DstMP, is a unique parameter that can indicate the severity of space weather. All storms having high mean DstMP (≤−250 nT), which corresponds to high amount of energy input in the magnetosphere–ionosphere system in short duration, are found associated with severe space weather events that caused all known electric power outages and telegraph system failures.© 2016 Balan et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.BackgroundGeomagnetic storms are disturbances in earth’s mag-netic field produced by enhanced solar wind–magne-tosphere coupling and ionosphere–magnetosphere plasma coupling (e.g., Svalgaard 1977; Gonzalez et  al. 1994). Scientific analysis of geomagnetic storms lead to improved fundamental understanding of the earth’s sur-rounding space weather environment (e.g.,  Prölss  2004; Kamide and Balan 2015). Applied analysis of the storms enables the assessment and mitigation of space weather-related hazards, for example, on satellite systems and orbits, satellite communication and navigation, over-the-horizon radio communication, geophysical surveys, electric power grids, and oil and gas metal pipe lines (e.g., Daglis 2004). Of particular concern are the effects associated with rare but extremely intense geomagnetic storms (e.g., Hapgood 2011; Cannon et  al.  2013; Cliver and Dietrich  2013). A Carrington type event of 1859 (e.g., Carrington 1859) at present times, for example, can cause very serious social and economic impacts in the High-Tech society (e.g., Baker et al. 2008). It is there-fore important for scientific and technological reasons to search for some parameter(s) of space weather events that can indicate their severity.The disturbance storm time (Dst) index (Sugiura 1964) represents geomagnetic storms. The intensity of the storms, which is the maximum negative value of Dst (or DstMin) during the storms, has conventionally been con-sidered to represent the severity of space weather. How-ever, recently by analyzing the Dst data since 1998, we (Balan et al. 2014) showed that DstMin is an insufficient indicator, and the mean value of Dst during the main phase (MP) of the storms (mean DstMP) can indicate the severity of space weather in causing damages to techno-logical systems such as electric power grids and telegraph systems. In this paper, using all the Dst data available since 1957 and H component data during the Carrington event of 1859, we confirm that the mean DstMP is a unique parameter that can indicate the serenity of space weather.Dst data and analysisWe use the hourly Dst data of 1 nT resolution for 58 years available at Kyoto WDC since 1957, with no data gaps and no erroneous values (http://swclob-kugi.kyoto-u.ac.jp). The Dst index is obtained from the horizontal H component measured at four low-latitude stations (3 in north and 1 in south) outside the equatorial electrojet belt (Sugiura 1964; Sugiura and Kamei 1991). A distur-bance time series is estimated for each station by sub-tracting a non-storm quiet time baseline; and the  Dst time series is obtained as an average of the individual disturbance time series from the four stations. For the Open Access*Correspondence:  balannanan@gmail.com 1 Instituto Nacional de Pesquisas Espaciais, São Jose dos Campos,  SP 12227‑010, BrazilFull list of author information is available at the end of the articlePage 2 of 5Balan et al. Geosci. Lett.  (2016) 3:3 Carrington storm of September 1859, we use the H range data measured at Mumbai and reported by Tsurutani et  al. (2003). H range is similar to Dst but positive. For consistency, we take H range also as negative like Dst.The Dst storms are identified by developing a computer program which minimizes non-storm like fluctuations and avoids human errors. Figure  1, for example, illus-trates the procedure. The program first detects the nega-tive slopes in the Dst variation (green dots, Fig. 1a) and identifies the preliminary main phase onsets (MPOs) when Dst starts decreasing (red starts) and DstMin when Dst reaches maximum negative values (black stars, Fig. 1b). The program then applies the selection criterion (1) DstMin ≤−50 nT and MP duration >2 h, and criterion (2) absolute value of MP range, that is, IDstMPO −  Dst-MinI  ≥50  nT. By applying criteria (1) and (2), many short-period, non-storm like negative fluctuations of Dst are eliminated (Fig. 1c). With a view to further avoid the short-period positive deflections during MP and identify clearly separated storms, the criterion (3), that is, sepa-ration between DstMin and next MPO  >6  h, is applied. Further to avoid very slowly varying, non-storm like long duration decreases in Dst, the criterion (4), that is, rate of change of Dst during MP or (dDst/dt)MP <−5 nT/h, is applied, which results in the detection of the clear storm in Fig.  1d. By applying the procedure to the whole Dst data, the computer program identified 810 storms which include 365 intense storms (DstMin ≤−100 nT) and 39 super storms (DstMin ≤−250 nT).The estimation of the storm parameters are illus-trated using Fig.  1d which also shows the storm phases. The impact at the Earth of a coronal mass ejec-tion (CME) compresses the dayside magnetopause, intensifies its eastward directed current, and gener-ates a positive perturbation in  Dst, known as initial phase (IP) which usually lasts from tens of minutes to a few hours. It may be noted that IP is not well identifi-able in all storms. This is followed by the main phase (MP) when the field decreases due to the enhance-ment of the westward ring current, which begins with the southward turning of IMF (interplanetary mag-netic field) Bz and consequent solar wind–magneto-sphere coupling and loading of solar wind energy into the ring current; it lasts for several hours to over ten hours when IMF Bz remains southward. When IMF Bz returns to zero or turns northward, the solar wind forcing diminishes, ring current intensity eventually dissipates, and Dst recovers back to its near-zero pre-storm level recovery phase (RP) usually taking tens of hours to several days.The storms are analyzed for their important param-eters. Main phase duration TMP is the time interval between MPO and DstMin which are defined above and identified in Fig. 1d; UT hours of MPO and DstMin are noted. ∫DstMP is the integral (or sum) of Dst during MP. For storms with positive initial phase, it is the negative of the sum of the magnitudes of Dst from MPO to DstMin. Mean DstMP = ∫DstMP/TMP is the new parameter which indicates the strength of geomagnetic storms while Dst-Min represents their intensity, discussed in "Results and discussion" section. (dDstMP/dt)max is the maximum rate of change of Dst during MP, which is the maximum suc-cessive difference of Dst during MP.Results and discussionAlthough over 800 storms are identified, we consider the 39 super storms (DstMin  ≤−250 nT) and the Car-rington storm because storms weaker than super storms are unlikely to be associated with system damages (e.g., Baker et  al.  2008; Balan et  al. 2014). The characteristics of the storms such as date of DstMin, value of DstMin, time of MPO, time of DstMin, MP duration, mean DstMP, and (dDstMP/dt)max are listed in Table 1. The solar activity index (F10.7) is also listed.Figure  2 displays the characteristics of all 40 super storms arranged in the order of decreasing mean DstMP. For clarity, the scales of mean DstMP, (dDstMP/dt)max, and DstMin are limited. Purple color represents the storms associated with severe space weather (SvSW) events that caused electric power outages and/or telegraph system -400-2000a-400-2000b-400-2000cDst(nT)0 12 24 36 48 60 72 84 96 108-400-2000dHours(09 - 15 February 1958)IPMPODstMinMP RP∫ DstMP∫ DstMP = -2200 nTMean DstMP = -275 nTFig. 1 Example illustrating the identification of geomagnetic storms, see textPage 3 of 5Balan et al. Geosci. Lett.  (2016) 3:3 failures; and blue color corresponds to normal space weather (NSW) events that did not cause such damages, discussed below. The unique parameter that distinguishes SvSW and NSW events is the mean DstMP (panel a). All five storms associated with SvSW events (purple color) have mean DstMP ≤−258 nT and all remaining 35 storms associated with NSW events have mean DstMP  ≥−238 nT. Also, 20 of the storms associated with NSW events have lower DstMin (as low as −429 nT) than the high-est DstMin corresponding to SvSW events (panel c). Although (dDstMP/dt)max (panel b) is high (>95 nT/h) for SvSW events, it does not distinguish SvSW and NSW events because 8 NSW events have higher value for the parameter than the lowest value associates with SvSW Table 1 Characteristics of geomagnetic storms (DstMin ≤−250 nT)Date is of DstMin. T1 and T2 are times of MPO and DstMin. MPD is MP duration. F10.7 is on the day of DstMinNo Date DstMin (nT) T1–T2 (UT h) MPD (hr) Mean DstMP (nT) dDstMP/dt (nT/h) Daily 10.71 1859‑09‑01 −1710 05‑07 2 −700 1390 –2 1989‑03‑13 −589 14‑02 12 −357 111 2533 1958‑02‑11 −426 04‑12 8 −275 103 2244 2001‑11‑06 −292 02‑07 5 −259 168 2335 2003‑10‑30 −383 18‑23 5 −258 98 2686 1957‑09‑05 −324 14‑04 14 −239 90 2417 2001‑03‑31 −387 04‑09 5 −238 148 2458 1981‑04‑13 −311 00‑07 7 −235 75 2559 1967‑05‑26 −387 17‑05 12 −230 106 21910 2004‑11‑10 −263 02‑11 9 −229 43 10311 1989‑10‑21 −268 06‑17 11 −220 61 20612 1960‑04‑01 −327 10‑19 9 −217 56 20113 1957‑09‑13 −427 02‑11 9 −216 122 27214 2004‑11‑08 −374 20‑07 11 −209 96 12215 2003‑11‑20 −422 09‑21 12 −204 100 17116 1959‑07‑15 −429 09‑20 11 −204 92 25317 1958‑09‑04 −302 14‑23 9 −201 55 26118 1991‑10‑29 −254 23‑08 9 −188 29 26919 1991‑11‑09 −354 14‑02 12 −187 63 19420 1960‑04‑30 −325 13‑19 6 −184 151 16421 2000‑04‑06 −288 17‑01 8 −183 74 17822 2003‑10‑29 −353 07‑01 18 −179 95 27523 1989‑11‑17 −266 10‑23 13 −179 52 21524 1982‑09‑06 −289 00‑12 12 −175 44 17225 2000‑07‑15 −301 16‑01 9 −172 137 22026 1958‑07‑08 −330 10‑23 13 −170 92 24027 1982‑07‑14 −325 17‑02 9 −163 110 26928 1991‑03‑25 −298 15‑01 10 −157 63 23529 1970‑03‑08 −284 15‑23 8 −156 90 17330 1961‑10‑28 −272 10‑19 9 −154 62 8631 2001‑04‑11 −271 16‑24 8 −145 58 16032 1960‑11‑13 −339 17‑10 17 −142 81 17833 1957‑09‑23 −303 11‑07 45 −138 65 29734 1990‑04‑10 −281 23‑19 20 −133 45 14935 1960‑10‑07 −287 21‑01 28 −120 45 14536 1957‑01‑21 −250 14‑23 9 −116 80 21937 1992‑05‑10 −288 21‑15 18 −111 62 12738 1989‑09‑19 −255 15‑05 14 −106 40 19739 1986‑02‑08 −307 04‑01 45 −105 72 9440 1957‑03‑02 −255 21‑08 11 −83 100 180Page 4 of 5Balan et al. Geosci. Lett.  (2016) 3:3 events. While all 5 super storms associated with SvSW events occurred under high solar activity (F10.7  >225), the storms in general are independent of solar activity (panel d). Mean DstMP (≤−250 nT) therefore is a unique parameter of geomagnetic storms that can indicate the severity of space weather while the conventional param-eter DstMin is insufficient.The most famous severe space weather event (Car-rington 1859) caused telegraph system failures (event 1, Fig.  2) and produced the most extreme geomagnetic storm in known history (Tsurutani et al. 2003) with high-est values of mean DstMP (−700 nT) and DstMin (−1710 nT) though Akasofu and Kamide (2005) commented that such extremely high value of Dst is unrealistic. The famous electric power outage in Quebec on March 13, 1989 (e.g., Medford et  al. 1989) is associated with an extreme storm (event 2) of mean DstMP  <−350 nT. The SvSW event on February 11, 1958 (event 3), which caused fire and severe damages in the telegraph systems in Swe-den (e.g., Wik et  al. 2009), also produced an extreme storm (mean DstMP  =  −275 nT). The SvSW events on Nov 06, 2001 and Oct 30, 2003 (events 4 and 5) caused power outages in New Zealand (Marshall et  al. 2012) and Sweden (e.g., Wik et al. 2009) and produced extreme storms (mean DstMP < −255 nT).High value of the unique parameter mean DstMP cor-responds to high∫Dst occurring in short duration MP. For example, the most famous SvSW event has the highest∫Dst (or ∫H) occurring in the shortest MP of duration only about 2  h. Such storms are produced by CMEs having high front velocity ΔV (sudden increase over the background) and sufficiently large IMF Bz southward at the front (Balan et  al. 2014). The phys-ics of high mean DstMP therefore seems high amount of energy input into the magnetosphere–ionosphere sys-tem in short duration probably through continuous and rapid magnetic reconnection (e.g., Borovsky et al. 2008), so that the system responds impulsively. The impulsive response causes strong electric currents in the iono-sphere especially at high latitudes, which, in turn, leads to technological system damages through secondary electri-cal induction on ground systems.ConclusionsHigh value of mean Dst during the main phase of geo-magnetic storms (DstMP  ≤−250  nT), which represents Fig. 2 Parameters of super storms (mean DstMP, (dDstMP/dt)max, and DstMin) and solar activity index F10.7 arranged in the order of decreasing mean DstMPPage 5 of 5Balan et al. Geosci. Lett.  (2016) 3:3 high amount of energy input in the magnetosphere–ionosphere system in short duration, corresponds to the severe space weather events that caused all known elec-tric power outages and telegraph system damages. The information can help identify the characteristic param-eter of SvSW in other geophysical data and in the design of technological systems.Author details1 Instituto Nacional de Pesquisas Espaciais, São Jose dos Campos, SP 12227‑010, Brazil. 2 Indian Institute of Geomagnetism, Navi Mumbai, India. 3 National Cheng Kung University, Tainan, Taiwan.AcknowledgementsWe thank the Kyoto WDC for the Dst data (http://wdc.kugi.kyoto‑u.ac.jp/dstdir/). N Balan thanks CNPq of Brazil for an Invitation Fellowship.Competing interestsThe authors declare that they have no competing interests.Received: 22 December 2015   Accepted: 1 February 2016ReferencesAkasofu SI, Kamide Y (2005) Comments on the extreme magnetic storm of 1‑2 September 182 1859 by B. T. Tsurutani, W. D. Gonzalesz, G. S. Lakhina and S. Alex. J Geophys Res 110, A09226, doi:10.1029/2005JA011005Baker DN et al (2008) Severe space weather events—understanding societal and economic impacts. The National Academy Press, Washington DC, pp 1–144Balan N, Skoug R, Tulasi Ram S, Rajesh PK, Shiokawa K, Otsuka Y, Batista IS, Ebihara Y, Nakamura T (2014) CME front and severe space weather. J Geophys Res Space Phys. doi:10.1002/2014JA020151Borovsky JE, Hesse M, Birn J, Kuzentsova MM (2008) What determines the reconnection rate at the dayside magnetosphere? J Geophys Res 113:A07210. doi:10.1029/2007JA012645Cannon P et al (2013) Extreme space weather: impacts on engineered systems and infrastructure. Royal Academy of Engineering, London, pp 1–68Carrington RC (1859) Description of a singular appearance seen in the Sun on, 1859. Mon Not R Astron Soc 20:13–15Cliver EW, Dietrich WF (2013) The 1859 space weather event revisited: limits of extreme activity. J Space Weather Space Clim 3:A31. doi:10.1051/swsc/2013053Daglis IA (ed) (2004) Effects of space weather on technology infrastructure. Kluwer Academic, Dordrecht, pp 1–334Gonzalez WD, Joselyn JA, Kamide Y, Kroehl HW, Rostoker G, Tsurutani BT, Vasy‑liunas VM (1994) What is a geomagnetic storm? J Geophys Res 99:5771Hapgood MA (2011) Towards a scientific understanding of the risk from extreme space weather. Adv Space Res 47:2059–2072Kamide Y, Balan N (2015) The importance of ground magnetic data in specify‑ing the state of magnetosphere–ionosphere coupling: an historical account. Geosci lettMarshall RA, Dalzell M, Waters CL, Goldthorpe P, Smith EA (2012) Geomagneti‑cally induced currents in the New Zealand power network. Sp Weather 10:S08003. doi:10.1029/2012SW000806Medford et al (1989) Transatlantic earth potential variations during the March 1989 magnetic storms. Geophys Res Lett 16(10):1145Prölss GW (2004) Physics of the earth’s space environment. Springer, Berlin, pp 1–513Sugiura M (1964) Hourly values of equatorial Dst for the IGY. Ann Int Geophys Yr 35:9–45Sugiura M, Kamei T (1991) Equatorial Dst index 1957–1986, IAGA Bull. vol 40. ISGI Pub Office, Saint‑Maur‑des‑FossessSvalgaard L (1977) Geomagnetic activity: dependence on solar wind param‑eters. In: Zirker JB (ed) Coronal holes and high speed wind streams. Colorado Association University Press, Boulder, pp 371–441Tsurutani BT, Gonzalez WD, Lakhina GS, Alex S (2003) The extreme magnetic storm of 1859. J Geophys Res 108:1268–1275Wik M, Pirjola R, Lundstedt H, Viljanen A, Wintoft P, Pulkkinen A (2009) Space weather events in July 1982 and October 2003 and the effects of geomagnetically induced currents on Swedish technical systems. Ann Geophys 27:1775–1787

A new parameter of geomagnetic storms for the severity of space weather

https://geoscienceletters.springeropen.com/track/pdf/10.1186/s40562-016-0036-5?site=geoscienceletters.springeropen.com

A new parameter of geomagnetic storms for the severity of space weather

Abstract

Similar works

Full text

Available Versions

Crossref