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Statistical study of the time delay of ionospheric TEC storms to geomagnetic storms in Taoyuan, Taiwan
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ScienceDirect Advances in Space Research 65 (2020) 86–94 www.elsevier.com/locate/asr
Statistical study of the time delay of ionospheric TEC storms to geomagnetic storms in Taoyuan, Taiwan Yuqiang Zhang a, Zhensen Wu a,⇑, Jian Feng b, Tong Xu b, Zhongxin Deng b, Weimin Zhen b a
School of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710126, China b China Research Institute of Radiowave Propagation (CRIRP), Qingdao 266107, China
Received 13 May 2019; received in revised form 26 August 2019; accepted 8 September 2019 Available online 25 September 2019
Abstract We have studied the time delay of ionospheric storms to geomagnetic storms at a low latitude station Taoyuan (25.02°N, 121.21°E), Taiwan using the Dst and TEC data during 126 geomagnetic storms from the year 2002 to 2014. In addition to the known local time dependence of the time delay, the statistics show that the time delay has significant seasonal characteristics, which can be explained within the framework of the seasonal characteristics of the ionospheric TEC. The data also show that there is no correlation between the time delay and the intensity of magnetic storms. As for the solar activity dependence of the time delay, the results show that there is no relationship between the time delay of positive storms and the solar activity, whereas the time delay of negative storms has weakly negative dependence on the solar activity, with correlation coefficient 0.41. Especially, there are two kinds of extreme events: pre-storm response events and long-time delay events. All of the pre-storm response events occurred during 15–20 LT, manifesting the Equator Ionospheric Anomaly (EIA) feature at Taoyuan. Moreover, the common features of the pre-storm response events suggest the storm sudden commencement (SSC) and weak geomagnetic disturbance before the main phase onset (MPO) of magnetic storms are two main possible causes of the pre-storm response events. By analyzing the geomagnetic indices during the events with long-time delay, we infer that this kind of events may not be caused by magnetic storms, and they might belong to ionospheric Q-disturbances. Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: Ionospheric TEC storms; Time delay; Seasonal and local time dependences; Extreme events
1. Introduction Geomagnetic storm results when high-speed plasma injected into the solar wind from coronal mass ejections or coronal holes impinges upon Earth’s geomagnetic field. If the arriving solar wind plasma has a southward magnetic field, energy is coupled efficiently into Earth’s magnetosphere and upper atmosphere (Buonsanto, 1999). With the injection of energy, ionospheric parameters such as TEC, foF2 will increase or decrease significantly in a period of time, termed as positive or negative ionospheric storms, ⇑ Corresponding author.
E-mail address: [email protected] (Z. Wu). https://doi.org/10.1016/j.asr.2019.09.017 0273-1177/Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
respectively. These storms can then disrupt, and sometimes damage, a wide range of everyday technological systems that are often critical to the smooth functioning of modern societies. Therefore, it has always been a hot topic in space weather research. The field has been enriched by a remarkable set of comprehensive studies and review articles (e.g., Martyn, 1953; Obayashi, 1964; Matuura, 1972; Pro¨lss, 1995; Buonsanto, 1999; Mendillo, 2006). The statistics of the ionospheric storms and case studies are widely reported (e.g., Matsushita, 1959; Mendillo and Klobuchar, 1975; Essex et al., 1981; Titheridge and Buonsanto, 1988; Rodger et al., 1989; Balan and Rao, 1990; Mannucci et al., 2005; Astafyeva et al., 2015; Sun et al., 2017; Kashcheyev et al., 2018). Modeling studies have also been
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carried out to reproduce ionospheric storms and understand their physical mechanisms (e.g., Richmond and Matsushita, 1975; Fuller-Rowell et al., 1994; Burns et al.,1995; Lekshmi et al., 2007; Lu et al., 2008; Balan et al., 2010; Sojka et al., 2012; Joshi et al., 2016). Time delay is an important feature of ionospheric storm, and it has been discussed by previous studies (e.g., Balan and Rao, 1990; Gao et al., 2008; Liu et al., 2010, 2017). Balan and Rao (1990) investigated the dependence of ionospheric response on the local time of storm sudden commencement and to the intensity of the magnetic storms, using peak electron density and TEC data observed by a low- and a mid-latitude stations in North America during more than 60 SC-type storms. They found that the time delays associated with the positive responses are low for daytime SCs and high for night-time SCs, and it is just the opposite applies for negative SCs. Moreover, the time delays are inversely related to the intensity of the storms. Gao et al. (2008) analyzed foF2 observed by four ionosonde stations in East Asian sector during 515 magnetic storms from 1957 to 2006 and found that the time delay correlates well with the local time of MPO. For positive response, it is shorter for daytime MPO than night-time MPO, and it is opposite for negative response. However, there is no evidence for the relationship between time delay and intensity of magnetic storms. Liu et al. (2010) found that the time delays of ionospheric TEC responses to geomagnetic disturbances depends on season, magnetic latitude as well as local time based on the statistical study using 10 years of global ionosphere maps (GIMs) TEC data retrieved at Jet Propulsion Laboratory (JPL). However, the time delay in their paper is not relative to the MPO of the storm, the correlation coefficients give the time delay for the maximum ionospheric response to the maximum magnetic storm. Recently, Liu et al. (2017) made a statistical analysis of the ionospheric response in the American Sector during 217 geomagnetic storms from 2001 to 2015, and they found that mean time delay for negative storms is mostly longer than 10 h for all latitude zones, while shorter than 10 h for positive storms, except for low and equatorial latitudes. However, most of them are concentrate on the local time variation of the time delay, and few are aiming at the full features of the time delay of ionospheric storms. Taoyuan is located in the north crest of EIA in East Asia, the ionosphere has high day-to-day variety in this area. Until now, there is no specific study analyzing the full features of time delay in East Asian EIA region (best to our knowledge). Therefore, following their work, we have used GPS TEC data derived from IGS database during 126 magnetic storms from 2002 to 2014 in Taoyuan to study the time delay and various factors (include local time, season, storm intensity as well) affect it. This paper sets out the data and methods of analysis we used in Section 2. Section 3 describes the dependence of time delay on the season, local time, storm intensity, solar activity, and present the results of two kinds of extreme events: pre-storm
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response events and long-time delay events. The interpretation of the results will be presented in Section 4. Section 5 sums up. 2. Data and methods of analysis The hourly geomagnetic activity index (Dst) is downloaded from World Data Centre in Kyoto (http://swclobkugi.kyoto-u.ac.jp) with one-hour resolution. The magnetic storms are selected with minimum Dst < 50 nT. An additional criterion is based on the condition that the ionospheric response of multiple main phases can be accumulated. Therefore, according to Kamide et al. (1998)’s classification, single main phase storm is considered in this paper only. Finally, excluding magnetic storms with missing TEC data, 131 geomagnetic storms are selected. There is not an agreement on how to define a storm onset and there are two definitions: SSC and MPO. It should be noted that SSC is caused by the coupling of interplanetary magnetic field and magnetosphere, it does not cause significant magnetospheric energy to be injected into the ionosphere; but MPO corresponds to the onset of the magnetospheric ring current growth phase, which directly or indirectly affects the magnetospheric energy injection (Pro¨lss, 1995). Considering the time delay of ionospheric response is mainly due to the coupling of ionosphere-thermosphere-magnetosphere and some storms do not have SSC, we choose the MPO to define the storm onset. The TEC data used in this paper is derived from IGS (ftp://ics.gnsslab.cn) with 15 min resolution. This paper adopts the criterion proposed by Deng et al. (2010) and by Jin et al. (2019) for the determination of ionospheric TEC storm in China. The TEC perturbation index DI (t) is described in terms of percentage deviation of TEC observation from its monthly median values, DI ðtÞ ¼
TEC o ðtÞ TEC m ðtÞ DTEC ðtÞ ¼ TEC m ðtÞ TEC m ðtÞ
where TEC o ðtÞ and TEC m ðtÞ are measured and monthly median TEC for time, respectively. The positive (negative) ionospheric TEC storm is considered to occur when DI > 0.35 (DI < 0.3) and last for more than 6 h. Besides, the total duration of unsatisfactory DI does not exceed two hours. Then, the time delay can be obtained by calculating the time difference between the onset time of the ionospheric TEC storm and MPO. Although there are some consecutive ionospheric storms triggered by one magnetic storm, we only consider the time delay between the first ionospheric TEC storm and MPO. Fig. 1 illustrates the day to day variations of Dst index and DI index during the period of 02–05 June 2003 at Taoyuan. Dst variation is shown in Fig. 1a and the arrow ‘MPO’ marks local time of the MPO at 03:00 UT on 2 June. Fig. 1b shows the variation of DI during this period. The negative storm begins at 10:30 UT with DI less than 0.3 (arrow ‘1’) and ends at 16:45 UT when the DI index
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Fig. 1. An example for onset time of magnetic storm and ionospheric storm during the period 02–05 June 2003.
becomes greater than 0.3 (arrow ‘2’). The time interval between arrow ‘MPO’ and arrow ‘1’ is taken as time delay we studied. 3. Results According to the statistical conditions, a total of 131 geomagnetic storms can be found during 2002–2014. However, only 5 of them do not cause ionospheric storms, confirming the ionospheric storms can be easily triggered under the circumstance of magnetic storms. In the statistical analysis of the time delay, we have divided the local time into two parts: daytime (06–18 LT) and night-time (18–06 LT) and define summer (May to August), winter (January, February, November and December) and equinox (March, April, September and October) according to the classification of Lloyd season. Since (dayside) ionospheric density is produced mainly by solar EUV radiation (which is inferred by F10.7), we use F10.7 index to represent the solar activity when the MPO occurred, and they are downloaded from NOAA (ftp://ftp.swpc.noaa.gov/ pub/indices/old_indices/). It is generally accepted that the physical mechanism of positive storm and negative storm are different, therefore, the time delays of positive and negative storms have been discussed separately.
Fig. 2 shows the distributions of the mean time delay of positive (negative) storms on a basis of MPO occurred at daytime (night-time) in summer, winter, and equinox, respectively. It can be seen that the time delay has a close relationship with the local time of the MPO. The mean time delay of positive storms is shorter for daytime MPO than night-time MPO. Specifically, it reaches 17.2 h for summer daytime MPO and 15.5 h for winter daytime MPO, compared with 32.2 h and 24.4 h for night-time MPO in these two seasons. What’s more, it is most pronounced in equinox, with 13.5 h for daytime MPO and 37.1 h for night-time MPO. The situation is opposite for negative storms. It is 28.7 h and 34.6 h for daytime MPO in winter and equinox, compared with 14 h and 28.1 h for night-time MPO, except for negative storms in summer. The time delay of negative storms for daytime MPO is 5.6 h shorter than night-time MPO in summer. Gao et al. (2008) have drawn the same conclusion by analyzing foF2 observed by four ionosonde stations in East Asian sector during 515 magnetic storms. On the other hand, the time delay associated with the daytime MPO is shorter for positive storms than negative storms, whereas the opposite applies for night-time MPO. As shown in Fig. 3a and b, by comparing the time delay of positive/negative storms associated with daytime/ night-time MPO. As for the seasonal characteristics, Fig. 2a shows the mean time delay of positive storm. It is shorter in winter daytime than that in summer daytime, which is 15.5 h and17.2 h, respectively. In contrast, the mean time delay of negative storm is significantly shorter during summer daytime than that during winter daytime, which is 18.1 h and 28.7 h. It can also be found that the time delay of positive storm and negative storm caused by night-time magnetic storms is shortest in winter. It’s well known that the ionospheric seasonal anomalies are reflected at daytime (Rishbeth et al., 2000), so the seasonal dependence of the time delay of ionospheric storms triggered by magnetic storms which start at night will not be discussed here. In addition, the mean time delay of positive (negative) storms for daytime MPO is shorter (longer) in equinox than in solstice, it was 13.5 h (34.6 h) for equinox, 15.5 h (18.1 h) for summer and 17.2 h (28.7 h) for winter, which represents an evident semi-annual variation. Negative storms (b)
40
32.2
30 20
37.1
17.2
24.4 15.5
13.5
10 0
summer daytime
winter night-time
equinox
Time delay (hours)
Time delay (hours)
Positive storms (a) 40 30 20
23.7 18.1
34.6 28.1
28.7 14
10 0
summer daytime
winter
equinox
night-time
Fig. 2. Comparison of the mean time delay for different seasons and local time. (a) Mean time delay of positive storms; (b) Mean time delay of negative storms.
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40 30 20
18.1 17.2
(a) 34.6
28.7 15.5
13.5
10 0
summer positive storm
winter
(b)
Night-time MPO
equinox
negative storm
Time delay (hours)
Time delay (hours)
Daytime MPO
89
40 30
32.2 23.7
37.1 28.1
24.4 14
20 10 0
summer positive storm
winter
equinox
negative storm
Fig. 3. Comparison of the mean time delay of positive/negative storms for different local time (a) Mean time delay during the daytime; (b) Mean time delay during the night-time.
Balan and Rao (1990) found that the time delays are inversely related to the intensity of the storms. However, Gao et al. (2008) found that the time delay does not show a significant association with the intensity of the magnetic storms, after classified the storms according to the intensity of the storms. Through the scatter plot of time delay and storm intensity shown in Fig. 4, we can hardly find relationship between them, and the correlation coefficients is 0.07 for positive storm and 0.05 for negative storm, respectively. After classification, the mean time delay of positive (negative) storms are 21.5 h (29.5 h) and 23.3 h (23.6 h) for strong magnetic storms (Dst < 100 nT) and moderate magnetic storms (100 Dst < 50 nT), indicating there is still no association between them, consistent with the result of Gao et al. (2008). With regard to the above results, three factors may contribute to the differences. (1) The classification of the storms is based on different geomagnetic indices. The Dst index used by Gao et al. (2008) and this paper correspond to the symmetrical part of the equatorial ring current; Ap index used by Balan and Rao (1990) represents the global geomagnetic disturbances. (2) The data and method of analysis are different. Balan and Rao (1990) have used TEC/Nmax data and the time delay in their studies to refer to the time interval between the onset of SSC and time of maximum ionospheric response. However, Gao et al. (2008) have used foF2 data and the time delay in their work to refer to the time interval between the MPO and the onset of ionospheric storms; we have used TEC data the same
definition of time delay. (3) The ionospheric response to magnetic storms has longitudinal effect (e.g. Zhao et al., 2007; Lekshmi et al., 2011; Kuai et al., 2017). These three factors may also lead to differences described above. The solar activity dependence of the time delay is also examined. As shown in Fig. 5a, there is no relationship between the time delay of positive storms and the solar activity. On the contrary, the time delay has weakly negative dependence on the solar activity, with correlation coefficient 0.41 (Fig. 5b). It might be related to the mechanism of negative storms (e.g. Fuller-Rowell et al., 1994; Pro¨lss, 1995). Due to thermal expansion, the quiet time [O]/[N2] ration at all pressure (and height) level is smaller at solar maximum than at solar minimum. In such a background thermosphere, the chemical effects of the storm- time neutral winds can easily make the [O]/[N2] ratio much smaller at solar maximum than at solar minimum (Lekshmi et al., 2011). In other words, negative storms can occur easily at solar maximum than at solar minimum, and time delay of negative storms at solar maximum are smaller than at solar minimum. By contrast, the time delay of positive storms seem have no relationship with the solar activity, as there are many factors attribute to the positive storms, such as rapid strengthening of equatorial plasma fountain by enhanced eastward prompt penetration electric fields(PPEFs), rapid slow-down of both recombination processes and downward diffusion of plasma by the equator-ward neutral wind, and solar activities affect these physical process in complicated ways
Fig. 4. Scatterplots of the time delay against the corresponding geomagnetic storms intensity (minimum Dst). (a) Positive ionospheric storms; (b) Negative ionospheric storms.
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Fig. 5. Scatterplots of the time delay against the corresponding solar activities (F10.7). (a) Positive ionospheric storms; (b) Negative ionospheric storms.
events occurred during 15–20 LT and the corresponding magnetic storms are either SC-type or have weak geomagnetic disturbance within 7 h (not shown here) before the MPO. In addition to the pre-storm response events, there are also events with long-time delay. As for the long-time delay events, there is still no specific definition. Numerous studies have confirmed that the effects of magnetic storms on ionosphere can last to the recovery phase (e.g. Fuller-Rowell et al., 1994; Mendillo, 2006; Joshi et al., 2016, 2019), indicating the effects of magnetic storms on the ionosphere can last for a long time. However, in previous studies, magnetic storm seems to have significant effects on ionosphere since MPO, and negative storms during the recovery phase usually start one or two days after magnetic storms. As stated by Mendillo (2006), the prominent variations of ionosphere during magnetic storms will occur one or two days after magnetic storms, therefore, we define such events with time delay of more than 48 h and do not have ionospheric storm during the first 48 h after MPO as long-time delay events, and list all the events in Table 2. It can be seen from the
during different magnetic storms. As mentioned above, there are many drivers in the EIA region during magnetic storms, and these coupled drivers together with background thermosphere-ionosphere generate ionospheric storms and control their strengths in complicated ways. The enhancement of electron density in the ionosphere before geomagnetic storms is one of the open questions (e.g., Kane, 1973; Danilov, 2001; Buresˇova´ and Lasˇtovicˇka, 2007; Liu et al., 2008). In our statistical study, there are 6 events which time delays are less than 0. Table 1 lists the relative information of these events. In these 6 events, 4 of them correspond to the SC-type storms, the rest of them correspond to the Recurrent-type storms. The advance time is longer for Recurrent-type storms, which is 3 h, 6.25 h, respectively. Whereas the maximum advance time for SC-type storms is 1.75 h, suggesting that the advance time of pre-storm response events closely related to the type of the magnetic storms. In addition, only one of the pre-storm response events belongs to the negative ionospheric storm. By extracting the common features of these events, we find that all the pre-storm response Table 1 List of pre-storm response events. Date
Local time of magnetic storms
Type of magnetic storms
Type of ionospheric storms
Min Dst (nT)
Ahead of time (h)
2002-5-23 2003-6-24 2003-11-4 2004-1-22 2005-8-24 2008-10-11
20:00 18:00 15:00 19:00 15:00 16:00
SC Recurrent SC SC SC Recurrent
negative positive positive positive positive positive
109 55 69 79 184 54
0.5 6.25 0.75 1.75 0.75 3
Table 2 List of events with long-time delay. Date of magnetic storms
Local time
Date of ionospheric disturbances
Local time
Type of ionospheric disturbances
Min Dst (nT)
Time delay (h)
2002-2-6 2005-4-5 2005-6-23 2006-11-30 2011-10-25 2012-4-13 2012-6-12
2:00 3:00 14:00 5:00 3:00 11:00 3:00
2002-2-9 2005-4-7 2005-6-25 2006-12-2 2011-10-28 2012-4-15 2012-6-14
16:00 19:30 22:15 10:45 21:00 17:00 14:15
Positive Positive Positive Negative Positive Positive Positive
76 70 68 74 147 60 67
86 64.5 56.25 53.75 90 54 59.25
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table that these ionospheric disturbances mainly occur from noon to midnight, and most of them are positive disturbances (6/7). By analyzing the geomagnetic indices during these events, we infer that these disturbances are not caused by magnetic storms, and they might belong to ionospheric Q-disturbances. An example will be given in the following discussion. 4. Discussion At low latitude, ionospheric response to geomagnetic activity is very complicated for the existence of the EIA and many other drivers, including: (1) penetration electric field driven by the solar wind ranging from high latitude to equator; (2) the equator-ward neutral wind resulting from particle precipitation and Joule heating at high latitude, sometimes accompanied with TAD; (3) the disturbance dynamo electric fields produced by the globally altered thermospheric winds during magnetic storms; (4) composition changes driven by storm time altered neutral winds; (5) compression of the plasmasphere by the enhanced solar wind. These coupled drivers together with background thermosphere-ionosphere generate ionospheric storms and control their strengths in complicated ways. In addition to the ionospheric storms caused by magnetic storms, extreme climates such as thunderstorms and typhoons can also lead to ionospheric disturbances (e.g. Xiao et al., 2007; Mao et al., 2010). Therefore, the ionospheric response to magnetic storms depends not only on the intensity of the storms, but also on various parameters of the ionosphere-thermosphere system when the storms occur, which lead to the weak correlation between the time delay and intensity of corresponding storms. Even after classification, there is still no association between them. Wu et al. (2007) have found that TEC at equatorial anomaly crests yield their maximum values during the equinox months and their minimum values during the summer. In this paper, we found that the time delay of the ionospheric storm has significant seasonal characteristics, and corresponds to the seasonal characteristics of the ionospheric TEC in this region well, manifested as follows: if the NmF2 and TEC in one season are larger than the values of other seasons, the time delay of positive storms triggered by daytime MPO in this season must be lower, no matter for winter or equinox. It is opposite for the negative storm. In other words, in the case of magnetic storm, the larger the corresponding ionospheric background TEC is, the more likely the positive ionospheric TEC storm will occur, and the shorter the time delay will be. From physical mechanism perspective, large ionospheric background TEC means the ionospheric processes which produce ions and electrons are active, and these processes are easy to be triggered by magnetic storms. Therefore, the positive storm is more likely to occur under these circumstances. In this sense, ionospheric storm is an extreme manifestation of the seasonal characteristics of the ionosphere, and time delay is one of its manifestations.
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We found that the time delay of ionospheric TEC storms has obvious local time characteristics. That is, the time delay of the positive storms is smaller for daytime MPO than that for night-time MPO and opposite for negative storms, consistent with previous results. (Balan and Rao, 1990; Gao et al., 2008; Liu et al., 2017). Apparently, it is related to the local time variation of TEC during storms, and the thermospheric day-to-night circulation during storm time can explain it. (e.g., Fuller-Rowell et al., 1994; Mendillo, 2006). Compared with night-time, the positive storms are easy to be triggered during the daytime. Therefore, when magnetic storms occur in the daytime, the ionospheric positive storms will be triggered faster than in the night-time. The opposite situation applies for negative storms. In our statistics, we also find out that 15–20 LT is a special time interval. All the events with pre-storm responses occurred within this time window. It is not only related to local time but also related to the geographical/geomagnetic location of Taoyuan. Taoyuan locates at equatorial ionization anomaly region which is 25.02°N, 121.21°E and 15.1° in magnetic latitude. Feng et al. (2016) found that TEC in EIA region reaches its maximum at 12–16 LT and the EIA area can last for another 2–3 h after sunset because of the pre-reversal enhancement caused by eastward electric field during the period of 16–18 LT. Therefore, the ionosphere is highly active during 15–20 LT, and response quickly to geomagnetic activities. However, the active ionosphere just provides the background, magnetic activities are also required to trigger pre-storm response events. By comparing the type of the magnetic storms, we found that among the pre-storm response events, 4 events are SC-type storm and the rest are Recurrent-type storm which have weak geomagnetic disturbances before the storms. As for the pre-storm response events caused by SC-type storm, Balan and Rao (1990) found that more than one-third of the 68 SC-type storms commence before the onset of MPs. The weak geomagnetic disturbance before the MPO may lead to the pre-storm response events (Balan et al., 2011), partly explained the other three pre-storm response events in this paper. In addition, the pre-storm events occurred usually in the form of positive deviations, except for one event in the form of negative deviations. To further understand pre-storm response events, we need to combine more observation to provide a statistical picture and use ionospheric model to study the physical mechanism of this phenomenon. Regarding the events with long-time delay, we found that geomagnetic activities are very quiet during these events, so the ionospheric disturbances may not be caused by magnetic storms. As illustrated in Fig. 6, the MPO of the magnetic storm was at 1700 UT, 11 June 2012. Almost 60 h later, the ionospheric disturbance occurred during the time interval between arrow ‘1’ and arrow ‘2’. During the period of ionospheric disturbance, the geomagnetic activity is very quiet, indicating the disturbance may not be caused by geomagnetic activity, whereas it might belong to iono-
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factors affect its ionospheric state. Moreover, the morphology and mechanisms of Q-disturbances are still far from being well understood. Therefore, more observations and modeling are needed to give a comprehensive study of long-time delay events in this area. 5. Conclusion
Fig. 6. An example of the ionospheric disturbance with long-time delay during the period of 11–14 December 2012.
spheric Q-disturbances. On the other hand, the preference for positive storm probably be related to the seasonal and local time characteristic of the positive Qdisturbances. Mikhailov et al. (2004) found that positive disturbances are seen to be more numerous than negative ones at all latitudes. For positive Q-disturbances, semiannual variations with peaks around equinoxes dominate at high and middle latitudes, and more positive Qdisturbances take place at summer as decreasing latitude. Besides, winter season is the most preferable for negative Q-disturbances for all latitudes. These features are consistent with the seasonal characteristic of the long-time delay events in this paper. They also found that the positive Q-disturbances are more frequent in the evening and nigh-early morning LT sectors, there are also 50 percent (3/6) positive long-time delay events in the nighttime in this paper. Although we cannot obtain statistical results due to the insufficient sample size of these long-time delay events. The similarity of these two kinds of events still implies that the long-time delay events may be belong to the ionospheric Q-disturbances. As for the Q-disturbances, they are closely related to the problem of the coupling from below: the so-called meteorological control of Earth’s ionosphere (e.g. Danilov, 1986; Forbes et al., 2000; Rishbeth and Mendillo, 2001; Rishbeth, 2006). Mikhailov et al. (2004) have devoted themselves to the morphological analysis of F2-layer Qdisturbances using the worldwide ground-based ionosonde observations from high to lower latitudes in the Northern Hemisphere and demonstrated that the main morphological features of Q-disturbances could be explained within the framework of the contemporary understanding of the thermosphere-ionosphere interaction (Mikhailov et al., 2007, 2009, 2012). Depuev et al. (2008) considered the morphological picture of the disturbances in the F2 region of equatorial ionosphere under quiet geomagnetic conditions (Q-disturbances) and interpreted the picture. Taoyuan is located in the north crest of EIA, and there are many
We have presented a statistical study on the time delay of the ionospheric TEC storms in Taoyuan, Taiwan which is located in the north crest of EIA in East Asia. Our new statistical results are generally consistent with the previous statistical analyses for the local time dependence of the time delay (Balan and Rao, 1990; Gao et al., 2008; Liu et al., 2017). However, there are a number of new results that provide interesting insights into the time delay of this region. Our main conclusions are summarized as follows: 1. The time delay has a close relationship with the local time of the main phase onset (MPO) of the magnetic storm. It seems that the time delays of the positive storms are smaller for daytime MPO than those for night-time MPO and opposite for negative storms, consistent with previous studies (Balan and Rao, 1990; Gao et al., 2008; Liu et al., 2017), except for the negative storms in summer. 2. The time delay of the ionospheric storm has significant seasonal characteristics, and corresponds to the seasonal characteristics of the ionospheric TEC well, manifested as follows: if the NmF2 and TEC in one season are larger than the values of other seasons, the time delay of positive storm triggered by daytime MPO in this season must be lower, no matter for winter or equinox, and it’s opposite for negative storms, indicating that ionospheric storm is an extreme manifestation of the seasonal characteristics of the ionosphere, and time delay is one of its manifestations. 3. There is no correlation between the time delay and the intensity of magnetic storm, consistent with the result of Gao et al. (2008). Besides, the time delay of positive storms has no relationship with the solar activity, whereas the time delay of negative storms has weakly negative dependence on the solar activity, with correlation coefficient 0.41. 4. All of the pre-storm response events occurred during the special time interval: 15–20 LT, because ionosphere is very active during this period in the EIA area, manifesting the EIA feature at Taoyuan. We further check the common features of the pre-storm responses events and find that SSC and weak geomagnetic disturbance before the MPO are two main possible causes of the pre-storm response events. 5. The geomagnetic indices of events with long-time delay suggest that these events have no correlation with the magnetic activity, and they might belong to ionospheric Q-disturbances.
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Acknowledgment The study was supported by the National Key R&D Program of China (Grant nos. 2018YFF01013700). The geomagnetic data (Dst) was obtained from the World Data Center for Geomagnetism of Kyoto (http://wdc.kugai.kyoto-u.ac.jp). The ionospheric TEC data in Taoyuan was provided by IGS database (ftp://ics.gnsslab.cn). The solar activity data (F10.7) was obtained from NOAA (ftp://ftp. swpc.noaa.gov/pub/indices/old_indices/). References Astafyeva, E.I., Zakharenkova, I., Fo¨rster, M., 2015. Ionospheric response to the 2015 St. Patrick’s Day storm: a global multiinstrumental overview. J. Geophys. Res. Space Phys 120, 9023–9037. Balan, N., Rao, P.B., 1990. Dependence of the ionospheric response on the local time of sudden commencement and the intensity of the geomagnetic storms. J. Atmos. Terr. Phys. 52, 269–275. Balan, N., Shiokawa, K., Otsuka, Y., Kikuchi, T., Vijaya Lekshmi, D., Kawamura, S., Yamamoto, M., Bailey, G.J., 2010. A physical mechanism of positive ionospheric storms at low latitudes and midlatitudes. J. Geophys. Res. Space Phy. 115. Balan, N., Yamamoto, M., Liu, J.Y., Otsuka, Y., Liu, H., Lu¨hr, H., 2011. New aspects of thermospheric and ionospheric storms revealed by CHAMP. J. Geophys. Res. Space Phys. 116. Burns, A.G., Killeen, T.L., Deng, W., Carignan, G.R., Roble, R.G., 1995. Geomagnetic storm effects in the low- to middle-latitude upper thermosphere. J. Geophys. Res. Space Phys. 100 (A8), 14673–14692. Buonsanto, M.J., 1999. Ionospheric storms – a review. Space Sci. Rev. 88 (3), 563–601. Buresˇova´, D., Lasˇtovicˇka, J., 2007. Pre-storm enhancements of foF2 above Europe. Adv. Space Res. 39 (8), 1298–1303. Danilov, A.D., 1986. Meteorological control of the D region. Ionos. Res. 39 (in Russian). Danilov, A.D., 2001. F2-region response to geomagnetic disturbances. J. Atmos. Solar-Terr. Phys. 63, 441–449. Deng, Z., Liu, R., Zhen, W., Liu, D., Ma, B., Wu, Y., 2010. Ionospheric TEC Disturbances Based on GPS Observations. https://doi.org/10. 1109/ISAPE.2010.5696484. Depuev, V., Depueva, A., Leshchinskaya, T., 2008. Mechanism of formation of Q-disturbances in the F2 region of the equatorial ionosphere. Geomagn. Aeron. 48 (1), 93–102. Essex, E.A., Mendillo, M., Scho¨del, J.P., Klobuchar, J.A., da Rosa, A.V., Yeh, K.C., Fritz, R.B., Hibberd, F.H., Kersley, L., Koster, J.R., Matsoukas, D.A., Nakata, Y., Reolofs, T.H., 1981. A global response of the total electron content of the ionosphere to the magnetic storm of 17 and 18 June 1972. J. Atmos. Terr. Phys. 43 (4), 293–306. Forbes, J.M., Palo, S.E., Zhang, X., 2000. Variability of the ionosphere. J. Atmos. Sol-Terr. Phys. 62, 685–693. Fuller-Rowell, T.J., Codrescu, M.V., Moffett, R.J., Quegan, S., 1994. Response of the thermosphere and ionosphere to geomagnetic storms. J. Geophys. Res. Space Phys. 99, 3893–3914. Feng, J., Wang, Z., Shi, S., Zhang, B., 2016. Using IGS to analyze the variation of anomaly equatorial ionization. Sci. Survey. Mapping 41 (6), 44–47. Gao, Q., Liu, L., Zhao, B., Wan, W., Zhang, M., Ning, B., 2008. Statistical study of the storm effects in middle and low latitude ionosphere in the East-Asian sector. Chinese J. Geophys. 51 (3), 625– 634. Jin, S., Jin, R., Liu, X., 2019. GNSS Atmospheric Seismology. Theory, Observations and Modeling. Springer, Singapore. Joshi, L.M., Sripathi, S., Singh, R., 2016. Simulation of low latitude ionospheric response to 2015 St. Patrick’s Day super geomagnetic
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Statistical study of the time delay of ionospheric TEC storms to geomagnetic storms in Taoyuan, Taiwan Yuqiang Zhang a, Zhensen Wu a,⇑, Jian Feng b, Tong Xu b, Zhongxin Deng b, Weimin Zhen b a
School of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710126, China b China Research Institute of Radiowave Propagation (CRIRP), Qingdao 266107, China
Received 13 May 2019; received in revised form 26 August 2019; accepted 8 September 2019 Available online 25 September 2019
Abstract We have studied the time delay of ionospheric storms to geomagnetic storms at a low latitude station Taoyuan (25.02°N, 121.21°E), Taiwan using the Dst and TEC data during 126 geomagnetic storms from the year 2002 to 2014. In addition to the known local time dependence of the time delay, the statistics show that the time delay has significant seasonal characteristics, which can be explained within the framework of the seasonal characteristics of the ionospheric TEC. The data also show that there is no correlation between the time delay and the intensity of magnetic storms. As for the solar activity dependence of the time delay, the results show that there is no relationship between the time delay of positive storms and the solar activity, whereas the time delay of negative storms has weakly negative dependence on the solar activity, with correlation coefficient 0.41. Especially, there are two kinds of extreme events: pre-storm response events and long-time delay events. All of the pre-storm response events occurred during 15–20 LT, manifesting the Equator Ionospheric Anomaly (EIA) feature at Taoyuan. Moreover, the common features of the pre-storm response events suggest the storm sudden commencement (SSC) and weak geomagnetic disturbance before the main phase onset (MPO) of magnetic storms are two main possible causes of the pre-storm response events. By analyzing the geomagnetic indices during the events with long-time delay, we infer that this kind of events may not be caused by magnetic storms, and they might belong to ionospheric Q-disturbances. Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: Ionospheric TEC storms; Time delay; Seasonal and local time dependences; Extreme events
1. Introduction Geomagnetic storm results when high-speed plasma injected into the solar wind from coronal mass ejections or coronal holes impinges upon Earth’s geomagnetic field. If the arriving solar wind plasma has a southward magnetic field, energy is coupled efficiently into Earth’s magnetosphere and upper atmosphere (Buonsanto, 1999). With the injection of energy, ionospheric parameters such as TEC, foF2 will increase or decrease significantly in a period of time, termed as positive or negative ionospheric storms, ⇑ Corresponding author.
E-mail address: [email protected] (Z. Wu). https://doi.org/10.1016/j.asr.2019.09.017 0273-1177/Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
respectively. These storms can then disrupt, and sometimes damage, a wide range of everyday technological systems that are often critical to the smooth functioning of modern societies. Therefore, it has always been a hot topic in space weather research. The field has been enriched by a remarkable set of comprehensive studies and review articles (e.g., Martyn, 1953; Obayashi, 1964; Matuura, 1972; Pro¨lss, 1995; Buonsanto, 1999; Mendillo, 2006). The statistics of the ionospheric storms and case studies are widely reported (e.g., Matsushita, 1959; Mendillo and Klobuchar, 1975; Essex et al., 1981; Titheridge and Buonsanto, 1988; Rodger et al., 1989; Balan and Rao, 1990; Mannucci et al., 2005; Astafyeva et al., 2015; Sun et al., 2017; Kashcheyev et al., 2018). Modeling studies have also been
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carried out to reproduce ionospheric storms and understand their physical mechanisms (e.g., Richmond and Matsushita, 1975; Fuller-Rowell et al., 1994; Burns et al.,1995; Lekshmi et al., 2007; Lu et al., 2008; Balan et al., 2010; Sojka et al., 2012; Joshi et al., 2016). Time delay is an important feature of ionospheric storm, and it has been discussed by previous studies (e.g., Balan and Rao, 1990; Gao et al., 2008; Liu et al., 2010, 2017). Balan and Rao (1990) investigated the dependence of ionospheric response on the local time of storm sudden commencement and to the intensity of the magnetic storms, using peak electron density and TEC data observed by a low- and a mid-latitude stations in North America during more than 60 SC-type storms. They found that the time delays associated with the positive responses are low for daytime SCs and high for night-time SCs, and it is just the opposite applies for negative SCs. Moreover, the time delays are inversely related to the intensity of the storms. Gao et al. (2008) analyzed foF2 observed by four ionosonde stations in East Asian sector during 515 magnetic storms from 1957 to 2006 and found that the time delay correlates well with the local time of MPO. For positive response, it is shorter for daytime MPO than night-time MPO, and it is opposite for negative response. However, there is no evidence for the relationship between time delay and intensity of magnetic storms. Liu et al. (2010) found that the time delays of ionospheric TEC responses to geomagnetic disturbances depends on season, magnetic latitude as well as local time based on the statistical study using 10 years of global ionosphere maps (GIMs) TEC data retrieved at Jet Propulsion Laboratory (JPL). However, the time delay in their paper is not relative to the MPO of the storm, the correlation coefficients give the time delay for the maximum ionospheric response to the maximum magnetic storm. Recently, Liu et al. (2017) made a statistical analysis of the ionospheric response in the American Sector during 217 geomagnetic storms from 2001 to 2015, and they found that mean time delay for negative storms is mostly longer than 10 h for all latitude zones, while shorter than 10 h for positive storms, except for low and equatorial latitudes. However, most of them are concentrate on the local time variation of the time delay, and few are aiming at the full features of the time delay of ionospheric storms. Taoyuan is located in the north crest of EIA in East Asia, the ionosphere has high day-to-day variety in this area. Until now, there is no specific study analyzing the full features of time delay in East Asian EIA region (best to our knowledge). Therefore, following their work, we have used GPS TEC data derived from IGS database during 126 magnetic storms from 2002 to 2014 in Taoyuan to study the time delay and various factors (include local time, season, storm intensity as well) affect it. This paper sets out the data and methods of analysis we used in Section 2. Section 3 describes the dependence of time delay on the season, local time, storm intensity, solar activity, and present the results of two kinds of extreme events: pre-storm
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response events and long-time delay events. The interpretation of the results will be presented in Section 4. Section 5 sums up. 2. Data and methods of analysis The hourly geomagnetic activity index (Dst) is downloaded from World Data Centre in Kyoto (http://swclobkugi.kyoto-u.ac.jp) with one-hour resolution. The magnetic storms are selected with minimum Dst < 50 nT. An additional criterion is based on the condition that the ionospheric response of multiple main phases can be accumulated. Therefore, according to Kamide et al. (1998)’s classification, single main phase storm is considered in this paper only. Finally, excluding magnetic storms with missing TEC data, 131 geomagnetic storms are selected. There is not an agreement on how to define a storm onset and there are two definitions: SSC and MPO. It should be noted that SSC is caused by the coupling of interplanetary magnetic field and magnetosphere, it does not cause significant magnetospheric energy to be injected into the ionosphere; but MPO corresponds to the onset of the magnetospheric ring current growth phase, which directly or indirectly affects the magnetospheric energy injection (Pro¨lss, 1995). Considering the time delay of ionospheric response is mainly due to the coupling of ionosphere-thermosphere-magnetosphere and some storms do not have SSC, we choose the MPO to define the storm onset. The TEC data used in this paper is derived from IGS (ftp://ics.gnsslab.cn) with 15 min resolution. This paper adopts the criterion proposed by Deng et al. (2010) and by Jin et al. (2019) for the determination of ionospheric TEC storm in China. The TEC perturbation index DI (t) is described in terms of percentage deviation of TEC observation from its monthly median values, DI ðtÞ ¼
TEC o ðtÞ TEC m ðtÞ DTEC ðtÞ ¼ TEC m ðtÞ TEC m ðtÞ
where TEC o ðtÞ and TEC m ðtÞ are measured and monthly median TEC for time, respectively. The positive (negative) ionospheric TEC storm is considered to occur when DI > 0.35 (DI < 0.3) and last for more than 6 h. Besides, the total duration of unsatisfactory DI does not exceed two hours. Then, the time delay can be obtained by calculating the time difference between the onset time of the ionospheric TEC storm and MPO. Although there are some consecutive ionospheric storms triggered by one magnetic storm, we only consider the time delay between the first ionospheric TEC storm and MPO. Fig. 1 illustrates the day to day variations of Dst index and DI index during the period of 02–05 June 2003 at Taoyuan. Dst variation is shown in Fig. 1a and the arrow ‘MPO’ marks local time of the MPO at 03:00 UT on 2 June. Fig. 1b shows the variation of DI during this period. The negative storm begins at 10:30 UT with DI less than 0.3 (arrow ‘1’) and ends at 16:45 UT when the DI index
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Fig. 1. An example for onset time of magnetic storm and ionospheric storm during the period 02–05 June 2003.
becomes greater than 0.3 (arrow ‘2’). The time interval between arrow ‘MPO’ and arrow ‘1’ is taken as time delay we studied. 3. Results According to the statistical conditions, a total of 131 geomagnetic storms can be found during 2002–2014. However, only 5 of them do not cause ionospheric storms, confirming the ionospheric storms can be easily triggered under the circumstance of magnetic storms. In the statistical analysis of the time delay, we have divided the local time into two parts: daytime (06–18 LT) and night-time (18–06 LT) and define summer (May to August), winter (January, February, November and December) and equinox (March, April, September and October) according to the classification of Lloyd season. Since (dayside) ionospheric density is produced mainly by solar EUV radiation (which is inferred by F10.7), we use F10.7 index to represent the solar activity when the MPO occurred, and they are downloaded from NOAA (ftp://ftp.swpc.noaa.gov/ pub/indices/old_indices/). It is generally accepted that the physical mechanism of positive storm and negative storm are different, therefore, the time delays of positive and negative storms have been discussed separately.
Fig. 2 shows the distributions of the mean time delay of positive (negative) storms on a basis of MPO occurred at daytime (night-time) in summer, winter, and equinox, respectively. It can be seen that the time delay has a close relationship with the local time of the MPO. The mean time delay of positive storms is shorter for daytime MPO than night-time MPO. Specifically, it reaches 17.2 h for summer daytime MPO and 15.5 h for winter daytime MPO, compared with 32.2 h and 24.4 h for night-time MPO in these two seasons. What’s more, it is most pronounced in equinox, with 13.5 h for daytime MPO and 37.1 h for night-time MPO. The situation is opposite for negative storms. It is 28.7 h and 34.6 h for daytime MPO in winter and equinox, compared with 14 h and 28.1 h for night-time MPO, except for negative storms in summer. The time delay of negative storms for daytime MPO is 5.6 h shorter than night-time MPO in summer. Gao et al. (2008) have drawn the same conclusion by analyzing foF2 observed by four ionosonde stations in East Asian sector during 515 magnetic storms. On the other hand, the time delay associated with the daytime MPO is shorter for positive storms than negative storms, whereas the opposite applies for night-time MPO. As shown in Fig. 3a and b, by comparing the time delay of positive/negative storms associated with daytime/ night-time MPO. As for the seasonal characteristics, Fig. 2a shows the mean time delay of positive storm. It is shorter in winter daytime than that in summer daytime, which is 15.5 h and17.2 h, respectively. In contrast, the mean time delay of negative storm is significantly shorter during summer daytime than that during winter daytime, which is 18.1 h and 28.7 h. It can also be found that the time delay of positive storm and negative storm caused by night-time magnetic storms is shortest in winter. It’s well known that the ionospheric seasonal anomalies are reflected at daytime (Rishbeth et al., 2000), so the seasonal dependence of the time delay of ionospheric storms triggered by magnetic storms which start at night will not be discussed here. In addition, the mean time delay of positive (negative) storms for daytime MPO is shorter (longer) in equinox than in solstice, it was 13.5 h (34.6 h) for equinox, 15.5 h (18.1 h) for summer and 17.2 h (28.7 h) for winter, which represents an evident semi-annual variation. Negative storms (b)
40
32.2
30 20
37.1
17.2
24.4 15.5
13.5
10 0
summer daytime
winter night-time
equinox
Time delay (hours)
Time delay (hours)
Positive storms (a) 40 30 20
23.7 18.1
34.6 28.1
28.7 14
10 0
summer daytime
winter
equinox
night-time
Fig. 2. Comparison of the mean time delay for different seasons and local time. (a) Mean time delay of positive storms; (b) Mean time delay of negative storms.
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40 30 20
18.1 17.2
(a) 34.6
28.7 15.5
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winter
(b)
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equinox
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Daytime MPO
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40 30
32.2 23.7
37.1 28.1
24.4 14
20 10 0
summer positive storm
winter
equinox
negative storm
Fig. 3. Comparison of the mean time delay of positive/negative storms for different local time (a) Mean time delay during the daytime; (b) Mean time delay during the night-time.
Balan and Rao (1990) found that the time delays are inversely related to the intensity of the storms. However, Gao et al. (2008) found that the time delay does not show a significant association with the intensity of the magnetic storms, after classified the storms according to the intensity of the storms. Through the scatter plot of time delay and storm intensity shown in Fig. 4, we can hardly find relationship between them, and the correlation coefficients is 0.07 for positive storm and 0.05 for negative storm, respectively. After classification, the mean time delay of positive (negative) storms are 21.5 h (29.5 h) and 23.3 h (23.6 h) for strong magnetic storms (Dst < 100 nT) and moderate magnetic storms (100 Dst < 50 nT), indicating there is still no association between them, consistent with the result of Gao et al. (2008). With regard to the above results, three factors may contribute to the differences. (1) The classification of the storms is based on different geomagnetic indices. The Dst index used by Gao et al. (2008) and this paper correspond to the symmetrical part of the equatorial ring current; Ap index used by Balan and Rao (1990) represents the global geomagnetic disturbances. (2) The data and method of analysis are different. Balan and Rao (1990) have used TEC/Nmax data and the time delay in their studies to refer to the time interval between the onset of SSC and time of maximum ionospheric response. However, Gao et al. (2008) have used foF2 data and the time delay in their work to refer to the time interval between the MPO and the onset of ionospheric storms; we have used TEC data the same
definition of time delay. (3) The ionospheric response to magnetic storms has longitudinal effect (e.g. Zhao et al., 2007; Lekshmi et al., 2011; Kuai et al., 2017). These three factors may also lead to differences described above. The solar activity dependence of the time delay is also examined. As shown in Fig. 5a, there is no relationship between the time delay of positive storms and the solar activity. On the contrary, the time delay has weakly negative dependence on the solar activity, with correlation coefficient 0.41 (Fig. 5b). It might be related to the mechanism of negative storms (e.g. Fuller-Rowell et al., 1994; Pro¨lss, 1995). Due to thermal expansion, the quiet time [O]/[N2] ration at all pressure (and height) level is smaller at solar maximum than at solar minimum. In such a background thermosphere, the chemical effects of the storm- time neutral winds can easily make the [O]/[N2] ratio much smaller at solar maximum than at solar minimum (Lekshmi et al., 2011). In other words, negative storms can occur easily at solar maximum than at solar minimum, and time delay of negative storms at solar maximum are smaller than at solar minimum. By contrast, the time delay of positive storms seem have no relationship with the solar activity, as there are many factors attribute to the positive storms, such as rapid strengthening of equatorial plasma fountain by enhanced eastward prompt penetration electric fields(PPEFs), rapid slow-down of both recombination processes and downward diffusion of plasma by the equator-ward neutral wind, and solar activities affect these physical process in complicated ways
Fig. 4. Scatterplots of the time delay against the corresponding geomagnetic storms intensity (minimum Dst). (a) Positive ionospheric storms; (b) Negative ionospheric storms.
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Fig. 5. Scatterplots of the time delay against the corresponding solar activities (F10.7). (a) Positive ionospheric storms; (b) Negative ionospheric storms.
events occurred during 15–20 LT and the corresponding magnetic storms are either SC-type or have weak geomagnetic disturbance within 7 h (not shown here) before the MPO. In addition to the pre-storm response events, there are also events with long-time delay. As for the long-time delay events, there is still no specific definition. Numerous studies have confirmed that the effects of magnetic storms on ionosphere can last to the recovery phase (e.g. Fuller-Rowell et al., 1994; Mendillo, 2006; Joshi et al., 2016, 2019), indicating the effects of magnetic storms on the ionosphere can last for a long time. However, in previous studies, magnetic storm seems to have significant effects on ionosphere since MPO, and negative storms during the recovery phase usually start one or two days after magnetic storms. As stated by Mendillo (2006), the prominent variations of ionosphere during magnetic storms will occur one or two days after magnetic storms, therefore, we define such events with time delay of more than 48 h and do not have ionospheric storm during the first 48 h after MPO as long-time delay events, and list all the events in Table 2. It can be seen from the
during different magnetic storms. As mentioned above, there are many drivers in the EIA region during magnetic storms, and these coupled drivers together with background thermosphere-ionosphere generate ionospheric storms and control their strengths in complicated ways. The enhancement of electron density in the ionosphere before geomagnetic storms is one of the open questions (e.g., Kane, 1973; Danilov, 2001; Buresˇova´ and Lasˇtovicˇka, 2007; Liu et al., 2008). In our statistical study, there are 6 events which time delays are less than 0. Table 1 lists the relative information of these events. In these 6 events, 4 of them correspond to the SC-type storms, the rest of them correspond to the Recurrent-type storms. The advance time is longer for Recurrent-type storms, which is 3 h, 6.25 h, respectively. Whereas the maximum advance time for SC-type storms is 1.75 h, suggesting that the advance time of pre-storm response events closely related to the type of the magnetic storms. In addition, only one of the pre-storm response events belongs to the negative ionospheric storm. By extracting the common features of these events, we find that all the pre-storm response Table 1 List of pre-storm response events. Date
Local time of magnetic storms
Type of magnetic storms
Type of ionospheric storms
Min Dst (nT)
Ahead of time (h)
2002-5-23 2003-6-24 2003-11-4 2004-1-22 2005-8-24 2008-10-11
20:00 18:00 15:00 19:00 15:00 16:00
SC Recurrent SC SC SC Recurrent
negative positive positive positive positive positive
109 55 69 79 184 54
0.5 6.25 0.75 1.75 0.75 3
Table 2 List of events with long-time delay. Date of magnetic storms
Local time
Date of ionospheric disturbances
Local time
Type of ionospheric disturbances
Min Dst (nT)
Time delay (h)
2002-2-6 2005-4-5 2005-6-23 2006-11-30 2011-10-25 2012-4-13 2012-6-12
2:00 3:00 14:00 5:00 3:00 11:00 3:00
2002-2-9 2005-4-7 2005-6-25 2006-12-2 2011-10-28 2012-4-15 2012-6-14
16:00 19:30 22:15 10:45 21:00 17:00 14:15
Positive Positive Positive Negative Positive Positive Positive
76 70 68 74 147 60 67
86 64.5 56.25 53.75 90 54 59.25
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table that these ionospheric disturbances mainly occur from noon to midnight, and most of them are positive disturbances (6/7). By analyzing the geomagnetic indices during these events, we infer that these disturbances are not caused by magnetic storms, and they might belong to ionospheric Q-disturbances. An example will be given in the following discussion. 4. Discussion At low latitude, ionospheric response to geomagnetic activity is very complicated for the existence of the EIA and many other drivers, including: (1) penetration electric field driven by the solar wind ranging from high latitude to equator; (2) the equator-ward neutral wind resulting from particle precipitation and Joule heating at high latitude, sometimes accompanied with TAD; (3) the disturbance dynamo electric fields produced by the globally altered thermospheric winds during magnetic storms; (4) composition changes driven by storm time altered neutral winds; (5) compression of the plasmasphere by the enhanced solar wind. These coupled drivers together with background thermosphere-ionosphere generate ionospheric storms and control their strengths in complicated ways. In addition to the ionospheric storms caused by magnetic storms, extreme climates such as thunderstorms and typhoons can also lead to ionospheric disturbances (e.g. Xiao et al., 2007; Mao et al., 2010). Therefore, the ionospheric response to magnetic storms depends not only on the intensity of the storms, but also on various parameters of the ionosphere-thermosphere system when the storms occur, which lead to the weak correlation between the time delay and intensity of corresponding storms. Even after classification, there is still no association between them. Wu et al. (2007) have found that TEC at equatorial anomaly crests yield their maximum values during the equinox months and their minimum values during the summer. In this paper, we found that the time delay of the ionospheric storm has significant seasonal characteristics, and corresponds to the seasonal characteristics of the ionospheric TEC in this region well, manifested as follows: if the NmF2 and TEC in one season are larger than the values of other seasons, the time delay of positive storms triggered by daytime MPO in this season must be lower, no matter for winter or equinox. It is opposite for the negative storm. In other words, in the case of magnetic storm, the larger the corresponding ionospheric background TEC is, the more likely the positive ionospheric TEC storm will occur, and the shorter the time delay will be. From physical mechanism perspective, large ionospheric background TEC means the ionospheric processes which produce ions and electrons are active, and these processes are easy to be triggered by magnetic storms. Therefore, the positive storm is more likely to occur under these circumstances. In this sense, ionospheric storm is an extreme manifestation of the seasonal characteristics of the ionosphere, and time delay is one of its manifestations.
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We found that the time delay of ionospheric TEC storms has obvious local time characteristics. That is, the time delay of the positive storms is smaller for daytime MPO than that for night-time MPO and opposite for negative storms, consistent with previous results. (Balan and Rao, 1990; Gao et al., 2008; Liu et al., 2017). Apparently, it is related to the local time variation of TEC during storms, and the thermospheric day-to-night circulation during storm time can explain it. (e.g., Fuller-Rowell et al., 1994; Mendillo, 2006). Compared with night-time, the positive storms are easy to be triggered during the daytime. Therefore, when magnetic storms occur in the daytime, the ionospheric positive storms will be triggered faster than in the night-time. The opposite situation applies for negative storms. In our statistics, we also find out that 15–20 LT is a special time interval. All the events with pre-storm responses occurred within this time window. It is not only related to local time but also related to the geographical/geomagnetic location of Taoyuan. Taoyuan locates at equatorial ionization anomaly region which is 25.02°N, 121.21°E and 15.1° in magnetic latitude. Feng et al. (2016) found that TEC in EIA region reaches its maximum at 12–16 LT and the EIA area can last for another 2–3 h after sunset because of the pre-reversal enhancement caused by eastward electric field during the period of 16–18 LT. Therefore, the ionosphere is highly active during 15–20 LT, and response quickly to geomagnetic activities. However, the active ionosphere just provides the background, magnetic activities are also required to trigger pre-storm response events. By comparing the type of the magnetic storms, we found that among the pre-storm response events, 4 events are SC-type storm and the rest are Recurrent-type storm which have weak geomagnetic disturbances before the storms. As for the pre-storm response events caused by SC-type storm, Balan and Rao (1990) found that more than one-third of the 68 SC-type storms commence before the onset of MPs. The weak geomagnetic disturbance before the MPO may lead to the pre-storm response events (Balan et al., 2011), partly explained the other three pre-storm response events in this paper. In addition, the pre-storm events occurred usually in the form of positive deviations, except for one event in the form of negative deviations. To further understand pre-storm response events, we need to combine more observation to provide a statistical picture and use ionospheric model to study the physical mechanism of this phenomenon. Regarding the events with long-time delay, we found that geomagnetic activities are very quiet during these events, so the ionospheric disturbances may not be caused by magnetic storms. As illustrated in Fig. 6, the MPO of the magnetic storm was at 1700 UT, 11 June 2012. Almost 60 h later, the ionospheric disturbance occurred during the time interval between arrow ‘1’ and arrow ‘2’. During the period of ionospheric disturbance, the geomagnetic activity is very quiet, indicating the disturbance may not be caused by geomagnetic activity, whereas it might belong to iono-
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factors affect its ionospheric state. Moreover, the morphology and mechanisms of Q-disturbances are still far from being well understood. Therefore, more observations and modeling are needed to give a comprehensive study of long-time delay events in this area. 5. Conclusion
Fig. 6. An example of the ionospheric disturbance with long-time delay during the period of 11–14 December 2012.
spheric Q-disturbances. On the other hand, the preference for positive storm probably be related to the seasonal and local time characteristic of the positive Qdisturbances. Mikhailov et al. (2004) found that positive disturbances are seen to be more numerous than negative ones at all latitudes. For positive Q-disturbances, semiannual variations with peaks around equinoxes dominate at high and middle latitudes, and more positive Qdisturbances take place at summer as decreasing latitude. Besides, winter season is the most preferable for negative Q-disturbances for all latitudes. These features are consistent with the seasonal characteristic of the long-time delay events in this paper. They also found that the positive Q-disturbances are more frequent in the evening and nigh-early morning LT sectors, there are also 50 percent (3/6) positive long-time delay events in the nighttime in this paper. Although we cannot obtain statistical results due to the insufficient sample size of these long-time delay events. The similarity of these two kinds of events still implies that the long-time delay events may be belong to the ionospheric Q-disturbances. As for the Q-disturbances, they are closely related to the problem of the coupling from below: the so-called meteorological control of Earth’s ionosphere (e.g. Danilov, 1986; Forbes et al., 2000; Rishbeth and Mendillo, 2001; Rishbeth, 2006). Mikhailov et al. (2004) have devoted themselves to the morphological analysis of F2-layer Qdisturbances using the worldwide ground-based ionosonde observations from high to lower latitudes in the Northern Hemisphere and demonstrated that the main morphological features of Q-disturbances could be explained within the framework of the contemporary understanding of the thermosphere-ionosphere interaction (Mikhailov et al., 2007, 2009, 2012). Depuev et al. (2008) considered the morphological picture of the disturbances in the F2 region of equatorial ionosphere under quiet geomagnetic conditions (Q-disturbances) and interpreted the picture. Taoyuan is located in the north crest of EIA, and there are many
We have presented a statistical study on the time delay of the ionospheric TEC storms in Taoyuan, Taiwan which is located in the north crest of EIA in East Asia. Our new statistical results are generally consistent with the previous statistical analyses for the local time dependence of the time delay (Balan and Rao, 1990; Gao et al., 2008; Liu et al., 2017). However, there are a number of new results that provide interesting insights into the time delay of this region. Our main conclusions are summarized as follows: 1. The time delay has a close relationship with the local time of the main phase onset (MPO) of the magnetic storm. It seems that the time delays of the positive storms are smaller for daytime MPO than those for night-time MPO and opposite for negative storms, consistent with previous studies (Balan and Rao, 1990; Gao et al., 2008; Liu et al., 2017), except for the negative storms in summer. 2. The time delay of the ionospheric storm has significant seasonal characteristics, and corresponds to the seasonal characteristics of the ionospheric TEC well, manifested as follows: if the NmF2 and TEC in one season are larger than the values of other seasons, the time delay of positive storm triggered by daytime MPO in this season must be lower, no matter for winter or equinox, and it’s opposite for negative storms, indicating that ionospheric storm is an extreme manifestation of the seasonal characteristics of the ionosphere, and time delay is one of its manifestations. 3. There is no correlation between the time delay and the intensity of magnetic storm, consistent with the result of Gao et al. (2008). Besides, the time delay of positive storms has no relationship with the solar activity, whereas the time delay of negative storms has weakly negative dependence on the solar activity, with correlation coefficient 0.41. 4. All of the pre-storm response events occurred during the special time interval: 15–20 LT, because ionosphere is very active during this period in the EIA area, manifesting the EIA feature at Taoyuan. We further check the common features of the pre-storm responses events and find that SSC and weak geomagnetic disturbance before the MPO are two main possible causes of the pre-storm response events. 5. The geomagnetic indices of events with long-time delay suggest that these events have no correlation with the magnetic activity, and they might belong to ionospheric Q-disturbances.
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