Long-term observation of electrical discharges during persistent Vulcanian activity
Caron E.J.Vossen, Corrado Cimarelli, Alec J.Bennett, Andre Geisler, Damien Gaudin, Daisuke Miki, Masato Iguchi, Donald B.Dingwell, 2021
a) Map showing Minamidake crater on Sakurajima volcano, the two thunderstorm detectors, KUR and HAR, and the Japan Meteorological Agency (JMA) branch office in Kagoshima city. In the inset: Digital elevation model of vents A and B of Minamidake summit crater and Showa crater obtained by SVO. b)Sensor KUR at Kurokami branch observatory with Minamidake crater erupting in the background.
Very low frequency and wide-band lightning detection networks can detect major volcanic plumes via their intense electrical and lightning activity. However, the high number of non-detected explosive episodes confirmed by direct observations, reveals the limits of these systems in the detection of the more frequent smaller ash-rich explosive events. Here, we use a data-efficient thunderstorm detector to observe electrical discharges generated from July 2018 to January 2020 by the persistent Vulcanian activity of Minamidake crater at Sakurajima volcano in Japan.
Two thunderstorm detectors recorded the electrical activity produced by explosions at Minamidake crater from a distance of 3 and 4 km from the active vents. The instruments measured the induced current due to the change in electric field with time within the extremely low frequency range (1-45 Hz). Using a volcanic lightning detection algorithm together with the catalogue of volcanic explosions compiled by the Japan Meteorological Agency (JMA) and Tokyo Volcanic Ash Advisory Center (Tokyo VAAC), the number of electrical discharges, the electrical discharge rate and the total amount of measured voltage were determined for each individual explosive event. In addition, the start of the electrical discharges was compared to the explosion onset as provided by the JMA (with a one-minute time resolution).
The sensors detected electrical discharges in 71% of the 724 recorded explosions. Our detection algorithm successfully recognises the presence/absence of electrical discharges with an accuracy of 73%. We find a non-linear positive correlation between the number of discharges and the plume height. Moreover, we find that the maximum electrical discharge rate and the maximum amount of measured voltage by a single discharge also increase with plume height. Fracto- and tribo-electrification appear to be the dominant plume electrification mechanisms. Even for the few explosive events that exceeded the 10 ◦C isotherm, the timescale of electrical activity seems to be too short for ice nucleation to make a significant contribution to the plume electrification. Finally, for 12% of the electrically-active explosive events, discharges were detected by the sensors more than a minute before the JMA explosion onset.
Our results show the capability of our detectors in pinpointing the inception of electrified explosive episodes in real-time and in providing an indication of the magnitude of each explosion, demonstrating their effectiveness as a cost- and data-efficient instrumentation for the monitoring of explosive ash emissions at active volcanoes.
Different temperature regions (shades of blue) as a function of height above sea level. The temperature data has been smoothed using a one-dimensional Gaussian filter with a standard deviation of 1σ. The corresponding calendar months are denoted at the top of the figure with a single letter. Vertical lines indicate plume height for explosions at Minamidake during the observation period. Note that the plume heights are reported as metres a.c.r. (i.e. 1117 m a.s.l. for Minamidake). Red lines indicate electrically-active explosions recorded by at least one sensor. Black and grey lines indicate explosions that did not produce anyelectrical discharges and explosions with no associated sensor data, respectively. Dashed lines indicate explosions with unknown plume height to which an arbitrary value of 1000m a.c.r. has been assigned. Gaps correspond to explosions that coincided with meteorological thunderstorms. (For interpretation of the colours in the figure(s), the reader is referred to the web version of this article.)
The monitoring of volcanoes is improving rapidly (Sparks et al., 2012). Lagging somewhat behind however is the detection andmonitoring of volcanic ash plumes, especially in remote areas. Air traffic is particularly at risk (Song et al., 2016), and demands for improved early warning systems of volcanic ash emissions. Intense electrical activity and lightning in volcanic plumes sug- gest that electrical monitoring of volcanoes can aid ash emission detection. Volcanic lightning can be detected both instantaneously and remotely, providing the prospect of real-time monitoring at remote volcanoes.
Volcanic lightning (VL) has been universally observed at vol- canoes around the world, with 2010 Eyjafjallajökull (Bennett et al., 2010), 2015-2016 Sakurajima (Aizawa et al., 2016; Cimarelli et al., 2016), 2015 Calbuco (Van Eaton et al., 2016), 2017 Bogoslof (Van Eaton et al., 2020) and 2018 Krakatau (Prata et al., 2020) eruptions being the most recently studied. Using laboratory exper- iments, numerical modelling, field observations and knowledge of thundercloud electrification, researchers have studied the source of VL and its relationship with eruption parameters, such as mass eruption rate (Hargie et al., 2019), plume height (Bennett et al., 2010; Behnke et al., 2013, 2014) and grain size distributions of the eruption products (Gaudin and Cimarelli, 2019). Several vol- canic plume electrification processes that lead to charge separation have been proposed. These include fracto-electrification (the emis- sion of charged species from freshly fractured surfaces, Dickinson et al., 1988; James et al., 2000), tribo-electrification (electron trans- fer through the collision of ash particles, Lacks and Levandovsky, 2007; Cimarelli et al., 2014; Gaudin and Cimarelli, 2019), ice nu- cleation/riming (Arason et al., 2011; Prata et al., 2020; Van Eaton et al., 2020), interaction with (sea)water (Björnsson et al., 1967; Büttner et al., 1997; James et al., 2008) and, to a lesser extent, nat- ural radioactivity (Clement and Harrison, 1992; Aplin et al., 2014; Nicoll et al., 2019). Thomas et al. (2010) have distinguished three types of VL based on when and where it occurs in the plume: 1) Vent discharges, also known as Continual Radio Frequency (CRF), are observed in the gas-thrust region at the inception of the explo- sion (Thomas et al., 2007, 2010). Behnke et al. (2018) found that a single CRF signal has a duration of 160 ns and a length scale of around 10 metres. 2) Near-vent discharges, in contrast, have a total duration of several ms (Aizawa et al., 2016). They have typ- ical lengths of few tens to hundreds of metres and occur at the transition between the gas-thrust and the convective phases of the plume (Cimarelli et al., 2016). These values are 1-2 orders of mag- nitude smaller than those of thunderstorm lightning (Aizawa et al., 2016). 3) Plume volcanic lightning is that element of VL which is most similar to thunderstorm lightning, both in length and dura- tion. These flashes can have a length of several kilometres and a duration of 30-650 ms (Thomas et al., 2007; McNutt and Williams, 2010).
Current global thunderstorm networks have demonstrated to be capable of detecting VL during major eruptions. This is especially so for plumes reaching altitudes at which ash becomes an effective catalyst for ice nucleation (Maters et al., 2019, 2020), between the 13 ◦C and 23 ◦C isotherms (Durant et al., 2008), although this temperature range can be extended several C depending on the nucleation mode (Durant et al., 2008), chemical composition, crystallinity and mineralogy (Genareau et al., 2018; Maters et al., 2019, 2020). These lightning detection networks commonly work in the very low to high frequency range, as the electromagnetic emis- sion of thunderstorm lightning is strongest in the radio spectrum (Bennett, 2017). Examples include the Earth Networks Total Light- ning Network (ENTLN) (Lapierre et al., 2018; Prata et al., 2020), the Global Volcanic Lightning Monitor of the World Wide Lightning Lo- cation Network (WWLLN) (Behnke and McNutt, 2014; Van Eaton et al., 2016; Haney et al., 2018; Hargie et al., 2019), Vaisala GLD360 Global Lightning Detection (Pattantyus and Businger, 2014; Haney et al., 2018; Van Eaton et al., 2020) and the Arrival Time Differ- ence Network (ATDnet) operated by the UK Met Office (Bennett et al., 2010; Arason et al., 2011, 2013). The scattered geographical dis- tribution of the sensors however, leads to reduced sensitivity and therefore to delayed or no detection for a relatively high number of explosive episodes, which do not produce sufficient high-current discharges. Thus, the applicability of these systems to the detec- tion of smaller (and often more frequent) ash-rich explosive events is limited. In contrast, Lightning Mapping Array (LMA) sensors installed on-site have detected electrical discharges in relatively small explosive eruptions with high precision (Behnke et al., 2018). However, these sensors currently have the drawback that they are expensive and data-intensive. Here, we present a novel, long-term and data-efficient method to monitor continuously active volca- noes using a different type of thunderstorm detector, which we have tested in situ at Sakurajima volcano, Japan.